Artificial intelligence: Why a digital base is critical
Early
AI adopters are starting to shift industry profit pools. Companies need strong
digital capabilities to compete.
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.
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 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. 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.
By Jacques Bughin and Nicolas van Zeebroeck
https://www.mckinsey.com/Business-Functions/McKinsey-Analytics/Our-Insights/Artificial-intelligence-Why-a-digital-base-is-critical?cid=other-eml-alt-mkq-mck-oth-1807&hlkid=91d17b6a2e7648f09bfec12533b61a11&hctky=1627601&hdpid=8cdb0413-e1c4-4e22-89d0-8f57b1559de3
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