October 20, 2026 | data²

A Decision Record™ is an answer to a business question that comes with its proof attached: the evidence traced back to the source, the rule that applied, any conflicts between records, and a confidence level.

Definition: Decision Record. A Decision Record is the written output of an AI decision that a reviewer can check. It holds the answer, the evidence traced back to the source record, the governing rule, any conflicts, and a confidence level. If an auditor, a board or a regulator asks "how do you know," the Decision Record is the reply.

Most AI platforms generate answers. data² provides the evidence behind the answer.

That difference matters most to operating companies. A decision is not done when someone makes it. It is done when it holds up in front of the people paid to question it. An answer with no evidence behind it is a starting point. A Decision Record is something you can defend.

What should an AI Decision Record contain?

An AI Decision Record should contain five fields: the answer, the evidence, the governing rule, the conflicts and the confidence. Each field answers a question that an auditor or a board member will ask sooner or later.

With all five fields, a reviewer can check the work without redoing it. Missing any one sends someone back to rebuild the file by hand.

Two more things matter. A named person reviews and approves the finding, so a person owns the decision. And the record is kept. The person who questions a decision months from now should get the same record back, not a reconstruction of it.

How do you make an AI decision auditable?

You make an AI decision auditable by making every part of it checkable by someone who was not in the room. In practice, that comes down to five habits.

1. Tie every fact to a source record. If a number appears in the answer, the reviewer should be able to open the record it came from. A fact with no source is an opinion.

2. Name the rule. An answer is only as strong as the rule behind it. Say which contract term, policy or approval limit applied, and point to where it lives.

3. Show conflicts instead of smoothing them over. Real records disagree. A ticket says one thing and an invoice says another. An audit-ready decision shows that conflict and explains how it was resolved.

4. State the confidence. Not every answer deserves the same weight. Saying so up front is more credible than sounding certain about everything.

5. Keep a person in the approval. Auditors and boards want to know who decided. The AI can find and prove the issue. A named person signs off.

Accuracy is the base of all five. A record full of traced evidence still fails if the reasoning behind it is wrong. That is why reView™, data²'s product, is built on patented AI designed to stay accurate (US Patent 12,339,839) and delivers 95%+ accuracy. The goal is defensible AI decisions: answers that stay correct when someone checks them, not just answers that sound right.

How do you trace an answer back to the source record?

You trace an answer back to the source by connecting the records where they already sit and keeping the link to each one as the answer is built. The trace is not added at the end. It is carried through the whole process.

The facts behind a real decision are rarely in one place. In an energy company, the invoice sits in the ERP. The field ticket sits in a ticketing system or a scanned PDF. The rate schedule sits in a contract folder. The approval sits with whoever signed it. Each record is correct on its own. Nobody puts them side by side until something goes wrong.

reView connects the records companies already have, where they already sit. It reads across those siloed systems and builds a working picture of how they relate. Every fact keeps its source. When reView returns a Decision Record, each piece of evidence points back to the document and page it came from.

That is what evidence-grade decision intelligence means in plain terms. The answer and the proof arrive together, and the proof is good enough to hand to a reviewer.

One example: the field ticket gap

At an oil and natural gas E&P company, a service invoice billed $189K. The field ticket for the same job confirmed $134K of work. Both cleared every AP control. The $55K gap only showed up when the invoice, the field ticket and the contract rate sat side by side.

Here is how that finding reads as a Decision Record:

• Answer: $55K of the invoice is not supported by the field ticket.

• Evidence: the $189K invoice and the $134K field ticket, each traced to its source record.

• Governing rule: the contract rate schedule for that work.

• Conflict: the invoice and the field ticket disagree by $55K.

• Confidence: stated on the record, so the reviewer knows how much weight it carries.

No one had to rebuild the file. This is what decision intelligence for oil and gas looks like when it is built for review, not just for speed.

How is it different from a dashboard or a chat answer?

A dashboard shows you what happened. A chat answer tells you what the AI thinks. A Decision Record shows you what the records support, why, and how to check it.

Both tools are useful. Neither was built to stand up in an audit.

A dashboard is good at trends and totals. It rarely tells you which record a number came from, which rule applied, or where two systems disagree. When a board member asks about one line, someone still has to go and find the proof.

A chat answer is fast and easy to read. But you can't always see which sources it used, whether they conflicted, or how sure it is. To defend it, you have to check it yourself first, and that checking time eats the speed.

A Decision Record is built to be checked. It is also built to be acted on. Findings come back ranked by what matters most and tied to a measurable outcome, such as dollars to recover or a payment to hold. A team can work the list from the top.

Put simply, the four things that set a Decision Record apart are:

• Accuracy: patented AI designed to stay accurate, with 95%+ accuracy.

• Traceability: every conclusion traced to source evidence.

• Context: records connected across siloed systems, so the answer reflects the whole picture.

• Actionability: prioritized findings tied to measurable outcomes.

You can see how these fit together on the reView platform page. For background on the category, read Decision Intelligence, Explained.

Frequently asked questions

What is a Decision Record in AI?

A Decision Record is the output of an AI decision that a reviewer can check. It holds the answer, the evidence traced back to the source, the governing rule, any conflicts, and a confidence level.

Who uses a Decision Record?

Operating companies use Decision Records when they have to defend a decision to auditors, boards or regulators. Typical users include controllers, internal audit, AP and payment integrity teams, and project controls.

Does a Decision Record replace human judgment?

No. The AI finds the issue and assembles the proof. A named person reviews the record and approves the decision.

How is a Decision Record different from an audit trail?

An audit trail logs who did what and when. A Decision Record explains why an answer is correct. It shows the evidence, the rule and any conflicts behind it.

Do you need to move data into a new system to get a Decision Record?

No. reView connects the records companies already have, where they already sit, and traces each answer back to those records.

See a Decision Record for yourself

The easiest way to judge a Decision Record is to read one. If your team has to stand behind its numbers in front of auditors, a board or a regulator, we'd be glad to walk you through a full Decision Record from our demo environment, field by field.

Book a walkthrough