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Data Leaders Say ‘AI Paralysis’ Stifling Adoption: Study
Workers in the data industry may be intimidated by the AI hype sweeping the world as a new report shows few are using the emerging technology.
4 Min Read
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At a Glance
- Report shows a lack of governance framework leads to slow AI adoption.
- CIOs and other IT leaders need to create an easy-to-understand narrative around AI adoption.
- AI adoption should start with specific use cases, instead of using the technology just to use it.
Fear and a lack of strategy may be holding the data industry back when it comes to artificial intelligence.
A new report from UK data consultancy firm Carruthers and Jackson released Thursday shows 87% of data leaders report few or no employees using artificial intelligence in the workplace -- despite a rapid pace of AI tool adoption by organizations. The company’s annual Data Maturity Index also showed that 61% of those data leaders said employees lack the basic data literacy required to use AI in a safe and compliant way.
But the disconnect is not all to blame on reluctant employees. The report shows that 41% of data leaders have little or no governance framework as they wait to see how global AI regulations shake out.
While AI is not new in the data industry, the public’s fascination with generative AI has fueled a veritable gold rush for industries to adopt the emerging technologies for a competitive advantage. But the lack of safety guidelines and organizational framework and training may be suffocating AI adoption efforts, according to the report.
Caroline Carruthers, CEO of Carruthers and Jackson, tells InformationWeek the firm’s study shows that while data leaders are laying the groundwork for AI adoption, defining clear goals for use is crucial going forward.
The “GenAI Hammer”
“What happened is everybody got ahold of the GenAI hammer, and now everything looks like a nail,” she says, adding that CIOs and CDOs must do their best to articulate the technical needs to non-technical members of the C-suite. “I do think there’s a disconnect between the CIO and CDO and the chief executive. We should not, in the data and technology space, expect people to understand the layer of complexity that we have to deal with. What we should be doing is taking that complexity and creating a story and a narrative, so it makes sense to the other people in our organization and businesses we work with.”
The report also showed that data governance has stalled just as AI is being adopted across industries. Of data leaders surveyed, 41% reported that their organization has little or no data governance framework, and 27% said their company has no data strategy. When asked how their organizations are adapting to AI, 40% of data leaders expressed concerns about AI-related governance, compliance, and regulations.
Just 4% of data leaders said a high number of employees at every level of their organizations were using GenAI, and 26% reported that AI wasn’t being used at all.
That reluctance on the part of employees goes back to a lack of framework for AI, and employees who may not understand the technology because of a lack of training. Employees also may be fearful about AI potentially replacing their jobs. “I think we have to be very cognizant of that fear existing. And to be quite frank, if we pretend it doesn’t exist, and that it won’t happen, we will be lying people … your post doesn’t deliver mail on a pony anymore,” Carruthers says.
However, Carruthers says, ignoring AI training won’t allay workers’ fears. “AI should be freeing us up to do the more interesting stuff. It should be unleashing our creativity, unleashing our enthusiasm and giving us time to solve the really complex, interesting stuff,” she says. Organizations need to “dive into [AI] and set up ways of helping people learn new tools and being fair and reasonable with them,” she adds.
Starting With Purpose
Finding the right use cases for AI within an organization is the first logical step, Carruthers says. “Everything comes back to purpose,” she says. “What problems are we trying to solve? What value are we trying to create? What difference are we trying to make? In any of those questions, you understand the problem, understand what you’re trying to get, you understand the difference you’re going to create, and then figure out the right tool to solve that problem.”
For CIOs, implementing a framework for AI adoption should start with purpose, as well as understanding AI tools. “It’s like any other set of tools,” Carruthers says. “But AI definitely is an incredibly powerful tool, and you will fall by the wayside if you don’t get your head around it.”
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