A recent IBM survey of 2,000 executives on their expectations for artificial intelligence (AI) in 2030 revealed something noteworthy. They unsurprisingly predicted that AI investment would surge (from already high levels) and that 79 percent expect AI would contribute significantly to their revenue. Strikingly, only 24 percent “clearly see” where that revenue would come from. Such lack of clarity might seem like a bad sign when most AI projects have failed to generate a return on investment, but it is actually exactly what we should expect from a truly revolutionary innovation, and it makes clear that the greatest businesses challenges posed by AI would be managerial, not technological.
Revolutionary innovations rarely announce their business models — or even their use cases — in advance. They usually start with a simple one-to-one replacement where the innovations are a better or cheaper way of doing something that companies already do. Over time, users realize that they offer new and powerful capabilities. That is when their real impact kicks in, and it explains why the same survey reports that executives expect their AI spending to shift from efficiency gains to product and service innovation.
Properly utilizing these new capabilities usually requires businesses to reorganize in every way. That shift is usually the biggest roadblock to a new technology fulfilling its promise.
We have been here before. When electricity first entered US factories in the late 19th and early 20th centuries, its economic returns were disappointing. Thomas Edison invented the electric lightbulb in the 1870s, but by 1900, less than 5 percent of the power used by US factories came from electric motors. Instead, power came from steam engines — often ones shared by multiple factories — which drove machines via line shafts. Electric lighting allowed factories to work far more efficiently at night, but their fundamental operations remained unchanged.
The real transformation came later, once smaller and cheaper electric motors made it possible to abandon centralized power. Machines could be distributed more flexibly, and workflows could be rearranged to follow the logic of production. Just as important, these new processes could only be properly utilized by workers who were trained differently, had more independence and, eventually, were paid better. That reorganization made possible the moving assembly line and, with it, mass production on an entirely new scale. That is when electricity became a truly revolutionary innovation. It did not just make factories cheaper to run, it changed what factories could produce, how quickly they could adapt and which firms survived. Entire industries followed.
AI might be inching out of its replacement phase and into its rearchitecture phase. Automating routine tasks and optimizing workflows is like swapping steam engines for electric motors. Useful, necessary and limited. The harder and more consequential work lies in redesigning processes, products and decisionmaking when machines can generate content, interpret unstructured information and act autonomously within defined boundaries.
That kind of redesign is uncomfortable and often expensive (pity the early 20th-century industrialist whose factory had to be rebuilt thanks to some dancing electrons). It does not fit neatly into existing organizational charts or capital budgets. It often produces temporary declines in measured productivity as firms experiment, fail and relearn how work should be done.
When information technology was being rolled out in the 1970s and 1980s, this effect was so pronounced that Nobel Prize-winning economist Robert Solow said: “You can see the computer age everywhere but in the productivity statistics.” And the combination of the difficulty of redesign and the unknowability of what these new capabilities would be used for makes it difficult or impossible for executives to answer the seemingly basic question of where, exactly, the revenue is going to come from.
History suggests that demanding too precise an answer too early is a mistake. The executives who installed electric motors in their factories in 1905 had no way of knowing that assembly lines would soon transform manufacturing or that their factories would soon be producing products that did not exist yet. The ones who required a detailed forecast before reorganizing production were the ones most likely to be left behind.
The same risk exists today. If you only see AI as a cost-cutting tool, you might protect margins in the short run, but you might also be trapped by optimized versions of soon-to-be-obsolete business models. Once competitors begin to redesign offerings, pricing and customer relationships around AI-enabled capabilities, incremental efficiency gains would no longer be enough.
None of this guarantees that today’s AI bets would pay off. Many early electrified factories failed. Many — likely most — AI-driven initiatives would, too. However, the beginning of wisdom is admitting what you do not know, and the executives admitting they do not yet know how AI would generate revenue are the ones who understand that the next phase of this technology is not about execution within known boundaries, but about discovering entirely new ones.
By the time the destination is obvious, someone else would have already reached the goal.
Gautam Mukunda writes about corporate management and innovation. He teaches leadership at the Yale School of Management and is the author of Indispensable: When Leaders Really Matter. This column reflects the personal views of the author and does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners.
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