You can't really "opt out" of multicloud anymore. Between cloud concentration risk, SaaS sprawl, and increasing regulatory expectations, most enterprises end up operating across multiple clouds whether they planned to or not. The hard part isn't having two or three providers. It's the hidden operational complexity that follows: demand management with each provider, consistent enforcement of controls, and environments that keep getting harder to run.

That complexity multiplies fast. Multicloud becomes an "N problem" because it is not just clouds — it is clouds times lines of business, times product teams, times data and tooling. Every combination creates more decisions to make and more things to keep consistent: identity, networking, policy, observability, cost, delivery. Traditional engineering execution alone can't scale to that.

The only sustainable approach is to treat multicloud strategy as a product: define customers and outcomes, set boundaries, build a capability map, and run a roadmap with measurable results.

Then AI lands on top of all of this. Used well, AI can reduce the burden of N-scale complexity across architecture, organisational change, and engineering workflows. Used without a solid foundation, it accelerates chaos: faster changes, more drift, more inconsistency, and more risk.

In this talk, I'll share the pre-work that makes the difference — the technical foundations and product management discipline that allow AI to help rather than hurt. No silver bullets, just hard-won patterns that scale.

You'll learn:

How to frame multicloud as a product with clear users, boundaries, and success metrics
How to reduce N-scale complexity using capability-based planning and "golden path" thinking
Where AI helps (and where it amplifies disorder) in platform and architecture work
Practical patterns for demand management, guardrails, and measurable adoption

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