Ninety-five percent of organizations investing in generative AI have seen zero measurable return, according to MIT's 2025 GenAI Divide report. That's after pouring an estimated $30–40 billion into it. The models aren't the problem. The problem is what happens to your team's work *between* the tools, and nowhere is that clearer than with the decisions you make in meetings.
Every week your team agrees on dozens of things in a call, then watches them blur. Someone misremembers who owned what. An AI summary records a decision nobody actually made. Two people leave the same meeting with two different plans. The missing ingredient has a name borrowed from security and forensics: decision provenance—the traceable record of what was decided, by whom, and on what basis.
It's the decision audit trail your meetings never had. Gartner just named "digital provenance" one of its Top 10 Strategic Technology Trends for 2026. The firm warns that companies who ignore it could face billions in compliance risk by 2029. This guide breaks down what decision provenance means for teams, why today's AI meeting tools quietly break it, and how to build it into every meeting—so the decision still exists, and still makes sense, a month after the call ends.
What Is Decision Provenance?
The term comes from computer science. In a 2019 IEEE paper, Cambridge researchers Jatinder Singh and Jennifer Cobbe coined "decision provenance" to describe tracing the chain of inputs, logic, and downstream effects behind an automated decision. The goal was accountability: when a system decides something, you should be able to reconstruct why.
Teams need the same thing for human decisions—and almost nobody has it. Knowledge workers already lose close to 60% of their time to "work about work"—clarifying, coordinating, and chasing down what was decided instead of doing it. A decision audit trail answers four questions about any choice your team makes:
- What was actually decided, not just what was discussed
- Who made the call and who owns the follow-through
- Why—the context, trade-offs, and data on the table at the time
- When it was decided, so you can tell a live decision from a stale one
Most teams can't answer even two of these a week later. Meeting minutes capture what was *said*. A transcript captures every word and no meaning. Neither captures the decision as a durable, attributable object. That gap is exactly what decision provenance fills.
Why AI Meeting Summaries Lose the Thread
You'd think AI note-takers would solve this. In a specific and dangerous way, they make it worse.
The obvious failure is hallucination—an AI inventing a fact. The subtler failure is misattribution, and it's everywhere. Speaker diarization (the part that decides who said which words) still trips on cross-talk, accents, and rough remote audio, so small attribution errors are routine. The summary records that "Priya approved the new timeline" when Priya only asked, "What if we pushed it back?" Tentative musing becomes a firm commitment. A question becomes a decision.
This is why AI meeting summary accuracy is a trust problem, not just a quality one. A clean-reading summary feels authoritative even when it's wrong, so nobody double-checks it. The team then executes against a decision that was never made, by a person who never made it. When you later ask who decided what in a meeting, the AI's answer is confident and unverifiable—the worst possible combination.
The deeper issue is structural. An AI built on a transcript is *reconstructing* provenance after the fact, guessing at attribution from audio. It's working backward from the thinnest possible signal. Provenance you have to reconstruct is provenance you can't trust.
Why Lost Decisions Happen at the Handoff, Not in the Notes
Even with perfect notes, most decisions die in the same spot: the handoff from the meeting to the work.
Here's the seam. A decision gets made on a call. The record—if there is one—lands in a doc. The work happens somewhere else entirely: a ticket, a chat thread, a project board. Each jump strips away context. By the time the decision reaches the person executing it, the "why" is gone and a one-line instruction is all that's left. Gartner notes that digital provenance breaks the moment a recommendation is "copied into a ticket, pasted into chat, or summarized into a runbook." The same is true for a human decision.
This is the real source of lost decisions in remote work—not bad memory, but too many seams. The average knowledge worker switches apps around 30 times a day, and context-switching taxes focus on every jump. Decisions made in one tool and executed in another lose their provenance in transit. It's also why managers end up running the meeting after the meeting—a second call just to confirm what the first one decided. Unnecessary, unfocused meetings already cost U.S. businesses an estimated $399 billion a year, and a decision that doesn't survive the call is a leading reason one feels wasted.
Atlassian's State of Teams 2026 put a number on the cost: fragmentation across tools drains an estimated $161 billion a year from the Fortune 500. A big chunk of that is decisions made, lost, and remade. It's the same force that quietly erodes institutional memory—knowledge that exists only in someone's head until they leave.
How to Build Decision Provenance Into Every Meeting
Building decision provenance isn't about more documentation. It's about capturing the decision once, at full fidelity, where it happens—and never letting it degrade after that. Four principles make it work.
Capture the decision the moment it's made
The best time to record a decision is the second it's made, in the room, with everyone present to confirm it. Provenance captured live is accurate by construction; provenance reconstructed later from a transcript is a guess. Name the decision out loud, write it where everyone can see it, and get a visible nod before moving on.
Record the decider, not just the decision
A decision without an owner isn't a decision—it's a topic. Every entry in your decision log should name the person accountable for the outcome, not just the group that discussed it. This is where meeting accountability actually comes from: a clear line from a choice to the single human who carries it. Ownership tools help, but the discipline matters more than any tool.
Keep the context welded to the call
A decision's "why" is as important as its "what." Six weeks later, "we chose Option B" is useless; "we chose Option B because the data showed X and we couldn't staff Option A in time" is reusable. Keep the reasoning, the trade-offs, and any visuals attached to the decision itself—not scattered across three apps where they drift apart.
Make it survive the jump to execution
Provenance only counts if it reaches the person doing the work intact. The fewer jumps between where a decision is made and where it's executed, the less of it you lose. This is exactly why Coommit folds video, a shared canvas, and contextual AI into one surface: the decision, its context, and the action plan live in the same place the conversation happened, so nothing has to be copied across a seam to survive. The AI isn't guessing who said what from audio—it's grounded in what the team actually wrote and drew on the canvas.
Decision Provenance vs. a Decision Log vs. Meeting Minutes
These three get conflated, but they sit at different layers:
- Meeting minutes are a chronological account of what was said. Useful for the record, weak for action—they bury the decision inside the discussion.
- A decision log is a focused list of decisions made, ideally with an owner and a date. It's a big step up, and worth keeping. (We've written a full guide to running a decision log.)
- Decision provenance is the layer above both: not just the list, but the full chain—decision, decider, context, and an unbroken link to the work that follows. A decision log is a snapshot; provenance is the chain of custody.
Put simply, the decision log vs meeting minutes debate misses the point. Minutes tell you what happened, a decision log tells you what was decided, and decision provenance tells you whether that decision still holds up and who's accountable for it. For distributed teams, that last layer is the one that stops the same debate from happening three separate times.
Conclusion
AI is about to make and execute decisions faster and in higher volume than ever—59% of teams already use AI agents at work, and daily AI use among desk workers has more than tripled in a year. That only raises the stakes for knowing which decisions are real, who owns them, and why they were made. Speed without traceability just means losing the thread faster.
Decision provenance is how teams keep up: a living decision audit trail that captures the call, the context, and the owner the moment a choice is made, then carries all three through to the work. The teams that build it stop relitigating settled questions and start compounding their decisions instead of repeating them. If your meetings keep ending in confident summaries and contradictory follow-ups, the fix isn't a better note-taker—it's a workspace that keeps the decision and its context in one place, from the conversation all the way to the plan.