Why AI’s biggest business challenge isn’t technology, but how companies reorganize operations around it

Subscribe to enjoy similar stories. A recent IBM survey of 2,000 executives on their expectations for artificial intelligence in 2030 revealed something noteworthy. They unsurprisingly predict that AI investment will surge (from already high levels) and that 79% expect AI will contribute significantly to their revenue.

But strikingly, only 24% “clearly see" where that revenue will 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’s actually exactly what we should expect from a truly revolutionary innovation, and it makes clear that the greatest business challenges posed by AI will 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’s 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, however, usually requires businesses to reorganize in every way. That shift is usually the biggest roadblock to a new technology fulfilling its promise. We’ve been here before.

When electricity first entered American 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% of the power used by American factories came from electric motors. Instead, power came