Service
AI and data diagnostic
For organisations that want to use AI without buying tools blindly. The diagnostic reviews processes, data, documents, risks, security, reporting and internal capacity before proposing automations or new systems.
Expected outcomes
What can remain after the work
- A map of critical processes and information sources.
- Prioritisation of AI use cases by value, risk and effort.
- Recommendations on data, security, training and governance.
- A practical roadmap with possible prototypes and pending decisions.
Related experience
Technical and operational proof
- Use of AI to support analysis, documentation, classification, prototyping and review.
- Experience in data models, operational platforms, dashboards and document automation.
- The judgement to tell an AI problem from a data problem and from a process problem.
Go deeper
Related articles
Organisational memory: keeping knowledge in the company when the AI tool changes
AI assistants need context, and that context is usually built inside a vendor's tool. Curating it as the organisation's own knowledge cards, with sources, an owner and a review date, makes it reviewable and portable.
Knowledge and context for AIWorking with AI assistants without losing the thread: rules and memory in the repository
If a project's context lives in a tool's chat, it is lost when the tool is closed. If it lives in the repository, any assistant or person can carry on from where the previous one left off.
Working method with AIAI proposes, a person decides: how to inventory AI use across your tools
Before debating which model to use, it is worth knowing, for each tool, what the AI does, what it never does, where it runs and who reviews its output.
AI governanceReady to start?
Take our diagnostic questionnaire to receive a detailed first reading of your case.