Our latest data show that about 90 percent of organizations have plans to use generative AI at some point. Despite these high levels of current and planned use, perceptions about who is adopting generative AI earliest shows a significant lack of alignment with overall adoption. The skew in perceptions, broken down by function, points strongly to a somewhat disturbing set of indications regarding how most organizations are approaching generative AI:
- Distributed, functionally oriented (not centralized) implementations
- Minimal if any centralized management and monitoring of initiatives
- Little cross-functional awareness regarding implementations of and plans for generative AI
These factors, stemming from the lack of alignment, create significant risk for duplicative efforts and perpetuation of data silos. In turn, duplication in efforts and data structures (and likely related software and systems) increase costs and decrease profitability for the organization—the bane of every C-level executive.
In the absence of clear centralized leadership and management of generative AI efforts, data leaders need to assert themselves and take the initiative to help ensure that likely disparate generative AI efforts flung throughout their organizations have a means by which to come together, and build awareness and understanding of how generative AI is being planned and implemented within their organizations.
We suggest creating and leading a cross-functional generative AI council. This structure can educate members about generative AI implementations and plans throughout the organization. It also can serve to raise business needs (such as data and content access) and challenges (such as data privacy) in the context of use of generative AI. Furthermore, such a council, through its aforementioned regular activities, can inform data-strategy decisions related to optimizing generative AI use and implementation throughout an organization.
Such efforts to build awareness and understanding regarding generative AI implementations, use, and challenges will provide a data leader with additional ancillary benefits. These endeavors also can help raise the profile of data-related issues often perceived by non-data-savvy C-level executives as having lesser strategic importance (such as data governance, data literacy, and unified data infrastructure). In addition, these efforts can help improve and shape perceptions of data as an enterprise-level strategic asset, rather than just the byproduct of systems that serve specific functional needs.
In time, if successful, an initially ad hoc generative AI council can act as a springboard for executives formalizing not only centralized management and monitoring of generative AI use and implementation throughout the organization, but also overall data strategy and data use. Most likely, this could come through a formal leadership position and dedicated team with access to significant resources.
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