CDAO Fall schedule

• Enhancing customer and employee experiences through intelligent solutions
• Driving sustainable growth by embedding AI into core business strategies
• Fostering a culture of innovation, continuous learning, and AI fluency
• Transforming business outcomes through measurable AI adoption and maturity progression

• The CDAO role has shifted from infrastructure custodian to strategic architect almost overnight. How are data leaders redefining their mandate as AI moves the goalposts on what governance actually means?
• Organisations have invested heavily in data quality and lineage, yet AI is exposing gaps that traditional frameworks weren't built to handle. Where are the most dangerous blind spots right now?
• As real-time decisioning becomes the expectation, how do you maintain meaningful oversight without becoming the bottleneck that slows the business down?
• The board relationship is changing fast. What does it take to translate data strategy into language that drives genuine executive commitment rather than polite nodding?
• If you could redesign your data organization from scratch today, knowing what you know about where AI is heading, what would you do fundamentally differently?

Panelists:

Kamal Distell, Vice President Enterprise Data – TRAVELERS

Trang Nguyen, Vice President, Data Science – PRUDENTIAL FINANCIAL

Elena Alikhachkina, Chief Data and AI Officer, TE CONNECTIVITY

Achuth Rao, Chief Product Officer & Head of Data & Analytics, AI Products – NEW YORK LIFE INSURANCE COMPANY

Moderator: Gorkem Sevinc, Co-founder and CEO - QUALYTICS

• Most organisations have the same tools, the same vendors, and broadly similar budgets. So why are some pulling away while others are still running the same experiments they ran two years ago?
• The companies making AI work in production share organisational and cultural characteristics that have nothing to do with technology. What are they and how do you build them deliberately?
• What does production-ready AI actually look like at an operational level, and how should CDAOs be measuring progress beyond the metrics that make board decks look good?
• The gap between AI ambition and AI execution is a leadership problem, not a technology problem. What does closing it actually demand from the person at the top?

• Every CDAO is making a foundational bet right now on how they structure their data and AI platform. Whether deliberate or by default, those decisions will constrain or enable everything that follows.
• The unified data platform has been promised many times before. What has genuinely changed in the last 18 months that makes platform coherence a real competitive differentiator?
• Where are the build versus buy versus partner decisions that look straightforward today but create serious lock-in problems down the road?
• What does the practical journey from fragmented tools to an integrated, AI-ready architecture look like for organisations that have already made it?

• Everyone wants to talk about models. Almost nobody wants to talk about the data preparation and quality engineering that determines whether those models are useful or dangerous. Why does the industry keep skipping this conversation?
• Data contracts, mesh architectures, and domain ownership models are all being adopted to solve fundamentally the same problem from different angles. Which approaches are proving durable in real enterprise environments?
• The organisations furthest ahead on AI often made boring, unglamorous infrastructure investments three to five years ago. How do you make that case to leadership when everyone wants to fund the exciting thing?
• If your data foundation were genuinely AI-ready, what would be measurably different about how your organization operates day to day?

Panelists:

Erin Fidyk, Director Oncology Enterprise Data Science – JOHNSON & JOHNSON

Moshmi Sanagavarapu, SVP Group Director, Data Analytics, OMNICOM-IPG MEDIABRANDS

Yogesh Bhardwaj, Sr. Director GSO Business Partner, Data & IT Shared Services, SENSATA

WNS Moderator

Not long ago, data was just a by-product of doing business. Then companies gave it infrastructure, a discipline, and a chief officer, and it became one of their most valuable assets.

What teams learn from that data is worth even more, but it has never had the same treatment. The findings, the context around them, and the approaches that produced them still get lost in decks, threads, and people's heads.

In ten minutes, see how Romb connects everything teams have learned and puts it to work across the organization, as a natural part of the day-to-day. Questions get answered from work teams have already done, new analysis starts with full context instead of from zero, and leaders see what is driving results across teams, with every answer cited to the actual work.

• We spent three years building AI that helps humans decide. We are now building AI that decides and acts on its own. That demands a fundamentally different kind of thinking. Are organisations ready for it?
• Over 70% of enterprises are using agentic AI in some form, but fewer than 20% have a governance model designed for autonomous agents. What does closing that gap actually require?
• Agentic systems fail differently from traditional software. They can fail silently, gracefully, and in cascading chains. How are organisations building the observability and intervention capabilities they need?
• At what point does an agentic AI system warrant the same level of scrutiny and validation as a human being given significant business authority?
  

Panelists:

Ravinath Kausik, Vice President, Data Science, AI and Analytics – THE HARTFORD

Tasneem Nipplewala, Head of Data, Cyber and AI Enterprise Architecture – MASSMUTUAL

Sakat Sankhla, Business Transformation & Product Leader, Modular Data Center for NAM – SCHNEIDER ELECTRIC

Celio Oliveira, Chief Data and AI Officer, FINANCE CANADA (GOVERNMENT OF CANADA)

Ewa Kozlowska, Senior Vice President, Treasury and Trade Solutions - Global In-Business Product Risk – CITI BANK

Mohan Krishna Rayapuvari, Sr Director AI & Machine Learning, MANULIFE

Moderator: Anjali Arora, Chief Technology Officer – PERFORCE

• Enterprise AI rarely fails because of technology alone. What does effective alignment between data, AI, risk, legal, security, and the business actually look like in practice? 
• Governance is often seen as the function that slows innovation down. How can leaders design governance models that enable responsible speed rather than create bottlenecks? 
• As AI moves from experimentation into production, ownership becomes more complex. Who should be accountable for decisions that sit across data, models, workflows, and customer outcomes? 
• The organizations scaling AI successfully are building repeatable operating models, not one-off pilots. What structures, decision rights, and cross-functional ways of working are needed to move quickly without creating unnecessary risk? 

Aiswarya (Ammu) Menon, Chief Data & AI Officer – NYC HEALTH + HOSPITALS

Amritha ArunBabu Mysore, Head of Product – Marketing and AI – STAPLES

David Krauza, Vice President, Enterprise Data Strategy, Products, and Governance – COMCAST

Nirav Parikh, Head of Enterprise Data and AI – MASS GENERAL BRIGHAM HEALTHCARE

Moderator: Taige Eoff, Go to Market Data Practice Lead, Google Cloud – GOOGLE

• Data and AI transformation require cultural change at least as much as technical change, yet most CDAOs are hired for their technical credibility. How do you develop the change of leadership capabilities the role now demands?
• Resistance to data-driven decision-making rarely looks like outright opposition. It looks like slow adoption, metric disputes, and business units building their own shadow data capabilities. How do you diagnose and address the real blockers?
• The data leader who tries to centralize everything creates bureaucracy. The one who decentralizes too aggressively creates chaos. How are practitioners finding the right balance between control and enablement?

Duyum Ryan, Finance Chief Operating Officer & SVP (Finance Data Strategy), THE CIGNA GROUP

Frederique De Letter, Head of Data Analytics & AI – PLANTE MORAN

Fariha Chaudry, Head of Engineering M&A – GE HEALTHCARE

Moderator: Greg Freeman, CEO & Founder – DATA AND AI LITERACY ACADEMY

• Data integration used to be a plumbing problem. In an AI-first environment where models need fresh, clean, and contextual data continuously, it has become a strategic capability. How should CDAOs be thinking about this differently?
• The volume, velocity, and variety of data sources has grown dramatically with AI adoption, including new sources like model outputs, vector embeddings, and agent action logs. How are organisations managing the integration complexity this creates?
• Many enterprises are still running batch pipelines in a world that increasingly demands real-time. What is the realistic migration path, and where should organisations prioritize the move to streaming first?

Moderator: Andrew Gallagher, Regional Director of Enterprise Sales – FIVETRAN

Richard Inserro, Director, Data Governance, Standards, and Assurance – MERCK

Aaron Chang, Director, Head of Data Science, Disease Area X – NOVARTIS

Aidar Kadess, Sr Director, Data and AI Governance – CLEAN HARBORS

Anu Sundaram, Vice President Business Analytics – RUE GILT GROUPE

Abstract:

• For twenty years, BI has been a spectator sport. We built dashboards, admired the charts, and then walked to another system to actually do something about them. Insight and action lived in different worlds.
• That gap is closing. This session shows what happens when analytics stops observing and starts acting when agentic workflows and self-service analytics fuse with operational systems to turn a finding into a fulfilled outcome without leaving the flow.
• Using Autonomous Data Analytics in ServiceNow, we'll demonstrate live: an AI agent that surfaces an anomaly, reasons about its cause, and triggers the workflow to resolve it governed, auditable, and in the hands of the business user, not just the data team.
• We'll tackle the harder questions, too. What does trust look like when analytics can act on its own? Where should the human stay in the loop? And what happens to the traditional dashboard when the answer to "so what?" is already in motion?
• Come see the end of the read-only era and what a decision that executes itself really means for your organization.

• Between new models shipping every month and regulations being rewritten just as fast, most enterprise AI architecture decisions are built on an assumption that today's constraints will hold. That assumption leads to pilots stalling out or landing in pilot purgatory.
• This session looks at why AI infrastructure needs to be built for change from the start, not retrofitted once the ground shifts. We'll walk through the architectural pattern that lets organizations adapt to new models, new regulations, and new data environments without rebuilding. Flexibility and control, delivered without the runaway costs typically attached to both.
• We'll share the architectural requirements: sovereignty, governance, and context, that separate AI systems built to stand the test of change. We'll show you how to design for adaptability so that new models, new regulations, and new data sources are things your AI absorbs rather than breaks under, and how to get there with an approach that makes your data sovereign by default for AI.

• Moving beyond pilots: what separates scalable AI execution from disconnected experimentation
• Building AI-ready foundations: data quality, governance, access, lineage, and trust
• Designing the operating model: ownership, accountability, and decision rights for enterprise AI
• Preparing for agentic AI: what changes when AI systems begin to act, not just advise
• Scaling responsibly: how to balance speed, innovation, risk, and measurable business value

This panel would explore how financial services organizations can move beyond reporting and dashboards toward analytics that actively shape business decisions, improve performance, and drive measurable outcomes.

• Where analytics teams still get stuck between insight generation and action
• How leaders can improve adoption of analytics across risk, finance, marketing, customer, and frontline teams
• What it takes to measure the real business impact of analytics beyond activity metrics

Moderator: Kate Butcher, GM, AI & Finance Transformation – ALTERYX

Mitch Holt, AVP – Head of Data & Analytics, Pet Insurance – METLIFE

Bill Carey, Managing Director, Client Analytics, Commercial Insurance Risk Control Services – LIBERTY MUTUAL INSURANCE

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.

• The EU AI Act, evolving US federal guidance, and sector-specific financial services regulation are creating a genuinely complex compliance landscape. How are organisations building frameworks that work across multiple regimes?
• Legal and compliance functions want to slow deployment until the picture is clearer. Business units want to deploy and deal with it later. How are data and AI leaders mediating that tension?
• Responsible AI has been a priority conversation for years. How much of it has translated into operational processes that actually change how systems are built, versus principles that sit in a document?
• Where do you genuinely believe the regulatory environment is heading in the next 24 months and how is that shaping your governance investments today?

Colleen Tartow, Senior Director, Enterprise Data Engineering – CAPITAL ONE

Theodora Skeadas, Trust and Safety Policy Manager – DOORDASH

• What does a successful transition from AI vision to measurable business value look like?
• Which business objectives should be prioritized when defining an AI strategy?
• How can organizations ensure alignment between business strategies, technology investments, and data capabilities?
• What are the key enablers of successful end-to-end AI transformation?
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• How to establish clear AI governance frameworks to define accountability, oversight, and decision rights?
• How to ensure data integrity and quality through standardized data management and validation processes?
• How to implement ethical AI principles covering fairness, transparency, and accountability?
• How to design explainable AI systems that build user trust and support decision-making?
• How to mitigate bias and unintended outcomes through continuous testing and monitoring?

• Identifying high-impact AI use cases that address real business challenges
• Linking AI initiatives to key performance indicators such as revenue, cost reduction, and productivity
• Assessing data readiness by ensuring quality, accessibility, and governance
• Evaluating technical and organizational capabilities to support AI delivery

Panelists:

Lauren (Liutong) Li, Executive Director, Head of AI & Innovation Product Strategy, NOVARTIS

Sreedhar Sistu, Vice President, AI Offers, SCHNEIDER ELECTRIC

Lily Li, Head of AI Adoption and Solutions, FRANKLIN TEMPLETON

• Defining a clear AI vision aligned with business strategy and measurable outcomes
• Building cross-functional collaboration between business, IT, data, and risk teams
• Embedding responsible AI principles into every stage of the lifecycle
• Prioritizing high-impact use cases that deliver tangible value quickly
• Establishing strong governance frameworks without slowing innovation
• Investing in talent development and fostering an AI-driven culture

Panelists:

Biswa Sengupta PhD, Chief AI Technologist: LLM Suite (CDAO), JPMORGAN CHASE & CO

Haroon Abbu, Senior Vice President, Digital Technology, AI & Data Analytics & CDAIO, BELL AND HOWELL

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.

• Data infrastructure investment is notoriously difficult to cost-justify in traditional financial frameworks because the value is cumulative, diffuse, and often realized by business units that didn't fund it. How are practitioners solving this problem?
• CFOs are increasingly being asked to approve significant AI and data platform spending with ROI projections that are genuinely hard to validate. What does a credible, honest investment case look like?
• How do you have an honest conversation with finance leadership about the fact that some of the most valuable data investments won't show a return for two or three years?
• What metrics have you found actually resonate with finance leadership, beyond the ones that data teams naturally reach for?

Diego De Aragao, Senior Vice President, Balance Sheet and Analytics, CITIGROUP

Duyum Ryan, Finance Chief Operating Officer & SVP (Finance Data Strategy) - THE CIGNA GROUP

Justin Heller, Former SVP, Chief Data Officer – Synchrony Financial

• A small number of organisations seem to improve their data and AI capabilities faster than everyone else, compounding advantages year on year. What are the operating practices and cultural habits that create that flywheel?
• The difference between organisations that plateau and those that keep accelerating is rarely about technology choices. What is it actually about?
• How do you build a data organization that learns systematically from both its successes and its failures, rather than moving on to the next initiative before the lessons from the last one have been absorbed?
• What is the one investment, structural decision, or cultural practice that you believe had the most outsized impact on your organization's data capability trajectory?

• Most organisations have a compelling AI strategy and a frustrating implementation reality. What are the structural, cultural, and operational factors that determine whether a data and AI programme scales or stalls?
• Scaling AI beyond a handful of use cases requires standardized platforms, shared infrastructure, and consistent practices across business units that often have very different priorities. How do you build that without becoming the team that slows everyone down?
• Where does centralization help and where does it hurt? The organisations furthest ahead seem to have found a specific balance between central governance and domain autonomy. What does that balance look like in practice?
• Executive sponsorship is easy to get at the start of a transformation and notoriously difficult to sustain through the messy middle. How do data leaders keep leadership genuinely invested when the results are still building?
• What does "scaled" look like as an end state, and how do you know when you have genuinely got there versus just having more activity?

Panelists:

Haroon Abbu, Senior Vice President, Digital Technology, AI & Data Analytics & CDAIO– BELL AND HOWELL

Moshmi Sanagavarapu, SVP Group Director, Data Analytics - Omnicom-IPG Mediabrands

Alexander Shvets, Head Medical & R&D Data Science & AI – IPSEN

Michael Mangione, Global Head - Digital & AI Experience Transformation - W. L. GORE & ASSOCIATES

Anu Sundaram, Vice President Business Analytics – RUE GILT GROUPE

• Some of the most impressive data and AI results in enterprise are coming from surprisingly small, tightly run teams. What are they doing differently that larger, better-resourced organisations should be paying attention to?
• Organisational complexity is one of the biggest hidden costs of data transformation. How do lean organisations stay fast and effective as they grow, and what do large organisations sacrifice when they scale?
• The tendency to hire for every gap rather than build versatile, high-trust teams creates coordination overhead that often outweighs the capability gained. What is the right philosophy for data team design?
• If you stripped your data organization back to its highest-leverage activities, what would remain and what would you realize you have been over-investing in?

• Which data quality challenges pose the greatest risk to AI success?
• Where do data silos limit visibility, insights, or innovation?
• Who owns accountability for AI-related decisions and outcomes?
• How important is explainability for business-critical AI decisions?

AI is expensive, operationally complex, and increasingly impossible to justify as a blanket investment strategy. As organisations move from experimentation to scale, the real constraint is no longer ambition, it's prioritization, cost discipline, and deciding what not to do.

• If AI is not cheap to build or run, what use cases should simply be stopped, even if they look innovative?

• Are organisations being honest about ROI, or are they subsidizing experimentation under the label of transformation?

• Who should have the authority to shut down AI initiatives that don't deliver measurable impact — the CDAO, CAIO, or the business?

Panelists:

Amin Assareh, Vice President, Data Science, FIDELITY INVESTMENTS 

Mitch Holt, AVP – Head of Data & Analytics, Pet Insurance – METLIFE

Santiago Ibanez, GENAI Lead – MAPRE

Moderator: David Woods, SVP – Precisely Global Services – PRECISELY

• Optimizing cost structures by balancing compute intensity, model complexity, and usage patterns

• Prioritizing performance through rigorous evaluation of accuracy, latency, and scalability

• Designing autonomous systems that can act independently while respecting defined boundaries

• Establishing control mechanisms to govern agent behavior, decision rights, and escalation paths

• How has the definition of “accountability in AI” evolved in the past few years?
• What does “success” look like for AI beyond model accuracy?
• What leading indicators should organizations track to ensure AI initiatives are on the right path?
• How are organizations preparing for evolving regulatory requirements around AI?

Panelists:

Ramesh Natarajan, Head - GenAI/ML Solutions, DAIICHI SANKYO US

Prashant Singh Tewatia, Vice President, JPMORGAN CHASE & CO

Jordi Rodriguez Oliver, Head of CoE (Center of Excellence), MABE GLOBAL

• How is AI governance formally structured across clinical, research, legal, compliance, privacy, and IT functions?
• How often are AI governance policies reviewed and updated as technology evolves?
• What governance principles determine whether specific datasets can be used for AI development?

Location: Pacific F-H

Join us on Day 2 for an informal networking lunch designed to bring together peers, speakers, and sponsors in a relaxed setting away from the exhibition hall, while still keeping you close to the energy of the event.

Use this time to pick up conversations from the morning sessions, share ideas with fellow data and AI leaders, and meet new contacts from across the community.

There will be no presentations and no pitches, just lunch, conversation, and the opportunity to build valuable connections.

• Trust in AI systems is eroding at roughly the same speed that AI adoption is accelerating. What is driving that erosion and what are organisations doing about it that is actually working?
• The employees who are closest to AI systems are often the most skeptical about them, because they see failure modes that leadership does not. How do you build internal trust with the people who know enough to be legitimately critical?
• Customer trust in AI-driven products and services is fragile and asymmetric. It takes a long time to build and can be destroyed by a single high-profile failure. How are organisations managing that risk structurally rather than reactively?
• Transparency about how AI systems work is increasingly expected by regulators, customers, and employees. How much transparency is genuinely achievable with complex models, and where does the honest answer fall short of what people want to hear?

 AI is no longer just a technology discussion—it is a business, governance, and leadership challenge. This keynote examines how boards and executive teams can move beyond experimentation to create measurable business value while maintaining trust, compliance, and accountability. Through real-world examples and practical frameworks, attendees will learn how to govern AI responsibly, prioritize high-impact opportunities, and position their organizations for long-term success in the AI era.  

• Data ownership disputes between business units are one of the most common and least discussed barriers to data and AI progress. How do experienced data leaders navigate these conflicts without burning political capital they cannot afford to lose?
• The CDAO is often asked to democratize data access while simultaneously being held responsible for governance and risk. Those objectives are in genuine tension. How do you manage it?
• Shadow data initiatives, rogue analytics teams, and business units building their own pipelines are symptoms of something. What are they telling you and how do you respond constructively?
• What is the hardest internal political challenge you have faced as a data leader, and what did it teach you about how organisations work versus how they are supposed to work?

Panelists:

Isa Terzi, Vice President, Data, ERGO NEXT INSURANCE

Justin Heller, Former SVP, Chief Data Officer, SYNCHRONY FINANCIAL

• Synthetic identity fraud, deepfake-assisted account takeover, and AI-generated social engineering are accelerating at a pace that is outrunning most institutions' detection capabilities. What does the current threat landscape actually look like for a tier one financial institution in 2026?
• Real-time fraud detection requires infrastructure and decisioning engines operating in milliseconds without false positive rates that destroy the customer's experience. What does the architecture that makes this work actually look like in production?
• The arms race dynamic means improvements in defensive AI tend to be matched relatively quickly by adversaries who have access to the same foundational technology. Where is the sustainable edge for financial institutions, and does it even exist?
• How are firms thinking about the liability and regulatory implications when an AI fraud detection system makes a consequential error, either missing genuine fraud or blocking a legitimate customer?

• Unlocking AI-driven growth while maintaining strategic and operational control
• Establishing governance and risk frameworks that enable innovation at scale
• Managing security, compliance, and regulatory challenges in a rapidly evolving landscape
• Building trust through responsible AI practices and transparent decision-making

Key Points:

• Ownership is a strategy decision, not a governance artifact: which few decisions carry the value, and which business leader carries the consequence when each is wrong. Settle both before the money moves. Which of your current AI bets could name them today?
• Diffused accountability is an operating model: every data and AI outcome has sponsors and none has an owner, because owning it puts one name on the hook for being wrong. That is a design, not an accident. Why does your organization keep choosing it?
• AI did not create the gap. It removed the people covering for it and now acts on it at machine speed: two teams report different numbers, both right, and the agent picks one. Whose review moves when it has been wrong for six months?
• One question in every funding meeting: whose objectives carry this outcome? Data leaders who ask it push the decision back to the executive who claimed it. Those who do not absorb accountability for outcomes they were never given. Which one are you?

Discussion points:

• How to prioritize AI use cases across the enterprise without chasing every new capability
• Where AI adds genuine value versus where traditional automation may be faster, safer, or more cost-effective
• How to turn AI strategy into execution across teams, processes, and business units
• What leaders need to consider when balancing innovation, risk, governance, and operational readiness

• The pipeline of future data and AI leadership talent is real but incomplete. What are the gaps between how organizations are developing the next generation of data leaders and what those leaders are actually going to need?
• Technical excellence gets people into senior data roles. It is rarely what determines whether they succeed once they get there. What are the non-technical capabilities that make the difference?
• Mentorship, sponsorship, and deliberate career architecture for data talent are all underinvested relative to their impact. What do organizations that do this well look like?
• What is the one thing you wish someone had told you earlier in your career that would have meaningfully changed your trajectory as a data leader?

• The CDAO and CAIO roles were often created independently, sometimes in competition with each other, and occasionally in confusion about where one ends, and the other begins. Now that both functions are maturing, what have you learned about how to make this partnership work in practice?
• The data layer and the AI layer are inseparable in theory but frequently siloed in reality. Where does the friction between these two functions live day to day, and what does it cost the organization when it goes unresolved?
• There is a version of this relationship where the CDAO and CAIO spend most of their time negotiating with each other over ownership, budget, and priorities. And there is a version where they operate as a genuine leadership unit that is stronger than either function alone. What is the difference between those two organisations and how do you deliberately build the second one?
• If you could say one honest thing to the person sitting across the aisle in the other community tonight, something you would not normally say in a formal setting, what would it be?

Adem Albayrak, Chief Data and AI Officer – ALZHEON

Jillian Landi, Chief AI Officer, NEEDHAM BANK

• What are the biggest barriers to operationalizing AI and analytics at scale?
• How do you ensure AI-generated recommendations are acted upon rather than ignored?
• What organizational changes are required to embed AI into day-to-day execution?
• How do you break down silos between strategy, operations, technology, and business teams?

• Human in the loop has become a reassuring but often meaningless shorthand for AI oversight. When systems are making thousands of decisions per second, what does meaningful oversight actually look like?
• The cognitive load of overseeing AI systems is a growing problem organisations are only beginning to grapple with. How do you design oversight that humans can actually sustain at scale?
• Different decisions warrant different positions on the spectrum from full human control to full autonomy. How are organisations making those calibration decisions systematically rather than case by case?
• The most dangerous moment in AI deployment is often not the launch, when everyone is watching, but six months later when oversight becomes routine and then perfunctory. How do you design against that?