A potential recipe for disaster proved to be the focus of every data executive’s agenda over the last year. A year ago many data leaders were caught off-guard. Employees embraced new gen AI tools with fervor, driving interest in all AI initiatives. Generative AI had penetrated the enterprise, with gen AI positioned in the Peak Of Inflated Expectation segment on the Gartner® Hype Cycle™ for Artificial IntelligenceI, 20231. At the same time, however, a survey of CDOs reported that over half of data leaders had not formally addressed growing adoption. Yikes!? Not really.
Fast-forward to the present, and data execs are sharing their lessons learned and best practices in planning, executing and scaling effective AI programs. To find out more about the journey to effective AI, we spoke to many AI-focused data leaders. Everyone had seen unsanctioned use of open source gen AI and had quickly moved to bring these activities in-house. The new demand was a catalyst to define (or refine) data and AI strategies and ensure the ability to execute them effectively at scale.
For most, the journey to effective AI starts with a good dose of education. Data leaders balance evangelism of AI’s promise with the challenges of implementation. It’s often a question of setting expectations. As Siemens Energy CDO Micheline Casey says, “Board members love AI. But it’s not a Magic 8 Ball. You can’t shake it and get an answer. We continue to educate our board on the capabilities and skills required to realize the benefits.”
Experimentation allows data teams to test ideas and build new skills. But it’s the organizational and process changes that ensure effective operationalization, expansion throughout the organization, and the embedding of new capabilities into the company’s DNA — an AI-driven transformation.
Investment must be deliberate and appropriately aligned with company goals. Avoiding the marquee projects, collaborating to identify common needs, coordinating the use of resources and ensuring reuse of AI models and data products are all best practices identified by data executives of Snowflake customers.
One of the recurring messages of our interviews was the view that the effective use of AI — and the data powering it — drives competitive advantage. As Beth Quinton, the CDO of Air Canada, clearly states, “We want to be on the winning side, and we think those who don’t experiment and invest will fall behind.” That message certainly resonates, as does the belief that those investments must span not only their technology but also their people and processes.
To learn more of the lessons learned and best practices shared by Snowflake’s customers read The Data Executive’s Guide to Effective AI: Best Practices from Data Executives for an AI Transformation Journey.
1Gartner Article, What’s New in Artificial Intelligence from the 2023 Gartner Hype Cycle,
https://www.gartner.com/en/articles/what-s-new-in-artificial-intelligence-from-the-2023-gartner-hype-
cycle. GARTNER is a registered trademark and service mark and HYPE CYCLE is a trademark of Gartner,
Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights
reserved.
