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Professional services firm Deloitte has a new report out this morning about A.I. adoption by corporations, and it is worth reading. (You can view the report here.) The main takeaway is that A.I. is becoming increasingly ubiquitous, and as it does, companies are going to have to think hard about how they can use the technology to differentiate themselves.
“Companies feel their window of competitive advantage is eroding,” Jeff Loucks, who heads Deloitte’s Center for Technology, Media and Telecommunications, which conducted the research behind the report, tells me. “It is harder to gain an advantage by simply being in the game.”
The report classifies businesses into three broad categories:
- In rough terms, about a quarter of businesses are what Deloitte calls “seasoned” A.I. adopters. These companies have put at least five A.I. systems into production and have a high degree of expertise in how to build, maintain and manage them.
- About 50% of companies Deloitte defines as “skilled” adopters: They’ve put between one and five major A.I. systems into production and have some expertise at how to run and manage them.
- Then there’s the final quarter of firms, which Deloitte calls A.I. “starter,” who are still playing around with pilot projects and don’t have as much confidence in how to build or manage their A.I. capabilities.
Loucks and his colleague Nitin Mittal, the partner in charge of what Deloitte calls its analytics and cognitive business, say the firm has noticed a few key differences between those veteran A.I. adopters and the rest of the pack: The more experienced business are more likely to see A.I. as a strategic technology. They are more likely to be using it to pursue new business models and offer new kinds of services, Loucks says, as well as to increase revenues in existing ones. In contrast, the A.I. novices, he says, are more likely to be focused on using A.I. to reduce costs and cut headcount.
That may explain why, while all companies are investing heavily in A.I., the more experienced firms are spending even more, with 68% saying they have spent more than $20 million. The A.I. veterans are also reporting faster gains from deploying A.I.: 81% said they expected to earn that investment back within two years.
The other big difference, Loucks says, is that the experienced firms have spent a lot more time thinking about what can go wrong—everything from algorithmic bias to operational disruption if the software fails to thinking about how to protect data from hackers—and have put in place frameworks, policies and procedures to mitigate those risks. Loucks says these companies are more likely to conduct audits of both their data and their algorithms, continually monitor their A.I. models to detect the situations where reality is deviating from the data the software was trained on (a phenomenon known as “model drift”), and train staff in issues around A.I. ethics.
That’s important because 56% of those surveyed told Deloitte that their organizations are slowing down the adoption of A.I. because of concerns about emerging risks. Regulation is also a big issue, with 57% of the executives Deloitte contacted saying they have “major” or “extreme” concerns about how regulation could affect their A.I. projects.
Deloitte also asked companies about their struggle to hire people with the right A.I. skills and shared some of those results exclusively with Eye on A.I. More than half of respondents—54%—said the skills gap was “moderate,” “major” or “extreme.” But, Loucks says, the number of companies saying they have an acute need for data scientists, A.I. researchers and machine learning engineers has actually come down slightly from last year’s survey. He says this is the result of the increasing ability of companies to implement off-the-shelf solutions for many machine learning problems that any software engineer can implement without specific data science or machine learning skills.
“But where skill gap has opened is around individuals with both business savvy and technology experience,” Mittal says. He says these people are essential to what he calls “mainstreaming” A.I. within an organization. These are the people who understand how A.I. can be used strategically by the business. They are also the people who lead the teams who actually have to use the A.I. software on a daily basis, and who will depend on its results.
As always, the business that wins is not necessarily the one with the best technology. It is about having the right people.
And with that, here’s the rest of this week’s A.I. news.
Jeremy Kahn
@Jeremyakahn
jeremy.kahn@fortune.com
