AI Adoption in the Enterprise 2020
Editor’s Note: AI is routinely cited as one of the biggest factors expected to disrupt business in the future — but how pervasive is Enterprise AI already? In the following post Roger Magoulas and Steve Swoyer interpret a December 2019 survey on the topic, analyzing the current landscape and the most common challenges to AI adoption.
Last year, when we felt interest in artificial intelligence (AI) was approaching a fever pitch, we created a survey to ask about AI adoption. When we analyzed the results, we determined the AI space was in a state of rapid change, so we eagerly commissioned a follow-up survey to help find out where AI stands right now. The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. The update sheds light on what AI adoption looks like in the enterprise — hint: deployments are shifting from prototype to production — the popularity of specific techniques and tools, the challenges experienced by adopters, and so on. There’s a lot to bite into here, so let’s get started. Key survey results:
- The majority (85%) of respondent organizations are evaluating AI or using it in production.1 Just 15% are not doing anything at all with AI.
- More than half of respondent organizations identify as “mature” adopters of AI technologies: that is, they’re using AI for analysis or in production.
- Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI.
- Though a problem, the lack of ML and AI skills isn’t the biggest impediment to AI adoption. Almost 22% of respondents identified a lack of institutional support as the most significant issue.
- Few organizations are using formal governance controls to support their AI efforts.
The takeaway: AI adoption is proceeding apace. Most companies that were evaluating or experimenting with AI are now using it in production deployments. It’s still early, but companies need to do more to put their AI efforts on solid ground. Whether it’s controlling for common risk factors — bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production — or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.
Analysis: The State of AI Adoption Today
More than half of respondent organizations are in the “mature” phase of AI adoption (using AI for analysis/production), while about one-third are still evaluating AI.2 This is close to a mirror image of last year’s AI survey results, when 54% of respondent organizations were evaluating AI and just 27% were in the “mature” adoption phase. This year, about 15% of respondent organizations are not doing anything with AI, down ~20% from our 2019 survey.
The upshot is that 85% of organizations are using AI, and (of these) most are using it in production. It seems as if the experimental AI projects of 2019 have borne fruit. But what kind?
Figure 3. Where AI projects are being used within companies
The bulk of AI use is in research and development — cited by just under half of all respondents — followed by IT, which was cited by just over one-third. (Respondents were encouraged to make multiple selections.) Another high-use functional area is customer service, with just under 30% of share. Two functional areas — marketing/advertising/PR and operations/facilities/fleet management — see usage share of about 20%. Clearly respondent organizations see the value of AI in a raft of different functional organizations, and the flat results from last year show a consistency to that pattern.
Common Challenges to AI Adoption
The acquisition and retention of AI-specific skills remains a significant impediment to adoption in most organizations. This year, slightly more than one-sixth of respondents cited difficulty in hiring/retaining people with AI skills as a significant barrier to AI adoption in their organizations. This is down, albeit slightly, from 2019, when 18% of respondents blamed an AI skills gap for lagging adoption.
Figure 4. Bottlenecks to AI adoption
Believe it or not, a skills gap isn’t the biggest impediment to AI adoption. In 2020, as in 2019, a plurality of respondents — almost 22% — identified a lack of institutional support as the biggest problem. In both 2019 and 2020, the AI skills gap actually occupied the №3 slot; this year, it trailed “Difficulties in identifying appropriate business use cases,” which was cited by 20% of respondents. A more detailed look at the bottleneck data shows executives selecting an unsupportive culture less often (15%) than the practitioners and managers (23%) who responded to the survey.
By a 2:1 margin, respondents in companies that are evaluating AI are much more likely to cite an unsupportive culture as the primary bulwark to AI adoption. This disparity is striking — and intriguing. Is it just the case that late-adopters are ipso facto more resistant to — less open to — AI? By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data. We saw in our “State of Data Quality in 2020” survey that ML and AI projects tend to surface latent or hidden data quality issues, with the result that organizations that are using ML and AI are more likely to identify issues with the quality or completeness of their data. The logic in this case partakes of garbage-in, garbage out: data scientists and ML engineers need quality data to train their models. Companies evaluating AI, by contrast, may not yet know to what extent data quality can create AI woes.
Figure 5. Bottlenecks to AI adoption with AI maturity level
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Roger Magoulas is a data science expert and was formerly VP of Radar at O’Reilly.
Stephen Swoyer is a researcher and analyst with more than 20 years of industry experience. His research has focused on business intelligence (BI), data warehousing, and analytics, along with the larger data management segment, for 15 years. Swoyer’s work combines a deep interest in BI and analytic technology with an alertness to the needs and priorities of the people who must use (or, often as not, dis-use) it. He’s a recovering philosopher and occasionally guest lectures on Kierkegaard, Nietzsche, and Heidegger at both Penn State and Vanderbilt.
