CXO Data Summit: Guest Agenda

Day 1. 29th June 2026

11:00 – 11:30 ARRIVAL

Arrival, Registration & Networking

• Registration and welcome: An informal start designed to allow meaningful first conversations before the programme begins
• Open networking: no structured introductions, no lanyards with titles, just peers

11:30 – 12:15 THE CXO DEBATE

Motion: This House Believes That Organisations Do Not Want to Be Data-Driven – They Just Want the Appearance of It

• The case for the motion: many organisations publicly champion data-driven decision making, yet continue to reward hierarchy, intuition, politics, and short-term commercial pressure over what the data is actually saying
• The case against: becoming truly data-driven is a long-term transformation journey, and resistance, complexity, and cultural inertia should not be mistaken for a lack of genuine intent.
• If organisations genuinely wanted to be data-driven, what behaviours, decisions, and leadership actions would need to change first?

12:15 – 13:15:  CXO Networking Lunch

13:15 – 14:00 CXO PANEL DISCUSSION

Panel: Designing a Modern Enterprise Data & Business Strategy – Defining and Implementing It

• What senior leaders have discovered when trying to align data and business strategy within real organisational conditions
• Why separating business strategy from data strategy is becoming one of the biggest blockers to transformation, AI adoption, and measurable value creation
• The three decisions that define success before a line of code is written: ownership, sponsorship, and how the organisation defines commercial impact
• How leading organisations are approaching data and business strategy differently in 2026, and what they’ve stopped doing that used to be considered best practice

Open Q&A: the panel takes questions from the room

WORKSHOPS

14:05 – 14:35.  Workshop 1: Data Architecture for AI-Ready Organisations

The Challenge

Most organisations are building AI ambitions on top of data foundations that were never designed to support them. Years of siloed systems, fragmented infrastructure, and deferred technical decisions are now surfacing as the single biggest constraint on AI delivery and retrofitting is proving far more expensive than the original business cases assumed.

• The architecture debt problem: what senior leaders inherited and what it is actually costing them
• Build vs. buy vs. bridge: the decisions that determine whether modernisation accelerates AI or delays it further
• What ‘AI-ready’ actually means in practice and the common gaps between the vendor definition and the organisational reality

Roundtable Challenge

• Where is the single biggest architectural constraint on your AI ambition right now? And is the business aware of what fixing it will cost?
• Your board has approved a major AI programme. Three months in, your team has discovered that the underlying data architecture cannot support it without 18 months of remediation. How do you have that conversation?
• What is the one architectural decision you wish you had made differently and what would you tell a peer who is about to make it now?

14:40 – 15:10. Workshop 2: Measuring Data ROI & Business Impact

The Challenge

Boards and CFOs are asking for ROI metrics that most data leaders cannot honestly produce. The frameworks built for capital investment decisions do not fit the nature of data and AI programmes yet the pressure to produce them has never been greater. The result is a credibility gap that is quietly undermining executive confidence in the entire data agenda.

• Why traditional ROI frameworks fail for data: what they measure, what they miss, and why applying them anyway does more damage than admitting the gap
• The proof points that actually move boards: what works, what doesn’t, and why the most credible cases are often the most specific
• Building a measurement approach that is honest about uncertainty without abandoning accountability

Discussion Questions

• How does your organisation currently measure the impact of its data investment? Does your CFO believe those measures and do you?
• What is the most powerful proof point you have used to justify data investment? What made it land when others hadn’t?
• If you were asked tomorrow to cut 40% of your data programmes, which ones would survive and on what grounds would you defend them?

15:30 – 16:00. Workshop 3: Governance, Ethics & Executive Oversight

The Challenge

Governance frameworks look credible on paper and fail in practice. The core problem is almost never the framework itself it is accountability. When everybody is responsible for governance, nobody is. And as AI makes more consequential decisions at greater scale, the absence of named, personal accountability is moving from a structural weakness to an existential risk.

• The accountability gap: why the most common governance failure is not poor design but the absence of anyone willing to put their name on the outcome
• Ethics as a practical leadership discipline, not a compliance exercise: the decisions that require a values framework, not just a legal one
• What executive oversight of AI and data decisions actually looks like when it is working and the warning signs when it is not

Case Study Discussion

• Your organisation has deployed an AI-assisted process that has produced a harmful outcome. The system was approved, tested, and used in good faith. Who is accountable and what does your governance structure actually say?
• Just because you can, doesn’t mean you should. What is the test your organisation applies to high-stakes data and AI decisions? Is it robust enough?
• Where is the single biggest governance gap in your organisation right now and does your board know about it?

16.05 – 16.35 Workshop 4: Neurodiversity, Cognitive Inclusion & Driving Change Without Authority

The Challenge

Many organisations publicly champion diversity of thought, cognitive inclusion, and innovation, yet in practice, much of it remains surface-level. Leaders are often expected to drive cultural and organisational transformation without owning the budget, authority, or decision-making power needed to create meaningful change.

For neurodivergent leaders in particular, this creates a constant tension between challenging systems and surviving within them. The result is often performative inclusion, where organisations want the appearance of diversity without the discomfort of changing the structures, behaviours, and power dynamics that exclude it.

The transformation paradox: being asked to drive change without the remit, influence, or backing to deliver it

• Performative inclusion vs. real inclusion: why many organisations want diversity visible, but not disruptive
• Navigating resistance: how leaders create momentum, influence stakeholders, and challenge systems from within
  
  Discussion Questions:
• How do you drive meaningful organisational change when leadership support exists more in language than in action?
• Where has diversity become a box-ticking exercise rather than a catalyst for changing how decisions are made, talent is supported, or innovation happens?
• What strategies have you used to influence transformation when you do not directly own the budget, authority, or organisational mandate to enforce it?

16:40 – 17:10.  Workshop 6: Monetising Data & Creating New Revenue Streams

THE CHALLENGE

The commercial case for data monetisation is often compelling in theory and difficult in practice. Legal constraints, governance obligations, partnership complexity, and the operational requirements of treating data as a product mean that most initiatives stall somewhere between business case approval and commercial reality. The gap between organisations that are generating revenue from data and those that are still planning to is widening.

• Where monetisation initiatives actually break down: the most common failure points and what distinguishes the organisations that get through them
• Internal data products vs. external commercialisation: the different risk, governance, and capability requirements of each
• The partnership and contractual models that enable data monetisation and the ones that have proved unworkable at scale

Scenario Exercise

• Your organisation has a data asset that a third party is willing to pay significantly to access. Walk through the decision: what questions do you need to answer before you can say yes, who needs to be in the room, and what would make you say no?
• Where is the closest thing to a revenue-generating data asset in your organisation right now? What is stopping it from being commercially active?
• What does ‘data as a product’ actually mean operationally in your context and who owns it?

17:15 – 17:45. Data-Driven Culture & Talent Transformation

THE CHALLENGE

Culture change is the one thing technology cannot do. And the talent challenge in the data and AI space finding, developing, and retaining people who can bridge genuine technical depth with business credibility is intensifying. Most organisations are competing for the same narrow pool while simultaneously asking more of the people already in their teams.

• The culture problem beneath the talent problem: why the organisations losing data talent are often the same ones that haven’t genuinely changed how they work
• Data literacy at scale: what it means to build an organisation where the capability to use data well is not confined to a specialist team
• The leadership behaviours that build and kill a data-positive culture without anyone intending either outcome

Roundtable Discussion

• In theory, we value data literacy across the organisation in practice, how many in this room can say they’ve seen it genuinely embedded beyond the data team?
• What is the one thing you have done as a senior leader that has had the most meaningful impact on your organisation’s relationship with data? Not a programme a behaviour or a decision.
• What is driving your data talent to leave and is the answer one your organisation is actually willing to act on?

18:15 – 18:55  CXO Pre-Dinner Networking Reception

19:00 – 22:30   KEYNOTE & GALA DINNER

Keynote Address & Gala Dinner

• A keynote address drawing on the themes that have surfaced across the day’s debates, panels, and workshops
• Followed by a formal gala dinner: the most important conversations of the summit often begin here
• Designed as an evening to build the peer relationships that make the next day’s discussions genuinely honest

07:00 – 08:00   BREAKFAST

Breakfast

• Informal breakfast and open networking the continuation of conversations from the previous evening

08:00 – 09:00   CXO THINK TANKS

CXO Think Tanks – Small Group, Deep Conversation

• Delegates self-select into small groups of 6–8 around the challenges most live for their organisation
• No facilitator, no slides, no agenda a genuine peer conversation under Chatham House rules
• Groups report back a single insight or question to the room before the morning’s panel
• Think tank topics drawn from themes submitted by delegates in advance

09:00 – 09:45   CXO EXECUTIVE PANEL

Data Sovereignty & Global Regulation Navigating the Patchwork

• How organisations operating across multiple jurisdictions are managing the increasingly fragmented global regulatory landscape for data
• EU AI Act, GDPR, UK data reform, emerging US federal frameworks: what the panel is actually doing rather than planning to do
• Sovereignty, vendor dependency, and the strategic risk of building critical infrastructure on infrastructure you don’t control
• What a genuinely resilient regulatory posture looks like and the common assumption that proves most dangerous when a jurisdiction changes its approach

WORKSHOPS

09.50 – 10.20 Workshop 7: Data Should Sit on the P&L, Not in IT

The Challenge

Most organisations still position data as a technology responsibility rather than a commercial capability despite expecting it to drive growth, transformation, efficiency, AI enablement, and competitive advantage. The result is a growing disconnect between where data sits organisationally and the value organisations expect it to deliver.

As pressure increases to prove ROI, many leaders are questioning whether current ownership models are fundamentally limiting data’s strategic impact.

• Data as infrastructure vs. data as a commercial asset: what current operating models reveal about organisational priorities
• Ownership, accountability, and influence: why many data leaders are responsible for outcomes without controlling the business decisions that shape them
• The funding challenge: whether positioning data within IT is preventing organisations from measuring, prioritising, and investing in it effectively

Roundtable Questions

• If data is expected to drive business value, should it still primarily sit within technology functions?
• What changes when data initiatives are measured against commercial outcomes rather than delivery milestones or platform implementation?
• Where does your organisation currently create friction between business strategy, data ownership, and accountability for value creation?

10:25 – 10:55   WORKSHOP 8: Real-Time Data & Decision Intelligence

THE CHALLENGE

The aspiration of real-time, AI-assisted decision-making is colliding with the practical reality of latent, incomplete, and inconsistently governed data. Most organisations have invested significantly in the concept of decision intelligence without yet resolving the data quality, latency, and trust problems that determine whether it actually works when a decision matters.

• The trust problem: why dashboards and decision-support tools are often available but not used and what that reveals about where the real work still needs to happen
• Real-time data at scale: the infrastructure, governance, and organisational changes required and the hidden costs that business cases rarely include
• When good enough is good enough: how to deploy decision intelligence practically without waiting for perfect data conditions that may never arrive

Table Scenario

• Your organisation has invested three years in a data platform that produces excellent, real-time dashboards. Eighteen months after launch, usage is at 25% of forecast. The CEO has asked you to explain why, and what you’re going to do about it.
• What is the real reason and how much of it is genuinely within your control?
• What would a decision that ‘sticks’ look like in your organisation and what conditions made it possible?

11:15 – 11:45   WORKSHOP 9: Building AI-Enabled Data Platforms

THE CHALLENGE

Every major cloud and platform vendor is promising a path to AI-enabled data infrastructure. The reality of building it at enterprise scale, in regulated industries, with existing data contracts, legacy obligations, and architectural constraints is substantially harder than the pitch suggests. Most organisations are navigating a build, buy, partner decision under commercial pressure and without a clear precedent to follow.

• The gap between what the platform vendors are promising and what enterprise organisations can realistically deliver on the timescales and budgets they actually have.
• Make-or-buy decisions in AI platform build: what factors are genuinely decisive and which ones are proxies for a conversation organisations haven’t had yet.
• Vendor dependency and lock-in risk: how leading organisations are managing the tension between speed-to-capability and long-term strategic flexibility.

Challenge Exercise

• Your organisation needs to choose between building a proprietary AI data platform, deploying a major vendor’s solution, or a hybrid model. You have 12 months and a fixed budget. What framework do you use to make that decision and who needs to be in the room?
• Where has vendor dependency created a strategic constraint in your organisation that you didn’t anticipate at procurement stage?
• What would ‘sovereign-ready’ AI infrastructure actually look like for your organisation and how far away are you from it?

11:50 – 12:20   WORKSHOP 10: Operationalising Advanced Analytics & AI

THE CHALLENGE

Getting AI from prototype to production is where most investment has quietly stalled. Organisations across every sector have run successful pilots and then watched them fail to scale. The gap between a compelling proof of concept and an AI capability embedded in the way the organisation actually operates is where the real transformation work happens and where most programmes are currently stuck.

• Why pilots succeed and programmes fail: the specific organisational, process, and cultural factors that determine whether AI moves from experiment to operation.
• The operationalisation bottleneck: the roles, skills, and governance structures that need to be in place before AI can be deployed at scale.
• What the organisations that have successfully operationalised AI have done differently including what they stopped doing.

Peer Exchange

• Name one AI initiative in your organisation that succeeded in pilot and failed to scale. What was the real reason and was the decision to stop it the right one?
• What is the organisational condition not the technical one that is most limiting your ability to move AI from experiment to operation right now?
• What would your organisation need to be true about its operating model for AI operationalisation to happen at the pace the business expects?

12:25 – 12:55   WORKSHOP 11: AI Governance & Executive Accountability in Regulated Markets

THE CHALLENGE

Regulated industries are caught between competitive pressure to deploy AI at pace and regulatory frameworks that are still being written. Personal liability for AI failures is becoming real through the EU AI Act, sector-specific guidance, and an evolving legal landscape and most governance structures were not designed with named executive accountability in mind. The organisations that get this wrong will not get a second chance.

• The personal liability question: as AI decisions become more consequential, which executives are accountable when something goes wrong and does your governance structure reflect that?
• How regulated organisations are building AI governance that satisfies regulators without creating a structure so cautious it prevents meaningful deployment.
• The board’s role in AI oversight: what it should understand, what it should approve, and what it should never be surprised by.

Accountability Discussion

• If your organisation’s most consequential AI system produced a harmful outcome tomorrow, who is the named executive accountable and is that person aware of the full risk profile?
• How does your governance structure handle the difference between AI decisions that go wrong because of bad data, bad design, or bad implementation? Does the distinction matter for accountability?
• What would a board that was genuinely well-governed on AI be asking your leadership team that yours is not asking yet?

13:00 – 14:00   CXO Lunch

14:00 – 15:30   STRATEGIC NETWORKING & PEER EXCHANGE

• Structured peer exchange: delegates are paired or grouped with counterparts from different sectors and organisations facing comparable challenges.
• An opportunity to continue conversations from the workshop sessions in a less formal setting.
• Advisory Committee members available for one-to-one conversations throughout.

15:30 – 16:00   CLOSING ADDRESS

The Road Ahead What This Community Is Taking Home

• A synthesis of the themes, tensions, and open questions that have run through both days
• What the community is converging on and where the honest disagreements remain
• The commitments delegates make before they leave: what they will test, change, or stop doing in the next 90 days