AI’s $15 trillion prize will be won by learning, not just technology

Leaders across business, government and civil society share genuine excitement about AI’s potential. Analyses consistently project a multitrillion dollar uplift. The World Economic Forum estimates AI could contribute up to 14% of global GDP by 2030, equivalent to about $15.7 trillion. Yet even as AI tools spread across enterprises, the productivity pay-off remains elusive: The UK’s labour‑productivity trend has deteriorated, and US productivity growth has been inconsistent and declining from the highs of the early 2000s. The paradox is clear: Many employees report “time saved” through AI tools, but organizations and economies are not seeing durable, enterprise-level gains.

As Chief Technology Officer at Pearson, my conclusion is that while AI investment is accelerating, enterprise productivity remains uneven because there isn’t enough focus on the entire system of work: Humans + technology working together. There needs to be greater emphasis on how we reimagine entire workflows afresh, going beyond the notion of implementing technology first, then training humans later. Instead, we need a more integrated approach that puts constant learning by both humans and AI agents at the heart of transformation.

Realizing AI’s potential requires more than scaling technology; it demands a partnership between people and technology, where learning and adaptation are embedded at every stage. Drawing on deep experience in learning science, we know that navigating this paradigm shift means empowering individuals and teams to thrive; not just through technology tools, but through the continuous development and collaboration that make those tools truly count. This approach ensures that AI serves as a force for good, elevating outcomes for people and organizations alike.

The real prize is building an augmented workforce where human expertise is accelerated and amplified by AI in the flow of everyday work, anchored in the authentic context of an individual’s skills, and orchestrating the right combinations across workflows.

Automation delivers quick, measurable efficiencies; AI-augmented workforces rewire how value is created. New Pearson research shows the greatest productivity gains come when AI is used to codify knowledge, orchestrate multi‑agent work, and embed rapid, continuous learning so people move from “doing” to discernment and creativity. In other words, AI should not replace judgement; it should raise the ceiling of what human judgement can achieve.

The biggest obstacle to unlocking this potential is a widening learning gap: While AI capabilities are advancing exponentially, workforce adaptation remains sporadic and superficial. Our research finds that organizations often “tick the box” on AI training initiatives, but fail to rearchitect tasks, roles and skills around human-AI collaboration. This gap explains why individual “time saved” hasn’t compounded to enterprise productivity – and is now the key risk to realizing the job transformation the World Economic Forum anticipates.

If we close the learning gap and scale augmentation, the economic upside is substantial. Our modeling across more than 300 knowledge‑intensive occupations shows AI-powered augmentation could add between $4.8 trillion and $6.6 trillion to the US economy by 2034 (around 15% of its current size), contingent on broad augmentation adoption. Combined with the Forum’s global outlook, the message is consistent: unlock new sources of growth by investing in people.

To close the learning gap, we need a fundamentally different approach to upskilling; one that moves beyond legacy training models to AI-enabled, human-centred learning. This means embedding learning directly into workflows, rather than pulling employees away for generic training sessions.

To enable this transformation, our research defines four key pillars at the core of a framework we call DEEP:

This is how transformation and feedback become the normal cadence of a business strategy; not a pilot programme, but a persistent capability that both prepares workers for future changes and makes transitions faster and more successful.

To realize AI’s $15 trillion promise, leaders must champion a new learning imperative that is human-centric, iterative and evidence-based. AI investment and learning must go hand in hand; neither can deliver sustainable impact alone. This requires moving beyond the false choice between automation and human workers to embrace workforce augmentation as the path to sustainable productivity growth.

C‑suites must invest in learning at scale by reimagining work around human‑AI collaboration, building trusted skills data, credentialling capabilities, and empowering Learning and Development leaders to define the future of work. This future depends on scalable learning systems, measurable skills ecosystems and trusted data to ensure augmentation strengthens, not erodes, human capability.