Set Up Company Context for AI

The problem with company knowledge

Most companies already have the knowledge. It is just not usable by AI yet.

The SOP lives in Notion. The real decision happened in Slack. The latest template is in Google Drive. The customer context is in HubSpot. The delivery status is in ClickUp. The finance exception is in QuickBooks and an email thread. The senior operator knows the actual rule, but it was never written down.

AI does not fix that automatically. If you connect a model to scattered context without defining what matters, the output gets louder, not better.

The goal is not to build a perfect knowledge base. The goal is to make one workflow legible enough for an agent and a human reviewer.

Start with one workflow

Do not organize the whole company first. Pick one workflow from the agent-ready workflow guide and build a context bundle around it.

Example workflow:

Client meeting notes to project update.

The context bundle might include:

1. Delivery SOP.
2. Current project board.
3. Recent meeting transcript.
4. Example of a good status update.
5. Rules for what counts as a blocker.
6. Slack channel for review.
7. Project manager as approval owner.

That is enough for a first useful loop.

The company context bundle

Create a small folder, doc, project source, or prompt packet with these sections:

Workflow:
What work is being supported?

Business goal:
Why does this workflow matter?

Inputs:
What information starts the work?

Source systems:
Where does each input live?

Relevant SOP:
What current instructions should the agent follow?

Examples:
What are 2 or 3 good completed outputs?

Decision rules:
How does a human decide what to include, ignore, escalate, or approve?

Output format:
What should the agent produce?

Destination:
Where does the output go?

Human review:
Who approves it?

Boundaries:
What should the agent never do?

If you cannot fill this out, the workflow needs discovery before automation.

Examples matter more than policies

Policies are useful, but examples teach taste.

Give the agent:

1. A good client update.
2. A bad client update.
3. A good support reply.
4. A bad support reply.
5. A correct CRM note.
6. A messy CRM note.
7. A clean invoice exception summary.
8. A confusing one.

Then explain why the good example is good. This helps the agent learn the company's operating style, not just the generic task.

Example:

Good status updates are specific, short, and mention owner, date, risk, and next action. They do not include raw transcript quotes unless a decision is disputed. They flag scope changes separately.

That kind of rule is gold.

Where context should live

There is no universal answer. Use the system your team already trusts.

Notion is good for durable SOPs, templates, and playbooks.

Google Drive is good for documents, spreadsheets, shared files, and client artifacts.

Slack is good for coordination, but weak as the only source of truth.

ClickUp, Asana, Linear, and Jira are good for work execution, ownership, and status.

HubSpot or Salesforce are good for account and deal context.

Obsidian is good for local knowledge, research, and personal operating systems.

GitHub is good for code, issues, reviews, and implementation history.

The AI layer should not replace these systems. It should connect the context they already hold.

Permissions and governance

Before connecting tools, decide what the agent can read and what it can change.

Use read access first. Add write access later.

For write actions, require approval when:

1. A message is sent externally.
2. A record is updated.
3. A task is assigned.
4. Money, legal, HR, or customer commitments are involved.
5. A workflow touches sensitive data.

Also decide which data should never be used. Some workflows need redaction, limited access, or a separate environment.

The best AI implementations make review obvious. Nobody should wonder whether an agent changed the source of truth.

Make context current

AI fails when the old SOP and the real workflow disagree.

Add a simple maintenance rhythm:

1. Review the workflow bundle every month.
2. Update examples after a major process change.
3. Remove stale templates.
4. Track the owner of each playbook.
5. Save prompts that worked.
6. Save failure cases too.

Failure cases are useful because they reveal missing context. If the agent made a bad assumption, write the rule that would have prevented it.

A useful first context audit

Ask your team these questions:

1. Where is the official SOP?
2. Where do people actually look?
3. Where do decisions happen?
4. Where does the finished work land?
5. Who knows the unwritten rules?
6. What examples prove what good looks like?
7. What data should not leave its current system?
8. What review step would make the first AI version safe?

The answers become your AI implementation map.

The HowDoWe.AI rule

Do not feed AI everything. Feed it the right context for the next workflow.

Company context is not a data lake. It is an operating map. The fastest AI ROI usually comes from teams that already document the work, then connect that documentation to the tools where work happens.