Track 1

Composable Infrastructure and AI Workload Optimisation: Combating Spiralling Power and Compute Costs

IDC research shows a combined 60% of organisations in Europe are either already using cloud to build AI solutions or else planning to do so in 2026. As AI moves from pilot into production, the infrastructure choices you make now will set the economics of the whole programme for years. And there have never been more options on the table.

You can run workloads on public cloud from the hyperscalers. You can keep them on private cloud or on-premises where control, latency, or data residency really matter. There are neoclouds built specifically for AI, offering GPU-dense compute at a fraction of what traditional providers charge. And out at the edge, AI- and TPU-enabled devices can now handle specific workloads on their own, away from hosted models, which cuts your reliance on centralised infrastructure and trims the token spend that goes with it.

AI sovereignty adds another dimension: IDC research shows that when asked how they have handled or expect to handle the migration of AI/Machine learning services to sovereign cloud, 80% said they will work with their existing cloud services provider to add sovereign cloud controls to the existing solution, while 20% said they will migrate the existing solution to a new solution provider that can offer sovereign cloud controls. Within all this, customers are hampered by challenges. IDC research consistently finds that users are hampered by high costs, high complexity and a lack of skills to enable their digital ambitions.

So how do you weigh all this up? Join Insight and IDC to work through the evidence behind each approach, where the real costs sit, and how to navigate your next move with confidence.

Sponsored By:

Rahiel Nasir

Rahiel Nasir

Research Director, European Cloud & Lead Analyst, Worldwide Digital Sovereignty

IDC

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Lee Wilkinson

Lee Wilkinson

Chief Strategist & Distinguished Technologist – EMEA Cloud Platforms

Insight

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