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What is Skills-Based Learning and Why It’s Important?

Skills-based learning is an educational approach that focuses on developing specific, practical skills and competencies rather than just acquiring theoretical knowledge. It emphasizes hands-on, experiential learning that equips individuals with the abilities they need to perform tasks, solve problems, and excel in real-world situations.

Embracing Skills-Based Learning in the Modern Job Market

As the job market evolves, skills-based learning has become a crucial component for professional success. This guide explores the benefits of incorporating skill-based learning techniques in education and career development , empowering adult learners and non-traditional students to excel in their chosen fields. By focusing on specific, relevant skills and using effective learning strategies, individuals can unlock new opportunities and achieve their career goals .

Delving into the World of Skills-Based Learning

Skills-based learning, often referred to as skill-based or competency-based learning, is an approach that emphasizes the development of specific, practical skills rather than the acquisition of broad, theoretical knowledge. This form of learning is tailored to the individual’s career goals and focuses on the mastery of skills that can be directly applied to real-world situations.

Traditional learning often follows a more linear path, with students acquiring knowledge through lectures and textbooks, then demonstrating their understanding through tests and assignments. In contrast, skills-based learning is more flexible and adaptable, allowing learners to focus on the skills that are most relevant to their career aspirations and personal interests.

Key Characteristics of Skills-Based Learning

Examples of skill-based learning techniques include hands-on training, simulations, and case studies. These methods encourage active engagement and facilitate the development of practical skills, empowering learners to excel in their chosen fields.  

Here are the main characteristics of skills-based learning:

• Practical Application : Learning is centered around practical tasks, projects, or activities that simulate real-world scenarios. This approach enables learners to directly apply what they’ve learned.
• Skill Development : The primary goal is to build specific skills, such as technical skills (e.g., programming, carpentry) or soft skills (e.g., communication, teamwork).
• Outcome-Oriented : Success is measured by the learner’s ability to demonstrate proficiency in a particular skill or competency, rather than by traditional academic assessments like exams or essays.
• Problem-Solving : Skills-based learning often involves problem-solving and critical thinking as learners tackle real challenges and learn to adapt and innovate.
• Hands-On Experience : Learners actively engage with the subject matter through practical exercises, experiments, or real-life tasks, fostering deeper understanding and retention.
• Personalization : Instruction can be tailored to each learner’s needs, allowing them to focus on the skills most relevant to their goals or career path.
• Lifelong Learning : Skills-based learning aligns with the idea that learning doesn’t stop after formal education. It encourages individuals to continuously acquire and refine skills throughout their lives.

This approach is particularly valuable in fields where practical expertise and hands-on experience are crucial, such as vocational training , technical education, professional development, and certain areas of higher education. Skills-based learning is often seen as a more practical and career-focused alternative to traditional academic learning, as it equips individuals with the abilities they need to succeed in the workforce and in various life situations.

Types of Programs and Schools That Focus On Skills-Based Learning

Many higher education programs incorporate skills-based learning to varying degrees, but some are specifically designed to prioritize practical skill development over traditional academic coursework. These programs are often found in fields where hands-on expertise is crucial. Here are several types of higher education programs that emphasize skills-based learning:

Vocational and Technical Colleges: These institutions offer programs that focus on specific trades or technical skills, such as automotive repair, welding , culinary arts, and healthcare. Students gain practical skills and often receive certifications or diplomas.

Apprenticeships: Apprenticeship programs combine on-the-job training with classroom instruction. They are available in fields like construction, electrician work, plumbing, and manufacturing. Apprentices work alongside experienced professionals to develop skills.

Community Colleges: Community colleges frequently offer associate degree programs with a strong emphasis on practical skills. These degrees can lead to careers in fields like nursing , information technology, and automotive technology.

Coding Bootcamps: These short, intensive programs focus on teaching coding and programming skills. Coding Bootcamps are designed to quickly prepare students for careers in software development and related fields.

Certificate and Diploma Programs:  Many colleges and online platforms such as Udacity offer courses that are designed to provide practical, job-ready skills in high-demand areas such as technology and business. These programs typically culminate in a certificate or diploma.

Trade Schools : Trade schools specialize in providing education and training for specific trades or industries. This includes fields like HVAC (heating, ventilation, and air conditioning), cosmetology, and construction management.

Nursing and Healthcare Programs: Programs for nurses and healthcare professionals, such as certified nursing assistant (CNA) or medical assistant programs , emphasize practical skills for patient care.

Culinary Schools : Culinary programs teach students cooking techniques and restaurant management skills, preparing them for careers in the culinary arts .

Design and Creative Arts Schools: Schools focusing on design, fashion, and creative arts offer programs that emphasize practical skills like graphic design , fashion design, and interior design.

Trade Apprenticeships in the Building Trades: These apprenticeships provide hands-on training in construction-related fields like carpentry, plumbing , electrical work , and masonry.

Emergency Services Training: Programs for emergency medical technicians (EMTs), paramedics, and firefighters emphasize practical skills for responding to emergencies and saving lives.

Applied Sciences and Engineering Programs: Some universities offer engineering and applied sciences programs with a strong practical component, allowing students to work on real-world projects and gain technical expertise.

Mini-MBA Programs (Non-Degree): Mini-MBA programs are shorter, condensed versions of traditional Master of Business Administration (MBA) programs. They are designed to provide students with a fundamental understanding of key business concepts and management skills in a shorter timeframe. Mini-MBA programs often emphasize practical skills and real-world applications, allowing participants to immediately apply what they learn in their work or business ventures.

Selecting the Right Skills for Your Skill-Based Learning Journey

Identifying the appropriate skills for your skill-based learning plan requires a thorough assessment of your personal strengths and weaknesses. Reflecting on your abilities and areas for improvement can help you determine which skills are most relevant to your chosen career path.

When selecting skills to develop, consider both hard skills, such as technical abilities, and soft skills, like communication and problem-solving. Transferable skills , those that can be applied across various industries and roles, are particularly valuable in skill-based learning. These versatile skills can provide you with a competitive edge in the job market and enable you to adapt to a changing professional landscape.

Creating a Successful Skills-Based Learning Plan

Developing a skills-based learning plan begins with setting clear and achievable goals for your skill development. By defining the skills you want to acquire and the level of mastery you aim to achieve, you can create a roadmap for your learning journey.

Next, identify the resources available for skill acquisition. Online platforms and courses, workshops and seminars, and books and articles are valuable sources of information and guidance. Explore these resources to find the most suitable learning opportunities for your needs.

Finally, establish a timeline for your skill development and mastery. This timeline will help you stay on track and monitor your progress, ensuring that you are consistently working towards your goals. Remember, skill-based learning is a continuous process, and your plan should be flexible enough to adapt to your evolving needs and aspirations.

Putting Skills-Based Learning Techniques into Practice

Active learning strategies are essential for effective skill development in a skills-based learning plan. Techniques such as project-based learning, problem-based learning, and collaborative learning encourage hands-on experience and real-world application, fostering skill mastery.

Integrating these techniques into existing educational and professional development programs can enhance their effectiveness and relevance to your career goals . By focusing on practical skills, you can maximize the value of your learning experience and accelerate your professional growth.

As you progress, it’s crucial to assess your development and adjust your learning strategies as needed. This ongoing evaluation ensures that you remain on track and continue to refine your skills, preparing you for success in your chosen field.

Establishing a Supportive Learning Network

Networking plays a significant role in skill development, as it enables you to connect with like-minded individuals, industry professionals, and mentors who can provide valuable insights and guidance. Building a strong network can enhance your learning experience, open doors to new opportunities, and help you stay informed about emerging trends and best practices in your field.

Additionally, establishing partnerships with industry professionals and mentors can offer you personalized advice, support, and encouragement throughout your learning journey. By leveraging these relationships, you can ensure that your skill development remains on track and aligned with your career goals.

Proving Your Skills for Career Growth

Demonstrating your skills for career advancement requires showcasing your accomplishments and expertise to potential employers. One way to do this is by updating your resumes and CVs with relevant skills and achievements. This information should be presented clearly and concisely, highlighting your unique value and capabilities.

Another effective method for displaying your skills is through portfolios and work samples. These materials provide tangible evidence of your expertise, allowing employers to see the direct application of your skills in real-world situations.

Finally, interviews and networking events offer opportunities to articulate your skill mastery and demonstrate your proficiency. By confidently discussing your skills and experiences, you can make a lasting impression on employers and increase your chances of career advancement.

Unlock Your Potential with Skills-Based Learning

As we conclude our exploration of skills-based learning, it’s evident that this approach is crucial for achieving career success in today’s competitive job market. By focusing on specific, relevant skills and implementing effective learning techniques, you can unlock new opportunities and advance in your chosen field. We encourage you to continue developing your skills and embracing lifelong learning as a key to personal growth and professional growth. At Pathways to Advancement, we’re here to support you with expert advice and resources for further skill-based learning opportunities. Discover more at Pathways to Advancement .

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What is the difference between skill and knowledge-based learning?

Gaining knowledge and putting it into practice is fundamental to learning, and the most effective way to learn is to build upon what you already know. Traditionally, education has followed the path to greater knowledge, but as skill gaps emerge, teachers must find new ways to address them.

Most educators today advocate for greater balance between knowledge and skills-based learning. But to teach effectively, it’s essential to understand the differences between these two methods. It’s also important to know how to implement them in a way that most benefits students.

Knowledge-based learning vs skills-based learning

Knowledge is the theoretical understanding of something, which is acquired through lectures and textbooks. Knowledge-based learning, therefore, refers to reading, listening, and watching to obtain the information needed before progressing to the next stage of learning.

Skills can be acquired by   doing,   and the best way to master something is through regular practise or trial and error. Skill-based learning aims to build upon knowledge by developing practical expertise in a particular area.

As an example, a carpenter requires the background knowledge of how to measure and read blueprints, before starting a project. This can help with a range of projects, but it’s not enough on its own. A carpenter also needs to develop skills to carry out specific tasks, such as crafting a kitchen cabinet or making a coffee table. 

As you can see from this example, having knowledge about something does not make you skilled in it—likewise, being skilled at something does not mean you have all the required knowledge to excel at it.

The carpentry example offers a very clear-cut distinction between these two learning methods. When it comes to early childhood teaching, such as in primary schools, skills-based learning aims to build upon the knowledge a student has gained in the classroom. This combination of knowledge-based and skills-based learning will help transform learners into independent thinkers and prepare them for challenges they may face in the future.

Why is skills-based learning important?

Skills-based education is essential for a few reasons:

• It promotes greater independence Students who learn through skills-based instruction are more likely to think at a higher level and solve problems on their own. This is important, particularly for primary school children who are being introduced to new ideas and concepts every day. Children at this age are just beginning to make connections between what they have been told and what they have experienced.
• It increases learning speed Students learn much faster when there are multiple ways for them to absorb information. Using a skill-based approach can further develop what they already know and help them grasp concepts quicker.
• Provides real-world experience Children have little real-world experience, which makes teaching theoretical subjects more challenging. By creating lessons that engage their reality, that they can experience and build upon, they will be able to catch on a lot faster.

Although the importance of a skill-based curriculum cannot be understated, knowledge is still a crucial foundation for students to be able to apply their skills and understand the broader reasoning behind what they are learning and why.

Understanding learning methodologies for early childhood education

There is a lot more you can understand about learning methodologies to make an impact on young learners. For example, what the characteristics of competency-based training is, what is meant by life skill education and much more. 

If you want to learn more, are interested in mentoring future generations and working with young children, then a career in childhood education might be your calling. Open Colleges’ courses in childhood education will provide you with the perfect foundation for starting your career in this field.

Visit the   Early Childhood Education and Care   course page now where you’ll find a selection of nationally recognised courses, including   CHC50113 Diploma of Early Childhood Education and Care . These courses will give you both the knowledge and skills you need to start a career in the growing and highly rewarding early childcare sector.

By Open Colleges

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Competency Based Education

Nov 01, 2019

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Competency Based Education. Cheryl Aschenbach – ASCCC North Representative Jarek Janio – Santa Ana College Jan Young – Glendale College. Overview. What is competency based education (CBE )? What does CBE look like in practice? How can you consider CBE in your discipline?.

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Competency Based Education Cheryl Aschenbach – ASCCC North RepresentativeJarekJanio – Santa Ana College Jan Young – Glendale College

Overview • What is competency based education (CBE)? • What does CBE look like in practice? • How can you consider CBE in your discipline?

Competency-Based Education The competency-based education (CBE) approach allows students to advance based on their ability to master a skill or competency at their own pace regardless of environment. This method is tailored to meet different learning abilities and can lead to more efficient student outcomes.

Competency-Based Education • Competency-based or Outcomes-based • Advancement based on mastery of outcomes rather than seat time or credit hours • Mastery demonstrated through assessments

Competency-Based Education • Changing paradigm: • Credit hour content mastery • Focus on teaching focus on learning • Time is constant/learning is variable time is variable/learning is constant • Greater focus on employer input regarding knowledge, skill, and aptitude (KSA) needs of future employees

Competency-Based Education • Colleges already do some CBE • Open entry/open exit courses in CTE and noncredit • Credit by Exam

Examples of CBE • Noncredit • Math and writing labs • Accounting (Glendale) Medical Front Office (Glendale) • High School Diploma classes (Glendale) • Keyboarding (Glendale) • CTE Certificates • Nursing • Welding • Noncredit pathways • Health Care Pathway: • Nursing Assistant • Medical Billing and Coding • Introduction to Medical Assistant • General Medical Office Clerk

Competency-Based Education Can more be done? • Can CBE be done in census classes? • Can more CTE education be CBE? • How can we accomplish this?

Implementing CBE • Identifying outcome competencies is key – all supporting learning objectives must be identified and sequenced within a program and courses • Objectives may be used rather than outcomes – usually require demonstration of more discrete skills or knowledge

Implementing CBE • Identify and reduce or eliminate ineffective structures, practices, and expectations that may have been historically justified but are now impeding the efficiency of student learning • Minimum hours & unit requirements • Minimum internship/externship hours requirements • Required skills practice regardless of current competence

Implementing CBE • Design classroom and lab experiences with pre-assessment opportunities • Based on outcomes or objectives, determine the appropriate assessment(s) and level of mastery • Pre-assessments may be the same as post-assessment, or the two may vary for greater demands on students • Pre-assessments may be integrated into early classroom activities or assignments rather than formalized as separate assessment opportunities • Instruction based on students learning, rather than faculty teaching. • Establish achievement benchmarks • Establish program exit criteria

Questions?Thank you! Cheryl Aschenbach: [email protected] JarekJanio: [email protected] Jan Young: [email protected] [email protected]

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OUTCOME-BASED EDUCATION (OBE)

Published by Shinta Hermanto Modified over 5 years ago

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Writing Effective Learning Outcomes Department for the Enhancement of Learning, Teaching and Assessment RGU:DELTA.

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An Overview of Service Learning: Building Bridges, Making Connections

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Nurturing 21st-century skills in the early years

Early years education must now, more than ever, needs to move towards experiential, exploration and inquiry based learning..

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• The pervasive influence of AI has transformed various aspects of daily life, with smart technology
• Generation Beta is the first to grow up immersed in AI-driven environments
• As technology continues to evolve, it's crucial to provide children with the skills and opportunities

We are currently living in times when AI has made everything around us smart, from phones to homes, healthcare, finance, entertainment and even education. Generation Beta, i.e. those children born between 2025 and 2039 are the first AI generation and are influenced heavily by everything smart. As technology continues to shape our future, it becomes increasingly important to equip our children with the right skills and opportunities to learn and adapt in this evolving world.

The following tips are shared by Anjali Motiani, Curriculum Head, Kido International Preschools and Daycare.

Gone are the days when children only gained structured knowledge in school or the educator was the sole source of knowledge. Now, an infinite amount of information is readily available at our fingertips at any time.

Children must learn skills such as flexibility, communication and collaboration to problem-solve and show adaptability and resilience towards change and challenge.

To achieve this, educators and parents must join hands to provide the right environment for a child to exercise and develop these 21st Century skills.

THINKING-BASED LEARNING:

It breaks away from the traditional approach of memorisation or rote learning to a more active method of learning any new concept through analysis and evaluation before concluding.

For example, what if a child from an early age becomes accustomed to taking a step back, evaluating and analysing their options and their choices before answering any decision-making question?

Teaching them to compare and contrast the alternatives instead of jumping to a reflexive response helps them give an informed answer.

The consistency in being exposed to this process would help a child’s brain develop analytical and critical thinking from a young age to the extent that it becomes second nature to them; and all this through exploration and experimentation.

DIGITAL LITERACY:

For young children today, gadgets and devices are a natural part of life and thus, introducing children to digital literacy becomes imperative. Instilling the right balance and approach towards screen and device use is of paramount importance.

Parents and educators need to equip children with knowledge and strategies for digital safety. In today’s day and age of technology, helping children digitally safeguard themselves improves their knowledge of the online world.

EMOTIONAL LITERACY:

As we prepare ourselves and our young learners for an unforeseeable future, it is equally important that we instil skills related to emotional literacy and self-regulation within our children.

Helping children identify, name and associate situations with specific feelings will help them learn how to regulate and manage those when required.

Additionally, it builds emotional sensitivity and teaches them sympathy and empathy while communicating with others. Being in tune with one’s emotions ultimately leads to self-awareness, stronger self-esteem and self-confidence, forming healthy relationships and higher resilience.

Early years education must now, more than ever, needs to move towards experiential, exploration and inquiry-based learning. We must do so to help children connect with the content and formulate the right questions that will make them curious and critical thinkers.

Educators must undergo rigorous training and professional development that will provide them with practical tools and knowledge to support Generation Beta’s needs in the classrooms.

At the same time, parents also need to develop their flexibility, collaboration skills, empathy and emotional literacy to become the guiding mentors children need. Published By: vaishnavi parashar Published On: Mar 19, 2024 READ | Lok Sabha Elections 2024: How to apply for voter ID card online

A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

• “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
• “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
• “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

• data scientist:
• prompt engineering
• in-context learning
• bias detection
• pattern identification
• reinforcement learning from human feedback
• hyperparameter/large language model fine-tuning; transfer learning
• data engineer:
• data wrangling and data warehousing
• data pipeline construction
• multimodal processing
• vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

• Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
• Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
• Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

• Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
• Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
• Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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