The agenda.
Delegate Registration
Welcome Drinks
Breakfast
Opening Comments
International Keynote: From Specialist to Generalist – Truly Becoming the AI People Leader of Tomorrow
Every AI leader in the room got here from somewhere else. Data, risk, transformation, HR, marketing; there's no accepted training ground for this job, because the job hasn't existed long enough to build one. What's emerging instead is a genuine split: hire the deepest technical expert you can find or hire someone who knows exactly what they don't know and builds a team around the gap.
This session makes the case for the second model. You'll hear why the strongest AI leaders aren't the ones who can build the stack themselves, they're the ones who know which questions to ask and who to bring into the room when the answer needs a specialist; how to resist the pull toward becoming a jack of every trade, from risk to engineering to change management; and why this role sits so awkwardly between CTO, COO and CFO that most people doing it feel like outsiders in their own organisation. You'll leave with a clearer view of what to build your own capability around, and what to deliberately leave to someone else.
Partner Keynote — TBC
Think Tanks — Round 1
Value & Measurement
What Are You Actually Solving For?
Nobody's short on AI ideas. Everybody's short on an agreed definition of what winning looks like.
Join your peers to discuss:
- If you polled five people in your organisation on what "AI value" means, how many different answers would you get?
- How much of your current AI activity exists because someone was afraid of being left behind, rather than because it solves a defined problem?
- When cost saved and revenue generated point in opposite directions, which one wins where you work?
- If you had to defend your AI investment to a sceptical board member in two sentences, what would you say?
- What would you stop doing tomorrow if "impressive AI use case" stopped being a good enough reason on its own?
People & Processes
Who's Actually Accountable Once Agents Are Doing the Work?
Half the org chart is now doing work that's never once shown up on a performance review.
Join your peers to discuss:
- If an agent drafts, decides or launches something, who owns the outcome, and does HR's performance management process reflect that yet?
- What does managing performance look like when you're accountable for a mix of people and agents doing the same category of work?
- Which parts of someone's job have quietly moved to an agent without their role, or their performance review, ever being updated to say so?
- What new handoffs exist between people, agents and governance that didn't exist eighteen months ago, and who's watching them?
- What's the job title you're hiring for that won't mean the same thing in eighteen months, and are you being honest with candidates about that?
Data & Technology
Do You Actually Trust What You're Building On?
AI is only as reliable as the data and vendors underneath it, and most organisations have never properly tested either.
Join your peers to discuss:
- Where is someone quietly redoing AI's work by hand right now, and does anyone above them know that's happening?
- When a vendor's model changes overnight, how exposed is your organisation, really, and has anyone mapped that?
- Which vendor contract only survived its last renewal because nobody had time to build the case to cut it?
- With agents increasingly touching systems humans used to guard manually, where has your security thinking not caught up yet?
- Build, buy or rent, what's driving that decision, cost, speed, control, or just whichever vendor got to you first?
Adoption & Scaling
What Breaks When You Go from Pilot to Everyone?
The thing that worked in a contained pilot rarely survives contact with the whole organisation, governance included.
Join your peers to discuss:
- What worked perfectly as a pilot that you're now realising doesn't hold up at full scale?
- At what point does "moving fast" stop being an advantage and start being the thing governance must catch up on afterwards?
- How much change is your organisation willing to absorb at once, and has anyone been honest about that number?
- Who decides when something's ready to scale, is that the same person who decided it was worth piloting?
- What would it take for your risk function to say yes faster, without taking on more risk?
Coffee Break & Networking Break
Partner Breakouts
Partner Breakout — TBC
Partner Breakout — TBC
Breakout Sessions
Breakout: Data as the Foundation – Compliance as an Afterthought or Genuine Resilience?
Every leader in the room agrees data foundations matter. Almost none of them can get the investment approved, because "fix the data" has never once made a board's pulse quicken. This session puts that failure on the table: if a genuinely AI ready foundation is what determines whether the flashy use case even works, why does it keep losing the funding argument?
And once you've built it, look at whose infrastructure it's sitting on, a handful of providers, mostly offshore. You'll leave with the argument you've been missing, and the harder question underneath it: is your data sovereignty position a compliance afterthought, or something you've genuinely built resilience around?
Breakout: Governance & Risk – Ensuring Continuous Maturity for Continuous Change
Ask why an organisation is moving slowly on AI, and you'll get "risk appetite" as the answer every time. It's usually the wrong answer. Most of what gets labelled caution is an understanding gap, a board or a risk function that doesn't yet know what "good" looks like well enough to say yes, especially once AI stops offering the kind of deterministic evidence regulators are used to.
This session tests that claim directly: what would it actually take, concretely, for your organisation's most cautious voice to say yes, and is the gap between that and where you are today really about appetite, or about a conversation nobody's had properly yet? You'll leave with a sharper read on which one is true where you work.
Partner Breakouts
Partner Breakout — TBC
Partner Breakout — TBC
Networking Lunch
Panel Discussion: The Economics of AI – Finding the Balance Between the Artificial and the Human
Every AI leader is being asked to prove what AI is worth, and every AI leader is answering that question differently. Some think ROI is the wrong frame entirely; others are still building the business case number by number. Costs are moving faster than most finance functions can track, and the loudest success stories from other organisations don't always survive scrutiny.
We'll discuss:
- Is the ROI question even the right one, or are we forcing an AI conversation into a measurement model built for traditional tech?
- If the work was never properly costed before AI touched it, what exactly is the improvement number being measured against?
- When another organisation reports a headline saving, is it something you could replicate, or marketing dressed as measurement?
- If every AI vendor is still pricing below their real cost to win market share; once prices rise, where does a human become the cheaper option?
- Where does workforce transformation settle once that cost equilibrium plays out, rather than where it happens to sit today, mid-shift?
Afternoon Activities
Wine Tasting
Golf
Pickleball
Paint & Sip
Workshop: Jobs to Tasks – Redesigning Decision Making, Not Headcount
Every AI headcount conversation defaults to the same blunt question: how many roles does this remove? Thing is, nobody's job disappears in one piece; jobs are really twenty or thirty tasks wearing a single title, and it's the tasks, not the titles, that AI is reaching into.
This workshop starts from the task, not the headcount. You'll map how decision authority moves through a function today, where it's genuinely delegated, where it's assumed, where nobody has ever written it down, and pressure-test which decisions truly need a person against which are process dressed up as judgement. Bring a real decision from your own organisation; you'll leave with a working task map for it.
Pre-Dinner Drinks Reception
Gala Dinner
Breakfast
Opening Comments
Partner Keynote — TBC
International Keynote: Building the Literacy – Developing a Truly AI-Driven Culture
Most organisations think AI literacy means teaching people to write a better prompt. It doesn't. The real barrier isn't a skills gap, it's genuine fear and misunderstanding, and no business case survives a room that hasn't been brought along yet.
This keynote gets specific about what building literacy at scale actually looks like: structured programmes that move people from basic understanding through to hands-on use case design, informal champion networks that scale faster than any formal training budget, and segmentation that treats your keenest adopters differently from your most sceptical holdouts.
Partner Keynote — TBC
Coffee Break & Networking Break
Think Tanks — Round 2
Value & Measurement
What Are You Actually Solving For?
Nobody's short on AI ideas. Everybody's short on an agreed definition of what winning looks like.
Join your peers to discuss:
- If you polled five people in your organisation on what "AI value" means, how many different answers would you get?
- How much of your current AI activity exists because someone was afraid of being left behind, rather than because it solves a defined problem?
- When cost saved and revenue generated point in opposite directions, which one wins where you work?
- If you had to defend your AI investment to a sceptical board member in two sentences, what would you say?
- What would you stop doing tomorrow if "impressive AI use case" stopped being a good enough reason on its own?
People & Processes
Who's Actually Accountable Once Agents Are Doing the Work?
Half the org chart is now doing work that's never once shown up on a performance review.
Join your peers to discuss:
- If an agent drafts, decides or launches something, who owns the outcome, and does HR's performance management process reflect that yet?
- What does managing performance look like when you're accountable for a mix of people and agents doing the same category of work?
- Which parts of someone's job have quietly moved to an agent without their role, or their performance review, ever being updated to say so?
- What new handoffs exist between people, agents and governance that didn't exist eighteen months ago, and who's watching them?
- What's the job title you're hiring for that won't mean the same thing in eighteen months, and are you being honest with candidates about that?
Data & Technology
Do You Actually Trust What You're Building On?
AI is only as reliable as the data and vendors underneath it, and most organisations have never properly tested either.
Join your peers to discuss:
- Where is someone quietly redoing AI's work by hand right now, and does anyone above them know that's happening?
- When a vendor's model changes overnight, how exposed is your organisation, really, and has anyone mapped that?
- Which vendor contract only survived its last renewal because nobody had time to build the case to cut it?
- With agents increasingly touching systems humans used to guard manually, where has your security thinking not caught up yet?
- Build, buy or rent, what's driving that decision, cost, speed, control, or just whichever vendor got to you first?
Adoption & Scaling
What Breaks When You Go from Pilot to Everyone?
The thing that worked in a contained pilot rarely survives contact with the whole organisation, governance included.
Join your peers to discuss:
- What worked perfectly as a pilot that you're now realising doesn't hold up at full scale?
- At what point does "moving fast" stop being an advantage and start being the thing governance must catch up on afterwards?
- How much change is your organisation willing to absorb at once, and has anyone been honest about that number?
- Who decides when something's ready to scale, is that the same person who decided it was worth piloting?
- What would it take for your risk function to say yes faster, without taking on more risk?
Fireside Chat: The Enterprise AI Operating Model – It Was Never Really About Structure
Roll out an AI agent inside an approval chain built for a slower world and you'll get the same ten signoffs you had before, just slower to complain about it. Most organisations don't have an AI adoption problem, they have an operating model that was never built to let AI's benefits actually land, and the choice isn't really centralised versus federated, it's how much of that underlying structure, decision rights, accountability, the approval chain itself, you're actually willing to change.
This session maps the real spread of operating models being run right now, not as a template to copy, there's no universal right answer, but as a set of practical, tried and tested approaches matched to where an organisation sits in its own adoption journey. Getting this right is what secures the trust the rest of the programme keeps returning to; you can't earn trust in customer-facing AI on top of an operating model that was never rebuilt to support it.
Closing Keynote: Trust First, Customers Second – The Use-Cases for Long-Lasting Success
Every organisation says the same thing about customer-facing AI: employees first, customers second. It isn't just a sequencing preference, it's an admission that trust must be earned internally before it's safe to risk externally, and most organisations, including some everyone assumes have already solved this, haven't earned it yet.
This closing session looks at what trust actually requires once AI is customer-facing: data reliable enough that the answer is genuinely correct, transparency about whether a customer is talking to a person or a machine, and a plan for what happens the day it gets something wrong in public. You'll hear what the honest state of play looks like right now, including from organisations further ahead than most, and where the gap between reputation and reality still sits.
Closing Comments
Networking Lunch