As enterprises rush to embed AI across workflows, productivity stacks, and employee experiences, a critical gap is beginning to emerge between AI deployment and measurable business value.
While many organisations are successfully enabling employees with AI tools, far fewer are redesigning workflows, learning systems, and skills architectures required to sustain long-term gains.
In an exclusive interaction with Express Computer, Dave Treat, Chief Technology Officer, Pearson, speaks about why enterprises are struggling to operationalise AI at scale, the growing importance of “skills intelligence”, and how AI-driven workforce transformation will increasingly depend on continuous learning embedded directly into everyday work.
AI adoption is being mistaken for productivity transformation
According to Treat, one of the biggest misconceptions enterprises currently have is assuming that faster work automatically translates into better business outcomes.
“Companies launch AI tools, see people work faster, and assume results will improve. But real productivity is about better outcomes — quality, customer impact, cost, and decisions, not just speed,” he says. He explains that many organisations still approach AI adoption as a technology rollout rather than a business redesign initiative. “A common mistake is to roll out technology first and think about training later.”
Instead, Treat believes enterprises need to begin with the business objective itself and redesign work around that outcome. “The better approach is to start with the business goal, redesign the workflow, and then decide what humans do, what AI does, and what people need to learn while doing the job.”
For Treat, learning and AI can no longer operate as separate enterprise functions. “When learning and AI are built into work together, the gains last longer.”
He also references Pearson’s research report Mind the Learning Gap: The Missing Link in AI’s Productivity Promise, which argues that enterprises will only unlock sustained AI value if they invest in continuous learning alongside AI tools and platforms.
The biggest barriers to AI scale are not the AI models
Treat says the primary challenges are often organisational rather than technological. “The biggest problems are not the AI tools themselves. Problems can arise in how those tools are applied and integrated without appropriate learning or infrastructure.”
One of the most significant issues enterprises face today is fragmented skills visibility across the organisation. Skills information is spread across many places — HR, learning tools, and work systems. Because it isn’t connected, leaders can’t clearly see which skills drive performance and business results.
At the same time, many enterprises continue to treat learning as a one-time intervention instead of an ongoing operational capability. “Learning is treated like a one-time training programme. But with AI, it is more successful when deployed in the flow of work, people learning and getting feedback and guidance in real time, right when they need it.”
Treat adds that AI deployments often expose deeper process inefficiencies already present within organisations. “AI often shows where processes are weak or confusing. If you don’t redesign the work at a role or even task level, what people do, what AI does, and how the steps connect, then AI won’t scale.”
For this reason, he believes process transformation is becoming just as critical as AI deployment itself.
To address this, Pearson has developed what it calls the D.E.E.P. framework that is Diagnose the tasks to improve, Embed learning into daily work, Evaluate progress using data, and Prioritise it as a business investment.
Skills intelligence will become central to enterprise competitiveness
Treat believes enterprises now need to rethink how they define, measure, and operationalise skills across the workforce. “To build real ‘skills intelligence’, companies need to stop treating skills like a one-time checklist.”
Pearson’s AI-powered communication coaching tool integrated into Microsoft 365 is an example of how learning systems are becoming embedded directly into work environments. “We built an AI-powered tool integrated into Microsoft 365 which gives people personalised feedback on their vocabulary and communication skills.”
The larger goal, according to Treat, is to make learning contextual and immediately applicable rather than isolated inside LMS environments. “When learning happens inside the day job, not in a separate LMS, people build skills and use them immediately.”
Treat asserts that this model creates a far stronger link between learning investment and measurable enterprise outcomes. “Learning and AI need to move together, in the flow of work, to create measurable productivity and performance.”
Verified digital credentials could become workforce trust infrastructure
As AI reshapes hiring models and workforce expectations, Treat sees verifiable digital credentials becoming increasingly valuable, provided they reflect real capability rather than participation alone.
He points out that employers now require more reliable signals of practical capability as roles evolve faster and traditional hiring indicators become less effective. He believes the real value of credentials lies in portability, trust, and skills verification. “When credentials are verifiable and portable, they reduce friction on both sides. Employers hire with more confidence, and individuals can signal readiness without relying solely on proxies like tenure or titles.”
AI transformation requires CHROs and CTOs to work together
Leadership alignment is becoming one of the defining factors behind successful enterprise AI adoption. “AI adoption succeeds when leaders treat it as a work redesign, not just a tech rollout,” he says.
He specifically highlights the need for closer collaboration between HR and technology leadership functions. “Real ROI comes when the CHRO and CTO co-own it.”
This includes redesigning workflows, defining accountability models, and deciding how AI and humans collaborate operationally.
India could become a major AI-skilling acceleration market
Treat also highlights the growing importance of ecosystem collaboration between academia, enterprises, and technology providers.
Pearson is currently working with organisations including Microsoft, Amazon Web Services, Google Cloud, and Tata Consultancy Services to build AI-aligned skilling pathways.
Treat avers collaboration between academia and industry will become increasingly critical in markets like India where digital transformation and workforce scale intersect simultaneously.
India is uniquely positioned to accelerate AI-led workforce readiness because of its scale, talent depth, and digital ambition. “In markets like India, where demand, talent, and digital ambition intersect, collaboration becomes a force multiplier.”
The broader shift, he argues, is moving away from static learning models towards continuously evolving readiness systems. “The shift is from a handoff model to shared accountability, where readiness is built continuously, not tested at the point of hire.”
By combining enterprise technology ecosystems with learning science expertise, Treat believes organisations can create far more effective workforce development models.