CDAO Dallas schedule

• Who should own the return on AI, and how do you make that ownership stick before 2027 budgets lock?
• What do you promise a board that expects AI value reporting as standard, and how do you survive that promise twelve months later?
• What separates a scalable use case from an expensive pilot, and who has the authority to stop an initiative that underperforms?
• What did 2026 teach the leaders who still got funded, and what should you renegotiate about your own mandate now?

Bo Gu, Director of Data Science & Generative AI — AT&T 

Tufan Arikan, Head of AI and Innovation — AIMBRIDGE HOSPITALITY 

Moderated by: Yahia Zemoura, Senior Vice President - Director, Enterprise Data and Analytics — HILLTOP HOLDINGS 

• Walk through twelve agentic AI solutions built in one year: which scaled, which stalled, and why.
• The numbers: cost, adoption and measured results for the ones that worked.
• What failed, and the stop decisions and readiness gates behind it.
• What to put in place before your next agent ships.

 A ten-minute, evidence-led case: one deployed system, its full cost, the baseline, the outcome and the lesson learned.  

• What is working at your scale, and what did you get wrong the first time?
• Who owns AI in your organization, is that settled, and what happens to the data leader's remit if it is not?
• What does the analytics function become when the business asks its questions through AI rather than through your team — which roles disappear, which appear, and what do you stop owning?
• How do you close that team gap, and which dashboards, reports and BI processes do you retire on the way?

Karthik Ilangovan, Vice President, Data Analytics and AI/ML — MODE GLOBAL 

Yahia Zemoura, Senior Vice President - Director, Enterprise Data and Analytics — HILLTOP HOLDINGS 

Tejas Shinge, Director, Head of Analytics – GAINSCO 

Moderated by: Priya Narayan, Managing Director of Technology – SOUTHWEST AIRLINES 

• Walk through a live readiness-gate system that grades data before AI is allowed to touch it.
• Insight into the quality and governance thresholds - and their measured effect on agent behaviour and error rates.
• Get the numbers: implementation cost and results after 12 months in production.
• How to build a readiness investment case a board will fund.

• If the Texas Attorney General or your auditor asked today, what could you hand over, and how do you use the NIST AI Risk Management Framework as the spine of that evidence?
• How do you convert AI principles, inventories and risk assessments into controls that run automatically across systems and agents, and which one do you automate first?
• How do data, technology, security, risk, legal and privacy teams operate a single, proportionate governance model across Texas law, the EU AI Act and sector rules without tripling the workload?
• What must be in place before 2027 audit, board and regulatory scrutiny, what can safely wait, and who signs off on each?

Shahran Haider, Deputy Chief Data Officer — NYC HEALTH + HOSPITALS 

Vijay Gandra, Global Chief Data Officer — ACRISURE 

• Where does accountability sit when an AI agent acts?
• When agents do the work, who owns the outcome — and what does your function's headcount and shape look like in 2027 as a result?
• What decisions are you no longer willing to delegate to a machine, and how did you draw that line with your board?
• How can leaders discover, register and govern unapproved AI agents before they create unmanaged risk or duplicate activity?

Jayant Kumawat, AI Solutions and Platform Lead — PEPSICO 

Srinivasa Murthy, VP, Lead Engineer — U.S. BANK 

Siloed operating models, inconsistent data quality, and mounting technical debt are the biggest obstacles standing between CIOs and the data-driven outcomes their businesses expect. Open Data Infrastructure (ODI) offers a way through: by separating storage from compute, adopting open standards, and ensuring interoperability across systems, CIOs can consolidate fragmented environments, improve the reliability and availability of enterprise data, and reduce the maintenance burden of legacy point-to-point integrations. The result is a data foundation that's easier to govern, cheaper to run, and flexible enough to support both today's analytics workloads and tomorrow's AI initiatives, without locking the organization into a single vendor's roadmap.

Join us to discuss:

• Building a data foundation that can actually support AI initiatives, not just today's analytics workloads
• Modernizing data infrastructure to eliminate silos and pay down technical debt without disrupting ongoing operations
• Improving data quality and availability as the prerequisite for trustworthy, data-driven decision-making
• Balancing the push for open, interoperable architecture with governance, security, and cost predictability

Niraj Vora, Principal Sales Engineer, Enterprise — FIVETRAN 

• What AI actually costs to run: inference economics, vendor spend, cost-to-serve.
• How to pressure-test the numbers behind your ROI story.
• Benchmark cost baselines with peers, off the record. 

Devdas Gupta, Senior Manager, Software Development & Engineering Lead – CHARLES SCHWAB 

• How do you decide between reducing, redeploying and reskilling when AI changes a role, and when do you hire talent versus growing it?
• When several transformation programmes run at once, how do you govern the workforce decisions that cut across them, so a choice in one doesn't undo another?
• What must line up across business, technology and culture for change to stick, and what breaks when one of the three is missing?
• Who owns the decision, what capability do managers need now that they didn't three years ago, and who is building it?

Tom Krusch, Vice President, Transformation – AT&T

• Decide whether data quality belongs on the board dashboard alongside cyber and financial risk.
• Who reports data quality to the board, on which metrics, and at what cadence?
• How to make the case if your board does not yet want it.

Ishita Malhotra, Data Science and Analytics Manager — GOLDMAN SACHS 

• Put your hardest question to a sitting – SUBMIT HERE.
• Insight into budget fights, failed projects, vendor pressure, and career risk.
• What skills will matter most in 2027 - and how to win the fight for talent.

• Where does ownership of AI risk sit - data office, security, or the business?
• How to brief the board on a threat model that learns.
• What is most critical in the early hours of a deepfake or AI-fraud incident - and how to structure the immediate response.
• Assess whether your own agents are now an attack surface - and how to close it.

Rajendra Elumalai, Vice President and Principal Engineer – U.S. BANK 

• What will boards expect from the data and AI function over the next 12 months?
• Which foundations, operating-model changes and measurements cannot wait for the next planning cycle?
• What should every data leader leave ready to do differently on Monday?