AI productivity is not the CIO's responsibility alone

With UK productivity showing signs of recovery, Jim Hinchliffe, Commercial Director at Kaplan UK - a leading provider of professional education, apprenticeship training and enterprise upskilling - considers how CIOs can shape the extent to which AI delivers meaningful productivity returns - when success depends on much more than technology.

New analysis from the Resolution Foundation offers encouraging news for the UK economy. Annual growth in output per hour has averaged 1.1% over the past two years, after a 0.7% annual decline in the preceding two.

The improvement coincides with rapid AI adoption, although it is too early to know how far the two are connected. It nevertheless sharpens the question facing technology leaders: is AI genuinely improving productivity?

Businesses are investing in a technology whose applications are still emerging while expecting evidence of commercial returns. Although implementation may sit with the CIO or IT function, the factors that determine success extend beyond technology.

Making AI's impact tangible therefore requires technology leaders to align business priorities, workforce skills and organisational culture.

Start with the problem, not the technology

For CIOs seeking to demonstrate a return, the first question is where AI can make a meaningful difference. Technology works best when it addresses a specific problem or opportunity.

Organisations should identify where productivity improvements are needed and how AI might deliver them. Priorities will vary, from strengthening client services through better analysis and reporting to accelerating internal processes or improving risk management.

Only then can organisations choose between an off-the-shelf assistant and a specialist solution. Success should be measured against the original business objective, not adoption levels. For smaller firms with tighter resources, getting that choice right is particularly important given the proportional impact AI could have on productivity.

Technology adoption is not the same as capability

Even when the right applications have been identified, putting AI tools in employees' hands will not automatically improve productivity. People need the skills and judgement to use them effectively.

An employee may be comfortable using an AI assistant without understanding how to evaluate its output, recognise its limitations or apply it appropriately. Encouraging adoption without clear guidance can introduce errors and create professional, ethical or regulatory risks.

In a compliance-sensitive process, for example, an assistant might confidently recommend an action that breaches requirements. Without subject knowledge and critical evaluation skills, an employee may not spot the mistake.

The challenge goes beyond operating a tool. Data literacy, critical thinking and human judgement are essential, requiring cooperation between IT, business leaders and learning and development teams.

Building a culture that delivers results

Workforce development must be part of AI strategy from the outset. Training should reflect employees' roles and emphasise applying skills, not simply familiarity with new tools.

Some employees will need foundational AI skills for everyday tasks; others will require specialist expertise in critical business areas. Broader capabilities also matter, including opportunity identification, systems thinking and measuring improvement.

Employees are often best placed to see how AI could improve the processes they handle each day. Giving them the confidence to identify opportunities, test approaches and assess results can turn adoption into continuous improvement.

Clear communication is equally important. People must understand what the organisation wants to achieve, how they are expected to use AI and where human oversight remains essential. Without that clarity, adoption can become a source of frustration rather than productivity. This responsibility can't rest with the CIO alone. Technology teams can implement systems, but the wider business must establish the skills and working practices to use them well.

Measuring what matters

Demonstrating AI's return on investment requires organisations to look beyond the technology itself. A tool may help someone produce a report or complete an administrative task faster, but the more important question is what that efficiency enables.

Can employees redirect time towards higher-value work? Are processes becoming more effective? Is service quality improving? These outcomes, rather than the number of licences issued or prompts submitted, should determine whether investment is meeting its objectives.

The CIO should identify opportunities and ensure suitable technology is in place. But they need to work alongside other departments and leaders in a business to ensure AI delivers meaningful value, like increasing productivity.

As AI evolves, organisations will need the internal skills to reassess opportunities and adapt their approach. That ability to learn and adjust is more valuable than committing prematurely to a fixed set of tools.

There is no definitive answer to how far AI is contributing to the UK's productivity recovery. For businesses, the priority is clear: judge AI by the improvements it enables and develop the people using it as seriously as the technology itself. Sustainable returns require collaboration between technology leaders and the wider organisation.