By Dave Treat, Chief Technology Officer, Pearson
For years, many organizations treated growth as a hiring consideration: more demand required a greater headcount. AI breaks that equation by shifting the constraint from capacity to capability. Access to tools helps, but access alone doesn’t translate into productivity. This is where learning comes in, embedded in the flow of work.
Hiring alone won’t close the gap
Early in the AI cycle, the default response was predictable: hire specialists, stand up pilots, then hope the organization absorbed the change. But AI is moving faster than most operating models can adapt, and faster than the skills pipeline can replenish. Research found that around two-thirds of learners, higher education leaders, and employers describe AI-driven workplace change as “very fast” or “extremely fast,” yet only a quarter believe universities are keeping pace. The lesson is simple: you can’t hire your way to enterprise AI adoption if work stays the same and people aren’t equipped to apply AI with judgment.
AI is already here, govern it
In 2026, the question is no longer whether AI will show up in your enterprise, as it already has. The question that employers should be asking is whether it shows up in a governed, productive way. When formal provision is thin, people assemble their own tool stacks. Learners report relying far more on independently sourced tools than institution-provided ones, creating a “shadow AI” environment where usage patterns form without consistent guidance on transparency, ethics, or data security. This is why governance has to enable learning and safe experimentation so people can use AI openly, verify outputs, and understand where accountability sits.
Build capability, don’t just automate
The most pragmatic leaders are moving from a “replacement” mindset to an “augmentation” mindset. The economic upside of getting this right is material: AI-powered augmentation of knowledge work when paired with effective learning could add between US$4.8 trillion and US$6.6 trillion to the US economy by 2034 according to studies.
The cost of misalignment is also measurable, with inefficient career transitions and learning gaps associated with roughly US$1.1 trillion in annual losses in the United States equating to about 5% of 2023 GDP. That’s not an “education problem” or an “HR problem.” It’s a system problem where technology, work design, and learning architectures are not moving in lockstep.
Translating capability into productivity
To be ‘AI-ready’, we need human capability to work effectively alongside intelligent systems, combining tool fluency with judgment, ethics, and durable human skills. This includes skills such as practical fluency in using AI tools, redesigning workflows based on where AI creates the most value, and being able to evaluate and verify AI outputs, Meanwhile, human skills such as communication, collaboration, adaptability and judgement will become even more valuable.
Productivity shows up when learning becomes infrastructure—woven into daily execution rather than bolted on afterward.
How leaders can enable better outcomes from AI integration
To achieve AI outcomes rather than AI adoption, leaders need to align Technology, HR, and business leaders around how work gets done, how skills are built, and how governance is enforced. This is where the CHRO–CTO partnership matters most.
Start with work, not tools - Identify where AI can augment outcomes at a task level and be explicit about the human checks. What does the system do by default? When must people verify or override? Who owns the decision?
Make practice measurable - Build structured opportunities for people to practice AI in real workflows, so learning shows up in execution. Certification can also play a useful role in validating applied capability.
Let governance enable speed - Set clear data, usage, and accountability guardrails that make responsible AI use easier.
Invest where adoption compounds - Prioritise capability in the people who shape daily work, across managers, instructors, and internal experts. When capability is uneven, outcomes are too.
AI will continue to embed itself in the workplace, but the differentiator in who gains the most will be the organizations which can build and govern the human capability to use it well. Leaders who pair augmentation with learning, in the flow of work, will see compounding returns.
