What Is an AI Trust Score? Definition, Formula, and How to Calculate It
By Anshul Gupta · Published 2026-09-10 · Technology & Innovation
Short answer: An AI Trust Score is a metric that measures how much AI-assisted work in a team was completed correctly the first time, without needing rework. It’s calculated as (Work Completed minus Rework Required) divided by Work Completed, multiplied by 100. A Trust Score of 90% means 90 percent of a team’s work, whether AI-assisted or not, was genuinely right the first time it was marked done.
The Formula
Trust Score = (Work Completed - Rework Required) / Work Completed x 100
This is one of the core metrics inside the Accountable AI Framework (AAF), built specifically to give Agile teams a number that standard Scrum or Kanban metrics don’t produce on their own.
A Worked Example
Say a Scrum team completes 40 story points in a sprint. Of that work, 5 points later needed rework, a bug fix, a corrected calculation, a missed requirement caught after the fact.
Trust Score = (40 - 5) / 40 x 100 = 87.5%
That 87.5% tells the team something velocity alone never will: 12.5 percent of what looked like a successful sprint actually wasn’t right the first time.
Why Standard Agile Metrics Don’t Capture This
Scrum’s velocity counts completed story points per sprint. It has no built-in way to subtract points that later came back as rework. A sprint can look identical on a velocity chart whether the team’s work was rock-solid or riddled with quiet corrections, because velocity only measures completion, not correctness.
The same gap exists in Kanban, where cycle time measures how fast a card crosses the board, not whether it was ever genuinely reviewed before reaching Done. Trust Score exists specifically to fill that blind spot.
Trust Score vs AI Error Rate: What’s the Difference?
These are often confused, but they measure different things.
Trust Score looks at all work, AI-assisted or not, and asks what percentage was right the first time.
AI Error Rate looks only at AI-assisted work specifically, and asks what percentage of that subset needed correction.
A team could have a healthy overall Trust Score while still having a concerning AI Error Rate, if the rework is concentrated specifically in AI-assisted items rather than spread evenly across all work. Tracking both numbers separately is what reveals that pattern.
How to Track Trust Score Without New Software
Trust Score doesn’t require a dedicated analytics platform. In JIRA, for example, it can be calculated with two saved filters, one for total work completed in a sprint, one for items that were reopened or required correction, then applying the formula to the resulting counts. Most teams start by tracking this manually in a spreadsheet, updated at the end of each sprint or cycle.
Frequently Asked Questions
What is a good Trust Score for an Agile team?
There’s no universal benchmark, but a Trust Score that is declining sprint over sprint, or sitting noticeably below a team’s historical average, is a signal worth investigating, particularly if AI-assisted work has increased during that same period.
Does Trust Score only apply to AI-assisted work?
No. Trust Score measures all completed work. It’s most useful when tracked alongside AI Error Rate, which isolates the AI-assisted subset specifically, so a team can see whether declining trust is coming from AI-assisted items or from the team’s work generally.
Can Trust Score be tracked in Scrum, Kanban, and SAFe equally?
Yes. The formula is framework-agnostic. In Scrum it’s typically calculated per sprint, in Kanban per a chosen time window, and in SAFe it can be rolled up across an Agile Release Train for a Program Increment.
Is a 100% Trust Score the goal?
Not necessarily, and a suspiciously perfect score is worth double-checking. It can mean the team is genuinely excellent, or it can mean rework isn’t being tracked accurately. Pair Trust Score with Override Rate for a fuller picture of whether review is happening at a genuine, meaningful depth.
Trust Score is one of five core metrics inside AAF, the Accountable AI Framework. Learn the complete formula set, including AI Error Rate, Override Rate, and Effective Velocity, at https://www.agileaipro.com/certifications