How to automate policy-based decisions: a step-by-step guide

The steps

1. 1

   Choose the decision

   Pick one that is frequent, costly when inconsistent, and has a policy — written or in experienced people's heads. Eligibility, routing and pricing are common first choices.

2. 2

   List the outcomes

   Eligible, ineligible for a named reason, refer to a person. Every case must land in exactly one; a default outcome catches the rest.

3. 3

   Write the conditions for each outcome

   In the order they should be checked. Where the policy says “below the minimum for the product”, the condition compares two facts rather than hard-coding a number.

4. 4

   Separate the numbers

   Move limits, rates and thresholds into reference tables keyed by the things they depend on, with effective dates, so a new rate sheet is a table edit rather than a rule change.

5. 5

   Define the calculations

   Write down how each derived number is computed — ratios, payments, maximums — including rounding. Use exact decimal arithmetic for money.

6. 6

   Connect the data

   For each fact the policy uses, name its source: the application, a document, a bureau, an internal system. Decide what happens when it is missing.

7. 7

   Decide who reviews what

   Which outcomes go to a person, which group, and whether one approver is enough or several must agree. Put these routes in the workflow, not in email.

8. 8

   Decide where AI helps

   Reading documents, summarising notes, classifying requests — inputs to the policy. Keep the policy decision itself on rules and people, and record AI output.

9. 9

   Test against past cases

   Run real historical cases through the automated policy and compare with what was decided. Every difference is either a bug or an undocumented policy.

10. 10

    Deploy a fixed version

    Run in a test environment first, then deploy a pinned version to production, so you always know which policy is live.

11. 11

    Record every decision

    Keep the inputs, the policy version, the branch that fired, any approvals and the outcome — in a record that cannot be quietly edited.

12. 12

    Change it the same way

    Policy changes go through the same path: edit, check what the change affects, test, deploy a new version.

Common mistakes

• Automating everything at once. Start with one decision; the second goes faster.
• Hard-coding numbers in rules. Every rate change becomes a rule change. Use tables.
• No default outcome. Cases that match nothing fall through silently. Make the default an explicit refer.
• Letting AI decide. A model's judgment in the moment is hard to explain and can differ between identical cases. Use AI for inputs.
• No record of the version. Without it, “why was this decided?” has no answer six months later.

Tools that help

Decision automation platforms cover these steps in one product: rules and tables, calculations, data connectors, approval routing and a decision record. Rules engines cover the rules step and leave the rest to developers. Workflow tools cover the routing and connections but not the policy. Enterprise suites cover most of it at larger scale and cost.

Where Miji fits

Miji follows these steps directly. You describe the operation and its AI drafts a workflow to review; the policy is versioned rules that read as conditions or a decision table; limits live in effective-dated lookup grids; calculations are exact; approval gates and work queues handle referrals; test and production environments keep versions pinned; and a hash-chained audit trail records each decision with its rule version. Before a change goes live you can test it against sample inputs, replay past cases on the draft in bulk and see every changed outcome, shadow it beside live traffic, and run scenario sets with expected outcomes — see policy testing.