CDAO Chicago schedule

•    AI everywhere, value nowhere: why scaling AI still fails to deliver ROI
•    From data chaos to decision velocity: breaking silos fast enough to compete
•    Talent, tools, and tech sprawl: who really owns the modern data stack?

•    Aligning AI and analytics initiatives with real business outcomes
•    Building a trusted data foundation to scale AI successfully
•    Moving from insights to action with advanced analytics and AI
•    Measuring and sustaining value across the AI journey

•    Looking back, what is the single decision you would change and why?
•    What early signal or risk did you underestimate at the time?
•    What would you prioritize in the first 90 days if you were starting again today?
•    What advice would you give executives facing the same challenge now?

•    How do you decide which data initiatives truly drive business impact?
•    What criteria help you prioritize data projects when resources are limited?
•    How do you measure whether a data initiative is actually moving the needle?
•    What common mistakes prevent data projects from delivering real business value?

•    Who truly owns the data product lifecycle in your organization, and what breaks when ownership is unclear?
•    What’s the hardest leadership decision you’ve had to make when a data product wasn’t delivering value?
•    How do you balance speed to market with governance without killing innovation?
•    Should data products be killed like any other product, and who has the authority to pull the plug?

•    What’s the real cost of not unifying data, and who’s actually paying for it?
•    Is data unification a technology problem or a leadership problem in your organization?
•    At what point does data unification start driving growth, not just efficiency?
•    What’s the biggest myth organizations believe about achieving true data unification?

•    How can data & analytics automation tools and frameworks evolve to address future compliance challenges?

•    How is GenAI reshaping data fabric architectures?
•    What new metadata challenges does GenAI introduce?
•    How should governance models evolve for GenAI?
•    How can automation improve trust and data quality in GenAI systems?
•    What are the key lessons learned from real GenAI implementations?

•    If people matter more than platforms, why do most data transformations still start with tools?
•    What leadership behaviors most often undermine a data-driven culture, without realizing it?
•    How do you hold teams accountable for data outcomes, not just data delivery?
•    At what point does investing in technology stop delivering value if people aren’t truly enabled?


 

•    Data enables new revenue streams beyond traditional products
•    High-quality data drives smarter, more profitable decisions
•    Data-driven products scale with minimal incremental cost
•    Strong data capabilities create sustainable competitive advantage

•    Data quality and governance as the true scaling limits
•    Shared analytics and data models for agent collaboration
•    Measuring value across distributed intelligence
•    When data complexity erodes expected returns

•    How has GenAI changed the definition of “trusted data,” and are today’s governance models still fit for purpose?
•    Where are organizations most exposed today: data quality, security, privacy, or model misuse—and why?
•    How do you balance speed to innovate with GenAI against the risk of losing control over data and IP?
•    Who should ultimately own data trust in the GenAI era: IT, security, data leaders, or the business, and how is accountability enforced?

•    Turning data into revenue while maintaining customer trust
•    Balancing personalization and privacy at scale
•    Building transparent and ethical data monetization models
•    Aligning data value creation with governance and compliance

•    Where are AI and GenAI truly improving the customer experience, not just automating it?
•    How do you balance personalization with trust and data privacy in AI-driven customer journeys?
•    What customer insights are only possible today because of GenAI?
•    How do you measure real customer-centric impact from AI initiatives beyond efficiency gains?

•    How agentic AI is moving beyond rule-based systems to make dynamic, context-aware decisions — transforming how organizations trade, manage risk, and operate.
•    Why the future isn’t just about smarter models, but networks of AI agents working together to forecast, adapt, and respond in real time.
•    How quantum breakthroughs are unlocking unprecedented speed and complexity in areas like optimization, portfolio management, and compliance.
•    What leaders must do today to leverage agentic intelligence and quantum technologies to stay ahead in an increasingly autonomous and unpredictable landscape.

 

•    How future-focused CDAOs are aligning data strategy with long-term business goals, customer-centric innovation, and emerging technologies like GenAI
•    Building agile data organizations that can move fast—balancing governance with experimentation, and enabling real-time, high-impact decisions
•    Translating data investments into tangible business outcomes by embedding analytics into core operations, managing risk, and demonstrating value to the C-suite

•    Why do most data transformations fail despite strong investment and executive support?
•    What leadership decisions make the biggest difference between failure and success?
•    What should leaders do differently in the first year to avoid common pitfalls?

•    How do you ensure data strategy is tightly aligned with business priorities?
•    What frameworks or criteria help prioritize data initiatives by business impact?
•    How do you balance quick wins with long-term strategic data investments?
•    What role should business leaders play in prioritizing data projects?

•    What must CDAOs stop doing today to stay relevant in 2027 and beyond?
•    Which capabilities will define a successful CDAO in the next three years—technical, organizational, or political?
•    Will AI elevate the CDAO role or make it obsolete—and what determines the outcome?
•    What uncomfortable bets should CDAOs be making now to future-proof their data organizations?

•    Why classical analytics is hitting its limits—and where quantum can break through
•    How AI and quantum together accelerate modeling, optimization, and insight generation
•    What’s real today versus what’s still experimental in quantum-driven analytics
•    Preparing data, skills, and strategy for a quantum-enabled future 

•    Analytics helps identify weak signals before disruption becomes visible
•    Predictive insights shift organizations from reactive to proactive decisions
•    Scenario modeling improves strategic resilience under uncertainty
•    Data-driven foresight accelerates response and innovation
 

•    Moving beyond pilots and hype to real-world deployment
•    Why the most effective AI agents augment human judgment, not replace it
•    Trust, not technology, as the main barrier: data quality, hallucinations, and compliance
•    Turning agents into measurable business value, not impressive demos

•    Shifting from intuition-led decisions to evidence-based leadership
•    Turning data and analytics into measurable business outcomes
•    Leading cross-functional collaboration across business, data, and technology
•    Building a data-first culture through literacy, trust, and adoption

•    At what point do AI agents stop being tools and start acting as true teammates in D&A teams?
•    How must data and analytics operating models change to effectively integrate AI agents at scale?
•    What new skills and roles become critical when humans and AI agents work side by side?
•    How do teams maintain trust, accountability, and data governance when AI agents take on more autonomy?
•    What early wins should organizations target to prove the value of AI agents in D&A?