Pearson warns AI gains hinge on urgent workforce reskilling

Research from Pearson suggests that AI-driven productivity gains are fundamentally linked to continuous workplace learning and robust skills development.

The report models the potential economic value of integrating AI deployment with structured learning programmes, estimating a significant uplift for the US economy. Specifically, the findings indicate a potential boost of between USD $4.8 trillion and USD $6.6 trillion by 2034, provided that employers augment existing roles with AI while ensuring staff acquire the necessary skills to utilize the technology effectively.

Pearson notes that even the lower end of this estimated range represents approximately 15% of current US GDP. Consequently, the report argues that employers risk forfeiting these substantial gains if they focus exclusively on technology roll-outs while overlooking the critical need for comprehensive training and organisational change. By prioritising the human element of digital transformation, businesses can better ensure that the implementation of AI results in tangible economic growth rather than untapped potential.

Productivity gap

Pearson's report frames the issue as a "learning gap". It said companies have committed large sums to AI infrastructure and models, yet they have produced few clear examples of enterprise productivity gains outside software development work.

Furthermore, Pearson observed that many businesses have primarily focused on achieving returns from AI through labour substitution, a strategy linked to heightened uncertainty and anxiety within the workplace. Whilst workers frequently report significant time savings when using AI tools, the report suggests these efficiencies have yet to translate into broader economic uplift at scale.

This discrepancy highlights the risk of prioritising cost-cutting over value creation, suggesting that without a strategic shift towards augmentation and retraining, the potential for widespread prosperity remains unrealised.

"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," said Omar Abbosh, Chief Executive Officer, Pearson.

"As a learning company, we see that the biggest obstacle to AI adoption is the lack of human skills to work alongside these technologies. Addressing that will support workers, boost their confidence with new technology, and drive the ROI outcomes that businesses want," said Abbosh.

Roadmap steps

Pearson set out a framework for workplace learning that it said should run alongside technology deployment. It described a shift from a model where firms deploy technology first and then expect staff to adapt.

The report said organisations should plan at the level of tasks rather than at the level of whole roles. It said employers should decide which tasks AI will augment and which tasks remain with people.

It presented four steps under what it calls the DEEP Learning Framework. The steps include diagnosing and defining a task-level augmentation plan, embedding learning into day-to-day work, evaluating skills progress for an AI-augmented workforce, and prioritising learning as a strategic investment.

Pearson also pointed to the pace of AI adoption among consumers and knowledge workers. It said AI reached more than one billion users in three years, while training and skills development did not keep pace.

The report links that gap to a combination of economic and emotional pressures. It said workers face increased risk of job loss and a loss of competitiveness when employers deploy AI without structured learning and redeployment pathways.

Workforce urgency

Pearson cited a World Economic Forum estimate that 59% of the global workforce will need reskilling by 2030. The report said that forecast underlines the need for new learning models inside organisations.

The report's economic estimate focuses on the US, but its workplace argument targets multinational employers and large-scale adoption. It frames AI as a general-purpose technology whose benefits depend on complementary investments in people, processes, and measurement.

Pearson said its findings draw on quantitative analysis combining its Faethm data with a review of academic and industry research, plus expert interviews. The company said this approach allowed it to model both the economic impact of AI-augmented work and the organisational practices linked to closing skills gaps.

The report positions learning metrics as an operational issue. It links measurement to workforce planning and the tracking of progress towards an AI-augmented workforce.

Pearson's work arrives as employers weigh AI spending against near-term cost pressures. Many companies have deployed chatbots and automation tools in customer support, marketing, software development, and internal operations, but they have struggled to quantify gains across functions.

"Every positive scenario for this AI-enabled future is built on human development," said Abbosh.

Pearson said employers that align AI deployment with training and task redesign could see stronger productivity outcomes over the next decade.