There was a minor hiccup today with Microsoft Copilot in the UK. It might have felt like a minor blip, but it’s a sign of something bigger. AI isn’t just a shiny add-on anymore — it’s woven into the way we work. When it goes down, even briefly, the ripple effect is real.
Microsoft confirmed via its official status page that users in the UK — and parts of Europe — experienced intermittent failures after an unexpected surge in traffic overwhelmed parts of the Copilot delivery stack. Engineers rolled back recent changes and rebalanced traffic to restore service. In short, the playbook worked, and the outage was contained quickly.
But here’s the question: whilst this incident wasn’t prolonged, what if it was? What if Copilot, or any Generative AI services stayed offline for hours or even days? For many organisations, that might not be just an inconvenience — it might be a continuity scenario.
When AI services go offline, the impact isn’t just about convenience — it touches core business operations:
Critical workflows stall
Automated processes like approvals, ticketing, and code generation can grind to a halt if they depend on AI-driven steps.
Decision-making slows down
Teams lose instant summarisation and insight generation, forcing manual analysis and delaying responses.
Support and service backlogs grow
Helpdesk and triage systems that rely on Copilot for quick resolutions revert to manual handling, increasing response times.
Integration ecosystems break
API-driven workflows and connectors fail, requiring developers to implement emergency fallbacks or manual interventions.
Right now, most organisations still have old processes and human know-how to fall back on. But fast-forward a year or two, when those manual paths are forgotten — that’s when an outage becomes a real continuity risk.
So, if AI services are now part of the critical path, how do we plan for the day they’re not available? The answer isn’t to avoid AI — it’s to build resilience around it. That means thinking ahead, documenting alternatives, and making sure your teams know what to do when automation fails. Here are some practical steps to get started.
Keep the manual playbook alive
Every AI-driven critical process should have a non-AI fallback documented in your BCP.
Build AI outage scenarios into your planning
Test what happens when AI isn’t there. How long before things grind to a halt?
Avoid single points of failure
Don’t let AI become the only way a process runs. Keep diversity in tools and skills.
Communicate and educate
Make sure teams know what to do when automation fails — and that it’s okay to go old-school for a while.
If you’re wondering where to begin, this quick checklist can help you turn theory into action. Use it as a starting point for updating your business continuity plan and stress-testing your workflows against AI outages.
Regional processing and compliance-driven localisation are great for sovereignty, but they add complexity — and complexity means more ways for things to break. This isn’t just an IT problem; it’s an operational resilience issue. Treat AI like any other critical system: build redundancy, demand transparency, and test your fallback plans.