Decision Tables: What They Look Like for Business Users
Every company has policies. Loan approval criteria. Claim routing logic. Pricing rules. Compliance checks.
In most companies, policies live in someone’s head, a shared spreadsheet, or buried in application code.
The problem isn’t complexity. It’s that the people who own the policy have no direct access to it. Every change means cross-team coordination and waiting.
What is a decision table?
A decision table is exactly what it sounds like. A table. Rows and columns. If you’ve used a spreadsheet, you already know how to read one.
Each row is a rule. The columns on the left are the conditions. The columns on the right are the outcomes. That’s it.
Here’s the simplest possible example. One condition, one result:
| Credit Score | Risk Category |
|---|---|
| > 750 | Low |
| 680 - 750 | Medium |
| < 680 | High |
Three rules. That’s the policy. Not a description of the policy, not a requirements document for someone else to implement. The actual rule, ready to run.
Now layer in more conditions. Same format, same clarity:
| Credit Score | Debt-to-Income | Loan Amount | Decision |
|---|---|---|---|
| > 720 | < 35% | Any | Approved |
| 680 - 720 | < 40% | < $500K | Approved with conditions |
| 680 - 720 | < 40% | >= $500K | Referred to underwriter |
| < 680 | Any | Any | Denied |
More columns, more rules. The policy owner owns the table. When a regulation changes the threshold from 680 to 660, they change one cell.
Why this matters
Traditionally, this kind of logic ends up embedded in application code. The approval threshold, the debt-to-income cutoff, the routing logic… mixed in with database calls, error handling, logging, and technical plumbing. Not because anyone designed it that way, but because that’s how systems were built.
When regulations change, updating logic takes cross-team effort and time, rather than allowing business teams to make changes directly.
A decision table separates policy from code. It makes rules visible, testable, and easy to update. Engineers focus on the platform, not manual changes.
What it looks like in practice
We built a decision table experience inside Aletyx Decision Control that works exactly like this. You open it, and it looks like a spreadsheet. Because that’s the format people already think in.
In this video, you can see the full cycle: start with an empty screen, define your decision logic in a familiar spreadsheet-like interface, and have a running decision service in under a minute.
But here’s where it gets interesting.
AI helps you build it
Instead of manually entering every rule, you describe what you need in plain language. “Add a rule for applicants over 65 with income below $30K that routes to manual review.” The Aletyx AI Assistant modifies the table. You review the change, accept or adjust, and move on.
It also generates test cases automatically, covering every condition in your table so you know it works before it goes live. Not some of the conditions. All of them.
One-click publish
Tools like Aletyx Decision Control let business users publish decision tables directly. One click makes it a live service for applications. No waiting for release cycles. The policy changed, it was tested, and it’s live.
And under the hood? The decision table is fully compliant with DMN 1.6, an open industry standard. No vendor lock-in, no proprietary format. Your logic is portable and standards-based, but you never have to think about that.
What changes when policy owners have direct access
Instead of requirements documents that get translated into code that nobody can verify back against the original intent… the policy is authored directly. In a format that is the executable logic.
When a regulation changes, the person who understands the regulation makes the update. Tests it. Publishes it. The audit trail shows exactly who changed what, when, and why.
No translation layer. No lost intent. Everyone works on what they do best.
Where this is going
This is foundational. Decision tables that policy owners manage directly are powerful on their own. But as AI agents become part of how enterprises operate, these same decision tables can become the policies that govern what AI can and can’t do. The rules the AI must follow.
More on that soon. For now, the starting point is simple: your business logic should be visible, testable, and owned by the people who understand it best.
Try it yourself
Aletyx Decision Control is available today. If your team manages business policies and wants a platform where the people who own the rules can author, test, and publish them directly, get started now.
A tech preview of the AI Assistant and new Decision Table experience is open. Request access to try it and share feedback.
Key Takeaways
- A decision table is a simple table of rules: conditions on the left, outcomes on the right. If you can read a spreadsheet, you can read a decision table.
- Decision tables separate business policy from application code, making rules visible, testable, and changeable without a release cycle.
- AI can accelerate authoring: describe what you need in plain language, and the Aletyx AI Assistant builds and tests the table for you.
- Decision tables built on DMN 1.6 are standards-based and portable, with no vendor lock-in.
- As AI agents enter the enterprise, decision tables can become the policies that govern what AI can and can’t do.
FAQ
Do I need to know DMN to use a decision table?
No. DMN 1.6 is the open standard running under the hood, but you never have to interact with it directly. The experience looks and feels like a spreadsheet.
Can decision tables handle complex logic, not just simple lookups?
Yes. Decision tables scale from a single condition with three rules to dozens of conditions with hundreds of rules. The format stays the same. You can also combine multiple decision tables together for more complex scenarios.
How is this different from managing rules in a spreadsheet?
A spreadsheet is a document. A decision table in Aletyx Decision Control is executable. You author it, test it, and publish it as a live service. It runs. A spreadsheet just sits there until someone manually translates it into code.
What happens to my existing business logic in code?
Decision tables don’t require ripping out existing systems. They can run alongside your current applications, gradually taking over the policy logic that today lives in code. Engineers keep the architecture, and the policy moves to where it belongs.
Who typically authors decision tables?
Business analysts, policy owners, compliance teams, and domain experts. Anyone who understands the business rule. The Aletyx AI Assistant helps with authoring and test generation, so the barrier to getting started is very low.
Is this only for financial services?
No. Any industry with business policies benefits from decision tables. Healthcare, insurance, manufacturing, retail, government. Anywhere a decision needs to be consistent, explainable, and auditable.
