Many teams begin an AI project by asking which model or platform to use. A more useful first question is simpler: can the agent find the right answer in the knowledge your team already relies on?
Prepare the knowledge before you expand the agent's autonomy.
What "AI-ready" data actually means
AI-ready data is not a special file format. It is business knowledge that is relevant to a real task, easy to retrieve, clear enough to interpret, tied to an owner and date, and limited to the people who should see it. That gives an agent a better working context without pretending that context removes the need for human judgment.
In a retrieval-augmented generation workflow, the system searches a knowledge source and passes useful context to the model before it answers. Microsoft's RAG guidance describes this as combining information retrieval with generation and highlights governance and access as part of the design.
AI readiness is a knowledge-management problem before it becomes a model-selection problem.
1. Start with questions, not files
Do not begin by uploading the largest folder you can find. Start with the questions people ask while doing one specific job: checking a policy, preparing a customer reply, finding an onboarding step or deciding which internal form applies.
Capture ten to twenty real questions from the people who do that work. Keep the wording they actually use, including shorthand and incomplete context. This gives you a test set for the knowledge later and prevents the agent from being designed around polished demo prompts.
- What decision or task is behind the question?
- What information must be current for the answer to be useful?
- What would a wrong answer cost the team or customer?
2. Choose the source of truth and its owner
A knowledge base becomes unreliable when two documents quietly disagree. Before an agent sees a policy, identify which source is authoritative, who owns it, when it was last reviewed and what should happen when it expires.
The owner does not need to be a data engineer. It can be the operations lead, HR manager, finance controller or another person who already knows when the rule changes. Their job is to keep the source current and decide which related notes are context versus official guidance.
Several versions are available, but nobody knows which one is current.
One owner, review date and escalation path are attached to the guidance.
3. Rewrite documents so the right section can be found
Documents written for people often depend on layout, shared history or a conversation that happened elsewhere. For an agent, make the important context explicit. Give each document a clear title, scope, audience, effective date, definitions, steps, exceptions and links back to the original source.
You do not need to rewrite an entire archive. Start with the pages that answer your question set. Remove duplicated introductions, stale instructions and unexplained acronyms. Keep exceptions beside the rule they modify instead of hiding them in a separate appendix.
- Can a reader understand this section without a meeting?
- Are the definitions and exceptions next to the relevant rule?
- Can the source, date and owner be carried with the content?
4. Keep access and freshness in the design
A correct answer is still a bad answer if it exposes information to the wrong person. Decide which groups can retrieve each source and what the agent should say when a user lacks permission. Treat access rules as part of the knowledge design, not as a cleanup step after launch.
Freshness matters too. Set a review rhythm for content that changes often, and give the agent a path for saying that a source may be outdated. The NIST Generative AI Profile is a useful reference for thinking about risks, documentation and evaluation across the AI lifecycle.
Knowledge that cannot be safely accessed, dated or challenged is not ready to power an agent.
5. Test with real questions before adding tools
Test the knowledge source before you give the agent permission to send messages, update records or trigger workflows. Ask the questions you collected earlier and record whether the answer used the right source, handled an exception, made uncertainty visible and stayed within the user's access.
A failed test is useful information. It may reveal a missing document, a conflict between sources, an unclear term or a question that should go to a person. Fix the knowledge or the workflow boundary first; do not hide the problem by making the agent sound more confident.
- Did it retrieve the correct source?
- Did it distinguish a rule from an exception?
- Did it know when to stop and ask for review?
A practical seven-day starting plan
You can create a useful first knowledge set without cleaning the whole company archive. Keep the scope narrow enough that one person can review every source and one small group can test the questions.
- Day 1: choose one workflow and collect real questions.
- Day 2: list the sources people use today.
- Day 3: name the owner, date and access boundary for each source.
- Day 4: rewrite the smallest set of documents needed for the questions.
- Day 5: assemble a test set and expected answer notes.
- Day 6: test retrieval, uncertainty and permission behavior.
- Day 7: decide what to fix, defer or prototype next.
Frequently asked questions
The right preparation depends on the workflow, but these questions are useful in almost every first conversation about an AI agent's knowledge.
What does AI-ready data mean?
AI-ready data is relevant business knowledge that can be found, understood, tied to an owner and date, and shown only to people who are allowed to see it.
Do I need to clean every company document before building an AI agent?
No. Start with the small set of documents needed for one real workflow. Remove obsolete or conflicting material, then expand only when testing shows a clear need.
Should I use a knowledge base or train a model on company data?
For many workplace questions, a retrieval-based knowledge source is the simpler starting point because the agent can look up current documents without retraining a model.
How do I test whether company data is ready for an AI agent?
Use a small set of real questions from the people who do the work. Check whether the agent finds the right source, handles exceptions, names uncertainty and respects access boundaries.
Prepare your first agent workflow - hands-on
If you want to turn a real workplace process into a working prototype, the Applied AI Agents workshop is designed for that next step. Bring one workflow, map the knowledge it depends on, and learn how to test an agent before it is given broader responsibility.