Executives globally are investing heavily in AI, yet many admit they are not yet seeing a meaningful impact on productivity. This paradox doesn’t suggest AI is overhyped, but that the focus is misplaced.
The real opportunity and challenge are transforming culture, process, and business strategy. But disproportionate attention is given to AI model performance and technical benchmarks, massive financial investments and so-called global struggles among competing AI companies.
A recent Hoover Institution report confirms this disconnect. Survey findings from almost 6000 CEOs, CFOs, and executives from 14 industries across the UK, Germany, U.S. and Australia found that 70% of companies are using AI, yet over 80% report no AI impact on productivity.
Simultaneously, analysts say 75% of CFOs plan to increase tech spending this year. This transformation gap – the space between investment and impact – is one of the most important challenges for leaders to solve today.
Important AI success factors missed
The Hoover report’s analysis is revealing, not for what it contains, but for what is not included such as words like “culture” and “transformation.” And there is no discussion about workplace learning, training or education. Culture eats strategy for breakfast, even when it’s about AI.
Contrast this with the CEO of a leading AI model company who shared that he spends around 40% of his time on company culture. And when looking at how AI value is created, top performing companies dedicate 70% of their efforts to people, processes, and cultural transformation, 20% to the surrounding ecosystem of data and technology, and 10% to the algorithms, finds the Boston Consulting Group.
Admittedly, culture can be a tricky thing to define, and there is no single right answer. But what gets recognised and rewarded, how decision-making is shared and delegated, and how people behave when there’s pressure, are good indications of what a company’s real culture is beyond the values it lists on its intranet and website.
The report also highlights CEOs “using AI” which sounds like confining it to a tool to log into for a bit, rather than understanding it as an execution layer across operations and workflows in the way WiFi and the internet are integrated into daily life in the shop, office, and field. And while leading by example and executive sponsorship of critical transformation projects is important, should we view CEOs logged into AI chatbots for hours each day as a measure of success? I don’t think so.
AI needs to speak the C-suite language
With the right culture and AI capabilities, a wide range of work will be elevated and make today’s automation intelligent, autonomous and adaptive. Intelligent operations and customer AI ROI is essential for business – more throughput, less downtime, better product quality and adaptive processes along with more loyalty and satisfaction among customers. These need to translate into language the C-Suite speaks – revenue growth, profit, EBITDA, and earnings per share. The Hoover report is on the right path in framing productivity in relation to sales per employee.
Strategy and culture investments can show how AI agents for frontline workers can lower employee churn and repeat hiring costs, AI personalisation for shoppers increases basket spend, and deep learning on the conveyor belt eliminates sunk costs addressing product quality and recalls. Senior leaders want to see AI investment deliver both operational and financial ROI.
I also agree with Professor Carl Benedikt Frey who argues that AI won’t deliver lasting prosperity if it’s mainly used to automate what we already do. Great leaps come from new industries, not faster repetition.
With AI capabilities, valuable workforce hours will be unlocked. But it will be a missed opportunity if these hours are only allocated to doing more of the same at best, or nothing at worst, because the culture isn’t ready for AI-related innovation and experimentation. Those “higher value tasks” we talk about really can be “great leap” moments.
Change requires imagination
Some may be tempted to fall back on the eponymous paradox of Professor Robert Solow, the Nobel Prize-winning economist who said, “You can see the computer age everywhere but in the productivity statistics.” With hindsight, we know that Solow’s paradox isn’t about technology.
Computers supported various business functions, but it wasn’t until the 1990s that productivity growth really took off. It was not merely a technology availability and adoption lag, but a business transformation lag, what economists call total factor productivity. It’s a challenge to accept for a business world that measures progress on a quarterly basis. We can see positive outcomes at an organisational level across industries, but new ways of working have not caught up at an industry-wide level yet.
It’s hard to imagine what does not yet exist, whether that’s new ways of working, new job roles, new companies, or whole new industries. But we can be confident in the human capacity to meet this opportunity. One research paper found that half of employment growth between 1980 and 2007 took place in occupations with new job titles and new tasks.
Technology often moves faster than the businesses that want to harness it. The AI adoption window has narrowed compared to previous technologies, but it also means simple adoption will become a baseline rather than a competitive advantage. AI initiatives need to be directly tied to key operational, cultural, and financial investments and metrics. Great leaps that make work better every day and advance how companies operate is a much heavier lift that requires more than AI alone.
Tim Stoddard
Tim Stoddard is Senior Vice President and General Manager EMEA at Zebra Technologies.
