CDAO Dallas schedule

• Who should own 'Return on AI' and how do you make that ownership stick before 2027 budgets lock
• What do you promise a board that now expects AI value reporting as standard, and how do you survive the promise twelve months later?
• How does the CDAO mandate change once AI has a P&L, and what should you renegotiate about your own role now, while your influence is rising?
• What did 2026 teach the leaders who still got funded?

• Which readiness gates separate a scalable use case from an expensive pilot before major funding is committed?
• What are the non-negotiable kill criteria and who has the authority to stop an underperforming initiative?
• How do leaders redesign workflows, roles and hand-offs so that value reaches the P&L rather than remaining in a demonstration?
• How do leaders redesign workflows, roles and hand-offs so that value reaches the P&L rather than remaining in a demonstration?
• What to stop, change or refuse to fund to ultimately make the programme more successful?

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

• Which dashboards, reports and BI processes should be retired, automated or retained.
• How do leaders govern shared metrics and semantic definitions as teams increasingly ask questions through AI?
• How must analytics teams change their product, engineering and business-partnering model to deliver faster, trusted decisions?

• 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.

• How do leaders turn AI principles, inventories and risk assessments into runtime controls rather than static policy documents?
• What evidence should organisations capture across AI systems and agents, including authorised data access, configuration, decisions, overrides, monitoring and incident handling?
• How can data, technology, security, risk and legal teams establish one proportionate governance model across TRAIGA, EU AI Act readiness and sector-specific expectations?
• What must be in place by the end of 2026 to make 2027 audit, board and regulatory scrutiny manageable?

• Where does accountability sit when an AI agent acts - data office, business owner, risk, or vendor?
• Tips on designing agent identity, permissions and decision boundaries that hold in production.
• How can firms build practical human-oversight controls into every agent workflow?
• How can leaders discover, register and govern unapproved AI agents before they create unmanaged risk or duplicate activity?

• Agree stop/scale criteria and sunset rules for AI investments.

• How to apply portfolio discipline ahead of the 2027 budget season.

• Compare real kill decisions with peers. 

• 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. 

• Confront the workforce math: roughly 205,000 AI-linked layoffs already reported in 2026 - while most employers reskill at the same time.
• What actually works in reskilling and redeployment - and what we owe our people.
• Debate the fight for talent: hire it, or grow it? 

• Decide whether data quality belongs on the board dashboard alongside cyber and financial risk.

• Who reports data quality to the board and on what metrics?

• How to make the case if your board does not yet want it. 

• 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.

• 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?