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Moderator: Asha Saxena, Founder & CEO, WLDA: World Leaders in Data & AI
Panelists:
- Anu Krishnan, Chief Data & Analytics Officer, Chevron
- Scott Richardson, Chief Data & Analytics Officer, First Citizens Bank
- Ido Biger, EVP, Chief Information & Data Officer, Delek US Holdings, Inc.
- Chris Satchel, Managing Director Tech & Digital, CD&R
During the DataIQ 100 Summit in Nashville, a panel of senior leaders from energy, banking, private equity, and industrial operations discussed how expectations of the CDO role are rapidly changing. What began as a governance and data management function is now being recast as a driver of AI readiness, business transformation, and organizational velocity.
The discussion focused on the realities of influence, such as earning executive trust, prioritizing finite resources, reshaping business processes, and helping organizations understand what AI can (and cannot) realistically deliver.
Questions Explored
- How has the CDO role evolved over the last decade?
- What do CEOs and boards now expect from data leaders?
- How should CDOs explain foundational data investments to the C-suite?
- What role do storytelling and influence play in successful transformation?
- How do organizations balance AI ambition with legacy systems and limited resources?
- What separates AI experimentation from real business transformation?
- How can CDOs build organizational buy-in beyond IT?
- What practical operating models are helping enterprises scale AI adoption?
Translate data into business language
One of the clearest topics from the panel was that technical competence alone no longer defines successful CDOs (which was echoed in the recent The End of AI Theatrics report). The role increasingly depends on the ability to communicate business value in language the board and executive peers understand.
Chris Satchell said the strongest data leaders are “great storytellers” who can explain why data matters “in your business terms.” He added that “the board wants to hear about the business outcomes.”
That expectation is reshaping the role as boards are now actively asking about AI readiness, data maturity, and whether the organization has the right leadership in place to operationalize both. Chris noted that when evaluating companies, his team increasingly asks the question “do you have a great data executive?” early in the process.
The implication is that CDOs are no longer back-office operators and have become strategic translators between technology capability and enterprise value.
AI finally gave data leaders leverage
Several panelists acknowledged that, for years, foundational data work struggled to attract executive attention or funding, and now AI changed that equation almost overnight.
Anu Krishnan described years spent defending investments in MDM, lineage, and governance tooling, stating that “I used to call it the plumbing.”
Now, however, executives understand that poor data produces poor AI outcomes, stating “we finally have a carrot with AI.”
Scott Richardson described a similar shift. Data management was once treated as “a necessary evil,” but AI reframed data as a strategic business asset.
What matters here is budget availability and the fact that AI has changed the political position of the CDO. Panelists agreed that executive attention is now arriving earlier, higher up the organization, and with greater urgency.
Re-engineer processes
The panel repeatedly warned against confusing tactical automation with enterprise transformation.
Anu cautioned that many organizations are becoming overly focused on agentic AI without fundamentally redesigning how work happens: “You’re shaving off parts of the business process, but you haven’t really reimagined the whole business process.”
She argued that real value creation will require combining agents with deeper machine learning and full process redesign.
This distinction matters because many current enterprise AI deployments still sit in the productivity enhancement category. The panelists suggested the real competitive advantage will come from organizations willing to rethink workflows from first principles.
Make the business own the transformation
A recurring point across the discussion was that AI programs fail when they remain stuck within the box of being a technology project. Chris summarized CD&R’s approach: “I haven’t seen a successful tech project ever, but I have seen successful business projects powered by tech.”
Speakers stressed that CDOs should actively hand ownership to business leaders. That includes giving operational teams visibility, recognition, and accountability for outcomes.
Ido Biger described how operators became champions once they were given tools and autonomy, whereas Chris went further, advising leaders to “give away your initiatives, make it the business owners initiative.”
That shift creates stronger adoption because business leaders become financially and operationally invested in delivery.
Build internal AI movements
Scott described how First Citizens Bank created an internal “AI volunteer army” to crowdsource momentum and uncover hidden talent across the organization. The strategy reflects a broader pattern emerging where AI adoption accelerates when organizations create grassroots participation rather than relying on centralized teams.
Scott explained how his team uses internal communities, collaboration channels, and public experimentation to surface employees willing to challenge existing ways of working.
This matters because most enterprises do not have enough specialized AI talent to scale transformation centrally. Internal mobilization becomes a force multiplier.
The CIO-CDO relationship is becoming existential
Multiple panelists emphasized that AI readiness depends heavily on alignment between data and technology leadership. Ido stated that CDOs operating without strong CIO partnership face structural disadvantages: “You need him or her at your side.”
Scott reinforced this, noting that delivery speed, engineering culture, and modern SDLC practices often become transformation bottlenecks long before AI models do.
As expectations rise, the separation between infrastructure, engineering, data, and AI strategy is becoming increasingly impractical.
The full article and learnings are available to DataIQ clients on our members only hub.
