The diffusion of a new technology, whether ATMs in banking or radio-frequency identification tags in retailing, typically traces an S-curve. Early on, a few power users bet heavily on the innovation. Then, over time, as more companies rush to embrace the technology and capture the potential gains, the market opportunities for nonadopters dwindle. The cycle draws to a close with slow movers suffering damage.
Early AI adopters are starting to shift industry profit pools
Our research suggests that a technology race has started along the S-curve for artificial intelligence (AI), a set of new technologies now in the early stages of deployment.
It appears that AI adopters can’t flourish without a solid base of core and advanced digital technologies. Companies that can assemble this bundle of capabilities are starting to pull away from the pack and will probably be AI’s ultimate winners. Executives are becoming aware of what is at stake: our survey research shows that 45 percent of executives who have yet to invest in AI fear falling behind competitively. Our statistical analysis suggests that faced with AI-fueled competitive threats, companies are twice as likely to embrace AI as they were to adopt new technologies in past technology cycles.
AI builds on other technologies
To date, though, only a fraction of companies—about 10 percent—have tried to diffuse AI across the enterprise, and less than half of those companies are power users, diffusing a majority of the ten fundamental AI technologies. An additional quarter of companies have tested AI to a limited extent, while a long tail of two-thirds of companies have yet to adopt any AI technologies at all.
The adoption of AI, we found, is part of a continuum, the latest stage of investment beyond core and advanced digital technologies. To understand the relationship between a company’s digital capabilities and its ability to deploy the new tools, we looked at the specific technologies at the heart of AI. Our model tested the extent to which underlying clusters of core digital technologies (cloud computing, mobile, and the web) and of more advanced technologies (big data and advanced analytics) affected the likelihood that a company would adopt AI. As Exhibit 1 shows, companies with a strong base in these core areas were statistically more likely to have adopted each of the AI tools—about 30 percent more likely when the two clusters of technologies are combined.
These companies presumably were better able to integrate AI with existing digital technologies, and that gave them a head start. This result is in keeping with what we have learned from our survey work. Seventy-five percent of the companies that adopted AI depended on knowledge gained from applying and mastering existing digital capabilities to do so.
This digital substructure is still lacking in many companies, and that may be slowing the diffusion of AI. We estimate that only one in three companies had fully diffused the underlying digital technologies and that the biggest gaps were in more recent tools, such as big data, analytics, and the cloud. This weak base, according to our estimates, has put AI out of reach for a fifth of the companies we studied.
Leaders and laggards
Beyond the capability gap, there’s another explanation for the slower adoption of AI among some companies: they may believe that the case for it remains unproved or that it is a moving target and that advances in the offing will give them the chance to leapfrog to leadership positions without a need for early investments.
Our research strongly suggests that waiting carries risks. Early movers appear to be racking up performance gains, and AI investments by first movers are also setting the stage for a second wave of gains. After realizing initial business-model improvements through AI, it seems, companies use the profits to invest in additional AI applications, adding further to their margins.
To provide a more detailed picture of AI leaders and laggards, we examined four levels of internal diffusion of both AI and digital technologies across six industries.
Our analysis suggests that power users of AI with a strong digital base can boost profits by one to five percentage points above industry averages (Exhibit 2). The analysis showed that profits among companies in the bottom two tiers—companies, in each industry, that had yet to diffuse AI and had a weak or no footing in digital technologies—were significantly below industry averages. In finance, where AI and digital technologies are creating greater competitive differentiation, the profit gap is wider than it is in construction, where (so far) AI and digital strategies have been relatively uncommon.
Reaching a tipping point?
Interestingly, the downward pressure on margins for the greater number (long tail) of companies in the lower two quadrants is greater than the uplift experienced by the smaller circle of companies that have either broadly adopted AI or are testing it (about 35 percent of our sample). This suggests that AI and digital competition are depressing overall industry margins. Our prior research on core and advanced digital technologies found that industries reach a tipping point once 15 percent of revenues shift to digital attackers and very fast followers. While AI competition isn’t in this zone yet, our model indicates that revenue shifts are moving toward it as the diffusion of AI accelerates over the next five years.
The number of companies applying the full range of AI technologies, of course, is still small, and many of the most advanced power users in our research, notably, were digital natives. But the competition is stiffening—fast followers are responding as they see profits drained by attackers. Companies that have a strong base in digital capabilities will benefit, since they can move more quickly to adopt AI. Companies with a less favorable digital foundation will need to line up new talent and rev up their digital-transformation efforts.