CDAO Chicago schedule

•    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

•    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?

Up to 95% of all AI deployments fail to deliver measurable ROI. Why? And what can enterprise leaders do about it? New global quantitative research from Actian shows a clear pattern: organizations lacking data products and enforceable data contracts are far more likely to fail. Based on survey data from enterprise data leaders, this session reveals what separates scalable AI from perpetual pilots and failed experiments. Attendees will leave with concrete design, governance, and operating practices they can apply immediately to their next AI initiative.

Agentic AI is transforming enterprise AI from answering questions to executing operational workflows. As a result, real-time, governed business context is becoming the new bottleneck for enterprise AI. In this session, K2view will show why traditional data architectures fall short, and how Business Operational Context becomes the data foundation for AI execution, validation, and continuous improvement. Attendees will learn a practical architecture pattern for decoupling AI agents from data handling, reducing token cost and governance risk, and enabling enterprise-scale agentic AI.

•    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?

Panelists:
Kyongche Kang, Analytics Branch Chief Data Scientist, UNITED STATES ARMY RESERVE
Smaran Mandala, Senior Director, Analytics and Innovation Partner, JLL
Lev Tyomkin, Vice President, Equity Research Data Strategy, JEFFERIES
Christopher Jones, Senior Director, Baseball Analytics, CHICAGO CUBS
Dhruv Baronia, Head of Analytics, NORTHERN TRUST

Moderator:  Cameron Cross, Partner, Technology & Experience, WEST MONROE

Track Panel Discussion: Understanding the Need for Speed: Making AI a Priority for Every Part of the Business
·    Why should AI be a priority for every part of the business today?
·    What are the biggest challenges companies face when scaling AI across the organization?
·    How can leaders ensure AI initiatives deliver real business value?
·    What steps can organizations take to accelerate AI adoption across teams?
 
Panelists: 
Madhu Mukherjee, Vice President of Transformation & Data Science, Gannett| USA TODAY NETWORK 
Daniel Chertok,  Sr. Manager, Applied AI, BMO U.S. 
Mir Ali, Senior Director - Data & Analytics, THE HERSHEY COMPANY 
Ashok Kumar Somi Jeevan - Director, AI / ML, Advanced Analytics and Analytics Solution Architecture
Joshua Burkhow, Chief Evangelist, ALTERYX

Moderator:  Jeannie Furlan, Financial Data Strategy VP, MUTUAL OF OMAHA

•    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?

Panelists:
Robert Cooper, Director, Analytics, CHICAGOATLANTIC
Latha Ramamoorthy, Vice President - Technical Product/Program Manager, JPMORGAN CHASE
Ravi Salavi, VP, Data Analytics Strategy & Capabilities, TRUSTAGE
Geetha Kagathi, Vice President of Application Development, TRANSUNION
Shawn Tumanov, Head of AI Governance and Enablement, ALIGHT INC.

Moderator: Flo Castro-Wright, Chief Data Officer, ILLINOIS DEPARTMENT OF INNOVATION & TECHNOLOGY

•    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?

•    If data readiness dictates AI success, why do most agentic initiatives still start with advanced models instead of the data estate?
•    How do you successfully transition your data and tech teams from manual "performers" to "conductors" who orchestrate AI workflows?
•    At what point does ad-hoc AI tool adoption turn into a liability if your organization lacks a formalized corporate standard for engineering fluency?
•    How do you enforce rigorous enterprise guardrails and human governance without completely choking team innovation?




 

We love the idea of “done.” But if you think data can be finished… you’re thinking about it wrong. In this keynote, we will explore why the best data strategies don’t chase completion – they build for constant evolution.
•    Why treating data like a project is quietly holding teams back
•    The hidden cost of “final” 
•    How to shift from delivery mindset → product mindset
•     What high-performing teams do differently
•     Practical ways to design for change instead of reacting to it

•    Why scattered AI cost, routing, and governance are symptoms of one architectural gap, not three separate problems
•    How intent-and-identity routing cuts token spend without asking anyone to use AI less
•    What it takes to govern autonomous agents at the boundary before they reach sensitive data

 

•    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?

Panelists:
Veronica Butler, Director of Data and Program Evaluation, SUNSHINE ENTERPRISES
Vinod Surasani, Sr. Software Engineer, RBC
Shuen Mei, VP, Director of Software and Data Engineering, HARBOR CAPITAL ADVISORS, INC
Ashish Garg, Principal Product Manager, WALMART
Shawn Tumanov, Head of AI Governance and Enablement, ALIGHT INC.

Moderator:  Shane Wiggins, VP, AI Strategy & Products, BIGEYE

•    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

•    Turning data into measurable customer and business value with AI 
•    Real-world lessons from building AI-powered products 
•    Scaling AI adoption through governance and actionable frameworks

•    Treat cost and usage data as a governed enterprise data product
•    Serve trusted cost intelligence through both dashboards and MCP-enabled conversational analytics
•    Enable leaders, engineers, analysts, and AI agents to ask governed questions over standardized cost data.
•    Connect cost signals to engineering action, business ownership, and AI-ready governance

•    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?

•    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

•    Owning enterprise AI strategy, not just data governance
•    Shifting from control and compliance to value and speed
•    Redesigning operating models for human + AI collaboration
•    Proving business impact while governing AI risk and trust

 

What and the Why?
1. AI is reshaping work faster than annual planning cycles can keep up. This session introduces a methodology for moving workforce planning from reactive headcount forecasting to proactive, intentional workforce design.
How?
2. We'll walk through the 5S Framework (Signal, Segment, Simulate, Shape, Sustain) — a structured approach for proactively modeling workforce futures and making deliberate "build, buy, borrow, or automate" decisions before they become urgent.
So What/Where Is This Going?
3. We'll explore how organizations can embed workforce design as a continuous operating practice — not a once-a-year exercise — so that people strategy keeps pace with AI strategy.

•    Defining meaningful AI ROI: Moving beyond cost savings to measure business impact and strategic value. 
•    From pilots to enterprise value: Key success factors for scaling AI initiatives that deliver measurable outcomes. 
•    Aligning AI investments with business goals: Prioritizing use cases that drive sustainable growth and competitive advantage.

• Why agent authorization has become a data-leadership problem, not just a legal one
• The real reason agentic pilots stall before production: sign-off, not model quality
• Designing delegated authority — consent, scope, revocation, and audit — for agents acting on a user's behalf
• What recent regulation signals about the audit trail enterprises will be asked for next

•    Reimagining enterprise investigations with AI-assisted workflows
•    Orchestrating AI agents, enterprise knowledge, and human expertise into a unified decision-support system
•    Designing explainable, human-in-the-loop architectures for high-stakes enterprise decisions
•    Practical implementation patterns for building trustworthy AI systems that support risk, compliance, and operational decision-making

•    Key strategies for scaling AI initiatives from pilot projects to enterprise-wide adoption. 
•    Building the data, governance, and operating model needed to drive sustainable AI success. 
•    Measuring business impact and demonstrating ROI through real-world AI implementation