Data Executives on AI: 5 Themes Heard at COLLIDE 2024

COLLIDE Data + AI Conference 2024The Top 5 Things Data Executives Are Saying About AIWatch the full session · 21 min

At COLLIDE 2024 in Atlanta, Brian Mink, Co-Founder and President of AI Connect, opened day one with a talk on "The Top 5 Things Data Executives Are Saying About AI." Drawing on surveys, executive roundtables, webinars and the organization's executive events, Mink outlined what senior data and AI leaders were telling AI Connect (AI Connect) in 2024. COLLIDE was held October 10-11, 2024, at the Sandy Springs Performing Arts Center.

Why do data executives feel behind on AI?

The first theme was perception versus reality. Mink said that when AI Connect invites executives to join a panel or roundtable, "over and over" the response is that the company is doing some things with AI but is not ready to talk about it, or feels far behind. He put the share at "90% plus" of panelists and roundtable and dinner participants.

He described a "benchmark anxiety," stronger among more senior leaders, about discovering that their organizations are less mature than peers. In practice, he said, "everyone is kind of in the same boat." Executives cited poor data quality, weak governance, missing in-house expertise and a lack of the right solutions. Once they compare notes with peers, Mink said, many feel relieved and leave with a roadmap.

Mink said AI Connect's community included over 100,000 data and AI professionals, about 55% executives and 45% practitioners, with roughly 250 events since 2012.

How are executives handling top-down pressure on AI?

The second theme was pressure from above. Mink said ChatGPT changed public awareness of what AI can do, and CEOs and boards of public companies were asking their chief data officers why the company was not doing more with generative AI.

His answer was that "business tends to move a lot slower than technology." Something possible in theory is not automatically reliable, accurate or responsible, and it can create liabilities. He said the most valuable executives explained to the C-suite where quick wins and returns were possible and where there was no need to rush.

"AI is a tool, it's not a goal," Mink said. Organizations should start with business problems and strategic priorities. He also encouraged leaders to reuse existing frameworks for data governance and privacy, as they did with the internet, email and smartphones, while planning for what AI may do in three, five or ten years.

Why is data strategy central to AI strategy?

Third, Mink said executives emphasize that "in many ways the best AI strategy is an effective data strategy." Quality data is foundational, he said, and it requires continuous data management as new use cases emerge. He cited unstructured data as an area where generative AI opens up value that data scientists previously could not reach.

He also flagged infrastructure as under-discussed. Generative AI uses large amounts of energy, water and processing power, he said, which strains both organizations and downstream stakeholders such as the environment.

What does human-centric AI mean for companies?

The fourth theme was that AI should be human-centric. Mink said executives ask what the point is of building something just because it can be built. He suggested three practices:

• Think about augmentation, not only replacement, of workers.
• Invest in upskilling and reskilling, since some people will be displaced.
• Keep a human in the loop for decisions involving major ethical questions, with a defined process for what is acceptable.

How should organizations address bias in data and AI?

Fifth, Mink said now is the time to address bias and equity. That means building ethical frameworks, deciding what AI will not be used for, seeking out inclusive data sets and developing bias mitigation strategies.

The reason, he said, is simple: "If your data is biased, the AI is going to compound that bias." He warned that models trained on data that does not represent underrepresented populations can lead to critical decisions built on skewed foundations.

Mink closed with the overarching point that AI is both revolutionary and evolutionary, and that "the evolution has to catch up to the revolution." The technology is moving quickly, he said, while the slower work of governance, data quality, data management and ethics has to be put in place now.

Where can leaders keep the conversation going?

Mink said the executive conversations behind this talk took place at AI Connect's executive events, including its ALIGN AI Executive Summit series and COLLIDE. Those continue in 2027. The ALIGN AI Executive Summits are invitation-only gatherings planned for eight U.S. cities in 2027: Atlanta, New York City, Dallas, Chicago, San Francisco, Boston, DC and Los Angeles. Details are on the ALIGN page, and registration is coming soon.

Frequently asked questions

What are data executives saying about AI?

Mink described five themes: feeling behind, top-down pressure, the need for data strategy, human-centric AI, and addressing bias and equity.

Why do executives feel behind on AI?

He said they cite data quality, weak governance, limited in-house expertise and missing solutions, and that peer benchmarking shows most organizations are in the same position.

Why does data strategy matter for AI?

Mink said quality data is foundational and needs continuous management as new use cases emerge.

How should companies address AI bias?

He recommended ethical frameworks, inclusive data sets, bias mitigation strategies and human oversight.

This article is based on remarks by Brian Mink, Co-Founder & President, AI Connect at COLLIDE Data + AI Conference 2024 on October 10, 2024. More coverage: COLLIDE AI Conference.

Filed under: AI Strategy & ROI, People, Culture & Leadership, AI Governance & Trust