For a long time, a strange thing has sat at the center of modern work. We’ve treated it as normal.
A group of people gets into a room. They talk through priorities, tradeoffs, risks, decisions, dependencies, tensions, and next steps. They negotiate reality together in real time. They do the messy human work of deciding what matters.
And then the meeting ends.
After that, one person is expected to go translate whatever just happened into something the organization can use. They write the notes. They summarize the decisions. They assign the actions. They clean up the ambiguity. They interpret what people meant. They decide what to leave out. They update the project management tool. They turn a living conversation into a static record.
That person is often a manager.
And we’ve been using humans to do a job that is both too mechanical and too lossy for humans to do well at scale.
Meetings are where organizational reality gets negotiated
The interesting AI shift isn’t that it will replace all thinking. It’s that it will make us more aware of which kinds of work humans should still be doing.
One answer is clear.
Humans should spend more time doing what only humans can do well together: making tradeoffs, negotiating meaning, aligning around uncertainty, resolving tension, committing to action, deciding what matters.
That work happens most powerfully in synchronous conversation.
Most meetings are terrible. But when people are in the same room, physical or digital, and have to respond to each other in real time, something happens that no document or workflow tool can replicate.
Ambiguity gets surfaced. Assumptions collide. People challenge each other. Misunderstandings become visible. A decision can actually become shared.
That’s a higher-fidelity coordination moment than a status update in a tool or a polished strategy memo. A strategy document matters. A plan matters. A procedure matters. But they are downstream artifacts. The conversation is where the reality changes.
The bizarre inefficiency at the center of work
Once you see that, the current workflow looks ridiculous.
We gather the expensive humans. We do the expensive human work. Then we ask one of those humans to do clerical translation after the fact.
That person becomes the bottleneck between human coordination and organizational action.
They are expected to remember: what was actually decided, what people only seemed to agree on, what was deferred, what was assigned, what was implied, what still felt unresolved, and which part of the discussion mattered enough to log.
That is not a neutral act. It is interpretive. It is filtered. It is biased. It is incomplete.
The notes are never the meeting. The project management update is never the conversation. The official recap is always a compressed, partial rendering of what actually happened.
And yet many organizations still rely on that rendering as if it were reality.
This is one reason so much work feels fuzzy. People aren’t lazy. Teams aren’t stupid. We are simply forcing human beings to manually re-encode shared reality after the richest moment of shared sense-making has already passed.
That is terrible system design.
AI changes the threshold of what feels absurd
In a world where AI can draft documents, generate prototypes, summarize research, analyze transcripts, and route information between systems, the manager-as-transcription-layer model starts to feel even more irrational.
You can feel the absurdity more sharply.
AI can write code, synthesize a market landscape, and generate ten versions of a board memo in seconds. But after a meeting, we still expect someone to manually type out who is doing what by when. We still expect a person to open a project management system and reconstruct the discussion from memory. We still expect middle managers to spend energy converting live coordination into admin artifacts.
That doesn’t just feel inefficient. It feels primitive.
The more capable AI becomes, the more intolerable this clerical burden will feel. And many organizations are still running managers as translation middleware — not leaders, not facilitators of judgment, just translation middleware.
That is the layer AI will absorb.
What AI should actually do
The right frame isn’t AI replacing human judgment. It’s this: humans should do more of the work only humans can do well, and machines should do more of the work humans should never have had to do in the first place.
After a real coordination moment, AI should be able to capture what was decided, identify what was agreed, spot what remained partial, register who owns what, and turn the output of the conversation into something that other humans and machines can actually work from.
That is not replacing the meeting. That is not replacing leadership. That is not replacing accountability.
It is removing the clerical translation layer that currently sits between conversation and execution.
Humans negotiate reality together. AI turns that negotiated reality into structured, usable downstream artifacts. That makes sense.
This is not about making people speak like machines
There is an important tension here.
You can’t build systems like this without some ontology. The machine needs structure. It needs categories. It needs a way to distinguish a real commitment from vague agreement. It needs to know the difference between a decision and a partial one. It needs some way of recognizing ownership, alignment, and next steps.
That can sound rigid.
But the point is the opposite of rigid. The machine should carry most of the framework burden so that humans can remain relatively natural.
People should not have to leave every meeting and fill out a ritualized template to prove they coordinated. They should not need to remember which workflow category applies to which discussion. They should not have to behave like clerks for the system.
What they do need, over time, is to become slightly more explicit about a few very basic coordination signals.
What did we decide?
What are we aligned on?
Who owns this?
What happens next?
By when?
That is not bureaucracy. That is the minimum viable structure of coordinated action. When those things never become explicit, much of what organizations call coordination is just socially acceptable ambiguity.
Better AI should make better meetings more valuable
There’s a tempting fantasy that AI will reduce the need for conversation. I think the opposite happens.
As AI makes drafting, producing, analyzing, and executing easier, the real bottleneck shifts upstream. The scarce work becomes judgment, tradeoffs, clarity, alignment, priority, commitment.
The more execution accelerates, the more valuable high-quality human coordination becomes.
The meeting of the future should not be a place where people passively report status so that someone can later type it into a system. It should be a place where people do the highest-value work: sense-making, decision-making, alignment, conflict resolution, real commitment formation.
If AI can reliably capture the outputs of those conversations, people can spend less time on clerical aftercare and more time actually thinking together.
That is a more interesting future of work than “AI summarizes your meeting.”
The manager’s role does not disappear. It changes.
The mechanical parts of management become harder to justify as human labor.
The manager who mainly exists to collect updates, restate priorities, translate decisions into tasks, chase follow-up, and manually maintain the system of record — that role is standing on unstable ground.
The manager who can set direction, create clarity, hold tension, facilitate judgment, surface tradeoffs, protect focus, and help groups reach durable commitments — that role becomes more valuable.
Less transcription. More facilitation. Less clerical reconciliation. More structured human coordination.
AI doesn’t replace management. It strips management down to the parts that are actually human.
Why this matters beyond productivity
Every time a meeting ends and one person manually reconstructs what happened, the organization loses signal.
It loses nuance. It loses dissent. It loses uncertainty. It loses the exact form of the commitment. It loses the difference between what was said and what was assumed. It loses the shape of the conversation that produced the outcome.
That loss compounds. Then we wonder why teams revisit decisions, why ownership feels muddy, why strategy doesn’t translate into execution, why meetings multiply, why coordination feels exhausting.
The problem isn’t that people failed to think. It’s that the outputs of thinking were never captured cleanly enough to travel.
My bet
My bet is that this old system will start to feel increasingly absurd.
We’ll look back and wonder why, after a room full of people did the hard work of making sense together, we handed the burden of translation to one person with a notes document and a project management tab.
We’ll wonder why we tolerated so much loss between conversation and action. We’ll wonder why managers spent so much time encoding reality instead of helping shape it.
The organizations that move first won’t be the ones that use AI to generate more internal sludge. They’ll be the ones that use AI to protect the highest-value human work and remove the clerical friction around it.
Better conversations, clearer commitments, cleaner transfer of reality between groups, and a system that lets both humans and machines act from the same structured understanding.
AI as the semantic layer between human coordination and machine execution.
That’s the future worth building toward.
Growth Wise is a Decision Reliability platform. It instruments the human coordination layer so organizations can see which decisions are closing, which delegations are landing, and where coordination is breaking down.