CDAO Fall schedule

• 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?

• 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?

• 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?

• 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?

• How are firms practically applying model risk management frameworks that weren't designed for this generation of technology?
• The speed at which AI models are being deployed in financial services is outpacing the speed at which model validation teams can operate. How are firms managing that gap without either paralyzing innovation or accepting unquantified risk?
• Regulators in the US, EU, and UK are each approaching AI oversight differently, and firms with cross-border operations are caught in the middle. What does a governance framework look like that satisfies multiple regulatory regimes without becoming an operational nightmare?
• Where do you draw the line between AI-assisted human decision-making and autonomous AI decision-making in a financial services context, and who in your organization owns that line?

• The universe of alternative data available to asset managers has exploded. How are leading firms separating the genuinely signal-generating data from the expensive noise?
• Where is the next generation of durable informational advantage actually coming from?
• Large language models can now process and synthesize unstructured financial data at a scale that was previously impossible. How is this changing the research process, and what does it mean for the fundamental analyst whose job was built on doing that synthesis manually?
• Acquiring alternative data creates its own governance, privacy, and regulatory obligations that are not always fully considered before purchase. What does a mature alternative data programme look like from a governance standpoint?
• How are quant and fundamental investment teams learning to work together as AI blurs the boundary between systematic and discretionary approaches?

• As AI makes more consequential decisions, who is accountable when something goes wrong? How are organisations restructuring roles and escalation paths to give a clear answer to that question?
• Most organisations are still figuring out how the CDAO and CAIO relate to each other and to the CIO, CTO, and CFO. What does the right executive structure actually look like in practice?
• Agentic AI takes sequences of autonomous actions with real-world consequences. How do you maintain meaningful human oversight of something designed to operate without constant human input?
• What does it take to make responsible AI a daily operational practice embedded in how teams build and deploy systems, rather than a policy document that nobody reads?

• The gap between generating an insight and acting on it has historically been measured in days. The organisations closing that gap to seconds are building a fundamentally different competitive capability.
• Embedded analytics, edge decisioning, and real-time AI inference are converging to put intelligence directly into operational workflows. Where are the use cases with the clearest measurable impact right now?
• Most analytics platforms were designed to answer questions. The next generation needs to trigger actions. How does that shift change what you need from your technology, your data, and your people?

• 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?

• Demand for data and AI talent continues to massively outstrip supply, and the most sophisticated organisations are competing against each other, against Big Tech, and against hedge funds for the same small population of people. What are the talent strategies that are actually working?
• What are the new capabilities that matter most and how are organisations finding or building them?
• Many organisations are finding that reskilling existing analytical talent is more effective and more durable than hiring externally for AI roles. What does a serious internal AI capability building programme look like?
• How are you thinking about the relationship between human data and AI talent and AI systems themselves, as AI increasingly takes on tasks that junior analysts used to do?

• Most AI business cases are built on projections that look compelling in a slide deck and become difficult to defend six months after deployment. How do you build ROI frameworks for AI investments that remain credible through implementation and beyond?
• There is a significant difference between AI that reduces cost, AI that generates revenue, and AI that builds strategic capability. How should the measurement approach differ depending on which type of value you are pursuing?

• AI-powered fraud is evolving faster than most financial institutions' detection capabilities. Synthetic identity fraud, deepfake-assisted account takeover, and AI-generated social engineering are all accelerating. How are firms keeping pace with adversaries who have access to the same foundational technology?
• Real-time fraud detection at scale requires data infrastructure, model deployment pipelines, and decision engines that can operate in milliseconds without false positive rates that kill the customer experience. What does the technical architecture that makes this possible actually look like?
• Collaboration between financial institutions on fraud data and threat intelligence is one of the most powerful tools available, and also one of the most legally and competitively complex. How are firms navigating that balance?

• Generative AI can now do in minutes what a junior analyst spent a week doing. That is not a marginal efficiency improvement; it is a structural change to how investment research organisations need to be staffed, organised, and led.
• There is a difference between AI that augments investment judgment and AI that replaces it. Where are the most sophisticated asset managers drawing that line today, and how are they expecting it to shift over the next three to five years?

• 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?

• 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?

• Enterprise AI deployment has now been running long enough to generate real lessons. Not the sanitised case studies that make it into conference presentations, but the genuine mistakes and surprises that changed how practitioners think.
• What did the optimists get right that the cynics missed, and what did the cynics get right that the optimists are still reluctant to admit?
• Boards and leadership teams have cycled through excitement, impatience, and disillusionment with AI. How do data and AI leaders maintain sustained organisational commitment across those mood swings?
• Looking back at the decisions you made two or three years ago on strategy, architecture, and team design, which are you most grateful for and which are you still paying for?
• If you could give one piece of honest, unvarnished advice to a CDAO or CAIO who is 12 months into the role, what would it be?

• 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?

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?

Khizar Hayat, Chief Data Officer & Chief Operating Officer – DAKOTA

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

• 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?

• 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?

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

• 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?
  

 Elena Alikhachkina, Chief Data and AI Officer, TE CONNECTIVITY  

• Unstructured data, documents, audio, video, contracts, emails, has historically been too expensive and too complex to use at scale. Generative AI has changed that calculus almost overnight. Where are the highest-value enterprise applications emerging?
• Most organisations are sitting on years of unstructured data that contains genuine institutional knowledge. What does it take to unlock that asset responsibly and at scale?
• The governance challenges around unstructured data are meaningfully different from structured data. How are data leaders building the frameworks to manage unstructured data in AI pipelines without creating new privacy and compliance exposures?
• Which industries and use cases are seeing the clearest early returns from unstructured data AI applications, and what does the adoption curve look like for the rest of the market?

• The CDAO and CAIO roles were created to solve different problems but are increasingly inseparable in practice. Where does the real friction between these functions live, and how are organisations resolving it before it becomes a structural problem?
• Data strategy and AI strategy are often developed separately and then expected to align. How do you build genuine joint ownership of the decisions that sit at the boundary?
• The reporting line for CDAO and CAIO functions varies enormously across organisations. How much does structure matter versus the quality of the working relationship between the people in those seats?
• What are the decisions that look like CDAO decisions or CAIO decisions but are actually only good decisions when both functions are genuinely in the room together?

• The pipeline of future data and AI leadership talent is real but incomplete. What are the gaps between how organisations 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 organisations 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?

• AI systems frequently fail not because the model is wrong but because the humans working alongside them do not trust, understand, or engage with the output in the way designers expected. How do you build AI that accounts for real human behavior rather than idealized human behavior?
• The adoption gap between deploying an AI tool and having it genuinely change how people work is wider than most organisations anticipate. What determines how quickly and completely that gap closes?
• Change management for AI is different from change management for other enterprise technologies because the system is making judgments, not just processing transactions. How does that change the human dynamics of adoption?
• What does a genuinely human-centered AI deployment process look like, and how do you know when you have got it right?

• Different industries have reached very different levels of AI maturity, and for very different reasons. What are the most valuable transferable lessons that financial services, healthcare, manufacturing, and retail are generating for each other?
• Regulated industries have had to build AI governance frameworks that unregulated industries are only now beginning to need. What can sectors like financial services and healthcare teach everyone else about governing AI under pressure?
• The talent, infrastructure, and organisational design challenges of AI at scale look broadly similar across industries even when the use cases are completely different. Where is cross-sector peer learning most underutilized?
• Which industry is doing something with data and AI right now that the rest of the room should be watching closely and why?

• The gap between AI leaders and AI laggards is widening faster than most people anticipated. What happens to the organisations that are still in early stages of their data and AI journey as the leaders pull further ahead?
• Agentic AI, multimodal models, and real-time decision intelligence are converging into something that looks qualitatively different from the AI landscape of two years ago. How should data and AI leaders be positioning their organisations for what comes next?
• The pace of change in foundational AI capability is forcing organisations to make architectural and strategic bets on platforms and approaches that may look very different in 18 months. How do you make durable decisions in an environment that keeps shifting underneath you?
• If you had to bet on the two or three developments in data and AI that will most significantly change how enterprise organisations operate in the next three years, what would they be?

• The CDAO and CAIO roles are genuinely hard in ways that are rarely acknowledged publicly. The expectations are extraordinary, the politics are intense, and the technology keeps changing. What keeps the best practitioners in this work and what gives them energy when it is difficult?
• The moments that define a data leader's career are rarely the ones that appear in the official success story. What have been the defining moments, the real ones, that shaped how you lead?
• The industry has made significant progress on data and AI in the past five years but there is a long way still to go. Where are you most optimistic and where are you most honestly concerned?
• What does success look like for you, not for your organization's AI programme, but for you personally as a leader in this space, five years from now?