Your Multicloud Strategy Is a Product Problem - Treat It Like One

Multicloud presents unavoidable operational complexity. Treating multicloud strategy as a product is recommended for sustainable management. The session covers how to utilize AI effectively to manage multicloud complexity.

Key Takeaways:

• Treat Multicloud as a Product: Define customers and outcomes, set boundaries, and manage with a capability map.
• N-Scale Complexity: It's not just about clouds but involves various lines of business, product teams, data, and tools.
• AI Integration: Used well, AI can reduce complexity across architecture but can cause more chaos if misapplied.

Challenges in Cloud Migration:

• Initial reasons for migration were cost optimization and agility.
• However, migration led to increased costs and complexities such as ungoverned workloads and non-compliant systems.

Strategic Recommendations:

• Capability-Based Planning: Focus on demand management, guardrails, and measurable adoption.
• Demand and Portfolio Management: Manage resources and prioritize tasks effectively.
• Think Product Mindset: Solving multicloud's complexity requires innovative product management strategies.

Conclusion:

The presentation highlights the need for a structured product mindset in tackling multicloud strategy complexities, with an emphasis on the proactive use of AI and strategic capability management for sustainable operations.

This is the end of the AI-generated content.

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