Businesses are reporting productivity gains from artificial intelligence, but many are struggling to show what those improvements are actually worth. An EY Parthenon CEO Outlook Survey released in October found that half of 1,200 CEOs surveyed across 21 countries saw AI as the biggest contributor to productivity gains over the past year. Yet 23 percent said those gains were being absorbed before reaching the bottom line, while only 16 percent said they had clear, real-time visibility into AI costs and returns.
For companies, the challenge is moving from demonstrating that AI can improve a process to proving that the improvement creates enough value to justify the investment. An employee might use AI to complete a task in one hour instead of three. That creates additional capacity, but it does not automatically mean the company has saved two hours of salary costs.
The financial benefit depends on what happens with that extra time. Speaking to Newsweek, Tahir Nisar, professor of strategy and economic organization at the University of Southampton in England, said businesses should focus on what changes as a result of AI rather than simply measuring how widely it is used. The important question is not how many employees are using AI, but whether it is reducing costs, increasing revenue, improving quality or enabling people to do more valuable work.” That distinction can make the difference between a productivity claim and a financial result.
Nisar said businesses should establish a credible comparison before making a claim about AI's impact. Comparing AI-enabled teams or processes with credible baselines or similar non-AI groups can help distinguish the impact of AI from changes in demand, staffing, management or wider market conditions.” Taylor Treese, founder of Timbuc, an advisory platform that uses AI to analyze company data and generate growth plans, said businesses often struggle to prove the value of advisory services and technology investments because they fail to establish a baseline before implementation. "Coaching and consulting ROI [return on investment] was never a coaching problem.
It was a baseline and measurement problem," he told Newsweek. "Historically, business coaching has suffered from a measurement problem: ROI was tied to subjective recollection rather than hard financial data. We built TIMBUC to turn business diagnosis and growth execution into an objective science." Iavor Bojinov, associate professor of business administration at Harvard Business School, told Newsweek companies must build measurement into AI deployments from the outset rather than trying to calculate ROI after implementation.
General Motors has said it is trying to build that discipline into its AI projects by starting with specific business and engineering problems and setting measurable outcomes before expanding a deployment. Jason Fischer, GM's executive director of virtual integration engineering, said the company looks at factors including speed to market, productivity, cost, quality and safety. Its approach includes pilots, measurement against corporate performance indicators and human oversight before wider deployment.
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