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Digital Business Models: All You Need To Know
Published: 06 September, 2023
Table of Contents
In today’s world, there is a widespread acknowledgment of the presence and validity of modern technologies and software solutions. The term “Artificial intelligence” is now a household term, with most individuals having a basic understanding of its meaning. However, a persistent area of ambiguity, even among some chief executive officers (CEOs), pertains to the concept of digital business models and the factors that make them exceptionally successful.
Recognizing the importance of digital business models transcends mere relevance; it stands as a strategic necessity for organizations across various sectors and scales. Within this context, Digital Leadership steps in to offer Digital Transformation Solutions and Business Model Strategy services, equipping organizations with the necessary tools, technologies, and strategies to adeptly navigate the complexities of this transformation. Our expertise extends to assisting businesses in rethinking their current models or crafting entirely fresh approaches that harmonize seamlessly with the digital terrain.
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In an era marked by rapid technological advancement and evolving consumer behaviors, gaining a deeper comprehension of these digital business models is imperative for anyone looking to thrive in the ever-changing landscape of commerce. In this discussion, we will explore what digital business models entail and delve into the reasons behind their remarkable effectiveness in today’s business environment .
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What are digital business models?
Let’s delve into the fundamental underpinnings of digital business models . As with all comparatively new terms, there is no clear agreement on a universally valid definition. However, one can limit oneself to certain commonalities .
Interestingly, when you examine this definition closely, you’ll find that it shares substantial similarities with traditional business models . The key distinction lies in the integration of digital technologies. This begs the question: What sets digital business models apart and accounts for their significant success in today’s business landscape? To unravel this, we must embark on a deeper exploration of the intricacies that define digital business models and elucidate the precise factors contributing to their contemporary prominence.
Digital business models revolve around delivering added value to one or more customers through the strategic use of digital technologies. The ultimate objective is to ensure that the customer benefits derived from these digital solutions reach a level at which consumers are not only satisfied but also willing to pay for this value. Let’s delve deeper into the foundational principles that make digital business models so integral to contemporary business success:
- Digital Transformation: Digital business models are the result of a transformative shift in how businesses operate. They harness the potential of digital technologies to fundamentally reshape traditional processes and create innovative ways of delivering value.
- Customer-Centricity: Central to digital business models is a relentless focus on meeting customer needs and preferences. Through data-driven insights and personalized experiences, they aim to not only satisfy but delight consumers.
- Agility and Innovation: These models thrive on agility and rapid innovation. They empower businesses to adapt swiftly to changing market dynamics, experiment with new offerings, and iterate based on real-time feedback.
- Global Reach: The internet has erased geographical boundaries, providing digital business models with access to global markets. This expansive reach allows companies to connect with diverse customer bases and unlock new revenue streams.
- Data as a Driver: Data lies at the heart of digital business models . They leverage data analytics and insights to make informed decisions, identify emerging trends, optimize operations, and enhance customer experiences.
Importance of Digital Business Model
The importance of digital business models in today’s world cannot be overstated, and the reasons behind it are quite straightforward. It’s no longer a choice; it’s a necessity. The evolution of consumer behavior, where consumers now expect seamless digital experiences, coupled with businesses prioritizing customer-centric approaches, has given rise to an entirely new paradigm. Companies have realized that the technological capabilities that are now available to them are so much more than a website.
Simultaneously, consumers have developed a newfound trust in digital business models. What was once viewed skeptically as a “subscription trap” a decade ago is now exemplified by the likes of Netflix, and the traditional catalog people used to order from has transformed into the convenience of Amazon. This transformation underscores the imperative for businesses to embrace digital business models to remain competitive and meet evolving customer expectations.
Comprehending digital business models is pivotal for organizations aiming to thrive and excel. These models offer a multifaceted toolkit for businesses, providing advantages that extend far beyond their basic functions.
Characteristics of Digital Business Models
In the constantly evolving realm of contemporary commerce, digital business models have emerged as powerful catalysts, reshaping the very essence of how organizations conduct their operations and engage with their customer base. These models are marked by a distinct set of core qualities that collectively serve as the bedrock of their triumph and prominence in the fiercely competitive landscape of today, distinguishing them from classic business models . Let’s explore these fundamental characteristics more comprehensively to grasp their profound influence on the tactics and functioning of enterprises in this digital era.
- Digital Transformation: Digital business models represent a profound shift in business operations, encompassing the adoption of cloud computing, data analytics, artificial intelligence, and automation. These technologies enable organizations to not only optimize existing processes but also create entirely new ways of delivering value to customers.
- Digital Value Generation : The added value in digital business models can only be generated digitally, setting them apart from traditional models that primarily create value in analog form. While traditional models may undergo digital transformation , the core value remains unchanged, only altering the means of obtaining it. In essence, the Internet is the cornerstone of the digital business model’s core operations, without which its core business would not be possible.
- Customer-Centric: At the core of digital business models lies an unwavering commitment to understanding and satisfying customer needs. Leveraging data-driven insights, organizations can segment their customer base, personalize offerings, and anticipate customer desires, fostering deep customer loyalty.
- Data-Driven: Data serves as the lifeblood of digital business models. These models collect, process, and analyze data on customer behavior, market trends, and operational performance. The insights derived from data empower businesses to make informed decisions, refine strategies, and continually enhance the customer experience.
- Agility: Digital business models thrive on agility, allowing organizations to pivot swiftly in response to market shifts, emerging technologies, and customer feedback. This adaptability ensures that businesses remain competitive and innovative in a rapidly changing landscape.
- Innovation: A culture of innovation permeates digital business models. Companies are encouraged to experiment with emerging technologies, develop new products and services, and explore novel revenue streams. This commitment to innovation is vital for staying ahead of the competition.
- Global Reach: Enabled by the internet, digital business models transcend geographical boundaries. They provide organizations with unprecedented access to global markets, allowing them to connect with diverse customer bases and capitalize on international growth opportunities.
- Revenue Diversification: Digital business models often incorporate a variety of revenue streams. These can include subscription models, freemium offerings, advertising revenue, and data monetization. This diversification reduces reliance on a single source of income and enhances financial stability.
- Ecosystem Orientation: Many digital business models foster ecosystems that bring together various stakeholders, such as customers, partners, and developers. These ecosystems create a network effect, generating additional value and enhancing the overall customer experience.
- Efficiency: Efficiency gains are a hallmark of digital business models. Automation, streamlined processes, and optimized resource allocation not only reduce operational costs but also enable businesses to deliver products and services more efficiently and at a lower cost.
- User Experience Focus: Delivering an exceptional user experience is paramount. Digital business models prioritize creating intuitive, user-friendly interfaces and applications that enhance customer satisfaction, foster brand loyalty, and drive customer retention.
- Disruption: Digital business models have the potential to disrupt traditional industries by introducing innovative approaches that challenge established norms. This disruption can lead to the creation of entirely new markets and business opportunities.
- Scalability: These models are inherently scalable, allowing organizations to accommodate rapid growth without a proportionate increase in costs. Scalability is a critical factor in achieving sustainable expansion and competitiveness.
- Sustainability: Ensuring long-term sustainability is a key consideration. Digital business models focus on maintaining profitability by aligning revenue streams with operational costs, ensuring financial stability and continued growth.
Types of Digital Business Models with a Real-Life Examples
Let’s take a closer look at the individual models to understand how they work and how they are structured to align with digital business strategy. Because even if the differences sound simple, they are not always. And especially with digital business models, it is interesting to see how the revenue streams emerge again. While in the beginning there was a lack of definition, there are now more and more possible distinctions. The largest and most established models are the following:
- In this model, the approach is still relatively intuitive. The entire offer in the form of the product or service is provided free of charge.
- The “Free” model offers core products or services at no cost to users.
- Revenue is generated purely through advertising on the respective URL. through alternative means, such as advertising, freemium upgrades, or data monetization.
- Example: Facebook is a social media platform that offers its core services (connecting people, and sharing content) for free to users. It generates revenue primarily through digital advertising. Advertisers pay to display targeted ads to users based on their interests and behaviors.
- On-demand models provide immediate access to products or services when users need them.
- Examples include ride-sharing services like Uber and food delivery apps like DoorDash.
- Convenience and real-time fulfillment are key features.
- Example: Uber is a ride-sharing service that allows users to request rides on-demand using a mobile app. Users can request rides in real-time, and drivers respond to these requests, providing convenient transportation.
- E-commerce businesses sell products or services online, often through their websites or platforms like Shopify or WooCommerce.
- They can range from small online boutiques to large-scale retailers like Amazon.
- E-commerce often involves various business models, including B2C (business-to-consumer) and B2B (business-to-business).
- Example: Amazon is one of the world’s largest e-commerce platforms, offering a wide range of products for sale online. It operates both as a B2C (selling products directly to consumers) and a B2B (offering marketplace services to third-party sellers) e-commerce platform.
- Online marketplaces act as intermediaries connecting buyers and sellers.
- They often charge fees or commissions for transactions.
- Marketplaces can focus on various niches, such as products, services, or accommodation.
- Example: Airbnb is an online marketplace that connects travelers with hosts offering accommodations, which can be apartments, houses, or even unique stays. It charges hosts and guests fees for bookings made through the platform, acting as an intermediary.
- This model emphasizes access to goods or services rather than ownership.
- Businesses rent or lease products to users, offering cost-effective and sustainable alternatives.
- Car-sharing services like Zipcar and equipment rental platforms follow this model.
- Example: Zipcar is a car-sharing service that allows users to rent cars by the hour or day. Users access Zipcar’s fleet of vehicles when needed, avoiding the need to own a car themselves.
- Ecosystem models create an interconnected network of products, services, or platforms.
- They encourage users to stay within the ecosystem for various needs.
- Example: Apple Ecosystem includes hardware devices (iPhone, Mac), software (iOS, macOS), the App Store, iCloud, and other services. Users are encouraged to stay within the Apple ecosystem, as products and services work seamlessly together (e.g., iCloud for data storage). The Apple ecosystem is known for its seamless integration, such as AirDrop, which can create a sense of vendor lock-in , where users are incentivized to use Apple products exclusively.
- Experience-based models focus on providing unique and immersive experiences.
- Businesses charge for access to experiences, such as virtual reality (VR) experiences, live events, or themed entertainment.
- Example: Disneyland is a theme park known for providing unique and immersive experiences to visitors. Visitors purchase tickets for entry and pay for additional experiences and attractions within the park.
- Subscription models offer recurring revenue streams and build customer loyalty.
- Businesses often offer tiered pricing with varying features or content access.
- They require a focus on retaining subscribers and continuously providing value.
- Example: Netflix is a subscription-based streaming service that offers a vast library of movies and TV shows. It offers multiple subscription tiers with varying features and content access, including options for streaming quality.
- Open-source models involve sharing software, code, or intellectual property freely with the community.
- Revenue is often generated through support, customization, or premium versions.
- Example: Linux Operating System is an open-source operating system widely used for servers and embedded systems. Companies and individuals can use Linux for free, but revenue is generated through support services, certifications, and customized solutions.
- Hidden revenue models offer a free or low-cost product but generate income through less visible channels.
- For instance, some mobile apps collect user data and sell it to advertisers without explicit user knowledge.
- This model can raise ethical and privacy concerns.
- Example: Free Weather Apps , Some free weather apps collect user location data and weather preferences, which are used for targeted advertising and data monetization. Users may not be aware that their data is being used for these purposes, raising privacy concerns.
- Freemium model attracts users with free basic features while offering premium upgrades.
- They can be effective for software, mobile apps, and online services.
- Conversion rates from free to paid users are crucial for success.
- Example: Dropbox is a cloud storage service that offers free storage with limitations and premium plans with enhanced features. Users can store and share files for free, but premium users get additional storage and advanced sharing options.
- Example: eBay is an online marketplace where individuals and businesses can buy and sell a wide range of products. It charges sellers fees for listing items and final value fees for completed transactions.
- Digital advertising models generate revenue by displaying ads to users.
- Targeted advertising, programmatic ads, and native advertising are common approaches.
- Platforms must balance user experience with ad revenue.
- Example: Google Ads is an advertising platform that displays ads on Google search results and websites within the Google Display Network. Advertisers bid on keywords and use targeting options to reach specific audiences.
- Data-driven businesses gather and analyze user data to offer insights, targeted advertising, or market research.
- Strict data privacy regulations must be followed.
- Data is often sold to third parties or used to enhance products and services.
- Example: Facebook Data Usage , Facebook gathers user data to offer targeted advertising to businesses. User data includes interests, behaviors, and demographic information. Facebook must comply with data privacy regulations and guidelines.
- IoT businesses offer solutions for connected devices, such as smart home systems or industrial sensors.
- Data generated by IoT devices can be leveraged for analytics and insights.
- Security and privacy are paramount concerns.
- Example: Nest (by Google) offers smart home products, including thermostats and security cameras, that are part of the Internet of Things (IoT). Data generated by Nest devices, such as temperature and motion data, can be used to optimize energy use and enhance security.
- Blockchain-based businesses use decentralized ledgers for various applications.
- Cryptocurrency exchanges facilitate the buying and selling of digital assets.
- NFT (Non-Fungible Token) platforms enable unique digital asset ownership.
- Example: Bitcoin is a decentralized cryptocurrency that enables peer-to-peer digital transactions without the need for intermediaries like banks. It is used for secure and transparent digital transactions, and it’s also seen as a store of value.
How to Create a Digital Business Strategy
Crafting a robust digital business strategy has become nothing short of imperative. With technology continuously shaping the way we live, work, and interact, businesses of all sizes and niches must adapt to the digital age. Whether you’re an established corporation or a budding startup, a well-crafted digital strategy is a compass that can steer you toward growth, enhanced customer engagement, and operational efficiency.
We’ll walk you through the essential steps for developing a digital business strategy that harmonizes with your goals, capitalizes on the potential of digital technologies, and positions your enterprise for triumph in the digital era.
So, Choosing the right digital business model strategy is a pivotal decision. It requires a deep understanding of your target audience, a clear definition of your unique value proposition, and a comprehensive evaluation of your resources and capabilities. Here are some key takeaways to guide you in this process:
- Know Your Audience: Understand your customers’ needs, preferences, and pain points. Use data and analytics to gain insights into their behavior and expectations.
- Define Your Value Proposition: Clearly articulate what sets your business apart. How will your digital strategy address customer challenges or provide unique solutions?
- Leverage Technology: Embrace digital tools and platforms that align with your strategy. Whether it’s e-commerce, mobile apps, or data analytics, technology should support your goals.
- Stay Agile: Be prepared to adapt and iterate. Digital landscapes evolve rapidly, and your strategy should have built-in flexibility to respond to market changes.
- Invest in Talent: Build a team with the right skills to execute your digital strategy effectively. Training and upskilling may be necessary to keep pace with technological advancements.
- Measure and Analyze: Implement metrics and KPIs to monitor the performance of your digital initiatives. Regularly review the data to make informed decisions.
- Customer-Centric Approach: Put the customer at the center of your strategy. Tailor your digital offerings to meet their needs and provide exceptional user experiences.
- Stay Informed: Keep abreast of industry trends, emerging technologies, and competitors’ strategies. Continuous learning is essential in the digital business landscape.
Remember that there is no one-size-fits-all digital business model. Your choice should align with your industry, target market, and organizational strengths. By following these guidelines and staying committed to innovation and customer satisfaction, you can create a digital business strategy that not only survives but thrives in the digital age.
Business Model Vs Digital Business Model
A business model serves as a comprehensive framework that delineates the fundamental operations and sustainability strategies of a business. It encompasses diverse facets, including how the business delivers value to its customers, the channels employed to reach these customers, the relationships cultivated with them, the requisite resources and activities for value delivery, the revenue streams generated, and the associated cost structure.
In contrast, a digital business model represents a specialized subset within the broader business model framework, meticulously tailored to harness the capabilities of digital technologies and resources. It notably accentuates the strategic utilization of digital tools, platforms, data, and communication channels to elevate and revolutionize various aspects of the business. Digital business models frequently encompass:
- Digital Customer Engagement: Utilizing digital channels such as websites, mobile applications, and social media to foster customer interactions, offering personalized experiences and real-time connectivity.
- Innovative Revenue Streams: Pioneering revenue streams facilitated by digital technologies, which may encompass subscription-based services, data monetization, or digital product sales.
- Efficient Cost Structures : Optimization of operational costs through automation, cloud computing, and data analytics to enhance overall efficiency.
- Data-Driven Decision-Making : Heavy reliance on data analytics to inform strategic decisions, enrich customer experiences, and drive continuous business enhancements.
- Agility and Adaptability : Structured for agility, digital business models enable organizations to promptly respond to market fluctuations and technological advancements.
Connecting The Dots With Business Model Canvas
The Business Model Canvas (BMC) is a powerful tool in the realm of digital business models due to its adaptability and versatility. It offers businesses a structured framework to define, conceptualize, and iterate on their digital strategies. One of its key advantages is its ability to encourage customer-centric thinking, driving businesses to identify and address the evolving needs of their digital audience. You can download it now.
Furthermore, the BMC promotes innovation by allowing organizations to experiment with different components of their model, aligning well with the ever-evolving nature of digital technologies. Its agility enables rapid adjustments to respond to market dynamics and emerging opportunities. Ultimately, the BMC is a valuable asset for organizations navigating the intricacies of the digital age, aiding in the development of comprehensive and strategic approaches to digital business models. You can easily access and utilize it to refine your own digital strategies and models.
In conclusion, crafting a digital business strategy is essential in today’s tech-driven world. To remain competitive, leverage tools like the Business Model Canvas to align your operations with evolving customer expectations and digital opportunities. Your Business strategy should adapt to the dynamic digital landscape, incorporating data analytics, online platforms, and customer-centric approaches. Remember, there’s no one-size-fits-all solution; tailor your strategy to your industry and strengths. By blending digital strategy principles with the Business Model Canvas , you can navigate the digital age and ensure your business thrives amid constant change.
Frequently Asked Questions
1- what are the key elements of digital business.
Key elements of a digital business include:
- Digital Technologies: Utilizing tools like AI, IoT, cloud computing, and data analytics.
- Customer-Centricity: Focusing on meeting customer needs through personalization and user-friendly experiences.
- Data-Driven Decision-Making: Leveraging data for insights and informed choices.
- Innovation Culture: Encouraging creativity and experimentation to stay competitive.
- Agility: Adapting quickly to market changes and technological advancements.
- Ecosystem Engagement: Collaborating with partners, suppliers, and platforms.
- Efficiency: Optimizing processes for cost-effectiveness and productivity.
2- What is a digital business structure?
A digital business structure refers to the organizational framework designed to effectively operate within the digital landscape. It involves roles, responsibilities, processes, and technologies that support digital strategies. Common elements include digital teams, data analytics divisions, agile workflows, and technology infrastructure to facilitate digital transformation.
3- What are the 7 principles of a digital transformation strategy?
The seven principles of a digital transformation strategy are:
- Customer-Centricity: Prioritize understanding and meeting customer needs.
- Leadership Commitment: Engage leadership in championing digital initiatives.
- Innovation Culture: Foster a culture of experimentation and adaptability.
- Agile Methodologies: Implement agile practices for quicker responses to changes.
- Data-Driven Decision-Making: Base choices on data and analytics insights.
- Ecosystem Collaboration: Partner with external stakeholders and platforms.
- Continuous Learning: Invest in upskilling and learning to keep pace with digital advancements.
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The gartner digital business value model: a framework for measuring business performance.
Published: 17 October 2019
The DBVM is a set of common business outcomes across traditional and emerging digital business models. CIOs can use the corresponding catalog of leading indicators, which connects dependencies to desired business outcomes for planning and reporting, to guide investment decisions.
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Is Your Company Seizing Its Digital Value?
- Stephanie L. Woerner,
- Peter Weill,
- Ina M. Sebastian
The ways in which companies can create and capture value have changed profoundly. Most aren’t keeping up.
In the digital era, how firms create and capture value has changed profoundly. But with digital transformation, many firms are leaving substantial value on the table, getting caught up in “doing” digital transformation rather than staying focused on how they will create and capture value with digital. To do this, first companies need to understand the three different types of digital value: value from customers (cross-selling, increased loyalty, great customer experience); value from operations (increased efficiency, modularity and reuse of components, automating processes); and value from ecosystems (leveraging partners for both access to more customers and range of products and services). With these types of value in mind, firms can then take action to create digital value by: identifying domain opportunities; building mutually-reinforcing capabilities; tracking digital value with a dashboard; recruiting digital partners; and investing in digital savviness of everyone at the firm. Companies that do this will become truly “future ready.”
A global financial services firm we worked with really seemed to get the digital message. They hired a chief digital officer who led many locally successful projects to improve the customer experience. These included making it easier to move from in-person to online for certain tasks, plus targeted offers based on customer data. They felt confident they were creating great customer value. But there was a problem. Those local innovations ended up adding more complexity to the existing fragmented business processes, systems, and data. Although the customer experience often improved — and in some cases, revenue increased — the rise in the cost-to-serve eclipsed the gains and added other risks like cybersecurity and system crashes.
- SW Stephanie L. Woerner is the principal research scientist at the MIT Sloan School of Management and director of MIT Center for Information Systems Research (CISR). She is coauthor of Future Ready: The Four Pathways to Capturing Digital Value and What’s Your Digital Business Model? Six Questions to Help You Build the Next-Generation Enterprise .
- PW Peter Weill is a senior research scientist at the MIT Sloan School of Management and chairman emeritus at MIT CISR. He is coauthor of Future Ready: The Four Pathways to Capturing Digital Value and What’s Your Digital Business Model? Six Questions to Help You Build the Next-Generation Enterprise .
- IS Ina M. Sebastian is a research scientist at MIT CISR and coauthor of Future Ready: The Four Pathways to Capturing Digital Value .
Digital transformation takes a customer-driven, digital-first approach to all aspects of a business, from its business models to customer experiences to processes and operations. It uses AI, automation , hybrid cloud and other digital technologies to leverage data and drive intelligent workflows, faster and smarter decision-making, and real-time response to market disruptions. And ultimately, it changes customer expectations and creates new business opportunities.
While many organizations have undertaken a digital transformation in response to a single competitive threat or market shift, it has never been about making a one-time fix. According to MIT Sloan Management Review (link resides outside of ibm.com), "Digital Transformation is better thought of as continual adaptation to a constantly changing environment." Its goal is to build a technical and operational foundation, to evolve and respond in the best possible way to unpredictable and ever-changing customer expectations, market conditions and local or global events.
It's also worth noting that while digital transformation is something that businesses undertake, the effect goes well beyond business. As one expert at Red Hat® puts it, “Better living through software—that's what digital transformation is (link resides outside of ibm.com). How's that for a definition?” It's a solid definition, particularly if you think that 'better living' includes working and playing in a world that promises new opportunities, more convenience and greater resilience to change.
Digital transformation is why so many areas of business and life are fundamentally different from those of 20 years ago. It's also why we are now living in the digital age to one degree or another.
Customer expectations have always been the prime drivers of digital transformation. It began when a rush of new technologies made new kinds of information and capabilities accessible in new ways, such as:
- Mobile devices
- Social media
- The internet of things (IoT)
- Cloud computing
Pioneers— disruptors —such as Amazon and Netflix snatched market share from their competition by adopting these technologies to:
- Reinvent business models (ecommerce, electronic delivery).
- Optimize processes (supply chain management, new feature development).
- Constantly improve the customer experience (in-context customer reviews, personalized recommendations).
Competitors adapted to provide even more capability and convenience (or they struggled and maybe even disappeared). Today, customers expect to conduct all business digitally, wherever, and whenever, using any device, with all the supporting information and content they need close at hand.
Ultimately, digital transformation is about meeting these ever-escalating expectations. But often, an organization's entry point is a transformation initiative that addresses a specific means to this end, such as:
Adding AI and automation serve customers better and do higher-value work. They create intelligent workflows that simplify operating models, increase productivity and enable employees to make better decisions faster.
Digital transformation implements technologies and best practices for fast product creation, new customer experiences and new business models in response to shifts in competitive threats, market trends and customer expectations.
This process can include modernizing legacy technology to run on modern infrastructure and interoperate with modern applications. It builds resilience into systems and processes and assimilates applications and data from acquisitions or mergers.
Digital transformation empowers the business to adopt the widest possible range of solutions and services from ecosystem partners, industry solution leaders and multiple cloud service providers.
In 2020, the COVID-19 pandemic laid bare every organization's digital transformation efforts and progress (or lack thereof). Manufacturers learned just how quickly and effectively they could get new products to market. Retailers scrambled to provide customers new and safer ways to shop. Employers adopted or expanded technologies that let employees work from home.
Among operational workflows, supply chains were the most glaringly exposed. Supply chains are always vulnerable; according to the McKinsey Global Institute, supply chain disruptions lasting one month or longer occur every 3.7 years (1) . But shortly after the pandemic began, the United States suddenly imported almost 50% less from major trading partners (2) . Companies were forced to undergo years or decades worth of supply chain transformation in weeks or months.
The impact figures to be lasting. In a recent study, Twilio called COVID-19 "the digital accelerant of the decade." Prior to the pandemic in October 2019, industry analyst IDC forecast worldwide spending on digital transformation would reach USD 2.3 trillion in 2023 (3) . And in November 2021, IDC predicted that global spending on digital transformation will reach USD 2.8 trillion in 2025, more than double the amount allocated in 2020 (4) .
How businesses transform in uncertainty (PDF, 201 KB)
Virtually any digital technology can play a role in an organization's digital transformation strategy. But technologies that figure to play a central role today and in the near future include:
Artificial intelligence and automation
Artificial intelligence (AI) technologies, such as machine learning, enable a computer or machine to mimic the human mind's capabilities. AI learns from examples, recognizing objects, making decisions and more. When combined with automation, AI can infuse intelligence and real-time decision-making into any workflow. It can drive everything from innovative smart products, from increasingly personalized customer and user experiences to optimized workflows for supply chain management, change management and more.
A hybrid cloud is a cloud computing infrastructure that connects on-premises IT, public cloud and private cloud resources with orchestration, management and application portability. By creating a single, flexible, optimal cloud for running every workload—and by not locking an organization into a single platform or vendor—hybrid cloud provides the agility, scalability and resilience required for enduring digital transformation success.
Microservices is a cloud-native application architecture in which a single application is composed of loosely coupled, independently deployable components. Together with Agile or DevOps methodologies, microservices are an engine for creating or countering digital disruption. It enables organizations to deploy new software or product features daily or sometimes hundreds or thousands of times a day.
Internet of Things
The Internet of Things (IoT) are objects and devices equipped with sensors that collect and transmit data over the internet. IoT devices are where digital technology meets physical reality. Applications like supply chain logistics and self-driving cars generate real-time data that AI and big data analytics applications turn into automation and decisions.
Blockchain is a distributed, permanent and immutable ledger or record of electronic transactions. Blockchain provides total transaction transparency to those who require it and is inaccessible to those that don't. Organizations are using blockchain as a foundation for super-resilient supply chain and cross-border financial services transformations.
Digitization, or digitalization , is the conversion of paper-based information into digital data. Digitizing printed information may seem like an old practice, but it’s a component of digital transformation efforts in virtually every industry or sector. It’s also a cornerstone of foundational transformation initiatives in healthcare (electronic medical records or EMR), government and education
A hundred organizations detailing their digital transformation strategies will likely result in 100 different roadmaps. Still, most successful digital transformations are aligned to a company's business strategy and follow two principles:
- They work backward, starting with the ideal customer experience.
- They take a holistic approach to transforming operations.
Enterprises start by envisioning the experience they want customers to have with their product and their brand for as many months or years as they want them to be customers. This objective means they must analyze their market, along with technology trends, to forecast or anticipate how customer needs or expectations may change and to spot opportunities for disruption.
Organizations then determine how they must transform the digital business from end-to-end, including infrastructure, product development, operations, and workflows. And finally, they bring the customer experience to life and improve it continually in response to opportunity and change.
Develop a digital-first business model to gain a competitive advantage and fundamentally transform how you deliver value to customers. Accelerate your digital transformation journey with IBM’s business strategy and technology expertise.
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¹ 'Risk, Resilience and Rebalancing in Global Value Chains ' (link resides outside of ibm.com). McKinsey Global Institute, August 2020
² Covid 19 Digital Engagement Report (link resides outside of ibm.com - PDF, 7.4 MB). Twilio, Inc., page 5
³ Worldwide Spending on Digital Transformation Will Reach USD 2.3 Trillion in 2023, More Than Half of All ICT Spending, According to a New IDC Spending Guide (link resides outside of ibm.com). BusinessWire.com, October 28, 2019
⁴ New IDC Spending Guide Shows Continued Growth for Digital Transformation as Organizations Focus on Strategic Priorities (link resides outside of ibm.com). IDC.com, November 2021.
In digital and AI transformations, start with the problem, not the technology
Digital and AI transformations are everywhere. Almost every company has done, is doing, or plans to do one. But how can you make the changes stick? In this episode of the Inside the Strategy Room podcast, McKinsey senior partner Eric Lamarre talks about the critical elements of what it takes to rewire an organization through making fundamental changes to talent, operating model, and technology and data capabilities. He is coauthor with Kate Smaje and Rodney Zemmel of the Wall Street Journal bestseller Rewired: The McKinsey guide to outcompeting in the age of digital and AI . This is an edited transcript of their conversation. For more discussions on the strategy issues that matter, follow the series on your preferred podcast platform .
Sean Brown: What areas do you find executives struggle with the most when trying to harness technology’s potential?
Eric Lamarre: How to do it at scale. Most business leaders have had a chance to taste what technology can do. They have done successful pilots and experiments, but these are not moving the needle on company performance. This is what Rewired gets at: How do you move from pilots to large-scale implementation? It’s not really a technology problem but a talent and a data problem—how do you organize to deliver this at scale? Of course, technology is involved, but the organizational component is where the big surgery has to happen.
Sean Brown: What should be the starting point for a digital and AI transformation?
Eric Lamarre: It should always start with the business problem you want to solve. When it starts that way, there is usually a good ending because the problem eventually ties back to serving customers better and delivering more value for the company. When business leaders say, “That’s the problem I want to solve with technology,” it becomes easier to develop the technology road map to solve that problem.
Sean Brown: There is probably no bigger subject in tech now than generative AI. Are you seeing companies almost inventing problems to solve with gen AI?
Eric Lamarre: Yes. The conversations right now make it feel like a technology in search of a problem. Maybe that’s natural because when we try gen AI, it seems like a magical experience. That takes your mind to “Where else could I apply this?” It’s good to come back to the fundamentals, though—what are the pain points in the company?—then search broadly for the set of technologies that will address them. Sometimes, that will be gen AI, but that doesn’t mean gen AI is the place to start.
For example, if you’re a consumer packaged goods company, you can use gen AI in many places, but good old revenue growth management is about advanced analytics on pricing, demand, and promotions. I don’t think gen AI will do a good job on that problem, and it’s one of the top business problems for consumer goods companies. Gen AI is a bit of a super technology, but it shouldn’t take us away from the problems businesses need to solve.
The conversations [around gen AI] right now make it feel like a technology in search of a problem. Eric Lamarre
Sean Brown: One of the elements you highlight in the book as essential to long-term success in digital transformations is talent. What approach to talent do you recommend?
Eric Lamarre: In my experience, the conversation usually starts with, “All this AI stuff is wonderful, but we will never get the talent to do this.” In reality, traditional companies that are serious about digital transformations manage to get the right talent, but they have to be committed to a modern technology environment that will make it easy for employees to do their jobs. Talent wants to know that the company they join won’t cause their skills to atrophy because it’s using an old technology stack and software engineering methods. They will ask questions such as, “Tell me about your technology architecture. Tell me about how you manage data. Tell me about your software engineering method.” These questions will come very early in the conversation, and if it doesn’t sound like the company is serious, they will walk out because what they value most right now is their craft.
Sean Brown: Have you seen incumbent companies successfully develop their existing talent to manage new digital products or solutions?
Eric Lamarre: Yes. For example, a product manager might envision a solution. They will master the problem to be solved, develop the road map on how to solve it, and then guide their team to that solution. To understand the business problems, people need to have experience in the business. Usually, companies provide additional training and skills, but the most important skills are not those of a technologist but of a businessperson who understands enough about technology to imagine how it can solve the problem.
Sean Brown: What might cause a company to not capture the full value of a technology it implements?
Eric Lamarre: I’ll give you a concrete example. We developed technology for an airline to maximize the fill rate of its cargo space. It’s a highly profitable business for an airline, but maximizing that fill rate is difficult because you don’t know how many passengers will show up and how many will have suitcases, and those determine the extra space for cargo. It’s a beautiful problem for AI, and the technology we developed could say exactly how much extra cargo space there would be and what to charge for that space. But when we checked the planes, they weren’t flying with the cargo they were supposed to have. Why? The palletizing procedures at the airport were not quite right.
That is a lesson I have learned over and over: Whenever you develop a technology, there will be a secondary effect somewhere in the system that will prevent you from fully capturing the value. In this instance, the answer was to train operators at the airport on how to maximize pallet caseloads. That’s not a technology problem; that’s just a good old operational problem. Technology usually unveils a bottleneck in the process that needs to be solved to realize the technology’s value. Therein lies the importance of business leaders owning the end-to-end reimagination of the process because once they have deployed the technology, they need to play a key role in chasing down bottlenecks throughout the chain.
Sean Brown: In the book, you say, “You can’t outsource your way to success.” Can you elaborate?
Eric Lamarre: We find technology development is much more productive when done in-house. Why do I say that? If you’re a data engineer, a software engineer, or a machine-learning engineer working on a business problem, you can develop the right technology two to four times faster if you understand the context of that problem. And that doesn’t happen overnight. In-house technologists will also understand the context for the next incremental innovation on that problem and the one after that, and your whole innovation wheel starts to fly a lot faster.
Sean Brown: What role should external technologists or consultants play, then, in building an organization’s technological muscle?
Eric Lamarre: I face that question often, and my answer to clients is that they should not rely heavily on consultants. They should build that capability themselves. Developing that flywheel is not easy to start. How do you build a technologist bench? How do you show them the right way to work? How do you bring business to the dance? A third party that knows what they’re doing can accelerate this process, but you don’t want to completely outsource that capability. If you want it to become a source of competitive differentiation, you need to own it. You can’t outsource your way to competitive differentiation.
Whenever you develop a technology, there will be a secondary effect somewhere in the system that will prevent you from fully capturing the value. Eric Lamarre
Sean Brown: Does it help digital transformations succeed if business leaders try to shift employee mindsets to see the organization as a digital or tech company?
Eric Lamarre: Some companies truly embrace that. In the book, we talk about DBS Bank, an organization that viewed itself as becoming a technology company. But not every company likes that analogy. They feel their core business is not technology but mining or consumer goods, and technology is a complement, not the core. However, if technology is going to play a role in driving competitive differentiation—better-served customer, lower unit cost—the company has no choice but to become good at software development. No company would debate whether they need to be good at finance. Well, if you want to be good at running a company infused with technology, you have to be good at software development.
Sean Brown: Should businesses think of technology less as a separate department such as HR or finance and more as a fundamental aspect of the organization?
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Eric Lamarre: Yes. A central theme of our book is getting to a state I call “distributed digital innovation.” You might start with a handful of teams developing technology solutions. Their apps or models will show some value, but the rest of the company won’t be transformed. When you reach a rewired state, those few teams multiply a 100-fold and work in various parts of the organization—sales, supply chain, manufacturing, R&D. They serve the leaders of those different areas, developing technology to solve their problems. At that point, no one calls IT to develop a solution because each area has that capability. IT evolves into a distributed function with distributed technology capabilities.
Sean Brown: What role does the IT team take on in such a rewired organization?
Eric Lamarre: IT would become the bedrock enabling cybersecurity and distributing the tools and data needed for innovation. IT remains an important platform capability, but it’s no longer the sole engine of innovation. That belongs in the hands of the enterprise more broadly.
Sean Brown: How should the top executive team, including the CIO and the CFO, think about their roles in this new environment?
Eric Lamarre: Everybody around the CEO has a role to play in that transition. The CIO now needs to move to a model where they are enabling the rest of the organization to innovate securely, with access to the tools and data they need, and providing them with the development capacity that used to reside in IT. That flip is a major transformation of the IT function. For HR, the talent equation is massive. HR staff need to recruit from outside and upskill people in product management. In a large institution, tens of thousands of people may need to go through that HR transition. The head of HR also needs to figure out how to assess a data engineer on the basis of skills because skills become the currency, not how many people someone manages.
Then there is finance. How do you fund all of this? Before, when you had a big IT project, you debated it, built a business case, funded millions for the project, and mobilized a big team. Often, two years in, something would cause the project to go off the rails. That’s IT in the old days. IT in the new days gets funded differently, with many small teams. You can’t fund 500 different teams project by project; you have to move to something called persistent funding, where you are funding portfolios of small teams rather than individual projects. You continue the funding until solving the problem is no longer productive. Moving to persistent funding from project funding is a massive shift for finance.
You can’t outsource your way to competitive differentiation. Eric Lamarre
Now, think about the people who head control functions: risk management, compliance, and regulation. Now, they have 500 little teams innovating, so they are underwriting new risk. The role of the control functions needs to move upstream to guide that development. They need to ask before development has even begun, “Are there any risks these teams are undertaking that we should be monitoring?” If a team is working with customer data, there are data privacy risks, and you don’t want to tell the team six months into the project, “You didn’t handle data privacy, so we can’t use the work you did up to now.” These functions also need processes to monitor that the risks have been addressed.
Long story short, when you move to a distributed innovation model, everybody in the C-suite has a new job. Everybody has to drive a transformation of their area for the whole system to work. It becomes what we call in the book “the ultimate corporate sport,” and everybody’s got to play.
Sean Brown: Many executives fear the complexity of IT—you tug on one wire, and 14 things fall apart. How would you ease those concerns?
Eric Lamarre: It’s the main reason why we wrote this book. The technology field has become very complex. Take gen AI—executives are wondering, “Does it mean old AI is dead, and now I just focus on gen AI? By the way, I keep hearing about data engineers—what do they do? And data architecture, I don’t understand any of that. How does that work? And I’ve been hearing about agile for ten years. Is that still relevant?” I could go on and on. The lack of a common understanding around the executive table makes progress very difficult because it’s become a world of buzzwords.
To some extent, we wrote this book to “de-complexify” the space and focus on what matters to getting value from new technology. Typically, when an executive starts on this journey, I counsel them, “Go slow to go fast.” Take your team on a shared learning journey. Invest 10 or 15 hours to establish a common language and base of technology understanding, clarifying all the questions I just mentioned and others. Second, visit companies that are further ahead in such transformations. Get inspired by what their leaders achieved and build your own confidence that you can do it, too. After that investment—and it’s not such a big investment—the alignment is there, and the top team can start to play their respective roles in leading the technology transformation.
Eric Lamarre is a senior partner in McKinsey’s Boston office. Sean Brown is the global director of communications for McKinsey’s Strategy and Corporate Finance Practice and is based in Boston.
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Rewired for value: Digital and AI transformations that work
The rewired enterprise: How five companies built to outcompete
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Table of Contents
Today’s most successful brands are adopting new technology, enabling them to transform the way they do business. Next-generation technology such as Artificial Intelligence (AI) can be a game-changer when it comes to the customer experience. AI powers applications like chatbots, which can help answer questions for your site visitors. AI can even recognize and answer multiple forms of the same question and can be trained to give instant responses using your preferred voice and tone.
This type of innovation may be included in what’s known as a digital business model , a form of creating value based on the development of customer benefits using digital technologies. The goal of these digital solutions is to provide significant advantages that customers are willing to pay for and to ultimately improve aspects of your organization. This could range from how your company acquires customers to what products or services you provide.
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Digital Offerings vs. Digital Business Model
Sometimes we confuse digital offerings with digital business. A digital offering is an addition to existing offerings, such as a product pp, chatbot, control interface, etc. On the other hand, digital business models create value, bring a fresh perspective, and provide USP to the customers.
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Characteristics of Digital Business Models
How do you recognize a digital business model?
For starters, digital business models are known for having the following four distinguishing characteristics:
- The value is created using digital technologies. When a service is based on digital technologies, it’s recognized as a digital business model. Take Amazon, Google, and Facebook, for example. These giants wouldn’t exist without the internet.
- The digital business model is new to the market. An example of this would be when you request transportation via an app (such as Uber or Lyft) that matches your request with a driver.
- To become a customer, you need to use a digital channel. Digital business models often rely on digital channels (such as Amazon) that show advertisements when you search online.
- The unique selling proposition (USP) is created digitally. This means that a customer is willing to pay for your products or services, and many times monetized online.
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11 Most Popular Types of Digital Business Models
What type of digital business model should you employ?
The best course of action is to first consider your customer profile when selecting the technology that’s right for your brand. This may include things like pain points, interests, buying patterns, and demographic characteristics.
As per Benjamin Talin , a digital transformation expert there are 11 digital marketing models.
1. Free-Model (ad-supported)
A free business model is one that makes use of and is supported by ads from platforms like Google and Facebook. The idea behind this model is to offer a service for free, making the user the end product. The online user provides valuable information that helps the company easily display targeted ads.
2. Freemium Model
This model is commonly used and allows users to get free access to a basic version of a product. This version may be somewhat limited, but the user has the option to upgrade and pay for a premium version should they want additional features. A great example of this is Spotify — you can use it for free, but if you want higher quality and no ads, you need to pay a monthly subscription.
3. On-Demand Model
This model refers to a virtual product or service such as online video stores like Amazon Prime Video or Apple TV where you can watch a video for a certain period of time. Another example of this model is the freelance and gig economy platform Fiverr, where you book an individual and get charged based on the project.
4. eCommerce Model
Amazon was one of the first and most successful companies to adopt this digital business model of selling physical products online. Today, eCommerce is one of the best-known business models on the web.
5. Marketplace Model
This model refers to a two-sided marketplace where sellers and buyers use a third-party platform to trade goods and services. Examples of this business model are service-based Uber and product-based eBay and Etsy.
6. Digital Ecosystem Model
Digital ecosystems are currently one of the most complex yet robust digital business structures. Alibaba, Amazon, Apple, Google, Tesla, and other ecosystem orchestrators exploit the customer with various services across several platforms. Due to the "vendor lock-in" impacts their ecosystems produce, they may upsell existing clients and attract new ones with their knowledge and data.
Consider what services you use from Amazon, Apple, Google, Alibaba, and other companies, and how difficult it would be to meander from their digital services and choose something else. The lock-in effect is also a significant revenue driver in the future. You don't have to be an ecosystem orchestrator; you might be an ecosystem user or a supplier of ecosystem modules. PayPal is an excellent example of a modular supplier. It allows for frictionless payment across various digital business models and ecosystems.
7. Sharing Model / Access-Over-Ownership Model
It's all about "sharing," but in a professional sense. This approach enables you to pay for a product, service, or offer for a specified time without actually owning it. Renting a car (e.g., Zipcar), an apartment (e.g., Airbnb), or even industrial gear are examples.
Due to its ramifications on ownership and the resulting revenues you may produce, this was one of the most disruptive business models. Instead of only creating costs, an automobile may become a cash source.
8. Model of Experience
Adding value to items that would not be feasible without using digital technologies. Tesla, for example, revolutionized the automobile sector by incorporating digital services and even a digital ecosystem into its vehicles, which is now a primary engine for its business model.
Another approach to the experience model is to mix several experiences to build a new customer-centric ecosystem.
9. Model of Subscription
We're all familiar with Netflix and Office 365. These are excellent instances of the traditional subscription business. On a monthly/annual basis, the user receives access, updates, services, etc. Subscriptions are particularly popular for content, software, and memberships.
10. Model of Open-Source
One of the most successful open-source examples is Firefox. The software is available for download, usage, and contribution to the global community. Because it is free and many people contribute, it spreads quickly. Usually, it attracts many (free) resources to improve the software. Firefox's business strategy relies on search engines for royalties and partnerships.
Because you might not be able to exploit the software for a sustainable business model, open source isn't necessarily a business plan. Red Hat distributes Linux for free and then makes money via training, services, and hosting.
11. Model for Generating Hidden Revenue
Customers may not always be able to see revenue generation at first glance. Other value streams may emerge as a result of data collection and analysis. We know that there may be hidden business models underlying platforms and digital services. As we saw with the Mozilla example, where the open-source browser earns money from licenses to integrate other search engines.
It's critical for businesses to understand their potential and whether there are further opportunities to combine an existing business model with another to produce additional revenue. However, concealed money production might backfire when dealing with data and unknowing customers. Cambridge Analytica is a beautiful example of a backlash like this, which resulted in serious ramifications for both organizations.
Apart from these, a few more business models exist.
- Club Affinity- collaborations with other organizations
- Services with Automation- automating services that humans traditionally perform
- Digital Business Model of Bundling - related products are packaged together.
- Crowdsourcing- making contributions rewarding and straightforward users for their contributions (usually money or a charitable goal).
- Digital Business Model of turning Data-Into-Assets- applying cutting-edge technologies to old industries
- Digital Business Model of Disintermediation- instead of using an intermediary to supply a service or product
The opportunities for success are abundant as digital becomes the new normal. However, knowing your destination is critical before choosing the digital business model and setting out on our entrepreneurial journey.
How to Create a Digital Business Strategy
No matter your brand, creating a successful digital business strategy is crucial to success in 2021 and beyond.
When you leverage the power of technology to build your business, you will undergo what’s known as a digital transformation . There are so many exciting and new technologies continuing to emerge, which makes forging a digital business model a necessity for your strategy.
“Every industry and every organization will have to transform itself in the next few years. What is coming at us is bigger than the original internet and you need to understand it, get on board with it, and figure out how to transform your business.” – Tim O'Reilly , Founder & CEO, O'Reilly Media
4 Important Dimensions for Creating a Digital Business Model:
- WHO is your target customer and what are their needs?
- WHAT is the value proposition and which products does it generate?
- HOW is the value proposition delivered?
- WHY is the business model profitable?
5 Steps for Developing Your Strategy:
1. prepare a detailed business plan..
Outline what your business is, your goals and visions, and how you plan to achieve them.
2. Identify Your Target Audience.
Narrow your audience down to two or three buyer personas. Outline the solutions that your company will offer.
3. Develop a Strong Value Proposition.
How will your company stand out among the competition? Establish what differentiates you.
4. Determine Key Business Partners.
Select key partners and strategic alliances that can best help contribute to serving your customers.
5. Allow Room for Innovation.
When developing your business model, leave room for future innovation. It’s important to ensure that your plan meets the needs of your customers.
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A Systems View Across Time and Space
- Open access
- Published: 16 January 2023
Disruptive business value models in the digital era
- Navitha Singh Sewpersadh ORCID: orcid.org/0000-0002-3219-7974 1
Journal of Innovation and Entrepreneurship volume 12 , Article number: 2 ( 2023 ) Cite this article
The coronavirus pandemic illustrated how rapidly the global environment could be disrupted on many levels but also drive an acceleration in others. Business leaders are grappling with dysfunctional business models that are ill-equipped to manage the disruptive environment of growing artificial intelligence. Hence, this study examined the discontinuous shift in the scope and culture of business models by exploring interdisciplinary streams of literature. An integrative review methodology was used in this study to develop theoretical constructs relating to business model innovation in the services sector. Key propositions were an innovation continuum, a responsive business innovation model and value architecture, which inculcates a sustainable value creation proposition and market advantage. Businesses must continuously evolve on the high end of the innovation continuum to reduce the risk of innovation apathy and strategic myopia. A key contribution of this study was the interdependencies in value networks that allow for collaborative working and co-creation of resources, such as crowdsourcing, crowdworking and social media platforms. This study also showed the growing importance of a centre of excellence to function at the forefront of disruptive technologies. A key finding was the need for governance structures to recognise and manage the trade-offs between value drivers, which sometimes may conflict with societal benefits. The integrative review revealed that customer relationship management, global business services and artificial intelligence had not been unified in the extant literature, which makes this paper novel in its contribution to businesses struggling with or opposed to the digital revolution.
The evolution of technology has disrupted almost every business globally by continuously transforming, enhancing, and streamlining operational processes and procedures. Digitalisation Footnote 1 is disruptive and brings about discontinuous changes (Paiola & Gebauer, 2020 ), but it is a key element for new value-creation and revenue-generation opportunities for market competitiveness (Kamalaldin et al., 2020 ). Climate change, pandemics, environmental devastation and widening social inequalities have created an abrupt realisation that the existing business models are no longer ‘fit for purpose’. New practices, skills, operational processes, and business models are required to use artificial intelligence Footnote 2 (AI) to create value for customers (Sjödin et al., 2021 ). It is increasingly important for businesses to understand the evolving environment to assimilate for viability in the market and then innovate to gain a competitive advantage. Businesses face pressure to focus on achieving their non-financial goals and not just maximising profits (Rabaya & Saleh, 2022 ). The interconnected elements of environmental, societal and governance (ESG) have provided a catalyst to transform businesses to be more responsive toward the planet and people when pursuing profitability and growth. “The illiterate of the twenty-first century will not be those that cannot read or write, but those that cannot learn, unlearn and relearn” (Toffler, 1970 ). Refining, adapting, revising and reformulating a business model provides businesses with a roadmap for achieving holistic goals by harnessing the strategic advantages of AI technologies.
Digital transformations create new potential for organisations to redefine and optimise their operations by recognising the role of automation Footnote 3 in creating market differentiation and service excellence (Flyverbom et al., 2019 ; Zuboff, 1988 ). The COVID-19 pandemic affected critical business functions across organisations globally, thus serving as an accelerator of digital transformations and the reconfiguration of static business models. The pandemic affected how people operate and customer services are provided, particularly when governments imposed regulated lockdowns to protect human life. According to institutional theory, internal and external pressures (Zucker, 1987 ) accelerate the desire or compulsion to transform an organisation. One such pressure is disruptive digital technology, and the other is the pandemic. The traditional workforce has also been transformed into a blend of humans working collaboratively with AI.
A global survey conducted by Deloitte (2020) found that the largest concern for respondents during the pandemic was the viability of their business models. Some businesses led the business model innovation Footnote 4 , while other companies crumbled. As the contingency theory proposes (Lewin & Volberda, 1999 ), a suitable strategy is required to accomplish a strategic fit with an organisation’s market. Therefore, business model innovation is a key ingredient in underpinning a business resilience strategy, particularly with technological innovation rapidly changing the nature of work. These pressures to innovate in the digital era have widened the gap between innovators and stragglers in the business world. The advantages of conventional business processes that are human reliant are weakening, exposing the fragility of the human capital leverage model, which will be further impacted as AI evolves. Therefore, innovation laggards may fail should they not embrace the principle of accelerating disruptive technologies in their business models. As global economies face unprecedented disruption, a once disruptive business model can become static by becoming complacent or relying excessively on past strategies that may have become outdated. This risk of innovation apathy or myopia motivates businesses to have an agile business model that continually evolves with the disruptive digital era.
A business model is seen as a robust abstract instrument to model a framework for a company’s competitive stance (Hamel, 2000 ) by connecting technical potential with the recognition of economic value (Chesbrough, 2011 ). However, Teece ( 2010 ) argued that approaches to business models are diverse due to the absence of a theoretical grounding in economics or business studies. For this reason, there have been calls for research on business models and value propositions Footnote 5 focusing on market differentiation and industry disruption (Weinstein, 2020 ). Emerging market differentiators are concentrated on labour automation, such as Robotic Process Automation (RPA) and service bots used in Global Business Services (GBS) (OECD, 2007 ; SSON, 2018 ). However, codifiability and digitalisation in the global services literature are absent despite the advantages of the centrality of transaction costs and efficiencies (McWilliam et al., 2019 ). There is an ongoing call for researchers to adapt and extend how AI technologies can be aligned with business (Coltman et al., 2015 ; Santos et al., 2020 ; World Trade Organization, 2019 ). Moreover, a persistent gap exists in academic research regarding the business models using AI for digitalising Customer Relationship Management (CRM) in the global service sector. A necessary first step toward knowledge evolution and model building is a systematic exposition based on theory (Melville et al., 2004 ) and disruptive technology (Parmar et al., 2014 ) that drive an understanding of business model innovation (Teece, 2018 ) to capitalise on business opportunities that overcome pandemic challenges.
With digital servitisation Footnote 6 (Kohtamäki, et al., 2019 ; Vendrell-Herrero et al., 2017 ), the service sector is no longer operating as a separate category, since retailers and manufacturers are entering the service sector with smart services, such as Caterpillar, Michelin, Siemens and Voith Group. They transform their products by embedding software to communicate to the data cloud (Ng & Wakenshaw, 2017 ), which can then be analysed through advanced data analytics for co-created value-added services (Opresnik & Taisch, 2015 ). This study selected the service sector to examine business model innovation, since it is people-centred and an important contributor to the economic environment. A GBS structure was adopted in this study, because it allows the researcher flexibility to incorporate innovative systems with global mobility for the service sector’s offerings. The GBS business model also provides benefits of economies of scale, streamlined processes, superior service quality and scalability of operations through consolidating support functions into a single centre staffed with specialists. This article provides crucial theoretical framing by linking the CRM, GBS and service innovation technologies to business model innovation. This study contributes an innovation continuum, a responsive business innovation model and value propositions focused on market differentiation, service innovation and industry disruption. This study also provided a research agenda to catalyse future research.
This study employed a methodical means of assembling and synthesising previous research (Baumeister & Leary, 1997 ; Tranfield et al., 2003 ) through an integrative review process of experimental and non-experimental research with theoretical and empirical data (Whittemore & Knafl, 2005 ). This study adopted a concept-centric rather than a chronological or author-centric approach (Webster & Watson, 2002 ) due to the inclusion of four streams of literature: GBS, CRM, service innovation and business models.
As Webster and Watson ( 2002 ) envisaged, the research process started with a protocol development to create a defined body of literature for the theoretical development of a responsive business innovation model. The protocol had three phases, as depicted in Fig. 1 . The first phase mitigated the incompleteness risk of the literature review by systematically identifying and reviewing existing databases. While the second phase remedied the overlap from different databases by filtering for duplicates, the final phase focused on creating a consistent structure among all patterns. There was rigorous screening and appraisal of each paper to assess whether its content was fundamentally relevant. A final sample of 79 high-quality articles was selected to build the theoretical constructs for this study. Other articles published by technology or accounting firms in this paper’s literature review and results section were used to establish current market practices. Whittemore and Knafl ( 2005 ) stated that the suppositions of the integrative review could be reported in tabular or diagrammatic form. Since the study intended to develop a theoretical business model in the form of a diagram, a thematic analysis was used to consolidate further and conceptualise higher levels of themes, constructs, patterns and descriptions from articles associated with GBS, CRM, service innovation technologies and business models.
Phases of the integrative review.
A theoretical framing is required for constructing a response business model. A business model provides a rationale, design or architecture for strategic choices to create, deliver and capture value (Magretta, 2002 ; Osterwalder & Pigneur, 2010 ) by specifying the structural elements and technology to address the unmet needs and activities of customers (Teece, 2018 ). Accordingly, organisational theory (strategic decision-making), customer relationship management (customer needs), global business service (structure) and service innovation technology provide the grounding for this research.
The institutional theory provides a multifaceted business outlook on normative pressures from external and internal sources that influence organisational decision-making (Zucker, 1987 ). It determines conventional rules and assumptions (Oliver, 1997 ), whereby conformance to these norms is compensated through improved legitimacy, resources and survival capabilities (Scott, 1987 ). Institutions provide social structures, rules and resources that are important to the service sector. Adopting AI in the service sector differentiates the fourth industrial revolution from the third (Schwab, 2017 ), which triggers adaptive structural processes that progressively change the organisation’s social interaction rules and resources that determine decision efficiency outcomes (DeSanctis & Poole, 1994 ). In the knowledge economy Footnote 7 (Powell & Snellman, 2004 ), greater reliance is placed on the intellectual capabilities of intangible resources as opposed to physical resources for decision-efficiency outcomes.
Extrapolating these theories to the fourth industrial revolution, it is apparent that there are challenges that organisations face to conform to the normative pressures of digital disruption that depend upon each company’s specific circumstances (contingencies). “ A good business model begins with an insight into human motivations and ends in a rich stream of profits ” (Magretta, 2002 pg. 3). Each organisation needs to find a strategic fit within the knowledge economy to gain value-driving opportunities while accelerating its customer-centric initiatives. For this reason, the customer relationship management (CRM) literature provides a framework to delve into human motivations concerning their buying incentives, biases and emotional connections.
Customer relationship management
The core of CRM is understanding customer needs and leveraging that knowledge to increase a firm’s long-term profitability (Stringfellow et al., 2004 ). In the digital era, technology may be leveraged to be customer focused to understand customer needs better. For instance, probing large data sets (big data) may inform CRM strategies (Payne & Frow, 2005 ; Stringfellow et al., 2004 ). Customer data is a rich source of unstructured, voluminous and ambiguous data for further processing through analytics. Data analytics are recommended for managerial strategic decision-making, since it is grounded in evidence rather than perception (IBA Global Employment Institute, 2017 ; McAfee, et al., 2012 ). Knowledge gained from data analytics is essential for building close customer relationships for service differentiation, customer loyalty and value creation.
Irrespective of the industry, the desire to nurture customers is a key success factor driving the need for CRM differentiators to gain a strategic competitive advantage. However, Stringfellow et al. ( 2004 ) criticised knowledge-deficient models developed from superficial customer data (demographics and transactions), since these do not address the functional (purpose-fulfilling) and emotional requirements of customers. They used the study by Schneider and Bowen (1999) to illustrate that decision-making is not dictated by functional needs, since a man may pay double the price to buy a Ralph Lauren polo shirt instead of a similar unbranded polo shirt to fulfil his self-esteem needs. This diversity in customer decision-making illustrates that relational selling may sometimes outweigh value-based selling. Therefore, any customer-centric business model should understand that buyers are not always rational but emotionally guided. For this reason, sales or services can be categorised as value-based to fulfil purpose or relational to fulfil the emotional connections to the product or service.
Global business services
According to OECD ( 2007 ), business services are provided to other businesses instead of customers. Organisations wanting to reduce costs enter the outsourcing market for lower-cost business services. However, within a GBS, various processes and functions are shared and operate unitedly instead of using several shared service centres and dealing with outsourcing vendors independently. The principal objective of GBS is to provide business-to-business services at a reduced fee and at contracted levels of quality that improve practice through lean, cost-competitive, efficient and streamlined processes with an optimised cost structure (Daub et al., 2017 ; OECD, 2007 ; SSON, 2018 ). This goal is achieved by leveraging a range of enablers, including a robust customer interaction framework, standardisation, economies of scale, automation, organisational realignment, labour/robotic arbitrage, implementation of best practices and true “end-to-end” process optimisation (SSON, 2018 ). Thus, companies leverage a GBS model to gain market advantage and operational efficiencies through an agile, focused and leaner service organisation. GBS integrates services that forsake functional silos and transcends to a multifunctional collaborative approach. GBS has an amalgamated delivery model providing “back-office” services to a global customer base, such as accounting, finance, HR, IT and procurement, and increasingly moving to “front office” activities, such as sales, marketing, analytics and reporting (SSON, 2018 ). Currently, businesses are focussed on services related to their digital offerings and the analytics of their customers’ data. Geographical expansion, innovation quest and the adoption of new technologies are important in pursuing profits when competition is rife (Hodgson, 2003 ). GBS, with AI technology, has an opportunity to achieve scalability by integrating its multitude of centres into a single network to expand its range of business across the globe for a competitive advantage.
Most GBS users depend heavily upon intangible assets, particularly technological and service innovations (OECD, 2007 ). GBS centres can integrate automation, virtualisation and analytics, amongst other digital tools and capabilities, into their prevailing processes that provide more effective support to business units (Daub et al., 2017 ). Global organisations, such as Siemens, have incorporated a GBS-type structure into their global multifunctional business model that provides shared services to all Siemens businesses. The two fundamental principles that guide this organisation’s international services centres are customer satisfaction and continuous improvement through innovation (Siemens, 2020 ). For this reason, the GBS-type structure has extended to accounting firms, with their large global networks increasingly centralising certain remote auditing functions through technology and then outsourcing geographic-dependent work to their component auditors. For the longevity of any business, new organisational designs need to evolve that shape human workers, such as service innovation technologies.
Service innovation technologies
The innovation theory proposes that innovations diffuse from early adoption to widespread use (Rogers, 1995 ). However, innovations have a lag effect on their relative advantage (profitability, social prestige, other benefits) over its predecessor. In defining a technology readiness index ranging from innovators to laggards, Rogers ( 1995 ) elaborated on the speed of the adoption being positively related to the perceived benefits, compatibility with the company’s structures, ease of use and trialability (experimental capability). The innovation diffuses at the rate at which an innovation’s results are visible to others (observability). However, the complexity of the innovation is negatively related to the speed of the adoption. Understanding innovation theory is central to constructing or transforming a business model.
The quadruple-helix theory proposes that society can drive the innovation process to design sustainable strategies to achieve social innovations in a green economy (Carayannis et al., 2012 , 2020 ). ESG goals are increasingly being demanded by stakeholders to be incorporated into business models. The focus on ESG has led to traditional business models integrating sustainability while undergoing digital transformation. A sustainable business model delivers multifaceted value to a wider range of stakeholders when compared to the traditional business model (Bocken, et al., 2013 ). Digital technologies allow for strategic planning on economic, social, and environmental performance (Evans, et al., 2017 ). For instance, social network platforms may assist companies in achieving their ESG goals allowing companies to move closer to a green economy. Platforms are technologies that facilitate networking for companies to co-create with stakeholders (Allen, et al., 2009 ). A concept is drawn from the microworking philosophy (Howe, 2008 ), where a large dynamic network enables the organisation to connect with the internal and external environment for co-creation opportunities. Close company–customer collaboration allows for long-term value co-creation (Kamalaldin, et al., 2020 ), where customers co-produce services by providing insights. Types of co-creation opportunities are the wisdom of crowds Footnote 8 (Surowiecki, 2004 ), open innovation Footnote 9 (Chesbrough, 2003 ), crowdsourcing Footnote 10 (Howe, 2008 ) and crowdworking Footnote 11 (Ross, 2010 ). A common feature of these co-creation opportunities is that they all use an open call for knowledge to create innovative solutions. Amazon Mechanical Turk and Uber are examples of the crowdworking philosophy using digital platforms to build networks in the service sector. Leveraging society’s connectivity and responsiveness through platforms facilitates the collaborative designing of personalised products, services and experiences.
Technologies such as RPA and service bots have been widely adopted in the service industry. RPA interacts with the user interface of other computer systems using rule/logic-driven software robots (softbots) that are coded to execute a high volume of repetitive tasks without compromising the underlying IT infrastructure (Deloitte, 2018 ; van der Aalst et al., 2018 ; Willcocks et al., 2015 ). This technology dates to the Eliza programme’s interactive bots that enabled interaction between humans and machines using text-based communication (known as the Turing test) (Turing, 1950 ; Weizenbaum 1966 ). RPA follows prescribed protocols and procedures that increase the speed, accuracy, compliance and productivity of business processes. Footnote 12 Instead of multiple ERP solutions (taking data from one system and inputting it into another system), it is more cost-effective and efficient to integrate RPA into a company’s existing infrastructure and automate processes (van der Aalst et al., 2018 ). However, RPA is on the lower end of intelligent automation, since it uses structured logic and inputs to operate from simple to complex business tasks.
RPA with cognitive automation has allowed softbots to be more useful due to their superior intelligence. Softbots with machine learning Footnote 13 capabilities are designed to mimic human thought and action to manage and analyse big data with greater speed, accuracy and consistency than humans can achieve by leveraging different algorithms and technological approaches (Firstsource, 2019 ). Algorithms do not produce definitive solutions but present probability-based predictions for humans to evaluate and make informed decisions. Table 1 provides a summary of the Softbots.
Softbots are also known as service robots, chatbots, AI bots, AI assistants, virtual assistants or agents, and digital assistants or agents. This study adopts the term service robots, since they are most common in customer support or sales environments, where they are expected to serve customers. For instance, call centre jobs are labour-intensive and employing people’ around the clock’ for one or two late-night phone calls are costly. However, service bots can answer simple queries efficiently and far quicker than a person can. Service bots use Natural Language Processing (NLP) to develop logic from unstructured inputs for human interaction. Service bots with NLP, Natural Language Understanding (NLU) Footnote 14 and Natural Language Generation (NLG) Footnote 15 are distinguished from the greater domain of service bots due to their aptitude to employ language to converse with their clients. Table 2 shows the different types of service bots.
Kiat ( 2017 ) states that service bots can manage CRM quality by handling mundane tasks leaving salespeople to focus on high-value tasks, such as meeting customers and concluding company sales. In general, leads should be attended to within 5 min to convert them to paying customers, which would be achieved with service bots. Other advantages are:
Seamless interface: bots can recall their previous customer interactions and seamlessly verify customer data by linking to social media, so queries are addressed at a speed unmatched by humans. Service bots can also seamlessly transfer complicated cases to human operators, facilitating humans’ foci on higher value customer engagements.
Data enrichment: cost-effectively resolving data leakage problems, since humans often neglect to record key customer information from the various stages of the customer’s purchase process, whereas a service bot would automatically capture the discussion.
Service bots are key differentiators within the IT industry with improved revenue performance and customer value (customer contentment, service delivery and contact centre performance) (MIT Technology Review, 2018 ). Service innovation technologies are employed by renowned brands, such as Amazon, Netflix, Starbucks and Spotify, to name a few. Service bots work reliably and accurately around the clock while maintaining the same competence level without being distracted or fatigued. Service bots also do not have inherent limitations, such as becoming ill, going on strike or requiring leave. In 2019, the banking sector achieved operational cost savings of $209 million from employing service bots. Insurance claims management departments had cost savings of $300 million across motor, life, property and health insurance (Juniper Research, 2019 ). Artificial Solutions ( 2020 ) also reported that Shell attained a 40 per cent decrease in call volume to live agents due to their service bots, Emma and Ethan. They answered 97 per cent of questions correctly and resolved 74 per cent of digital dialogues. Similarly, the service bot Laura is digitally transforming Skoda (a Volkswagen Group’s subsidiary), where customers can discuss their vehicle needs and budget with Laura (Artificial Solutions, 2020 ). Therefore, digitalisation has resulted in customer relationships evolving from transactional to more relational.
Results: theoretical propositions
Several constructs emerged from the thematic analysis of the integrative review for developing a digital business model, reflected in Table 3 .
Using the people, process and technology (PPT) framework (Leavitt, 1964 ), these ten constructs from Table 3 and innovation capabilities are presented in Fig. 2 . This study has added governance to the PPT framework to form the PPTG framework. Governance is imperative for oversight over the value-creating activities (Sewpersadh, 2019a ) to balance the trade-offs from the synergistic benefits of lower costs, increased coordination, greater productivity and value delivery with the ethical and risk concerns over customer data.
In Fig. 2 , people have been expanded to include service bots. Collaboration between service bots, employees and customers are integral for value co-creation. Service bots cost-effectively record customer information from the various stages of their service interactions, allowing for data warehousing. Data warehousing is important for allowing data mining tools and the analysis of critical customer parameters.An ethics and risk officer will play a key governance role in overseeing the principles of fairness and ethics over emerging technologies, such as service bots. Increasingly companies integrate their AI technologies with social media platforms which necessitates the ethics and risk officer to detect, correct and prevent any biases that the service bots learn through the data they collect. For example, service bots may discriminate against customers based on their demographics (Puntoni et al., 2021 ). In 2016, Microsoft launched a service bot called Tay to research conversational understanding. This project failed, because the developers did not anticipate that some Twitter users would teach the bot to make racist, inflammatory and offensive tweets through its Twitter account (Berditchevskaia & Baeck, 2020 ). For this reason, recent studies proposed digital corporate responsibility to guide ethical dilemmas related to AI technology (Lobschat et al., 2021 ). There are also ethical and security risks when service bots impersonate humans (van der Aalst et al., 2018 ), since they may make improper judgements due to contextual changes that may remain undetected, leading to unintended consequences. For instance, service bots may make poor-quality recommendations that do not align with customer interests or may expose customers to vulnerable and risky situations (Mullainathan & Obermeyer, 2017 ). Service bots require service audits to prevent poor service quality outcomes. Service bots also have excessive access and privileges that place them at risk of cyber-attacks. The ethics and risk officer may assist in safeguarding data using surveillance methods to detect intelligent malware. Footnote 16 Research has found that customers are more likely to act unethically and misbehave (LaMothe & Bobek, 2020 ) when interacting with service bots. Therefore, service bots need to be monitored to detect and prevent these infringements.
In Fig. 2 , PPTG is improved with technologies for process value configuration. Technology with people allows for smart analytics on service value capture and optimisation. For example, service staff, key accounts managers and digital developers in Solutioncorp evaluate customer service data to identify priority areas for AI innovation (Sjödin et al., 2021 ). This dispersion of emerging technology gives rise to a disruptive landscape in the knowledge economy, necessitating more R&D and continual business model innovation. The three overarching themes from the constructs presented in Table 3 are innovation, sustainable business models and value creation, which will be discussed further below.
The rapid pace of the evolution in technology innovation accelerates the diffusion of innovations (Rogers, 1995 ). The increased R&D in innovation creates a continuum (Fig. 3 ), where companies are not statically classified according to their degree of innovation but rather placed on a continuum. Those businesses that recognise innovations’ relative advantages, compatibility and trialability (Rogers, 1995 ) will move to the higher end of the continuum. Although, a high-innovation company may not remain a disruptor in the market if it becomes complacent or myopic with its innovation strategy and neglects to continuously improve its business processes. This complacency can be explained by the icarus paradox, where success may lead to a path of convergence with an emphasis on the same strategies, which may simplify and desensitise divergent evolving demands (Elsass, 1993 ; Miller, 1990 ). Past successes promote a defensive mindset and overconfidence, resulting in the persistence of the same strategic formulas when executing innovative strategies is the most appropriate response (Sewpersadh, 2019b ) to the market’s changing needs. Thus, this paradox may lead to myopia, complacency and inertia. This complacency leads to a condition of ‘unconscious incompetence’, where the lack of knowledge of the availability of advanced technologies leads to suboptimal decision-making or decision paralysis on deploying such technologies. For this reason, the degree of innovation is bidirectional on the innovation continuum, which allows for the acceleration and deceleration of innovation investment. As business models transition from traditional to transformative ones, eventually evolving into disruptive ones, those with myopic capabilities soon find their business models antiquated. When companies intensify their investment in innovation, they adopt a futurist strategy allowing them to transition up the innovation continuum and challenge complacent companies.
Rogers ( 1995 ) cautioned that insufficient knowledge, inability to predict consequences or overzealous innovation investments might lead to over-adoption. Also, the complexity or incompatibility of innovations may not be suitable for some businesses, which may jeopardise their positioning on the continuum. For this reason, governance structures, such as a digitalisation committee, are important for moderating the firm’s adoption strategy. This committee will assess the suitability, acceptability, feasibility and sustainability of developing or acquiring innovations. Integrating stakeholder networks in collaborative activities creates trust-based relationships, legitimacy and good governance that allows for the acceptability of innovations. In Fig. 3 , governance optimisation is vital for ensuring value-maximising decision-making concerning value-creating activities for all stakeholders (Sewpersadh, 2019a ).
There could also be a reluctancy to allocate resources for R&D due to a digital paradox (revenue growth is not as expected despite the proven growth potential) (Gebauer, et al., 2020 ). For these reasons, value creation and governance optimisation are unidirectional factors in Fig. 3 and are placed on the high end of the continuum, where disruptive business models operate. Governance is essential to moderate the negative effects of an over-adoption, complex or incompatible innovations and the digital paradox. Good governance is also critical for balancing trade-offs when making strategic decisions. For instance, harmonising the need for legally protected intellectual assets for profit maximisation and sustainability with knowledge sharing to build collaborative networks.
Central to the innovation process is the need for firms to create and acquire “new combinations” of knowledge. Based on the resource-based theory, complementary assets and capabilities are scarce but valuable strategic resources, since they have strong path dependencies that are difficult to imitate (Barney, 1991 ), thus shaping the firm’s competitive advantage in the cooperative network. Since companies compete in a capital-intensive space, with barriers to entry and economies of scale, profits may be achieved with the legal protection of competitive advantages, such as closed innovation. Closed innovation is the internal research within a particular company that is generally protected by patents, so that access to that innovation is controlled by the rightsholder (Chesbrough, 2003 ). Progressively, open innovation has become a way in which key resources are obtained for the development and execution of innovation (Chesbrough, 2003 , 2011 ). Open innovation is a means of sharing costs, ideas, synergies and skills (Chesbrough & Crowther, 2006 ) from value networks to co-create innovation rather than an individual company outlaying capital to conduct R&D from scratch. For this reason, in Fig. 3 , the networking capabilities of a company also follow the direction of its innovation policy due to the collaborative work with extended networks that allow for the acquisition of external knowledge. As innovation diffuses, collaborators within forged networks stimulate newer co-created innovations with superior outcomes.
A significant limitation to knowledge sharing is the disclosure of internal knowledge to external collaborators (Cassiman & Veugelers, 2002 ), commonly referred to as the risk of knowledge leakage (Gans & Stern, 2003 ) or the “paradox of openness” (Laursen & Salter, 2014 ). This paradox describes the fundamental tension between knowledge sharing (value creation) and knowledge protection (value appropriation) in open innovation. Open innovation may increase the imitation tendency of mimetic companies, who benefit from incurring fewer costs and inefficiencies with access to extended networks. Therefore, a company’s position on the continuum and its competitive stance in the industry depends upon its ability to remain at the technological forefront. Consequently, open innovation also poses significant governance challenges to monitoring, controlling, and managing intellectual property rights in enterprise innovation (Graham & Mowery, 2006 ). Hence, risk-averse companies usually have linear business models with a unilateral dependency on internal resources. This tendency to be an information hoarder lends itself to a closed innovation competitive stance. For this reason, the company’s risk strategy must also be considered, since innovation pioneers may be more risk-tolerant than those with more traditional business models. As newer, more revolutionary technologies become available, static business models with poor networks risk being on the low end of the innovation continuum. Companies that have failed to keep at the forefront of technology do not have sustainable business models and may lose their extended networks.
Sustainable business models
The diminishing competitiveness of traditional business models (McGrath, 2010 ) has led to a fundamental rethinking of the firm’s value proposition for new prospects (Bock et al., 2012 ) on refining how an existing product or service is provided to the customer (Velu & Stiles, 2013 ). Reconceptualising structural elements for technology and resource capitalisation to create new activity frameworks and networks aimed at clear value propositions is known as business model innovation (Battistella et al., 2017 ; Hamel, 2000 ; Helfat et al., 2007 ). Therefore, business model responsiveness becomes a critical success factor in addressing challenges in the knowledge economy. A business model’s alignment and coherence should be mutually reinforcing and incorporate a response to the concomitant influence of contextual factors (Dehning & Richardson, 2002 ; Melville et al., 2004 ; Schryen, 2013 ) and lag effects on firm performance (Schryen, 2013 ). The responsive business innovation model, in Fig. 4 is a hybridisation of prior value models with interlinkages to current service technologies employed in the market, including digital platforms, crowdsourcing, blockchain, crowdworking, big data and service bots.
Responsive Business Innovation Model.
Figure 4 ascribes to Santos et al. ( 2015 ), where the model is more about “how is it being done?” than “what is being done? It incorporates an iterative strategy that maps cross-functional relationships between innovations and the underlying activities to be responsive to the evolving economic environment. Large corporates often use share centre services to support their network of firms under a GBS structure. However, with the evolution of AI, the GBS structure can evolve into a digital platform business model. A responsive business innovation model focuses on facilitating interactions across many shared centres by providing a governance structure and a set of standards, so that they operate as one cohesive ecosystem. It is an activity system with interconnected and interdependent activities to satisfy the market’s perceived needs (Foss & Saebi, 2018 ).
The responsive business innovation model enables the acquiring, developing, and integrating of key resources to overcome inertia. Introducing a new business model into an existing organisation is challenging and may require a separate organisational unit to redefine and reconfigure the model. For example, General Electric (GE) experienced business model transformation conflicts when they tried to adopt digital servitisation. There were conflicts between digital and physical service offerings, new ecosystem partnerships and traditional supply chain relationships, digital revenue and product sale models (Moazed, 2018 ). For this reason, positioning a Centre of Excellence (COE) is important, since it can provide the organisational structure, methodology, skills, tools and governance framework for handling the future innovation needs of a large global corporate (SSON, 2018 ). A GBS structure includes a COE for higher level business support and specialist work and thus is incorporated in Fig. 4 . COE comprise of a centralised specialist team to promote collaboration and provide higher value services, resulting in economies of scale. COEs focus on agility, Footnote 17 CRM and talent development while standardising and automating cross-function end-to-end process ownership), resulting in reducing costs and harnessing process efficiency (SSON, 2018 ). Examples of these are procure-to-pay (supply chain and accounting) and hire-to-retire (HR and accounting. The positioning of the GBS is better placed by groups of talent (area of expertise) rather than location, function or lowest costs.
The CRM literature provides a framework to delve into human motivations concerning their buying incentives, biases and emotional connections. For this reason, CRM is at the heart of the business model with AI differentiators (McAfee et al., 2012 ; Payne & Frow, 2005 ; Stringfellow et al., 2004 ) that responds to evolving consumer behaviour and expectations. The deep knowledge of consumers’ emotional and functional needs allows businesses to optimise capital to address those needs. This strategic response to customer needs and experience requires standardisation (lower costs, benchmark service quality) and differentiation (premium service). For instance, businesses could standardise business processes through RPA for efficiency gains but personalise services via service bots for market differentiation.
Service bots are key components of a digital strategy for entities searching for innovative and cost-effective means to build closer customer relationships (Artificial Solutions, 2020 ). With a GBS structure, the service bots may need to be multilingual due to the diversified client base. Furthermore, by integrating with social media (shown in Fig. 4 ), service bots can access clients’ online data and learn their preferences, sentiments, outlooks and proclivities. The data from clients’ online presence are often undervalued, but access to this enables businesses to transcend beyond basic business intelligence. Therefore, the service bot’s initial customer interaction will offer a superior service through seamless verification of personal information (similar to the Facebook sign-up process) and quick information transfer through hyperlinks. A seamless trail of conversations can be achieved whenever users swap from device to device (cross-platform Footnote 18 ), since this practice improves engagement and customer fulfilment (Artificial Solutions, 2020 ). The increased customer engagement means more actionable and enriched data to train service bots to personalise the customer’s experience. In so doing, service bots can service customers more competently and cost-effectively without human error (Artificial Solutions, 2020 ; Kiat, 2017 ).
A limitation of service bots is that humans can notice tone and subtext in a way that a service bot could never master. This disparity calls for cross-functional collaboration between service bots and higher skilled humans, transitioning toward blended workforces. Data-centric CRM harness the potential of big data to focus on not only the functional but also the deeper psychological aspects of buying behaviour (Stringfellow et al., 2004 ). Access to client data is essential for value creation (Paiola & Gebauer, 2020 ) to improve existing services and create novel innovations (Opresnik & Taisch, 2015 ) within the confines of privacy laws. Automating customer interaction with service bots (see Fig. 4 ) allows for a higher degree of message personalisation without increasing personnel costs. In-depth analysis of unstructured conversational data conveys perceptions on what is done well or what can be improved by the business to develop market differentiators for a strategic competitive advantage. Smart analytics, such as sentiment analysis, support businesses in gauging their customers’ mindsets Footnote 19 and analysing the customer’s journey more effectively while remaining within the confines of data safety legislation.
Strategy guides and shapes by including the company’s brand reputation, Fig. 4 . The iterative CRM engagement strategy and value outlook (short, medium and long term) is built from big data collected from the AI-led CRM and crowdsourcing from their networks. This process allows companies to leverage their large network of end-users to inform the co-created products, services and experiences. A large network also provides microwork opportunities through crowd-working platforms for comprehensive support and supplement human labour. However, managing the trade-offs between stakeholders, technology, and societal benefits is important. Stakeholder engagement is essential in identifying key stakeholder requirements for these benefits to occur. Accordingly, business models should recognise and incorporate environmental, social and governance (ESG) goals, whereby trade-offs must be managed. For instance, automation disrupts the human capital leverage model, in which a trade-off exists between harmonising the prospective savings from automation and the human impact of job losses. Due to the escalation of global warming, business models must also incorporate innovative sustainable environmental solutions (Carayannis et al., 2020 ). Therefore, innovations must be expanded beyond service innovations to ESG innovations.
In Fig. 4 , the benefits of using blockchain technology in a business model are also presented. Blockchain represents an endlessly accumulating list of records stored in “blocks” protected using cryptography principles (Arnaut & Bećirović, 2020 ). The peer-to-peer protocol ensures unambiguous and common ordering of all transactions in blocks, a process that guarantees consistency, decentralisation, integrity and auditability (Arnaut & Bećirović, 2020 ; Yuan & Wang, 2018 ). These features make the blockchain’s permanent ledger resistant to data manipulation, which is a value contribution to the company.
A business model’s lifecycle involves “periods of specification, refinement, adaptation, revision and reformulation” (Morris et al., 2005 pg.732). The business model’s initial period in the lifecycle has a process of trial and error, where core decision-making delimits the firm’s evolution. For this reason, a value creation cycle is essential to harness a sustainable competitive advantage by continuously refining, adapting, revising and reformulating a business model to counteract the limitation of becoming static. In Fig. 5 , the importance of the continual assessment of the contextual factors, and the suitability thereof, feed into the value creation cycle necessitating the need for change. However, the suitability of this change must be assessed in terms of the company’s contingencies. Research is necessary for informed decision-making on whether the change is incremental versus transformative to reap all the benefits and value that innovations offer. For value creation, the decision-making process should be free from bias and consider the business’s ESG values, goals, and trade-offs. It is also important to be cognisant that there is a time lag before benefits can be realised. A value architecture may also assist in alleviating some of the trade-offs, particularly structuring a digitalisation committee.
Value creation cycle.
The value architecture (Osterwalder & Pigneur, 2010 ), presented in Fig. 6 , allows a responsive business innovation model to capture and create market activation to build the deep, compelling experiences customers desire with service-related products. However, there is a need to balance the trade-offs between conflicting value drivers. For instance, costly R&D may have environmental consequences that conflict with the desire to provide a good return on capital. For this reason, a clear value preposition is the first step in the value architecture. A value preposition is the underlying economic logic explaining how value is delivered to customers at the appropriate cost (Magretta, 2002 ). The building blocks of value proposition, configuration, delivery and capture (Osterwalder & Pigneur, 2010 ; Osterwalder et al., 2005 ) must be considered to develop a sustainable competitive advantage for the organisation (Teece, 2010 ). While the value preposition remains customer centred, the value configuration and capture are focused on relational selling using technological innovations. While the value delivery is focused on efficiency and service optimisation using service innovations.
With the global environment moving so swiftly, multidisciplinary research is necessary to condense and intensify business knowledge. This study highlights the need to examine the discontinuous shift in the scope and culture of business models by exploring interdisciplinary streams of literature. An analysis of the recent literature revealed a lack of research fusing automated technologies in the business models of CRM-intensive companies. This study bridged the theoretical frameworks of organisational theories to learn how contingent characteristics influence the design and function of business models. A key contribution was the inclusion of structural elements (GBS, CRM and AI) to design a responsive business innovation model to create, deliver and capture value. It was established that AI-led CRM in a GBS structure yields a greater focus on generating innovative services that satisfy customers’ emerging needs as well as balance ESG goals. Instead of just customers just being consumers, they can be strategic networks to collaborate and co-create outcomes by integrating CRM and AI technologies into a GBS structure.
Global businesses must update their cost focussed models to transcend into the digital age by moving forward on the innovation continuum model and refocussing on customer-centric service innovations to thrive in this evolving environment. An over-reliance on past successful formulae and static business models leads to the eventual demise of AI-complacent companies. A prime example was seen during the COVID-19 pandemic when some businesses adapted swiftly to the enforced lockdowns using more digital avenues of earning revenue, while others failed to advance up the innovation continuum and closed their businesses, resulting in the loss of millions of jobs. The COVID-19 pandemic is not the only crisis faced by the global economy, since there have been other life-threatening epidemics, such as the Zika virus, MERS, Swine flu, SARS, Aids and Ebola. Businesses need to adapt to the ever-changing environment with cognitive flexibility and agility to transform their business in the wake of any crisis. Structures such as the COE may assist companies in averting the risk of unconscious incompetence in respect of evolving AI and place them at the forefront of the innovation continuum for sustained viability. Static business models can use existing digital platforms to enhance their services, enabling them to move up the innovation continuum. These businesses will have collaboration and co-creation opportunities from the large networks on the high end of the innovation continuum.
This article illustrated the benefits of AI, specifically how service bots can assist in creating new and improved business models in business-to-business and business-to-consumer markets with CRM adoption. Since service bots are a market differentiator, businesses at the forefront of service innovation are assured of resilience, even when faced with the threat of a pandemic. Service bots use real-time data to predict and influence customer behaviour, preferences, buying incentives, and spending tendencies. The un-leveraging of the human capital model has accelerated at an unprecedented level amid the COVID-19 pandemic and is foreseen as being at its most impactful in the post-pandemic period. The effects of AI technology on the human capital leverage model vary depending upon humans’ skills set. AI technology is negatively associated with low-skilled workers but significantly positively influences highly skilled workers.
Multinationals have better opportunities than single-country competitors to experiment with various business models in different geographies and then transfer those validated models to all geographies in which they can capture value (Teece, 2014 ). In the digital transformation era, customer-centricity and global marketplace competition, shared services have evolved from outsourcing to in-housing/re-shoring a GBS model for developing a single and consistent approach to providing internal customer services across functions and geographies. For GBS to stay at the forefront of service delivery development and remain competitive, GBS leaders must leverage and scale these new technologies. GBS’s global reach and governance, standardised processes, extended business process ownership and use of consistent operating models and technologies make them ideal candidates for implementing and delivering the aforementioned AI arbitrage benefits for their operations. This study has illustrated the tremendous strides made in AI technologies, whereby AI investment does not comprise resource-depleting disbursements but encompasses intangible assets through which the system autonomously learns and continually advances. These digital avenues provide key market differentiators in customer service.
Management cannot rely exclusively on in-house expertise and needs the benefits of mechanisms, such as crowdsourcing and crowdworking, to create a comprehensive sustainable business model. However, regulators need to be wary of the potential ramifications of crowdsourcing and crowdworking, since opportunistic companies may exploit these platforms for cheap labour. Blockchain must be considered when proposing disruptive models due to its revolutionary potential. As businesses move to scale their digital ingenuities, a focus is placed on the agility to respond to consumers’ evolving tastes with diminishing lag times due to the availability of real-time data.
Inevitable changes in business models are necessary as organisations shift how they create, capture and deliver value. For these reasons, this study developed key value drivers grounded in the theoretical framework. The key findings of this article are the various conflicting trade-offs between value drivers and ESG goals in digital business models that require executives to harmonise. Some examples of these trade-offs were:
the societal impacts of human job losses conflict with the efficiency and cost benefits of cognitive automation,
the utilisation of customer conversational data conflicts with remaining within the confines of data protection legislature,
the cost of software intrusion detection systems to avoid losing confidential data conflicts with the desire to maintain profitability margins,
the cost of innovation R&D conflicts with the desire to provide a good return on capital, and
the standardisation of processes conflicts with the customisation of services to avoid the loss of strategic competitive advantage.
This study identified governance as a key mechanism in managing ethical issues and risks. Concerns about consumer privacy may cause governments to prevent some important innovative developments in global services (World Trade Organization, 2019 ). Data security is a crucial concern for any business due to security risks when handling customers’ personal information. For example, in 2018, Facebook was guilty of invading users’ personal data and giving this information to other large corporations, such as Amazon, Microsoft and Spotify, to increase Facebook’s users and revenue (Dance et al., 2018 ). Although regulatory user protection laws exist, businesses must employ centralised data management with cognitive analytics capabilities, encryptions, independent security audits and codes of practice. Personal identifiable data is a highly valuable commodity in the digital age but is also unsafe, since any data breaches will result in customers losing trust. Kelley ( 2019 ) recommends that a successful security protocol is to program service bots to identify personal and/or sensitive information and treat it accordingly. Systems must be able to anonymise or pseudonymise conversational data, replacing identifiable data with placeholders, so users can still understand the intent for analytics purposes but not know the customers’ identity (Kelley, 2019 ). Despite the challenges of surveillance and privacy issues, digital technologies are increasingly central to people, organisations and societies (Flyverbom et al., 2019 ). For instance, the UK government has invested more than £1 billion into an AI industrial strategy (Berditchevskaia & Baeck, 2020 ), thus, illustrating that some countries have grasped the opportunity to build value-added resiliency into a service delivery model.
Companies should be aware of their business model lifecycle to avoid becoming stagnant. Therefore, it is recommended that they adopt a responsive business innovation model with a value-creating cycle to continuously refine, adapt, revise and reformulate their business model. To achieve this, companies should also have an innovation strategy driving a customer-centric service innovation culture while reducing costs and leveraging the finest skills. Organisations should consider establishing a COE with an innovation leader to be at the forefront of innovative technologies.
The COE would seize, assess and manage cognitive automation technologies for data governance. The COE is vital for providing leadership, driving change, and influencing business strategy and multiple onboard stakeholders across the business. COE’s essential function is driving an automation strategy as follows:
Develop an iterative strategy to extend and expand existing capabilities through automation.
Drive a holistic AI-enabled disruptive operating model, similar to the model proposed in this article, that is cost-efficient and leverages ‘fit-for-purpose’ technology to inspire ‘out-of-the-box’ thinking and nurture an entrepreneurial ethos.
Incorporate and harness a digital platform strategy management that accelerates the rate of digital platforms to realise cost savings and drive resiliency.
Initiate regular consolidating and mapping of business processes to identify areas of duplication and labour-intensive processes for an automation analysis to appraise potential benefits.
Create an AI-intensive GBS with an effective COE to use cognitive automation technologies in customer-centric service delivery.
Ensure CRM focuses on new customer onboarding forms and data-driven methods.
Benchmark against industry and competitors to ensure that the company’s technology has a competitive advantage.
Create consistent and frequent communication channels between COE and those charged with firm governance.
Design a data governance model to determine control and direct the use of data (how and for what purpose).
Create guidelines on data protection, privacy, intellectual property rights and ethical issues in data management.
It is also highly recommended that the public sector employs AI-intensive technologies, specifically RPA and service bots, that can streamline business processes. This sector’s work is extremely labour intensive, which is inefficient and resources depleting, given the recent rise in digital technologies. The large burden placed on taxpayers to supplement the ever-increasing public sector budgets is not met with improved outcomes. Lower level public officials’ mundane and repetitive work, such as capturing information from one system to another, using ineffective reporting templates and manual month-end tasks, are time-consuming, costly and widen the margin for human error. The public sector is also continuously dealing with fraud, tender bribes and schemes that impair its ability to deliver public services efficiently. The employment of digital agents can improve and expedite these laborious, inefficient and frustrating processes and, even more importantly, alleviate fraud to some degree.
Future research agenda
This study focused on the value of service innovation technologies in responsive business innovation models. However, there is an abundance of future research explorations in the list below, which is not exhaustive.
Research that empirically tests the customers’ satisfaction journey with digital workers versus human workers, particularly from a customer demographic perspective. For instance, NLP has made strides in making service bots more humanlike. However, there needs to be research that interrogates which customer demographics are more amenable to service bot services and which are not. Furthermore, research needs to be conducted on service bots’ ability to match their customers’ evolving needs.
There should also be studies examining the emotional consequences on customers when their needs are addressed by service bots, particularly from a customer demographic perspective and any potential extensions to service bot biases.
Research examining customers’ concerns over privacy and data leakages and which service bot interactions are more likely to trigger these concerns.
Investigations into the potential impact on the company reputation/brand when faced with negative service bot interactions and biases, amongst others.
Research investigating potential trust or control issues when customers and employees rely on work performed by service bots.
Public service sectors
As with institutional theory, government intervention is also necessary for a functional digital ecosystem concerning infrastructure and access to funding and investment resources. Studies should investigate government funding structures to encourage more innovative R&D.
An appraisal of the public sector’s readiness for the digital transformation of their business model, since automated processes will result in societal benefits of service efficiency and tax savings for citizens.
An empirical study on the suitability of a GBS innovation model for the external audit service. Due to the nature of their service, there is potential for a suitable fit.
Open source/collaborative technologies
An investigation into the use of open innovation systems and collaborative platforms in assisting start-up companies with their digital transformation.
Availability of data and materials
Freely available using online research databases.
Digitalisation or digital transformation is the use of AI technology in the business processes and activities of a company.
AI is distinct from conventional information technology and is defined as the ability to learn, connect, assimilate and exhibit human intelligence.
Automation is defined as the employment of technologies to perform a process or task that reduces human intervention.
Innovation in terms of this research refers to business model, service and technological innovation.
A value proposition enables stakeholders to understand how the business intends to use its strategic resources, which is then mapped to the business model.
The transition from products and add-on services to smart solutions with connectivity, monitoring, control, optimisation and autonomy is known as digital servitisation.
The knowledge economy is defined as production and services based on knowledge-intensive activities that contribute to an accelerated pace of technological and scientific advance as well as equally rapid obsolescence (Powell & Snellman 2004 pg. 201).
Wisdom of crowds uses a wide range of annotators to create large datasets, for example, Wikipedia.
Open innovation is the free flow of knowledge to accelerate internal and external innovation.
Crowdsource is an open call to internet users to get innovative solutions.
Crowdwork is “the performance of tasks online by distributed crowd workers who are financially compensated by requesters (individuals, groups, or organizations)” (Kittur et al., 2013 pg. 1).
Business processes are activities that underly value-generating processes such as transforming inputs to outputs (Melville et al., 2004 ).
Machine learning allows a machine to learn by using algorithms to analyse and draw inferences from patterns in data without direct intervention.
NLU helps bots understand the user by using language objects (such as lexicons, synonyms and themes) in conjunction with algorithms or rules to construct dialogue flows that tell the chatbot how to respond.
NLG enables bots to interrogate data repositories, including integrated back-end systems and third-party databases for information to be used to create meaningful and personalised responses that are beyond pre-scripted responses.
Intelligent malware is AI-based and exploits vulnerabilities by mimicking normal user behaviour to avoid being detected.
Agility can be described as a dynamic process of anticipating or adjusting to trends and customer needs without diverging from the company vision (Fartash et al., 2012 ).
Cross-platforms recognise inter-relationships and complimentary services through different software applications and devices.
Mindset is the attitudes and norms that either inhibit or encourage people’s or firms’ decisions.
- Artificial intelligence
Chief information officer
Centre of excellence
Coronavirus disease 2019
Enterprise resource planning
Environmental, social and governance
Organisation for economic co-operation and development
Natural language understanding
Natural language generation
Natural language processing
Robotic process automation
Research and development
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Sewpersadh, N.S. Disruptive business value models in the digital era. J Innov Entrep 12 , 2 (2023). https://doi.org/10.1186/s13731-022-00252-1
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Business models, value capture, and the digital enterprise
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Firms across all industries are embracing internet-based digitization strategies to expand or improve their business. In many cases, though, internet-based businesses pursue customer growth ahead of profits. The path to profitability, which is a core element of a business model, should not be an afterthought. A well-designed business model balances the provision of value to customers with the capture of value by the provider. The elements of a business model and the dynamic capabilities that help design, implement, and refine a model for an organization and its business ecosystem are reviewed. The article then translates these concepts with respect to key organizational design decisions such as that of licensing versus practicing an innovation, insourcing versus outsourcing, and building a business ecosystem.
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Digital Ecosystems: Issues of Creating and Increasing Value
Value Creation and Value Capture Through Internet Business Models
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As more and more elements of the physical world become sources of digital data, software is able to analyze, control, and interact with devices, equipment, and people. This has brought economy-wide changes from the disintermediation of traditional media to the introduction of 3D printing in factories. Inside companies, digitization has contributed to new business processes, new business models, and even new managerial models (Birkinshaw and Ansari 2015 ).
When a new idea is launched, either as a new enterprise or inside an established company, it needs to be supported by a value capture strategy if it is to have a chance of being more than a passing fad. Footnote 1 While this is self-evident for firms with lots of costly tangible (i.e., physical) assets, it is less obvious for creative firms, including pioneering born-digital companies, which, once the software has been built, often have low or zero marginal costs associated with providing a service. Such companies can be tempted to follow the path of the handful of successful web-based firms that built a large following by giving away a product before deciding how best to leverage their success. The reality, though, is that far more digital companies have made an initial splash only to fail in finding a way to monetize their user base.
Existing companies making physical products that launch a new digital platform, such as an automobile manufacturer providing in-vehicle mobile transactions, present an intermediate case. The company may already have income-statement discipline, but it may be tempted to subsidize the new business for an indefinite period in order to enable learning about digital markets and/or to block competing services from encroaching on its space.
Concern about how to capture value from internet-based activities is almost as old as the World Wide Web (e.g., Ghosh 1998 ). The original Napster, launched in 1999, was an early example, pioneering peer-to-peer file sharing with huge uptake but no expressed value capture model. The challenges quickly became apparent, including the danger of being late in a winner-take-all market niche and the difficulty of differentiating a product in a digital marketplace where potential customers can easily make detailed feature and price comparisons.
Ultimately, any privately offered digital service must pay its own way, directly or indirectly, by capturing a share of the value it creates if it is to be a sustainable business. Competition has become more global, technology more widely dispersed, and business ties more complex, requiring managers to think systemically about how they will accomplish this. The question addressed in this article is “how can companies, especially in the digital realm, gain confidence that they are on a path to profitability when they launch a business?”
While there is never any guarantee that a new business will be, or will remain, profitable, certain steps entrepreneurs can take in the early stages of creating a business will improve its chances. An essential step is taking the time to think through a business model.
A good business model explains how and why customers, suppliers, and complementors interact with the company through the digital interface. As circumstances change, it provides guidance as to the ways the value architecture can be altered and a systemic framework for maintaining overall coherence.
This paper begins by briefly examining the way competition has become more volatile and fast-paced in the digital era. It then introduces the business model concept as a system-level framework for navigating the next-generation competitive landscape by connecting innovation to value capture, including how an organization’s dynamic capabilities are used for business model design and implementation. The subsequent section focuses directly on some of the organizational design aspects of the business model, particularly whether an innovation should be exploited purely through licensing, how to draw the boundaries of the firm, and how to use a platform to leverage a business ecosystem. A final section summarizes the key points.
The pace of technological and business innovation seems to have accelerated in recent decades, with the result that sustainably profitable business models are harder than ever to craft. The Internet in particular enables the creation of new types of businesses and allows them to achieve global reach almost instantly. Traditional definitions of “an industry” are becoming outdated as digitization and networking drive convergence across numerous formerly separate realms of activity including banking, IT, advertising, social media, print, broadcasting, timekeeping, mapping, and insurance.
Many established companies are experimenting with the new possibilities in much the same way as start-ups. General Motors, for instance, is experimenting with a new model of car ownership that, for a monthly fee, allows customers to switch in and out of different types of Cadillac up to 18 times a year rather than own a single car (Colias 2017 ). Other firms, like Blockbuster Video, proved unable to respond innovatively and have vanished.
The nature of competition today is so different from the primarily scale-based competition of the previous century that it deserves to be called next-generation competition (Teece 2012a ). Next-generation competition is changing the way businesses compete, collaborate, and operate. In particular, the acceleration of competition places a premium on rapidly implementing (and continuously updating) novel business models.
The salient characteristics of next-generation competition include:
In standard formulations, such as Michael Porter’s “Five Forces” (Porter 1980 ), competition is determined primarily by the structure of input and end-user markets. Innovation is absent from the model, and structural change is rare. Today, incumbent firms struggle to gain durable advantage (D’Aveni et al. 2010 ). Disruptive innovation from start-ups and entry by firms from once-separate industries brings new competitive pressure that undermines existing business models. In 2009, RIM, the developer of BlackBerry mobile devices, was the second-most profitable company in the cell phone business. Following the successful entry of Apple, a computer company, and Google, an internet search company, into the mobile phone business, it remains an open question whether RIM can even survive. It is now primarily a software company, having sold its BlackBerry handset brand to a new entrant from China, TCL, in 2016.
A semi-globalized world
While the world remains in many ways at best semi-globalized (Ghemawat 2003 ), it is nonetheless a much more complex landscape today than even 20 years ago. For the latter half of the twentieth century, competitors to US firms were based in the USA, Europe, or Japan. Now globally competitive firms have emerged from Taiwan, South Korea, China, and other countries. A corollary of this is that the potential sources of innovation are also more dispersed. Firms need to optimize their costs globally and, if they decide to compete overseas, must learn what adaptations of their initial business model are needed in each new market.
Textbook treatments of innovation often assume that products depend on one, or a few, patented inventions, trade secrets, and trademarks. It has, however, been true for years that products of any complexity—either because of the number of parts or the number of functions—may read on hundreds, if not thousands, of patents, as well as numerous trade secrets. The smartphone is a contemporary example of this “multi-invention context” involving a mind-numbing range of patents for displays, processing, networking, design, and much more (Somaya et al. 2011 ). In the digital and e-commerce realm, an ever-expanding number of patents for software and business methods provide countless potential stumbling blocks for new ideas (Bessen 2012 ).
Competition is no longer so much firm against firm as it is ecosystem against ecosystem (Moore 1993 ). A business ecosystem contains a number of firms that work together (and also compete) to create and sustain new markets and new products. The co-evolution of the system is typically reliant on the technological leadership of one or two firms that provide a platform around which other ecosystem members, providing inputs and complementary goods, align their investments and strategies. The value capture component of a business model must find an acceptable balance between profits for the focal firm and the profitability of the firm’s ecosystem partners.
With next-generation competition, businesses need to think differently about their sources of advantage. A critical weakness in accounting and other financial perspectives is their limited ability to recognize the competitive significance of intangible assets, despite the fact that such assets are increasingly the essence of the contemporary business enterprise (Teece 2015 ). The most recognized of these assets is a firm’s intellectual property, such as patents and trade secrets. But there are other, and arguably more important, types of intangibles. In the management literature, a key idea of the 1990s was that a company’s strength lay in its “core competences,” the technologies and know-how that underlie and bring coherence to the company’s businesses (Prahalad and Hamel 1990 ). Since then, attention has increasingly turned to higher-level competences called “dynamic capabilities” (Teece et al. 1997 ). These enable a company to coordinate its various tangible and intangible resources to accomplish tasks such as new product development and business model design. Dynamic capabilities also contribute to the alignment of production plans with customer needs (and willingness to pay) by activities that can be summarized as “sensing, seizing, and transforming” (Teece 2007 , 2014 ). These capabilities are embedded in both organizational routines and managerial cognition.
To summarize, firms today must navigate a more complex innovation environment, build and maintain a richer set of alliances, and counter a wider range of competitors from both expected and unexpected quarters than ever before. It is vital for managers to understand and augment the full range of their organization’s capabilities if they are to design and implement innovative business models that track the evolution of technology and consumer demand.
Business models, one of the most vital tools for articulating the “architectural” design of a business so as to manage the complexity of next-generation competition, merit a closer look. In this section, we consider their definition, design, and implementation.
There are multiple definitions of the business model concept in use, and several comparisons have been published (e.g., Birkinshaw and Ansari 2015 ). The definition used here is that a business model “articulates the logic … that demonstrates how a business creates and delivers value to customers [and] outlines the architecture of revenues, costs, and profits associated with … delivering that value” (Teece 2010 , p. 173). Without the right balance between the creation, delivery, and capture of value, the model will not be in operation very long, at least not by a for-profit enterprise.
A business model has many moving parts, all of which must work together congruently. The model as a whole must also be aligned with the organization’s strategy, culture, and resources. These relationships cannot be optimized just through data analytics. Good business model design depends as much on art and intuition as it does on science and analysis.
A compact but fairly comprehensive list of business model components is provided by Schön ( 2012 ). His schema is similar to that of Osterwalder and Pigneur ( 2010 ) but further compiled into three main categories. Slightly adapted, the list is as follows:
Value proposition : product and service, customer needs, geography
Revenue model : pricing logic, channels, customer interaction
Cost model : core assets and capabilities, core activities, partner network
Extensions are possible. For example, some business model definitions incorporate strategy (e.g., Chesbrough and Rosenbloom 2002 ). While strategic analysis, such as which customer segments to target in which order, is inevitably tied to business model design, it as an analytically separate and more detailed exercise (Teece 2010 ).
With a digital setting in mind, El Sawy and Pereira ( 2013 ) included the design of a service platform and customer interface as part of business model design. This introduced questions such as whether the digital platform would be based on open or proprietary standards, and how easily the interface could be customized by users. These sorts of product design detail choices, part of the model's value proposition, will not be pursued further in the present article.
Business model design
The process of designing a particular business model is typically engaged by sensing the existence of customers with an unmet (or poorly met) need who are willing and able to pay for a potential product or service. A successful business model will provide a customer solution that can support a price high enough to cover all costs and yield profit that is at least sufficient to support the business and its growth.
Sensing takes place at all levels of the organization, with lower levels helping to provide information and insights about external developments to middle and top managers. A manager looking to profit in a particular technology area needs “generative sensing” through which various hypotheses about the underlying state of consumer demand are tested until a set of options can be validated (Dong et al. 2016 ).
Most “new” (to a given firm) business models will be similar to older ones, involving a permutation or hybridization of existing models. A typical example would be a firm that excels in a particular area of operations, e.g., running a restaurant chain, and then leverages its expertise into a services business such as supply chain consulting. While there are a finite number of business model types, the opportunities for recombinations are virtually endless.
A firm’s seizing capabilities come into play for the crafting of a revenue mechanism and the planning of the organization’s value chain, including the designation of which activities will be internalized and which will be left to outside suppliers. An important early decision is whether to test a business model on a segment of the potential user base before the new product or service is introduced more widely. This can help prove the concept behind the business model and allow an opportunity to make adjustments before large-scale commitments are made. However, it can also allow potential rivals valuable information and time to better position themselves to compete.
Tested or not, business models are seldom successful “out of the box” and must be fine-tuned—and sometimes completely overhauled—before they enable the business to become a profit engine. Start-ups generally find transformation easier than do mature firms because they have fewer established assets and procedures to reengineer. The “lean start-up” model that has spread well beyond Silicon Valley includes the capacity to “pivot,” i.e., to quickly test, discard, and replace ideas and business models that do not work (Ries 2011 ). This is especially relevant for software-intensive Internet-based business models (where pivoting is relatively easy because much software can be repurposed) and in circumstances where social media can provide fast feedback.
Business models can last for years or even decades, but they all have finite life spans. When changes are sensed in technology, consumer demand, or the competitive landscape, business model revision is needed. This is best undertaken before the need for change becomes obvious. Proactive sensing of the need for change is a dynamic capability that must be cultivated and built into the organization’s structure. Scanning and interpretive processes can be embedded throughout the organization, with open communication channels leading back to upper management so that information flows to where it is needed (Teece 2012b ).
Sensing must consider more than just technology; it entails awareness of large market trends and numerous other factors. A famous example is Kodak, which invented a mass-market film-based camera in the early 1900s and refined its designs incrementally over decades as it made most of its money from selling photographic film. Although a Kodak engineer demonstrated a digital camera prototype in 1975, the company’s film business was so profitable that it only slowly explored the digital camera opportunity, failing to recognize the technology’s disruptive potential. It continued to invest in R&D, with results that included the first megapixel image sensor. However, Kodak’s management had no sense of urgency, perhaps fearing cannibalization. In consequence, digital camera technologies were commercialized most effectively by other companies. Digital cameras came to market in the 1990s and thereafter followed the same “smaller, better, cheaper” trajectory as personal computers. Kodak responded in the 2000s to the faster-than-expected decline of its film business with entries in the digital camera and color printer markets, but it struggled to distinguish its products and finally declared bankruptcy in 2012. Although other leading camera firms also came under extreme pressure at that time, had Kodak’s management moved more quickly, it arguably would have bought itself valuable time to explore other options.
In some cases, an existing business model can be rejuvenated by altering only some of its elements. For example, Rolls-Royce changed its revenue model in the 1960s to "power-by-the-hour", or "jet engines as a service" (Rolls-Royce 2012 ). The customer, instead of paying the high fixed cost of the engine up front, pays only for the hours when the engine is operational. Rolls-Royce has strong incentives to keep the customer’s engines in good working order, as opposed to the old system in which its incentive was purely to sell engines. Furthermore, an hourly contract enables Rolls-Royce to dampen rivalry from third-party service providers, helping it capture the lucrative service relationship that has always been more profitable than selling the engines themselves.
Of prime import is how the elements of a business model create (or destroy) differentiation from competitors in the market. Although many business models, such as power-by-the-hour, can be copied by rivals, in practice it may take many years for this to occur. Rivals may calibrate their opportunities differently, and they may lack the organizational adaptability to switch business models. In other words, they may have weaker dynamic capabilities.
Pioneering a new business model is particularly important when the market has network effects and “installed base” characteristics such that the more users who are engaged, the greater the value associated with the platform (Katz and Shapiro 1994 ). In such cases, winner-take-all, or winner-take-most, competition is more likely (Teece 2013 ). This helps to explain why early Internet companies like Amazon that survived initial industry shakeouts have developed formidable leadership positions.
Business model implementation
In an existing company, the introduction of a new business model can prove challenging because of a cultural mismatch, the ability of existing businesses to influence budgets, or other reasons that start-ups do not face. This is particularly true when adding a next-generation business into a company that has been competing in more traditional ways. One solution is to set the new business apart, with its own premises and possibly even a different incentive system. This can work provided there is high-level support for the new venture. The capability of an established firm to experiment with new businesses while not undermining its existing revenue sources has been called ambidexterity (O’Reilly and Tushman 2004 ).
When a new business model is implemented in a start-up or within an established firm, the organization’s transforming capabilities come into play to configure, or reconfigure, the necessary resources and maintain organizational coherence. Radical business model transitions that change numerous elements of an existing model at the same time are unlikely to succeed without major financial resources and a steady commitment. For example, most taxicab companies are not attempting to replicate the ride-sharing capabilities of Uber or Lyft because those models are based primarily on software and data skills unavailable at low-tech cab companies. However, staying within their existing business model, taxis have improved quality as shown by a reduction in complaints in the New York City and Chicago areas (Wallsten 2015 ). Although a capability such as data analytics can be acquired, integrating an unrelated capability into an existing organization is challenging at best, disastrous at worst.
Business model implementation, like transformation more generally, involves closing capability gaps between the firm’s current activities and those required to enact the new business model (Teece forthcoming ). The gap closure can be accomplished through internal or external means, with the decision depending on a number of variables, including the strategic relevance of the capability, the time that would be required to “build” it, and the availability of the capability from third parties.
The gap identification process begins by examining the match between a proposed business model and the firm’s existing capabilities. An analysis of existing capabilities needs an objective point of view that is detailed and realistic. Organizational instincts tend to compel the exaggeration of current capabilities. The true magnitude of a gap may become apparent only after an organization falls short in executing a strategic initiative. The early phase of a project may be satisfactory, but as it progresses, problems begin to crop up, the senior team has to get more and more involved, and the goal slips further away. Management may have thought that a particular capability, such as supply chain management, was in place only to discover that it was inadequate to the needs of a new product or strategy.
To build a new capability, the people must be chosen correctly in terms of skills, creativity, and readiness to learn. Moreover, teams need time and guidance to develop their routines and develop working relationships. Even if much of the capability resides in a new piece of specialized equipment, it takes time for it to be fully understood and integrated into new routines then diffused to the divisions and geographies where it is needed. Many projects and programs fail because of an organization’s inability to develop and integrate the capabilities needed to deliver on a new objective. While building capabilities is hard, there is a silver lining; if well-built, they can be difficult for others to imitate and provide a basis for competitive advantage.
Many “ordinary” capabilities for production and administration are unlikely to be differentiators. They can be outsourced or developed to best-practice levels with the aid of consultants.
When a required new capability is less mundane, questions of how to acquire, whom to hire, and what to measure become harder to answer. Filling capability gaps involves more time, effort, and expense the larger the “distance” that separates the desired capability from what the firm is currently doing in technological, marketing, and business model terms (Teece forthcoming ).
Radical business model innovations are particularly challenging to implement because they involve addressing multiple gaps simultaneously, and the effort and attention needed to do so is non-linear with closing individual gaps. Large organizations are complex systems where every change can have a systemic impact on social, economic, and other dimensions.
Above all, the closing of capability gaps requires leadership by managers. An organization will not embrace transformation unless its managers are clearly committed. Employees will not engage in the necessary learning unless they are encouraged—and given the means—to do so. During the initial learning phase, the emphasis should be on effectiveness and improvement. A premature focus on financial performance can impair the ability to deliver better results in the longer run.
Capabilities can sometimes be obtained through merger or acquisition, but taking this perceived shortcut can introduce other problems. An acquired capability still needs to be learned and absorbed by the acquiring firm’s existing employees, which reduces the time saved. Moreover, management, prior to the acquisition, needs a deep understanding of the capability and how excellence is measured, failing which a hasty acquisition could turn out to disappoint.
Designing an organization and its ecosystem
Many new business models are based on a key business or technological innovation. A framework that can assist in choosing some of the organizational design aspects of a business model is the Profiting From Innovation (PFI) model (Teece 1986 , 2006 ). The PFI model, in the first instance, helps an innovator identify the key considerations for deciding a fundamental business model question: whether to license the innovation to other companies or to develop and commercialize it internally. The PFI model also helps to decide which functions to outsource and how external complementarities can be harnessed.
Licensing (or selling an early-stage start-up) potentially surrenders a larger share of the potentially available profits from an innovation but may be the surest route to monetization when direct commercialization by the innovator poses too many challenges for the inventor, e.g., when the capability (and financing) gaps are too numerous and large (see Teece 1986 , Figure 9). Moreover, for an innovation with multiple uses, licensing can potentially cover more of the potential applications than the innovating firm could exploit on its own.
However, licensing is subject to numerous pitfalls. A key question before choosing this business model concerns the strength of the relevant intellectual property regime. If the innovation can be imitated and intellectual property protection in the relevant jurisdiction is weak, then licensing may not be viable, and the innovator may be forced to integrate in order to have a decent chance of capturing some value.
The strength of intellectual property (IP) such as patents, trade secrets, and copyrights is often illusory. For instance, patents are not self-enforcing; patent infringement and trade secret misappropriation is frequent, sometimes leading to costly litigation. Moreover, many patents can be “invented around” at modest costs (Mansfield 1985 ; Mansfield et al. 1981 ). The pioneer’s business challenge is usually quite a hard one; followers often have it easier. To help with appropriability, the pioneer of a core technology can try to seek complementary patents on new features and/or processes and, in some cases, on designs, but these activities may delay efforts at licensing or commercialization.
A licensing model may also require that technology be transferred to licensees. That, too, involves costs. Moreover, the technology’s use may not be easy to monitor (Somaya et al. 2011 ). A further complication in recent times is the emergence of cybertheft and other cybersecurity problems. Being secure to market must often take precedence over being first to market.
Internal development and the boundaries of the firm
When a pure licensing model is rejected, the innovator must invest in direct (or joint-venture) commercialization. A key insight from the PFI model is that successfully commercializing an innovation requires complementary assets and technologies, often including other innovations, in order to provide a reasonably complete value proposition to users. Complements that exist and are competitively supplied can generally be outsourced or left to the ecosystem that builds products and services around the innovator’s platform. Various other types of complements require special attention:
A complement might not yet exist and need to be developed. That adds risk and delay to the commercialization path.
A complement may be highly specialized to the innovation being commercialized. If this is supplied by another firm, that firm may hold too much leverage over the innovator.
A complement may be in short supply or can become so in the event the innovation proves successful. This is a “bottleneck” that can attract a large share of the profit stream if not owned or otherwise controlled by the innovator.
A complement may be systemically linked to the focal innovation so that the two need to co-develop for future iterations of the product or service offering (de Figueiredo and Teece 1996 ). Leaving the complement in a separate firm risks losing control of the technological trajectory (Chesbrough and Teece 1996 ).
The issue of timing must also be considered. Decisions about where to draw the firm’s boundaries lead to a list of investments in assets and capabilities that need to be made to implement particular business models. Are other companies racing the innovator to market with similar products? Given the amount of time the contemplated investments would take, are rivals better positioned to compete? A positive answer here leads to a consideration of licensing.
This question of where to draw the boundaries of the firm’s activities is seldom straightforward and may need to be revisited periodically. Even a start-up offering a purely digital service must decide which corporate functions, such as HR and ad placement, can be rented from other service providers without harming its ability to build up its differentiated value proposition. And the challenge is even greater for products that straddle the digital-physical divide, even for formidable digital innovators like Google/Alphabet. In 2009, Google launched a project to develop technology for self-driving vehicles. In 2014, it announced that it was launching its own fleet of custom-designed driverless vehicles without steering wheel or pedals, suggesting that it might be becoming a car company (Walsh 2014 ). In 2017, not long after being established as a separate division called Waymo within Google’s Alphabet holding company, the company announced that, while it would not be bringing its own cars to market, it would be offering other car companies a platform consisting of its software and a set of sensors (radar, lidar, and vision modules) that it had designed (Higgins 2017 ).
Digital platforms and business ecosystems
If one applies the PFI model, many complements are likely left to other firms to provide. These firms, which may supply inputs, accessories, or ancillary services, add value to the focal firm’s innovation and constitute the innovator’s business ecosystem.
At the heart of most ecosystems lies the innovator’s platform. Although platforms are not entirely new, digital technologies have vastly expanded their reach by allowing the easy inter-operability of systems based on common standards. As a result, products that were once separate are more easily integrated, creating opportunities for new business models.
A platform is a combination of hardware and software that provides standards, interfaces, and rules that allow providers of complements to add value and interact with each other and/or users. Collectively, the platform innovator(s) and the complementors constitute an ecosystem that depends on continued innovation and maintenance of the platform by its owner(s) for success.
There are at least two basic types of digital platform, of which there are also numerous hybrid combinations (Evans and Gawer 2016 ). The first kind, a transaction platform , facilitates exchanges by otherwise fragmented groups of consumers and/or firms. A paradigm example is eBay, which allows huge numbers of individual sellers and buyers located anywhere in the world to find one another with an ease that was previously unimaginable. While digitization has enabled transaction platforms in a growing range of industries, this transactional type of platform is not entirely new. For example, the credit card industry has long provided a viable payment option that merchants will accept, that banks will join by issuing cards and processing transactions, and that cardholders find of value. However, the newer digital transaction platforms present an unprecedented opportunity to accumulate and leverage the knowledge voluntarily shared by its users (Brousseau and Penard 2007 ).
An innovation platform provides a base technology and a distribution system to which other companies can add their own innovations, increasing the value for the system as a whole. Apple’s “app” ecosystem is a well-known, and much copied, example of this. The PC ecosystem based on Intel chips and Microsoft software was an earlier instance.
Platform owners of either type must attract complementors. While the process is somewhat different depending on whether the complementors are partner businesses or individual consumers, both types of partner need to see a compelling value proposition to adopt the platform. Platform owners must also decide whether or not to impose an exclusivity requirement, such as an exclusive distribution agreement by a content provider with a digital media outlet. In other cases, a complementor (e.g., an application software provider) can participate in multiple ecosystems (e.g., both Apple’s iOS and Google’s Android).
The potentially disastrous effects of a failure to connect with ecosystem partners is demonstrated by the problems that a Japanese wireless carrier, NTT Docomo, had when it tried to take its domestic success overseas. Docomo’s i-mode system, launched in 1999, became one of the first successful wireless data services. The i-mode service, limited by the 2G cellular technology of the time, allowed keypad phones to access email and certain specially redesigned web pages. It also included a simplified version of an app store through which third parties provided i-mode users with paid services and content and shared the profits with NTT. Although i-mode was wildly successful—and profitable—in Japan, efforts to export it failed. NTT invested heavily in overseas partnerships, including a nearly $10 billion investment in AT&T Wireless in 2000, but failed to convince them to adopt the integrated i-mode business model (Kushida 2011 ). NTT also faced an equipment problem in export markets because the Japanese companies making i-mode phones had no presence outside Japan, where the wireless standards at the time were incompatible with those of most other countries. NTT had difficulty convincing the leading non-Japanese phone manufacturers, particularly then-dominant Nokia, to develop i-mode-compatible devices. Another element of the ecosystem, i-mode compatible content, was also in short supply. In 2002, NTT’s partners in the USA and Europe began to roll out i-mode-based services, but the uptake by consumers was poor, and in 2002, Docomo took a write-down of more than $1 billion on its overseas investments.
Global competition is placing a higher premium than ever before on astute management. Strategy and organizational design choices are immensely complicated in many markets today. Digitization, a driver of next-generation competition, adds flexibility but also speeds up the pace of competition. Tasks such as calibrating threats from rivals, cultivating cultural awareness, forming a patent strategy, and developing a portfolio of organizational capabilities must all be done more quickly and with less margin for error than ever before. In markets with network effects, the need to move early and rapidly must be balanced with the need for thorough analysis and thoughtful experimentation. An understanding of system-level concepts can help managers build robust, coherent organizations and strategies.
A critical tool for mapping a pathway to profit through the obstacle course of next-generation competition is the business model. It is in some sense the skeleton around which a company structures an internal organization, builds capabilities, and, of course, formulates strategy so that the company can make its way in the market. A robust business model provides a solid basis for success. Key principles include:
Good business model design requires deep knowledge of customer needs and the technological and organizational resources that might meet those needs.
Most new business model designs involve the hybridizations of others. An understanding of current business models at work in the market is essential.
The elements of a business model should be mutually reinforcing.
Business models evolve. They need to change in response to competition, imitation, or other changes outside the firm. In the longer term, they eventually need to be replaced.
The introduction of a new business model into an existing organization is often difficult and may require a separate organizational unit.
The design and implementation of a business model requires strong dynamic capabilities for sensing, seizing, and transforming. Customer needs must be identified, products developed, revenue and pricing mechanisms designed, and capability gaps closed. These are not one-time actions; they are processes involving learning loops that contribute to continually adjusting the business model so as to maintain the organization’s competitive edge. Conjectures must be formed and tested. Failures must be recognized, analyzed, and used as a springboard to new learning.
The Profiting From Innovation model provides an organized approach to thinking through critical business model issues. Is a pure licensing approach viable? Which assets will be in short supply? Which capabilities can be safely outsourced? How can a platform attract a robust business ecosystem?
Innovations do not exist in isolation; they interact or compete with numerous others. Changing one element of a business model (e.g., distribution channels) requires changing others (e.g., customer interaction, pricing). Companies do not go to market in isolation; in most cases, their products and services become more valuable to customers when combined with the products and services of other firms. Managers today must think systemically. Those who comprehend and implement system-level concepts such as business model, dynamic capabilities, and Profiting From Innovation will be less likely to lose themselves inside the forest among the trees.
This has been a long-standing focus of Teece’s research, as reflected in Teece ( 1986 , 1988 , and 2006 ).
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The authors are grateful to two anonymous reviewers for helpful feedback.
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Teece, D.J., Linden, G. Business models, value capture, and the digital enterprise. J Org Design 6 , 8 (2017). https://doi.org/10.1186/s41469-017-0018-x
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DOI : https://doi.org/10.1186/s41469-017-0018-x
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What is a Digital Business Model?
A brief explanation and introduction
Definition of digital disruption
A digital business model is a form of creating value based on the development of customer benefits using digital technologies. The aim of the digital solution is to generate a significant advantage for which customers are willing to pay.
The development of digital business models is an important task for companies being confronted with digitalization and digital disruption . The mere extension of an existing analogue business model by a digital component (e.g. online ordering of goods from a stationary retailer) is a preliminary stage, but not an independent digital business model.
Characteristics of digital business models
Digital business models have different characteristics, several of which usually apply simultaneously.
The added value generated would not be possible without the use of digital technologies. Amazon, Uber and Airbnb are companies that would have no business without the technologies of the Internet. Amazon might be a local marketplace today, Airbnb a room agency in several cities and Uber a taxi center or a carpool agency.
The business model is characterized by digital business innovation . Digital business models are based on services that are new to the market.
Customer acquisition and distribution are based on digital channels. Companies that develop and drive digital business models mostly use digital technologies to reach potential audiences. Sales are characterized by trends such as sales automation and early onboarding. (see Freemium Model)
Customers are willing to pay for the digital service or the service. Digital business models thus create a unique customer value that can be monetized.
The willingness of customers to pay and thus the independent creation of value is a striking feature of a digital business model. Purely digital services, e.g. the possibility of monitoring energy consumption via an app, are digital offers, but not digital business models.
Types of digital business models
Customers receive parts of the digital service (e.g. limited functions of software) free of charge. This serves to manage the onboarding process with as little sales effort as possible.
The Freemium model follows the principle of competence standardization: Previously personnel-intensive activities such as sales were made more efficient by automatic processes. The challenge for companies is to successfully manage the upgrade from the Freemium version to a paid version.
Similar to Amazon, a digital platform functions as an intermediary marketplace for products and services: Supply and demand are brought together. Digital business models that follow this model derive their added value from the fact that a large number of independent players are active on the marketplace and regular transactions take place.
The marketplace model can work alone or represent the extension of an existing offer of a company. A property management company that allows external service providers such as cleaning staff or the bakery service to be integrated into a tenant app already has a digital business model in the form of a marketplace – although in this case only on a very small scale.
Using instead of buying
This digital business model enables another form of use of an asset (e.g. software, automobile or machine). It is no longer the possession, but the consumption or use of an asset that is monetarized. Digital technologies make it possible to measure consumption or usage. In the field of car sharing, for example, both the rental and the return as well as the kilometres or miles driven are billed. In the case of machines, payments can be made, for example, according to the operating time, the number of units produced or other data retrieved from the machine.
Digital business models based on the “use instead of buy” principle can help companies to reach new target groups (e.g. those who have been reluctant to invest so far) or to remain competitive and to offer customers an attractive digital business model before potential competitors do so.
Example of a digital business model in the sharing economy.
Development of a digital business model
Toothbrushes can become digital business models.
There are a number of questions to answer when developing a digital business model. These questions are discussed as part of an innovation process in innovation management , often using methods such as Open Innovation . The focus is on future customer benefits.
This will be explained using the example of a toothbrush and a drilling machine. The customer’s benefit is not the possession of the toothbrush, but clean and healthy teeth. Also, owning a drilling machine is not the customer benefit, it is the hole. If it would be possible to sell holes in the wall digitally, this digital business model would certainly be a strong competitor for drill manufacturers.
The development of digital business models therefore begins with a profound analysis of a company’s future role in the market:
- What is the real problem behind buying existing products?
- Where do your existing products and services solve these problems well?
- Where do problems exist that have not yet been solved?
- In which areas does a product possibly create new problems that have not yet been solved?
- What problems and challenges do customers face in developing their own digital business models?
- What problems and challenges will customers face in the future?
Digital business models for a mechanical engineering company
Digital business models: New markets for mechanical engineering companies
This is illustrated by the example of a mechanical engineering company. So far, the machines have helped companies to manufacture products highly efficiently. However, extensive problems in connection with the use of the machine were not solved:
- Fluctuations in orders : The company has to cope with volatility in orders. Sometimes the machines are not working at full capacity, at other times production capacity would have to be increased. By connecting machines and developing a marketplace, the machine builder can offer valuable additional services: utilization-oriented invoicing ( use instead of purchase ) or a brokerage of production capacities (“marketplace model”) can be the basis for successful digital business models.
- Staff training : Monitoring the machine requires precise training, which is costly and time-consuming. Teams of experts must be on site in each shift to monitor the machine. Error messages must be detected and corrected on site. This requires precise shift planning to ensure that qualified staff are available around the clock. The machine manufacturer can develop digital business models as a solution here: for example an online academy for improved training or a 24-hour service center with networked technicians who monitor the performance of machines worldwide and, if necessary, switch on via web conferences.
- Lack of skilled workers : In future, customers of the machine manufacturer will have more and more difficulties finding qualified personnel to operate and maintain the machines. For the mechanical engineering company, this gives them the opportunity to establish their own worldwide online academy and offer qualifications as a digital business model. The development of digital business models is therefore initially based on an intensive analysis of current and future customer problems. The second step is a technology analysis: a list of the technological possibilities to solve the identified customer needs with the help of digital technologies.
A digital business model is the result of the interaction between customer needs and possible available technologies. Companies that develop digital business models often use innovation management methodologies here. Prototypes are developed, which are tested and verified in the market. When developing a digital business model, it is not crucial to develop the “perfect” digital business model from the very first second. The innovation process is iterative and characterized by many loops. The Innolytics Innovation Software supports idea generation and the development of a Digital Roadmap for digital business models.
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Business models guidebook.
It's not always clear what makes something 'digital .'
Even if we know the basics of business models , it may be hard to distinguish between analog and 'digital' versions. When people refer to 'digital businesses,' they are often referring to new value models more than specific uses of technology.
This is why digital modernization efforts, which upgrade tools and organize data, are not enough to truly transform businesses. New thinking, business models, and tools are also necessary. In this guide, we'll define the most common models for value creation. To avoid overwhelming detail, we won't attend to every business model or value proposition element, but you can learn more about those about in other parts of the Digital Fluency Guide .
Identify value models
Value models focus on things, people, ideas, or connections..
Across industries, there are four basic models for value creation:
- Asset Builders focus on making or owning things .
- Service Providers focus on people .
- Creators & Technologists focus on ideas like content, technologies, and data.
- Network Orchestrators focus on connections between parties.
Each model for value creation answers the question, "What business are we in?"
How a business thinks about value determines the value of the business.
A look at the highest-valuation us startups shows that all of them are focused on the exponential value models of ideas and networks..
In the chart shown above, we can see companies' multiplier, or price-to-earnings (P/E) ratio in publicly-traded stock markets, corresponding to their primary value model. Asset-focused businesses tend to be valued at about twice what their annual earnings are, while companies who focus on connections are valued at up to eight times their annual earnings. This is because as companies focus on value models with higher multiplier ratios, they are investing in and creating value through the power of network effects.
Digital businesses network their resources
Find ways to unleash network effects by linking things, people, ideas, and connections..
Regardless of your industry, network thinking can drive exponential business strategies.
Network effects can drive exponential growth in any industry across both analog and digital environments.
Digital businesses stand out because they effectively link those resources, using them to generate network effects wherein the companies get exponentially more valuable as more resources are connected.
Things (And Assets)
Asset creation (things).
What are our assets, and how do we best protect and leverage them?
What are our assets, and how do we best protect and leverage them?
Strategy : acquire, create, protect, and leverage assets
Resources : equipment, money, products, facilities, real estate
An asset creation model focuses on the production of things. Businesses with this model depend on the quality of their assets (food products, real estate, raw minerals, etc.) and their ability to reliably and efficiently deliver those assets to customers. These firms are constrained by their available inventory, buying power, and geographies. For example, an automaker can only make so many vehicles with the cash and lines of credit it has available. The value of an asset creation business is also limited by the supply of assets and demand for them in the market. For this reason, asset creation businesses tend to have a low degree of exponential multiplication.
People (and Services)
How do we attract the best talent and deliver the best experiences?
Strategy: deliver high-quality services through human talent and labor
Resources: customers, leaders, employees, partners
Organizations focused on a service-based model succeed based on the capabilities of their employees. They invest heavily in recruitment, talent, training, and customer service capabilities to create value and differentiate themselves from competitors. By focusing on customer experience, these companies transcend the market value of any tangible assets they may be providing. Classic examples of this value model include restaurants, medical offices, and consulting firms.
Successful service businesses often invest in and emphasize positive employee experiences to attract and retain talented people. Leading firms with this value model make decisions based on a shared purpose between themselves, their employees, and their customers.
A business that provides services has a moderate market value relative to its earnings.
Starbucks as a people business, not a coffee business
That’s Starbucks’ core strategy. Instead of focusing on the monetization of coffee (an asset), they offered a service: hosting an experience that's a third place between work and home. That's why the company is valued higher than just a coffee vendor—they’re not selling coffee, but an experience that happens to include coffee.
Ideas (Content, Technology and Data)
Ideas (intellectual property, technology, and data).
How do we create and share intellectual property? How do we design and build exponential value with machines and data?
Strategy: create, share, and/or sell ideas in the form of intellectual property, technologies, and/or data
Resources: copyrights, patents, technology tools like software, data, and data science models
Ideas-focused value models take knowledge and turn it into a reusable form. There are several key strategies that focus on idea generation, primarily the creation of intellectual property (such as brands, patents, and content), building reusable technologies (such as software or machine learning models), and the acquisition and processing of data.
Below, we'll explore the specific strategies which leverage ideas.
Ideas (intellectual property).
Strategy: create, share, and increase the value of intellectual property
Resources: brands and trademarks, content, patents, business processes
Ideas can take the form of reusable intellectual property (or IP). There are many varieties of IP; some of the most common are the creation of brands and trademarks, written or audiovisual content, patents, or reusable processes and procedures.
Some organizations are valued based on the recognition and reputation of their name or other parts of their brand.
Well-known brands like Nike are worth exponentially more than unrecognized shoe manufacturers with a comparable level of quality. Similarly, luxury brands like Gucci are valued based on their reputation and exclusivity, not just on the quality of their merchandise.
Business Processes and Procedures
The creation of a highly-replicable franchise, like McDonald's stores, encapsulates several of these kinds of intellectual property. Franchise models have evolved significantly in the digital era due to the availability of machine tools to further streamline and scale operations.
Some of the most common IP value models in the digital era are based on content. Traditionally, content would be created by a select group of writers, performers, or artists and then distributed through media companies; today's content models are more democratized. This is for several reasons:
- A lower cost of entry to content-production tools
- Less technical skill barriers (software tools automate labor, and existing creators often provide tutorials to each other)
- Near-zero friction to replicate content (in contrast to having to print physical copies of media)
- Easy ways to distribute and monetize content (often through platforms like Spotify or YouTube)
- Social movements toward co-creating content (among peers and between individuals and companies)
Example of Ideas (IP) Businesses
Brands who create or aggregate intellectual property include the New York Times, McDonalds, Eurovision, Al Jazeera, DStv, Columbia Music, Sam Smith (musician), Netflix, Gucci, and Apple TV+.
Strategy: Invest in re-usable digital tools
Resources: software, mobile apps, hardware devices, tech-specific patents, integration tools
'Idea generation' value models sometimes take the form of technologies. Hardware and software can be created once and then provide 'evergreen' value for an extended period over which they receive more iterative updates. Hardware giants like Intel and Nvidia have multi-year innovation cycles based on massive research & development budgets. Software behemoths like Microsoft create interoperable 'stacks' of technologies that can be used for many different functions, especially in the business world. Meanwhile, smaller startups often focus on specific or niche technologies which can be integrated into a more extensive technology solution, as Shazam did when they created a music-recognition tool; Apple later acquired them.
You can learn how data can create value by reading Creating Value with Data and exploring data types in the Data Sources Explorer .
Strategy: creating value by selling data as datasets or via products and insights gleaned from processing data, such as algorithms, data-driven investment strategies, business optimization, and/or product personalization
Resources: raw data, processed and aggregated datasets (big data), algorithms
Data-centric value takes the form of direct value (such as buying, aggregating, and selling or licensing datasets) as well as indirect value. This indirect value can take the form of insights from data (such as credit scores based on payment history or social media trend analysis) and algorithms, AI, and machine learning models (such as Google Image Recognition). This is in addition to the ways companies can use data to enrich other value models through personalization of offerings and apps, optimization of businesses, and improved decision-making on investments.
Networks (and Connections)
Networks: the connections value model.
Strategy: enable and amplify the exchange of value between parties
Resources: people, organizations, marketplaces, and machines
The network orchestration value model generates exponential results by connecting humans and/or machines to each other. Common analog examples include business networking organizations, brokers, alumni groups, and even neighborhood associations. In the digital world, networks include social media, app stores, and other online marketplaces. Through matchmaking between parties and the creation of common core resources, such strategies have very high leverage since much of the value in the network is created through things, people, and ideas that exist outside of the network host. There tend to be three sub-models for the connections value model: connecting people, networking machines, and hosting multi-sided platform marketplaces.
Below, we'll explore the specific strategies which leverage networks.
Strategy: match people to each other
Resources: individuals, groups, matchmaking tools, directories, gathering spaces, 'social objects,' community norms
The networking of people is essentially a function of creating spaces for people to connect (such as communities), matchmaking (to help people find each other), and furnishing common resources (such as brokerage services or software aids). This could take the shape of a business association in the analog world, where various professionals join via membership, have concierge-like services and special events to connect with each other, and directories or publications that help members stay connected and informed.
In a network of people, there are often ' social objects '—common points of discussion or sharing that create or reinforce relationships. In a traditional business association, these may be reports of meetings or deals between members, or announcements, events, and discounts. In an academic network, they might be publications. In a neighborhood association, they might be social events or safety updates.
In the digital context, online social networks provide many of these same functions (such as membership and profiles). Using recommendation engines , social networks help people find each other. Gathering spaces often take the form of activity feeds where members can create posts or other social objects, private groups and/or direct messages, and ways to share. Similar to analog networks of people, certain gate-keeping, identity-checking, norm-setting (like appropriate tone for a professional setting, or avoidance of incitements to violence), and other community functions need to be practiced. In the digital world, such functions are partly or wholly run by machines. Using machine learning algorithms to recommend and prioritize content is a massive undertaking with substantial social and ethical implications. Online social networks vary in business model, where some are supported by ads or paid content, others by community efforts, and yet others by paid membership.
Strategy: connect machines to each other and orchestrate them to achieve exponential results
Resources: technology infrastructures like servers, data and processing tools, APIs, authentication and orchestration, algorithms and other rules
Networks of machines are an emerging value model which goes beyond having technology or datasets and focuses on the connection and orchestration tools of devices that operate most of the time without human direction.
Examples of machine networks include server farms, internet-connected in-car navigation devices, application program interfaces (APIs), content delivery networks (CDNs), botnets (malware ), and blockchain-based cryptocurrencies like Bitcoin (where lots of machines perform math, log activity, and then redundantly check transactions across a distributed ledger to prevent fraud).
Networks (Multi-sided Platforms)
You can read more in Causeit's Guidebook to Multi-Sided Platforms .
Strategy: Host exchanges of value between parties
Resources: community, marketplaces, infrastructure, and data
Multi-sided platforms combine networks of people and/or machines and add a marketplace element for direct exchange of value between parties. The most commonly-known multi-sided platforms (or MSPs) are multi-seller e-commerce sites like eBay and the Amazon Marketplace and the mobile app stores hosted by Apple and Google.
What makes this value model distinct from just online sales or traditional marketplaces is the combination of digital infrastructure and datasets along with community elements.
What’s your value strategy?
Sort your company’s resources and activities into these four categories. Identify where your value model is currently focused. Nearly all companies have resources that include things, people, ideas and connections, but not all strategies leverage those resources the same way.
- Things: What are our assets and how do we best manage and monetize them?
- People: How do we engage the best talent and deliver the best experiences?
- Ideas: How do we create and protect intellectual property (including technologies and data)?
- Connections: How do we enable and empower value exchange between parties?
Every industry can leverage multiple value models. Understanding value models can unlock ways to innovate within an existing industry and prompt critical reflection on how resources, partners, activities, channels, and other business model elements might fundamentally shift in the digital era.
- Across industries, there are four basic models for value creation. Each model for value creation answers the question, "what business are we in?"
- Creators focus on ideas like intellectual property, technology, and data.
- Companies can sometimes increase their market valuation by expanding or shifting their value model towards intellectual property and/or network orchestration.
- Examine which value models are being used by your firm or organizations which interest you
- Leverage ' platform thinking ' to network your strategies and resources across several value models
- Broaden your value models into a complete business model to begin checking for feasibility
- Ground your value models in specific value propositions to your customers
- Explore how different value model strategies are leveraged within the same industry
- Explore company case studies by value model
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The constantly accelerating technological progress is propelling us with full speed into the fourth industrial revolution. The ongoing transformation will fundamentally change the scale as well as the complexity of how future businesses are done. Digital business models play a significant role in this transformational process, changing not only the speed in which commercial transactions are executed, but also strategies and marketing techniques by which customers are approached.
In order to understand the principles of digital businesses, we need to analyze them in several dimensions. First dimension concerns the digital business model from the corporate perspective. It explains the key economic drivers that allow digital businesses to grow fast, from small startups into global market leaders. The second dimension deals with paying customers and freemium users of digital services. Of main interest is how digital companies have changed the customers’ roles as well as the expectations of them. The third dimension covers the aspect of the society. Since digital businesses have penetrated almost every aspect of an individual´s daily life, we also need to consider how the core values of our society must be redefined to meet the new challenges.
Defining Digital Business Models
In digital business models the creation, delivery and capturing of the value proposition are based on digital technology.
The effective application of digital technology allows a unique value proposition to customers, which has the following characteristics:
- Everything: all products are available to the customer at once.
- Immediate: all products are instantly available; there is basically no transaction time or delay in delivery.
- Everywhere: all products or services are available at any geographical place as well as across various end-user technologies.
The near-perfect examples of this principle are video streaming services like Amazon Prime or Netflix: the customer can select to watch from a vast amount of videos, at any time of their convenience, on virtually any device and at any place they like. Obviously, digital technology is a fundamental prerequisite to the everything-immediate-everywhere proposition, making it unique to digital businesses.
In exchange to everything-immediate-everywhere, customers provide an enormous amount of data. These historic and real-time data are diligently analyzed by the delivering companies, allowing them to constantly improve key aspects of their respective business model: the product itself, its delivery to the customer, and the pricing of their service.
Why are Digital Business Models Scalable?
Scalability of a business model is the ratio between the increase of the product volume and the corresponding increase in the volume of required resources. In the case of digital business models, the scalability is only marginally related to additional investments or to fixed costs. This means that the marginal costs (costs required by producing one additional product) are very low for digital business: every new user only requires a minor increase in CPU, memory or bandwidth.
In contrast: the more “physical” a product is, the poorer the scalability. For example, physical goods are not arbitrarily scalable, as every product first needs to be produced at relatively high marginal costs. Furthermore, if there is a bottleneck in the production or delivery process– for instance the limited capacity of a warehouse—the corresponding business is not scalable at all.
Digital businesses rely on global and digital customer channels such as search engines and social media. Due to their high scalability, digital businesses can grow very fast and become in no time global payers. Famous examples include Uber, AirBnB, Netflix, and other such well-known “unicorns”. The potential of exponential growth allows them to endanger the old, established business models – not only for in the B2C but also in B2B sector.
Fast growth and favorable scalability are exactly the characteristics investors are looking for in digital business models, resulting in high volume investment in this sector.
The Dark Side of the Moon
Digital “pleasure bubbles”.
At first glance, digital businesses have miraculously transformed their customers to cosmopolites. We all enjoy an unlimited access to a vast number of products, services, and information from the whole world – all tailored for us and always available!
Is there a dark side to the digital cosmopolitanism? The trouble comes paradoxically with the increasingly efficient customization. Due to this customization, we may find ourselves inside of a digital ‘pleasure bubble’, where we only see information that is akin to what we have preferred or favored previously. I am sure you notice how efficient the self-learning algorithms in social networks are: some of your contacts’ updates disappear from your news feed, the advertisements show you products compatible to your last purchases, and your search engine makes smarter and smarter suggestions. This creates a feel-good cocoon around us, shielding us from unfamiliar products, possibly ‘disturbing’ opinions or different political convictions. Undoubtedly it is already happening and is not limited to consumer behavior. It may lead to a digital incapacitation of citizens and to a digital “bread and circuses” analogous to that in the ancient Roman Empire. In this way, digital technology leads to the opposite of cosmopolitanism: it reaffirms our biases and gives rise to parochialism, intolerance, polarization, and the closing of public spaces that are crucial to the democratic dialogues in liberal societies.
Whither Are We Abound?
Scalability also means that digital businesses will continuously seek to build closer relations to permeate all aspects of their customers’ daily lives, by improving the quality of the ‘docking station’ attached to the information bubble of the consumers. The key is to enhance the precision with which the consumer behaviors are understood and predicted. Two directions emerge as the most important ones:
1. Individual Psychological Level: psychometric consumer analytics
Customers of digital businesses produce a vast amount of data, all of which are instantaneously being analyzed. Beside the ‘click & like’ analytics, new trends are upcoming: sophisticated semantic analysis based on artificial intelligence (AI) algorithms as well as the use of psychometric models, such as ‘The Big Five (personality traits)’ (also known as the OCEAN model). Analytics companies prey on our waste use of social media, hereby coming to not only know us but be able to predict and manipulate us. We will then be not only provided with products we may want, but also addressed in the way most receptive to us possible. These new digital business models create individualized information, products, and services.
An impressive and disturbing example of the combinations of big data and psychometrics is Cambridge Analytica (CA). This company uses ‘data modeling and psychographic profiling to grow audiences, identify key influencers, and connect with people in ways that move them to action’. An outstanding analysis of its method as well as the role it played in the 2016 US presidential election can be found in: Das Magazin N°48-3 Dezember 2016 Article under the title: ‘Ich habe nur gezeigt, dass es die Bombe gibt’ (‘I Just Showed That the Bomb Does Exist’), link: https://www.dasmagazin.ch/2016/12/03/ich-habe-nur-gezeigt-dass-es-die-bombe-gibt/
2. Technological Level: Near Future Predictive Services
The upcoming loT trend is expected to generate an unprecedented amount of data, coupled with our enhanced technical capacity to analyze and store these data. Consequently, the predictive analysis will become available on a large scale. This means we may expect a fundamental shift from on-time-instant services to short-term predictive services. Some of the predictive services already in place are:
- PRECOBS: Near Repeat Prediction Method already used by the German police in several towns to forecast the commitment of ’near repeat crimes’, mainly applied to burglary prevention. For additional details see: www.ifmpt.de
- Autopilot crash prediction: Tesla autopilot seems to have predicted a crash and initiated and emergency break before the actual crash happened. For the video of this incident see here .
Near-future prediction will be based not only on deterministic calculations, but increasingly on artificial intelligence (AI). AI has a real potential to become a key disruptive technology of the future. Current AI-examples are numerous, beside prominent IBM’s WATSON and Google’s AlphaGo , there is even one curious oddity:
- AI as a member of the Board of Directors: in 2014 the Hong Kong based VC firm Deep Knowledge Ventures appointed an AI algorithm, called VITAL (Validating Investment Tool for Advancing Life Sciences), as a member of the Board of Directors. The key task of VITAL is to find and analyze information, the significance of which not obvious to humans. You can find more here .
Advantages and Disadvantages of Digital Business Models
Digital business models are unique in their value proposition and have a very attractive growth profile for entrepreneurs and investors alike. In addition, digital businesses change customers’ expectations on delivering value: ‘I want it all & I want it now’ has become a digital reality. At the same time, the customers are more than ready to pay for the everything-immediate-everywhere experience, with money but first of all with our data.
In summary, there is a trade-off between advantages and disadvantages of digital business models, from the corporates’, customers’, as well as societies’ point of view. The corporates are those who benefit the most from the current arrangement, enjoying the following advantages:
- unique value proposition based on everything-immediate-everywhere products and services
- high scalability with low marginal costs
- global reach and high growth rates based on digital customer channels such as social media and search engines
- investors’ darling: good chances to be well-funded by investors
However, the advantages to these corporates are accompanied by hefty personal and social costs, if they are left to develop unregulated, which includes, for instance:
- market monopoly: fast growth often leads to a ´winner takes it all market situation’. Example: we need only one Amazon instead of thousands of local shops or even other digital store fronts
- ‘pleasure bubble’: customers are manipulated by way of psychometric-based analytics. Example: your social media feed continuously nudges you to affirm your political bias
- users become the products. Example: freemium offerings of search engines or social media in exchange for data. In that case the true customers are the companies paying for the advertisement. Even when the users are indeed paying customers, the companies can still profit from analyzing or trading their data.
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