You’re stepping into a different kind of accountability this year
If you’re relying on the comfort of policies or frameworks to reassure you about AI, 2026 will feel different. Not because the technology suddenly changed. But because the expectations around oversight have become very real, very visible and very personal.
Whether you’re operating in the UK, dealing with the EU AI Act’s reach or handling clients in regulated markets, you’re now expected to understand — and stand behind — how automated systems influence decisions in your organisation. That’s the shift. You’re no longer reviewing theoretical governance models. You’re accountable for the outcomes those systems produce.
The gap you’re likely feeling — but haven’t said out loud
Most organisations didn’t adopt AI through a single strategic decision. It arrived quietly. A feature added to an existing tool, a SaaS product someone trialled, a workflow a vendor upgraded without much ceremony. Now those systems sit inside processes that matter, and you’re being asked to vouch for them.
If you’ve felt uneasy signing off on AI-related statements, you’re not alone. The issue isn’t resistance. It’s visibility. You’re expected to oversee systems that weren’t always designed with oversight in mind.
You’re being asked to explain decisions you didn’t watch being made.
Where this becomes a governance risk
AI influences decisions across hiring, finance, fraud management, risk scoring, verification and supply-chain activities. These aren’t background processes. They shape outcomes that regulators, customers and investors pay close attention to.
When those decisions are questioned, accuracy on its own doesn’t give you the footing you need. What matters is whether you can explain how the decision was reached and which factors influenced it.
The accountability sits with you, and you can only stand behind a decision you can understand. That’s why explainability is no longer viewed as a technical bonus — it has become a fundamental part of responsible oversight. In 2026, that expectation will become visible much faster than many organisations anticipate.
The areas most likely to expose pressure
AI tools that never had a clear owner
You may discover systems running in production with no single person responsible for monitoring behaviour, reviewing changes or validating performance. When something goes wrong, responsibility becomes difficult to trace. That ambiguity won’t hold under scrutiny.
Decisions shaped by systems that can’t be readily explained
If a hiring model rejects someone, or a fraud engine blocks a payment, or a risk score changes unexpectedly, someone will ask why. If it takes too long to find the answer — or if the answer isn’t clear — the problem becomes a governance issue faster than a technical one.
Controls that weren’t built for systems that evolve
Traditional internal controls assume stable behaviour. AI doesn’t work that way. Models shift. Data changes. Performance drifts. When controls fall behind the system they’re supposed to govern, you carry exposure even if everything appears compliant on paper.
What 2026 represents for you
This isn’t a year defined by the AI you’ve adopted. It’s defined by how strongly you can stand behind the decisions those systems influence.
You’re no longer being asked to oversee a category of technology.
You’re being asked to take ownership of outcomes.
If you can explain how your automated systems behave — and who is responsible for them — you’ll navigate 2026 with far more confidence than most. And if you can’t yet, this is the year to close that gap while it’s still manageable.
