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

Paul Dorasil, Head of Data and AI - DECA Dental Group

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

Deploying AI agents is one thing. Building an enterprise architecture that can support hundreds of them is another.

Should teams build their own agents or share reusable capabilities? How can agents access governed enterprise data without duplicating systems? Where does autonomy end and human oversight begin? And how do you keep inference costs, latency and security risks under control?

This session explores the architectural trade-offs behind scaling agentic AI, from context engineering and orchestration to governance, cost optimisation and operational resilience.

The goal: more autonomy, less complexity and an architecture built to deliver value at scale.

• 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.

• Everyone wants AI. But can your data stand up to scrutiny?

  When a single government record feeds 26+ agencies, trust, accuracy and accountability aren't optional. Drawing on a real-world state modernization project, this session explores what it takes to build reliable, defensible data foundations that make AI possible.

  Key Takeaways:

  • One record, 26+ destinations: Making interoperability work at scale without compromising data quality.
  • Governance you can prove: Building transparency, traceability and accountability into every step.
  • AI starts before the model: Why trustworthy data is the real foundation of AI readiness.

• Which operating models are actually working for enterprise data and analytics teams, and where are they falling short?
• How should responsibilities for data strategy, governance, analytics and AI be distributed across the organisation?
• As self-service analytics and AI become more accessible, how is the role of traditional BI and analytics teams evolving?
• What skills, structures and capabilities will data leaders need to build or rethink for 2027?

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

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

Paul Dorasil, Head of Data and AI - DECA Dental Group

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

Mohan Krishna Mannava, Head of Data Analytics and Business Intelligence -Texas Health Resources

Jason Klein, Director of Marketing -Alteryx

• Lessons from deploying AI in the emergency department, where decisions carry real-world consequences.
• Balancing innovation with safety, trust and regulatory compliance.
• Building accountability and human oversight into AI-driven decision-making.
• What data and AI leaders across industries can learn from high-stakes AI implementation.

• If the Texas Attorney General or your auditor asked today, what could you hand over, and how does the NIST AI Risk Management Framework anchor that evidence?
• How do you convert that evidence into controls that run automatically across systems and agents — and how does that work make you faster, not slower?
• What must be in place before 2027 audit, board and regulatory scrutiny, what can safely wait, and who signs off on each?

Vijay Gandra, Global Chief Data Officer — ACRISURE

Harvey Castro, Advisior to the CEO - Phantom Space

Nidhi Singh, Lead Data Engineer- State Public Health Data

Naga Mrunal, Lead Engineer - Humana

• 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?

Moderator: Satyam Mittal, Machine Learning Engineer – CHEWY

Jayant Kumawat, AI Solutions and Platform Lead — PEPSICO

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

Deeksha Yennam, Director Data Science – WALMART

Your data stack keeps getting more expensive. Your teams are still fixing broken pipelines. And now AI is adding another layer of cost and complexity.

So where is the money actually going?

This roundtable puts the economics of data infrastructure under the microscope. From the hidden costs of legacy integrations and manual data pipelines to unpredictable AI workloads and vendor lock-in, we’ll explore what’s worth keeping, what needs replacing, and where modernisation genuinely pays off.

Can open data architecture and automation actually reduce costs while delivering reliable, trusted data, or do they simply move the complexity somewhere else? And how do you modernize without disrupting the systems your business relies on?

The real question: are we building infrastructure that delivers value, or just paying more to keep it running?

Niraj Vora, Principal Sales Engineer, Enterprise — FIVETRAN

• What does it actually cost to operate your data and analytics ecosystem, from cloud infrastructure and data storage to BI tools, AI workloads and vendor contracts?
• Where are organisations overspending, and which costs are most difficult to track or justify?
• How can data leaders build credible cost baselines and demonstrate ROI across their technology investments?
• What practical steps are organisations taking to optimise spending without compromising data quality, performance or business value?

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

• How are AI agents, deepfakes and AI-powered fraud changing the risks employees and organizations face?
• What new skills and capabilities do employees need as AI takes over tasks and introduces new security and operational responsibilities?
• Where should accountability sit between data, security, technology, HR and business teams as AI becomes embedded in everyday workflows?
• How can organizations prepare their workforce to recognize emerging AI threats, respond effectively and embrace innovation without compromising security?

Tom Krusch, Vice President, Transformation – AT&T

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

• 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 
 Satyam Mittal, Machine Learning Engineer – CHEWY  

• What improves collaboration between data, technology and security teams, and where do ownership and accountability typically break down?
• How do organisations maintain visibility and control over the hidden risks of AI adoption, including shadow AI, third-party tools, data leakage and access permissions?
• How do you balance the need to accelerate innovation with appropriate governance, security and risk controls without creating unnecessary complexity or friction?
• How do we translate data, AI and cyber risks into business impact, enabling CISOs and CDAOs to communicate effectively with boards and drive informed decisions on investment, risk appetite and accountability?