The Cloud Is Now Just a Stage for Agentic AI

Jan de Vries · 2026-08-05

I walked into the Google Cloud Summit in London expecting the usual chatter about databases, serverless functions, and infrastructure tweaks. Instead, the cloud itself felt like a minor footnote, barely acknowledged near the registration desk. Almost every conversation on the exhibition floor had pivoted to one thing: agentic AI. The cloud has quietly faded into the background. It's no longer the star of the show—it's just the execution layer where autonomous systems run. That shift raises a practical question: which decisions should stay with humans, and which can we safely hand over to software that acts on its own? Many European businesses still treat AI as a basic productivity tool, something to shave a few percentage points off overhead costs. That defensive approach misses the bigger opportunity. Agentic AI isn't just about trimming budgets; it's a chance to fundamentally redesign how operations work. Instead of asking how AI can automate today's workflows, organizations should ask a harder question: if working hours and human attention limits no longer applied, would we design these workflows the same way at all? Probably not. ### The Workflow Is Not Sacred The rush to deploy AI agents across enterprises makes sense, but it's driven by a basic error: the assumption that every existing workflow deserves an AI agent. Most organizations start by mapping their current processes, then ask how an agent can automate a specific task. That seems logical, but it's the wrong order. The first question should be: why does this process exist in the first place? Many business processes were designed around constraints that no longer apply. They reflect human capacity limits, organizational silos, manual approvals, and legacy technology. Wrap an AI agent around one of those processes, and you might increase throughput, but you haven't improved the business. You've just built a faster version of something that shouldn't exist. The real goal is to find where complexity can be removed and where the operating model itself should be redesigned—not keep yesterday's practices on life support with more expensive tooling. Define the business problem first. Question why the current workflow exists. Only then figure out where autonomous software fits. ### Europe's Mindset Problem One statistic from the London summit stuck with me: Europe accounts for only about 8% of global AI spending. The explanation behind that number is telling. Too often, European businesses start with the technology itself. They invest in a platform, then go searching for a business problem to solve. But those companies aren't the ones succeeding. The ones achieving measurable results do the opposite—they start with a business problem first, then decide whether AI is the right tool to solve it. Starting with technology limits what you can achieve, especially if the focus is just cost-cutting. One speaker at the summit illustrated this with two financial institutions: - A European bank focused on cutting operational costs to fund its new AI projects. - A US bank used the technology to generate new revenue streams, reducing manual touch-points to turn an expensive cost center into an active revenue driver. Both institutions had access to the exact same technology. The difference lay entirely in their strategic ambition. Only one of them was asking, "How do we make more money with this?" ### Stop Using Legacy Systems as an Excuse Legacy systems are constantly blamed for stalling digital transformation, particularly in highly regulated sectors like FinTech. While older infrastructure presents real integration challenges, using it as an excuse for inaction is a strategic choice. Every established organization has core databases that can't be replaced overnight. But that doesn't mean every new operational model must inherit those same old constraints. Critical legacy infrastructure can be ring-fenced and left running to maintain stability, while new, autonomous operating models are built alongside it. The smartest companies treat legacy systems as a boundary condition, not a blueprint. They build new workflows that work around the old constraints, rather than forcing AI agents to work within them. That's the difference between incremental improvement and true transformation. If you're a leader in a European business, the takeaway is simple: stop asking what AI can do for your current processes. Start asking what your processes should look like if you were designing them from scratch today. That's where the real value lies.