Dive Brief:
- The top AI adoption barrier for enterprises is an inability to estimate and demonstrate the value of AI projects, according to a Gartner survey of 644 organizations in the U.S., Germany and the U.K. published in May.
- Implementation barriers include talent shortages, technical difficulties, data-related troubles and a lack of business alignment, according to the survey conducted in the last quarter of 2023.
- Still, the most frequent kind of AI solution organizations deploy today is generative AI. More than one-third of organizations are primarily using generative AI that is embedded in existing applications, such as Microsoft Copilot for 365 or Adobe Firefly.
Dive Insight:
While existing applications are placing generative AI within reach for the average business, many are finding it difficult to craft mature AI strategies, Gartner found.
The technology’s drawbacks further complicate matters. Generative AI has an accuracy problem, and bad models can introduce bias and exacerbate security problems. Nearly 2 in 5 organizations said they didn’t trust AI, according to Gartner.
Enterprises are also aware that employees, investors and customers have differing sentiments on the pace of innovation, dialing back some of the initial hype.
Mentions of AI and related terms dropped sharply this quarter compared to the previous four reporting periods, according to a Bloomberg Law analysis of earnings transcripts, with more than 80% of companies in major listings reporting so far.
Organizations have also found it difficult to move beyond the experimentation stages. The prototype to production lifecycle takes around eight months on average, according to Gartner. Less than half of AI projects make it to the production stages.
“Organizations who are struggling to derive business value from AI can learn from mature AI organizations,” Leinar Ramos, senior director analyst at Gartner, said in a statement. But it’s a small group: only 9% of respondents in Gartner’s survey are characterized as AI-mature.
“AI-mature organizations invest in foundational capabilities that will remain relevant regardless of what happens tomorrow in the world of AI, and that allows them to scale their AI deployments efficiently and safely,” Ramos said.
