The most dangerous AI assumption in government? That it will fit into your existing systems

AI is being promoted as a solution to public sector pressure. It can improve productivity, speed up triage, and enhance service delivery. But one assumption continues to introduce avoidable risk:

That AI can be integrated into existing systems without significant change.

Traditional software follows fixed rules. AI does not. It interprets patterns in data. This means results can shift and performance can degrade without clear warning. Attempting to add AI to legacy infrastructure or apply old governance models invites risk.

In our latest white paper: ‘AI readiness in government’, Nicholas Shearer says: “Something as big and disruptive as AI can’t be treated like buying Excel. You need end-to-end thinking.”

Several things need to change:

• Governance: Delivery needs decision points, escalation routes and ethics reviews embedded from the start.
• Testing: Standard QA methods are not enough. Models require fairness checks, live monitoring, and behavioural validation.
• Capabilities: Internal teams must build confidence in assessing and operating AI systems, not just using the tools.

Departments that continue with outdated assumptions will find themselves managing tools they cannot explain within systems they cannot control.

If AI is to deliver value, it requires preparation. That includes governance, assurance, and internal skills. Not in theory, but in practice.

AI deployment requires more than investment. It requires fit-for-purpose delivery conditions that allow the technology to work safely and transparently.

If you are under pressure to show AI progress while actively managing delivery risk, we can help. Our assurance teams work alongside yours to provide clarity, confidence, and control from the outset.