AI is becoming part of mainstream IT, but the purchase of a licence is the smallest part of adoption. The organisation must connect the technology to a valuable process while protecting information, assigning accountability and changing how work is performed.
UK government research published in 2026 found that business adoption remained modest and was concentrated in language and text-generation tools. This suggests many organisations are still at the experimentation stage rather than operating mature AI services.
1. Is there a defined business problem?
Start with a costly delay, repetitive task, knowledge bottleneck or service problem. “We need AI” is not a use case. Define the process, owner, baseline and measurable improvement first.
2. Is the information ready?
AI quality and risk are shaped by the information it can access. Establish where relevant data sits, who owns it, whether it is accurate and what must never enter a public model.
3. Are identity and security controls mature?
Confirm approved tools, user access, data-sharing boundaries, logging, retention and supplier risk. AI adoption can amplify weaknesses already present in cloud, identity and information governance.
4. Is there accountable governance?
Assign an executive owner, use-case owner and technical owner. Set an acceptable-use policy and a route for privacy, security, legal and ethical review proportionate to the risk.
5. Can the organisation deliver a controlled pilot?
Select a bounded use case, representative users and explicit success and stop criteria. Preserve human approval where errors could affect people, money, safety or regulated decisions.
6. Is adoption being treated as operational change?
Training is not enough. Redesign the workflow, clarify responsibilities, measure benefits and create a support model. A successful demonstration is not yet an operational service.
A simple decision rule
If fewer than four of these six conditions are credible, pause platform expansion and complete an AI readiness assessment. If four or more are credible, proceed with one bounded pilot and measure value, risk and adoption before scaling.
Responsible adoption
Where AI processes personal information, data-protection requirements remain technology-neutral. The ICO highlights fairness, security, data minimisation, individual rights and explainability as important lifecycle considerations.
Primary sources: UK Government AI Adoption Research · ICO guidance on AI and data protection