According to a Deloitte survey of director- to C-suite-level respondents, companies are struggling to move genAI projects into production.

As discussed by ZDNet, the survey, conducted between May and June, received responses from 2,770 director- to C-suite-level respondents across six industries and 14 countries. The survey also included interview feedback from 25 interviewees, who were C-suite executives and AI and data science leaders at large organizations.

“70% of respondents said their organization has moved 30% or fewer of their Generative AI experiments into production,” according to lead author Jim Rowan and team in the latest installment of the firm’s ‘The State of Generative AI in the Enterprise‘ report series. Here’s a graph from the report that illustrates the current state of implementation:

The lack of progress in production contrasts with the flurry of activity around the technology. “Two of three surveyed organizations said they are increasing their investments in Generative AI because they have seen strong early value to date,” reported Rowan and team.

Here’s why many companies are struggling to move genAI projects into production. Less than half of companies feel highly prepared for the most basic requirements, such as data management. On average, 45% of respondents said they were highly prepared concerning “technology infrastructure,” and 41% said they thought the organization was highly prepared for “data management”. 

The least-prepared areas, the responses show, were “strategy”, with 37% feeling their firm was highly prepared, followed by “risk and governance” (at 23%) and “talent” (at 20%).

Some qualitative remarks by executives interviewed revealed more detail on where that lack of preparedness lies. For example, a former vice president of data and intelligence for a media company told Rowan and team that the “biggest scaling challenge” for the company “was really the amount of data that we had access to and the lack of proper data management maturity.” 

The executive continued: “There was no formal data catalog. There was no formal metadata and labeling of data points across the enterprise. We could go only as fast as we could label the data.”

Rowan and team suggested in the report that data quality hinders many companies: “Data-related issues have caused 55% of the organizations we surveyed to avoid certain Generative AI use cases.”

The 31-page report from Deloitte has many more stats and findings, which you can check out here.

Bad data equals bad AI. Simple as that. Perhaps the push toward generative AI is forcing companies to address their data management shortcomings. Finally.

So, what do you think? Are you surprised that many companies are struggling to move genAI projects into production? Or did you expect it? Please share any comments you might have or if you’d like to know more about a particular topic.

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