The overlooked opportunity in AI: making people more productive

Much of the conversation around artificial intelligence focuses on the jobs it might replace. But what if the greater opportunity lies in helping people do their existing jobs better?

Are we asking the right question?

When businesses consider adopting artificial intelligence, one of the first questions is often how much work can be automated and, by extension, how many people might no longer be needed.

It's an understandable starting point. Employee costs are a significant part of most organisations' operating expenses, and technology that can perform work faster and at a lower cost naturally attracts attention.

But there is another way to approach the question. Instead of asking how many employees AI could replace, what if we asked how much more productive those same employees could become?

The distinction might seem small, but it could fundamentally change how businesses approach their AI investments.

The economics of doing more with what you have

Consider a business employing 100 people. One approach to AI adoption might focus on automating enough work to reduce the workforce by five employees. The financial benefit would largely come from the associated cost savings.

An alternative would be to use AI to improve productivity across the entire workforce by 10%. The second approach doesn't necessarily reduce operating expenses. Instead, it increases the organisation's capacity to deliver work, serve customers, improve turnaround times or pursue opportunities that existing resources couldn't previously accommodate.

Whether this creates more value than reducing headcount depends on the business. Increased capacity is only valuable if it can be put to productive use. Nevertheless, the potential is significant, particularly in businesses where growth is constrained by the availability of skilled employees.

Importantly, these two approaches are not mutually exclusive. But focusing exclusively on cost reduction risks overlooking the broader economic benefit of improving productivity.

There is evidence that this works

A 2025 study published in The Quarterly Journal of Economics examined the introduction of an AI assistant to more than 5,000 customer-service employees. Researchers found that access to AI assistance increased productivity by approximately 15%, measured by the number of customer issues successfully resolved per hour.

Perhaps more interesting was how those improvements were distributed. Less experienced employees benefited considerably more than their experienced colleagues. The AI assistant helped them access information, respond more effectively and develop skills that would ordinarily take longer to acquire.

In effect, the technology helped make the knowledge and practices of experienced employees more accessible to the broader workforce.

The results come from one organisation and one type of work, so they shouldn't be treated as a universal productivity benchmark. They do, however, demonstrate that meaningful improvements are possible without removing people from the process.

Where should businesses be looking?

Most organisations have employees who spend a meaningful portion of their day on activities that are necessary but don't necessarily make the best use of their skills.

Consider a financial analyst spending hours gathering and reconciling information before being able to interpret the results. Or an operations team working through routine exceptions, leaving limited time for more complicated cases that require experience and judgment.

These are areas where AI and automation could make a difference. The objective isn't necessarily to remove the employee from the process. It's to reduce the administrative and repetitive work surrounding the activities where their expertise is most valuable.

A financial analyst who spends less time preparing data has more time to investigate results. An operations specialist who processes routine exceptions more efficiently can devote more attention to complex cases. The value comes from allowing employees to concentrate on work that requires their knowledge, experience and judgment.

Of course, not every process needs AI. In many cases, conventional automation or simply improving an inefficient process may deliver better results at a lower cost. The starting point should always be the business problem, rather than the technology.

Productivity needs to be measured

There is a risk that organisations confuse adopting AI with improving productivity. Providing employees with access to AI tools doesn't automatically mean they will become more effective.

Businesses need to identify where employees spend their time, which activities create bottlenecks and what improvements would genuinely benefit the organisation. From there, the measures are relatively straightforward.

Are employees completing work faster? Has accuracy improved? Are customers receiving better service? Has the organisation increased its capacity without a corresponding increase in operating costs?

It's equally important to understand whether any time saved is being lost to additional checking, correcting errors or managing the technology itself. Without this understanding, productivity improvements can become difficult to distinguish from simply doing more work.

A different way to think about AI investment

There will undoubtedly be circumstances where automation reduces the need for certain roles. Ignoring that possibility would be unrealistic. But it shouldn't be the only outcome businesses consider.

Organisations invest considerable time and money in developing their employees' skills, knowledge and understanding of how the business operates. That experience has value, even if it doesn't always appear directly in financial reporting.

Technology that allows employees to apply that experience more effectively could offer a compelling return on investment. It may also create opportunities to grow without increasing costs at the same rate, improve service delivery and make better use of scarce specialist skills.

Perhaps the more useful question for business leaders isn't how many jobs AI can eliminate. It's how much more their organisations could achieve if the people they already employ were better equipped to do their work.