Answer: Start with one workflow that is frequent, measurable, information-heavy, frustrating to staff and low enough risk to test safely.

What this means in practice

Repetitive Follow-Up

Follow-up on bids, documents and open items happens every day, is easy to measure and stays low risk when people approve anything consequential.

Routine Research

Researching owners, projects and companies is information-heavy and read-only, which makes it a safe place to learn.

Document Intake And Classification

Sorting incoming documents and emails is high-volume work where a correct result is easy to check.

Meeting/Action Summaries

Turning meeting notes into summaries and action lists saves staff time and is easy for the meeting owner to verify.

Internal Reporting

Internal reports stay inside the company, so errors can be caught before they affect a customer or a project.

Data Movement Between Systems

Moving data between systems is often best handled by traditional automation. Use AI only where the input needs interpretation.

A practical construction test

  1. Define the business outcome.
  2. Measure how the workflow performs today.
  3. Identify what AI is being asked to interpret or decide.
  4. Define what remains under human authority.
  5. Test with real examples and acceptance criteria.
  6. Measure results before expanding scale or autonomy.

Where governance fits

The AI model can change. The company’s operating rules should not change silently with it. Catha’s Governance Cage is designed to preserve the business authority layer above the model and workflow.