11 February 2026

 

Artificial intelligence will not deliver sustained productivity gains unless it is paired with continuous learning and workforce development, according to new research from Pearson released at the World Economic Forum Annual Meeting in Davos, earlier this year. The findings challenge the assumption that AI investment alone will translate into higher productivity, arguing instead that skills development is the decisive factor in realising economic value.

Why AI’s productivity promise is falling short

Organisations worldwide are investing heavily in artificial intelligence infrastructure and tools, yet clear examples of enterprise-wide productivity gains remain scarce. Outside limited use cases, such as software development, many companies struggle to convert AI adoption into measurable improvements in performance or return on investment.

According to Pearson, much of the current focus remains on labour substitution rather than job enhancement. While employees may experience short-term efficiency gains or ‘time saved’ through AI tools, these benefits rarely scale into sustained economic impact or long-term value creation.

The research identifies a widening ‘learning gap’ as the central obstacle. AI technologies are being deployed faster than workforces are being prepared to use them effectively, leaving organisations with powerful systems but limited capability to integrate them meaningfully into everyday work.

Learning as the missing link

The report, Mind the Learning Gap: The Missing Link in AI’s Productivity Promise, concludes that the largest productivity gains will come from job augmentation rather than automation. Redesigning work so that AI supports human judgement, problem-solving and creativity is, according to the research, far more economically powerful than replacing people outright.

“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,” said Pearson CEO Omar Abbosh. He added that every positive scenario for an AI-enabled future depends on sustained investment in human development.

Pearson’s analysis estimates that augmenting knowledge workers with AI, when combined with structured learning, could add between US$4.8 trillion and US$6.6 trillion to the US economy by 2034, equivalent to around 15 per cent of current US gross domestic product. While the modelling focuses on the United States, Pearson notes that the same principles apply across advanced economies.

How productivity gains are defined

The research focuses on ‘knowledge workers’: professionals whose roles centre on analysis, problem-solving and the application of expertise rather than manual labour. Pearson assesses productivity gains by examining the current economic contribution of these workers, the degree to which AI can enhance their output, and how suitable different occupations are for AI-driven augmentation.

Without targeted learning strategies aligned to these roles, Pearson concludes that the projected productivity gains are unlikely to materialise.

Abbosh said the biggest obstacle to AI adoption is not the technology itself but the lack of human skills needed to work alongside intelligent systems. Closing that gap, according to Pearson, would support workers, build confidence in new tools and help organisations achieve the return on investment they expect from AI.

Rethinking ‘time saved’ as a success metric

The report also challenges how organisations measure the impact of AI. ‘Time saved’ is frequently cited as evidence of success, but Pearson argues that this is an incomplete and potentially misleading metric.

In the short term, AI can free employees to focus on tasks requiring judgement, creativity and critical thinking. However, Pearson finds few examples where these time savings have translated into sustained enterprise-level productivity gains. Too often, productivity narratives remain tied to workforce reduction rather than capability building.

AI, the research argues, can also improve productivity by transforming how people learn at work. Embedding learning directly into workflows, rather than separating training from daily activity, allows employees to receive real-time feedback and guidance as they adopt new tools.

A roadmap for AI-enabled learning

To address the learning gap, Pearson proposes the DEEP Learning Framework, a model designed to align AI deployment with skills development. The framework encourages organisations to diagnose which tasks should be augmented, embed learning into everyday work, evaluate skills progression using reliable data, and prioritise learning as a strategic investment.

According to Pearson, this approach requires closer collaboration between technology, human resources and learning teams. It is intended to be implemented incrementally, recognising that building an AI-augmented workforce involves cultural change as well as technical capability.

Automation and augmentation are not opposites

The research does not dismiss automation, but positions automation and augmentation as complementary strategies. While some tasks will inevitably be automated, Pearson argues that the most durable productivity gains will come from strengthening human roles rather than eliminating them.

Organisations that focus narrowly on short-term cost savings risk missing AI’s longer-term economic value. Without parallel investment in learning, AI adoption may increase employee uncertainty while failing to deliver meaningful productivity improvements.

What this means for business and education

For business leaders, the findings reinforce the need to treat AI strategy as a human development challenge as much as a technological one. Productivity gains are not automatic outcomes of AI adoption; they depend on whether people are equipped to work confidently and effectively alongside intelligent systems.

For students, educators and policymakers, the research highlights the growing importance of lifelong learning. As AI reshapes professional roles, continuous skills development will be central to economic resilience, workforce competitiveness and sustainable growth.

Pearson’s conclusion is unambiguous: AI’s productivity promise is real, but it will only be realised if learning keeps pace with technological change.

 

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