Knowledge work is changing quickly, and for many people, it’s opening up new possibilities rather than replacing what they do best. Roles built around thinking, analysing and applying information, including managers, analysts, engineers, consultants and researchers, are increasingly supported by AI tools that help people work smarter, spot patterns faster and spend more time on higher-value work.
Pearson’s 2026 Learning Gap report highlights a clear opportunity in this shift. AI only creates real economic value when people feel confident and capable using it in their day to day roles. Even as AI adoption grows, productivity in the UK and US has remained relatively flat, suggesting there is untapped potential. Closing the gap between workforce readiness and technology is where progress happens, enabling organisations to deliver better outcomes for employees, customers and the business as a whole.
“AI will drive profound long‑term change to business and industry. But leaders are under pressure to rapidly adopt AI and demonstrate a return on that investment, all while bringing worried employees along with this seismic shift. Every positive scenario for this AI‑enabled future is built on human development.” Omar Abbosh, CEO, Pearson
Augmentation vs Automation
When organisations think about AI, the conversation often comes down to two ideas. One is automation — using AI to replace tasks and improve efficiency. The other is augmentation — using AI to help people do their jobs better. These are sometimes treated as opposing choices, but in reality, most organisations need both.
“Organisations really need the benefits of both automation and augmentation. Automation is very good at productivity and efficiency, but it’s not very good at innovation, or at figuring out whether systems are working well, identifying when you need to retrain a model, etc.” Tom Davenport, Professor of Information Technology and Management, Babson College
Automation is very good at speeding things up and reducing costs. Where it falls short is in areas like creativity, judgement and understanding when systems need to change. Augmentation fills that gap by supporting people to think more clearly, work more efficiently and make better decisions.
“Ten percent cost savings is nice, but that’s not what excites businesses the most. It takes reengineering the workflows to get to significant growth.” Andrew Ng, Founder, DeepLearning.AI
Small cost savings are useful, but they rarely transform a business. Real growth comes from redesigning how work gets done and giving people the right tools to perform at a higher level.
What Does an Augmented Knowledge Worker Look Like?
Many roles can benefit from AI, but knowledge work offers some of the greatest potential. This is work where insight, experience and problem-solving drive long-term value. The augmented knowledge worker uses AI to organise information, speed up research, test ideas and manage complex tasks, while still applying human judgement where it matters most.
This is about much more than giving people access to AI tools or chatbots. True augmentation means rethinking how work is shared between people and technology. Instead of asking what a role looks like today, organisations need to ask what people should focus on, and where AI can provide support.
The D.E.E.P. Learning Framework
To make AI augmentation work, organisations need to rethink how they introduce new technology and skills. Rather than deploying tools first and training people later, people and AI should learn together from the start. The D.E.E.P. framework (Diagnose, Embed, Evaluate and Prioritise) offers a clear way to approach this.
Diagnose: Define Your Task
Success begins with understanding work at the task level. This is often led by experienced employees who are curious about AI and eager to experiment. These individuals help identify where AI adds real value and where it does not. Working alongside technologists, operations leaders and learning teams, they test use cases and help scale what works.
This approach balances clear strategic direction from leadership with hands on experimentation by people closest to the work. The focus is on how tasks, skills and processes will evolve as generative and agent based AI becomes part of daily work.
Embed: Learn Whilst You Work
Many AI training efforts fall short because they focus too much on tools and not enough on how people actually learn. A more effective approach is to build learning into daily work, supported by a strong culture of curiosity and sharing.
AI makes this easier by enabling learning that is timely and relevant. Instead of pulling people away from their roles for generic training, learning can happen in the moment, tailored to individual needs and reinforced through peer support.
Evaluate: Track What’s Changing
An AI-enabled workforce needs visibility into how skills are developing over time. Tracking progress helps ensure learning stays aligned with business goals. This includes building better skills data, using AI-supported assessments and testing skills in real work situations.
Modern systems make this more achievable. AI can analyse data that already exists across the organisation, helping leaders build a clearer and more up-to-date picture of workforce capability.
Prioritise: Treat Learning as an Ongoing Investment
AI is changing the role of Learning and Development teams. Rather than simply delivering courses, they are increasingly responsible for building and maintaining capability across the organisation. This means focusing on skills rather than fixed job titles and supporting continuous learning over time.
When organisations recognise and validate the skills people develop, they build trust in learning efforts and confidence in how talent is deployed. Learning becomes part of everyday work, not an extra task.
What Leaders Need to Do
Building an AI-augmented workforce is a long-term effort, not a one-off project. It requires clear leadership, steady investment and input from people across the organisation. Senior leaders set direction, while real progress comes from learning what works in practice.
HR and learning leaders play a central role in developing skills. Technology leaders need to adapt and scale AI initiatives as lessons emerge. CEOs must provide visible support, framing AI adoption as a shared journey rather than a top-down mandate.
Used narrowly, AI can reduce costs but weaken capability. Used thoughtfully, it strengthens human judgement, creativity and expertise. That’s how AI delivers lasting productivity gains and supports work that feels both effective and meaningful.