Enterprise AI planning guide
Use an enterprise AI readiness checklist before selecting a platform or starting a pilot
A productive evaluation starts with the work to be improved, the information it needs and the people responsible for the result. This checklist helps teams prepare evidence before committing to a product, model or implementation path.
Printable resource
Take the checklist into your evaluation workshop
Download the two-page PDF with space to mark each readiness question and a table of the evidence to prepare.
The checklist
Seven questions to answer before an enterprise AI pilot
What decision or task should improve?
Describe a bounded job, its intended result and the person or team that remains responsible for using that result.
Which knowledge is allowed to support it?
Identify representative sources, owners, currency expectations, permissions and the information that must stay outside the evaluation.
How will quality be judged?
Prepare realistic questions or cases, define acceptance criteria and agree who can judge relevance, accuracy and usefulness.
Where can the system and its data run?
Document infrastructure, networks, identity, data residency, operational ownership and the permitted paths to model providers.
Which model and workflow choices are required?
Decide whether local or external models are suitable for each step, and map the retrieval, validation, branching and integration needs around them.
Where must a person review or approve?
Make consequential decisions visible. Define the conditions for review, the information a reviewer needs and what is recorded when they approve or reject.
How will the solution be operated and changed?
Clarify monitoring, support, retention, incident response, workflow versioning and the boundary between the product team and the surrounding organisation.
Bring evidence, not assumptions
What to prepare for a useful evaluation
| Area | Prepare | Why it matters |
|---|---|---|
| Work | Representative tasks and intended outcomes | Keeps the evaluation tied to a real need |
| Knowledge | Approved examples, source owners and access rules | Tests retrieval quality and the data path |
| People | Users, reviewers and accountable owners | Shows where human judgement belongs |
| Technology | Environment and integration constraints | Prevents a polished demonstration from hiding an architecture mismatch |
| Operations | Support, monitoring and change expectations | Makes long-term ownership explicit |
Enterprise AI readiness questions
Is a proof of concept enough to show readiness?
A proof can reduce uncertainty, but it should use representative work, clear success criteria and the controls required for the intended environment. A compelling answer alone does not establish operational readiness.
Should we choose a model before defining the workflow?
Start with the task, knowledge and control requirements. Model choice is important, but it is one part of a wider information and workflow design.
What does human approval add?
A meaningful approval point lets a person assess context and suitability before a consequential process continues. Its timing, authority and record should be designed deliberately.
Use the checklist in a private product discussion
Bring one representative task, the relevant knowledge and the deployment questions you need to resolve.