Evaluate AI use cases, protect sensitive information, and introduce practical governance and human oversight for adoption.
AI adoption should start with a defined problem, suitable data and a clear way to evaluate results. We help organisations review use cases, integration risks and governance requirements so that teams can make informed decisions about pilots and deployment. Human oversight remains important wherever errors could affect people or business operations.
Define the intended outcome, data requirements, success measures and operational constraints.
Review what information is shared with AI services and how access, retention and permissions are controlled.
Consider unsafe outputs, prompt injection and tool permissions when reviewing AI-enabled workflows.
Set responsibilities, acceptable-use guidance, review points and a process for handling errors.
Clear scope, practical checks and a documented handover guide every engagement.
Agree the problem, users, data boundaries and the evidence needed to assess value and risk.
Review representative outputs and integration behaviour against agreed quality and security criteria.
Document safeguards, human review, monitoring and the conditions for expanding or stopping a deployment.
No. Defining the use case, data requirements and constraints helps establish the criteria for evaluating providers or existing tools.
No. Outputs need evaluation appropriate to their intended use, with human review and safeguards where mistakes could cause harm.
