Generative AI Smoke Is Clearing; Data Leaders Need A Game Plan

Our latest data show that between 72-82 percent of organizations use or plan to use generative AI at some point. Although that number is down from the nearly 90 percent rate reported in 4Q23, it still includes a strong majority of organizations. Calling the “bloom off the rose” that is generative AI seems premature. We firmly expect generative AI will continue to have a prominent and critical role among organizations and vendors, and not fizzle out after significant hype (including Google Glass, the Segway, the Pebble smartwatch, and even the Sony Betamax).

Understanding where we are and how we got here requires a look back into the relatively recent past. Remember that generative AI truly emerged into the mainstream consciousness in 1Q23. In that quarter, OpenAI announced a premium subscription model for ChatGPT, released ChatGPT4, an API for application developers, and plugin support, including browsing and code interpreter. Microsoft announced and delivered ChatGPT-powered features for Bing. Anthropic launched Claude, its ChatGPT alternative, as did Google with Bard.

This crescendo occurred well after organizations with fiscal and calendar years that coincide had established overall budgets and set spending priorities. Without a centralized, top-down mandate and corresponding funding, which many organizations lacked, the perceived mandate to adopt generative AI—and not potentially miss “the next big thing”—fell to specific functions and lines of business, both of which tend to have more discretionary spending available throughout the year, as well as an easier ability to adjust planned expenditures within an active fiscal year. User organizations and vendors largely were in some degree of reactive mode throughout the rest of 2023. Our data showed this clearly.

Jump to 1Q24, and our most recent data show that, quarter over quarter, organizations report lower rates of adoption, lower perceptions about the priorities driving use and adoption of generative AI, less bullishness about generative AI in general, and less of a need to invest time staying on top of the latest developments in generative AI. The answer to why these differences occurred is to focus on what primarily changed. The challenge for data leaders is how to place these changes, which reflect many factors, in the right context for all in the organization so that generative AI can be implemented, applied, and leveraged for business value in the best ways possible.

In an organization that lacks formal, centralized oversight and control of generative AI implementations, our recommendations remain appropriate for a data leader (see the Research Insight “Who’s Really Adopting Generative AI and Why It Matters That Everyone Knows”). In an organization that recently added centralized oversight and control of generative AI implementations, a data leader—if not already strongly involved or in this leadership position—should seek to serve as (at least) the “trusted advisor” on all things data related to the person newly responsible for generative AI. The data leader in such a capacity should almost think of her/himself as a function-neutral advocate for data and its proper use—both in “traditional” use as well as part of generative AI efforts—to make sure leadership understands “the big picture” and is best informed before making critical data-related decisions regarding generative AI strategies and implementations.

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