Excellence in AI Collaboration

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Most organizations still talk about AI like it’s a faster stapler, a productivity tool,

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a feature you enable.

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That story is comforting and it’s wrong.

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Work now happens through AI, with AI, and because of AI, drafting, summarizing, prioritizing,

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and quietly deciding what counts as reality.

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That makes humans more important, not less, because judgment, context, and accountability

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don’t automate.

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In the next few minutes, this gets reduced to a simple model.

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One has structural, cognitive, and experiential layers, and AI rewires all three.

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The foundational misunderstanding, deploy co-pilot as a feature toggle.

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The foundational mistake is treating co-pilot like a license assignment and a change management

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email.

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It is not the co-pilot is not a tool inside word.

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It is a participant inside the system that produces your organization’s decisions.

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That distinction matters because the moment AI starts drafting your work, summarizing your

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meetings and suggesting your next actions, you’ve changed the shape of collaboration.

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Not cosmetically, mechanically.

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Most leaders still think the adoption plan is, enable co-pilot, train prompts, publish a

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few do’s and don’ts, and then measure whether people use it.

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That’s feature rollout logic, and feature rollout logic fails when the feature becomes the

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narrator of the work, because AI doesn’t just increase output.

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It changes what gets noticed.

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When co-pilot summarizes a meeting, it compresses a messy hour into a tidy paragraph.

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When it drafts a proposal, it chooses a structure before your team argues about the problem.

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When it suggests action items, it decides what was important enough to become work.

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That’s not assistance.

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That’s framing, and framing is where power lives.

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So the actual problem isn’t, are people using co-pilot?

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Yes, the problem is, what decisions are now being routed through an AI-generated first

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draft of reality?

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This is where organizational entropy shows up.

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Policies drift, ownership blurs, people take shortcuts, and every shortcut becomes an

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entropy generator.

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Small exceptions that accumulate until you can no longer tell what the organization believes.

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But it’s not only what it has allowed.

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In practice, the deploy co-pilot mindset creates three predictable failures, first, invisible

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co-authorship.

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Someone drops a clean, confident draft into a channel.

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The team edits it.

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The deck ships.

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Nobody says how it was produced.

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Now you have an artifact with unclear provenance and unclear accountability if it’s wrong

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who owns the error.

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The person who clicked “generate”, the person who edited a sentence, or the leader who

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forwarded it with “looks good”.

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This is not a philosophical question.

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It’s an incident-review question.

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Second, speed up, coherence down.

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AI removes friction from producing text, so teams produce more.

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But friction wasn’t only waste.

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Friction was often the forcing function that made people explain their assumptions to each

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other.

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When the draft appears instantly, the team skips the shared sense-making that used to

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happen while building it.

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The result is fast alignment on words and shallow alignment on meaning.

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You’ll see it later as rework, decision reversals, and a strange kind of collective amnesia.

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I thought we agreed on this, followed by five people realizing they agreed on different

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interpretations of the same summary.

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Third, ownership migration.

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When Copilot becomes the default first writer, humans quietly move from authors to reviewers.

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Review feels efficient.

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It is also cognitively lazy.

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Editing a draft is not the same as constructing the model of the problem in your head.

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Review optimizes for polish.

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Authorship optimizes for understanding.

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Over time, if you don’t design against it, the organization stops building shared mental

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models.

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It starts consuming outputs that’s epistemic agency loss.

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In plain language, the team no longer owns how it knows.

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Now none of this means don’t use Copilot.

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It means stop pretending it’s a neutral productivity upgrade.

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It is a socio-technical redesign.

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The system behavior changes whether you plan for it or not.

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And if you want the cynical architectural truth, you are already redesigning your organization.

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You’re just doing it accidentally.

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So the executive question becomes simple.

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What kind of collaborators are you creating when AI is always in the room?

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That question forces a better definition of collaboration.

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Collaboration is not meetings and chat threads.

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Collaboration is the system your organization uses to turn partial information into decisions.

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Who contributes?

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What gets recorded?

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What gets ignored?

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How disagreement is handled?

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And who is accountable when it goes wrong?

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Once AI becomes a participant, it affects every step of that pipeline.

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It accelerates production.

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It compresses debate.

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It standardizes language.

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It makes outputs look finished earlier than they are.

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And humans, humans don’t become obsolete.

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They become the control plane.

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Humans still have to decide what matters, what trade-offs are acceptable, what risks are

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real and what context changes the meaning of the same facts.

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AI can propose, it can summarize, it can imitate certainty, it cannot be accountable.

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So before we talk about features, licensing or prompt libraries, the correct move is to

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map the collaboration system itself.

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That’s why the rest of this episode uses a three-layer model, structural, cognitive

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experiential, because if you only manage the surface layer, you get accidental, culture

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and conditional chaos.

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And once AI is a participant, accidental culture is not a rounding error.

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It’s the default.

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The three-layer model.

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Structural, cognitive experiential collaboration.

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Here’s the model that keeps this from turning into another AI changes everything monologue.

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Collaboration has three layers, structural, cognitive and experiential.

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Most organizations only manage the structural layer because it’s visible, scheduleable and

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reportable.

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Meetings, chat, documents, workflows, who’s invited, where the files live, which channel

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is official, it feels like collaboration because it’s where collaboration happens.

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And structurally correct work can still be cognitively wrong, and cognitively correct work can still

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fail if the human experience layer collapses.

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That distinction matters because AI touches all three layers at once.

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If you treat it like a productivity add-on, you’ll optimize one layer, usually structural

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speed, and then act surprised when judgment, trust and decision quality degrade.

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Start with the structural layer.

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Structural collaboration is the physical plumbing of work, the meeting cadence, the chat topology,

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the document lifecycle, the async versus sync balance, the workflow parts, the handoffs,

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the where does this live rules, it’s also the implicit rules.

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If something is in email, it’s formal, if it’s in chat, it’s provisional, if it’s in a document,

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it’s policy.

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Those conventions already determine who participates and what gets remembered.

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AI doesn’t just make this faster, it turns the plumbing into an output generator.

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Meetings become transcripts, recaps, tasks and artifacts, chat becomes draft first.

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Documents become the battleground where the final version appears early and the team

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edits around it.

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Structurally you get more artifacts, more traceability and more motion.

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That’s the easy part to see, now the cognitive layer.

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Cognitive collaboration is what teams do to turn ambiguity into action, since making ideation

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synthesis, trade off decisions, risk framing and the building of shared mental models.

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It’s the part nobody can point to on a calendar invite, but it’s the part that makes the calendar

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invite worth having.

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AI accelerates cognitive work by reducing search cost and offering synthesis, it also degrades

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cognitive work by making incomplete framing look complete.

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The moment the team treats an AI summary as the truth, instead of a compression, it stops

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doing the cognitive labor that creates understanding.

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You get faster convergence, but you lose the thing that prevents dumb decisions.

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Disagreement grounded in different models of reality.

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This is where work, graph and work IQ become leverage.

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In Microsoft terms, graph is the organizational memory of people, artifacts and activity signals

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across Microsoft 365.

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Work IQ is essentially the intelligence layer built on top of that.

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It’s how co-pilot stops being generic and starts being contextually dangerous, because when

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the system has context, it can root work more effectively.

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And when it roots work more effectively, humans are tempted to stop thinking about why

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it routed it that way.

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So the cognitive layer is not used co-pilot to brainstorm.

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The cognitive layer is preserve epistemic agency.

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Keep humans directing how the organization knows, not just what it produces, now the experiential

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layer.

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This is the part leaders like to treat a soft, which is why it keeps detonating projects.

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Global collaboration is psychological safety, autonomy, identity, authorship and meaning.

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It’s how it feels to contribute, disagree and be accountable.

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It determines whether people speak up when something is wrong, whether they challenge an

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AI-generated summary, and whether they feel like the work is theirs or just something they

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edited.

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AI changes the experience in subtle ways.

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It shifts status dynamics.

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It changes who speaks first.

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It makes critique feel like obstruction.

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It makes people worry they’re arguing with the machine, not debating a draft.

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And once people feel that silence becomes the path of least resistance, that’s not culture,

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that’s system behavior.

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And leadership has an obligation here either design across all three layers or accept accidental

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outcomes.

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Structural drift is measurable.

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Cognitive drift looks like alignment.

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Experiential drift looks like engagement is fine until retention and quality collapse.

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So we start with the three layer model because it forces a practical question when AI is

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a participant, where is the failure actually happening?

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And the answer is usually not where you’re looking.

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Structural.

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Meetings become artifacts, not events.

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Meetings used to be a temporary event.

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People talked, somebody took notes, and the real outcome lived in whoever remembered the

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argument afterward.

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That world is gone.

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In the AI-mediated workplace, the meeting is no longer the product.

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The artifact is.

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Transcript recap decisions, tasks, follow-ups and whatever gets forwarded to someone who

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wasn’t there.

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That’s the new unit of work.

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And structurally, this seems like an upgrade.

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This ambiguity, more traceability, fewer.

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What did we decide?

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Emails.

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Leadership loves it because it looks like maturity.

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Everything captured, everything searchable, everything packaged.

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But here’s what most people missed.

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The artifact doesn’t just record the meeting.

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It replaces the meeting as the organization’s memory.

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Once you have a recap, people stop asking humans.

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They ask the recap.

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They forward the recap.

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They build the next deck from the recap.

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And the recap becomes the truth through repetition, not because it’s accurate.

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That distinction matters.

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A transcript is raw.

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A recap is interpreted.

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It’s a compression algorithm applied to social reality.

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And any compression loses information, tone, hesitation, uncertainty.

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The moment someone said, I don’t know.

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The implicit risks people were nervous to name.

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AI is great at removing that mess.

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It is also great at removing the evidence that the room wasn’t actually aligned.

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So the structural change is straightforward.

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Attendance becomes optional, but documentation becomes mandatory.

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People follow meetings instead of joining them.

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They show up for the last five minutes and rely on the recap to tell them what happened.

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They trust the artifact more than the conversation because the artifact is tidy.

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The organization shifts from present-based work to artifact-based work.

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Now on paper, this sounds efficient.

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In practice, it has a specific failure mode.

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Meeting quality declines while documentation improves.

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Because once you believe the artifact is the real output, the incentives change.

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People stop fighting for clarity in the room.

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They assume the recap will sort it out.

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They stop restating decisions out loud because it’ll be in the notes.

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They stop doing the slow and annoying work of checking shared understanding because the

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system will generate a neat paragraph that looks like shared understanding.

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But that neat paragraph can be wrong in two ways, Commission and omission.

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Commission errors.

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It states something confidently that the room did not decide.

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It turns a suggestion into a commitment.

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It converts a tentative we should explore into, we will do.

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That’s not malicious.

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That’s how summarization works when the input is messy.

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Omission errors, it excludes the debate that mattered.

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It omits the minority view, the risk caveat, the constraint, the uncomfortable dependency,

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and once omitted, it doesn’t exist in the organizational narrative unless a human re-inserts

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it.

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That’s why meetings become artifacts is not neutral.

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It relocates power to whoever controls the artifact, not the senior person in the room.

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The person who edits the recap.

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The person who decides whether to accept the AI summary as is or rewrite it.

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The person who forwards it to leadership with one sentence of framing.

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And once you see that, you stop treating meeting recap as a convenience feature.

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It becomes governance.

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Because the meeting artifact is now a control plane object.

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It is the object that propagates decisions through the organization.

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It gets indexed, it gets quoted, it gets embedded into the next document.

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It becomes the input to copilot when someone asks, what’s the plan?

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So if you don’t design rules around artifacts, you don’t have meeting hygiene, you have narrative

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drift.

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A useful norm here is brutal and simple.

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Recap is input, not truth.

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The recap must point to decisions, owners, and assumptions, not just highlights.

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If the team can’t name what was decided, what was deferred, and what is explicitly uncertain,

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the meeting didn’t produce a decision.

250
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It produced noise with better formatting.

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And for high impact work, the artifact needs an accountable human signature, not as bureaucracy,

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as physics.

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Because the organization will treat that artifact as reality, whether you intended it or not.

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This is the uncomfortable truth.

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AI didn’t just make meetings easier to survive.

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It turned meetings into a publishing pipeline.

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And once you’re publishing, you’re governing.

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Structural chat shifts from dialogue to confirmation.

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Once meetings become artifacts, chat becomes the glue that pretends everything is aligned.

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That’s always been true in teams.

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The difference now is that copilot turns chat from a place where people think out loud

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into a place where people approve what already looks finished.

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Before AI, chat had friction.

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Someone typed a messy thought, got corrected, asked a question, refraised, and eventually

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the thread converged.

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It wasn’t pretty, but it forced the team to expose assumptions in public.

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The work happened in the gaps.

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The clarifying questions, the wait, what do you mean?

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The annoying back and forth that makes shared understanding real.

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Copilot changes who speaks first, because the person with the prompt can post a clean paragraph

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in 30 seconds, and the rest of the team is now reacting to something that looks like

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00:13:01,920 –> 00:13:02,920
a conclusion.

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00:13:02,920 –> 00:13:05,040
The default response is no longer exploration.

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It’s approval, minor edits, or silence.

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This is not because people got lazy.

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It’s because structure drives behavior.

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And when the first message is polished, disagreement feels like slowing down, not improving

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the work.

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Here’s what most people miss.

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Chat isn’t a conversation layer anymore.

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It’s an authorization layer.

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Threads compress.

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People stop asking questions because questions now look like incompetence, or worse, they

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00:13:23,800 –> 00:13:25,960
look like resistance to efficiency.

285
00:13:25,960 –> 00:13:29,440
Instead of what are we optimizing for, you get LGTM.

286
00:13:29,440 –> 00:13:32,040
Instead of what are the trade-offs, you get a thumbs-up reaction.

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00:13:32,040 –> 00:13:36,360
And the organization mistakes that, for alignment, it’s alignment on text, not alignment

288
00:13:36,360 –> 00:13:37,560
on meaning.

289
00:13:37,560 –> 00:13:40,760
This is where accountability starts to blur in a very specific way.

290
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Authorship becomes untraceable.

291
00:13:42,280 –> 00:13:45,320
In a draft first world, you can usually point to the author.

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00:13:45,320 –> 00:13:47,760
In a copilot first world, you can point to the messenger.

293
00:13:47,760 –> 00:13:48,960
Those are not the same thing.

294
00:13:48,960 –> 00:13:52,960
The person who posted the message may not have written it, may not understand it, and may

295
00:13:52,960 –> 00:13:54,760
not agree with the implications.

296
00:13:54,760 –> 00:13:59,200
They may simply be the one who could get a coherent paragraph out of the system fastest.

297
00:13:59,200 –> 00:14:02,320
So you get a new role in the organization, the prompt holder.

298
00:14:02,320 –> 00:14:06,040
Not the subject matter expert, not the accountable owner, the prompt holder.

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00:14:06,040 –> 00:14:10,280
The person who can reliably produce good enough language that the team can ship.

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00:14:10,280 –> 00:14:11,760
Over time, that shifts power.

301
00:14:11,760 –> 00:14:12,760
Quietly.

302
00:14:12,760 –> 00:14:15,320
Because whoever controls the first draft controls the range of debate.

303
00:14:15,320 –> 00:14:18,800
If the first draft frames the problem as we need to reduce costs, the thread will debate

304
00:14:18,800 –> 00:14:19,800
cost reduction.

305
00:14:19,800 –> 00:14:23,600
If it frames it as we need to reduce risk, the thread will debate controls.

306
00:14:23,600 –> 00:14:25,240
That initial frame becomes the rails.

307
00:14:25,240 –> 00:14:28,760
Most people won’t jump the rails because the rails look reasonable and the chat wants

308
00:14:28,760 –> 00:14:29,760
closure.

309
00:14:29,760 –> 00:14:33,920
This might seem backwards, but the fastest way to suppress descent is to make the proposal

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00:14:33,920 –> 00:14:35,000
look complete.

311
00:14:35,000 –> 00:14:36,880
And copilot is excellent at completeness.

312
00:14:36,880 –> 00:14:38,720
Now add another structural drift.

313
00:14:38,720 –> 00:14:42,240
Chat becomes the place where decisions get made without decision structure.

314
00:14:42,240 –> 00:14:45,280
People will say, well, if nobody objects by EOD will proceed.

315
00:14:45,280 –> 00:14:49,480
That’s a normal async pattern, but in an AI mediated environment, the proposal that nobody

316
00:14:49,480 –> 00:14:53,400
objects to is often an AI generated synthesis that feels authoritative.

317
00:14:53,400 –> 00:14:58,200
So silence becomes consent to the machine’s framing, not consent to a human plan.

318
00:14:58,200 –> 00:15:02,320
And the moment you normalize that, you’ve created conditional chaos.

319
00:15:02,320 –> 00:15:05,800
Decisions that look documented, but have no accountable reasoning behind them.

320
00:15:05,800 –> 00:15:07,640
This shows up later as conflict.

321
00:15:07,640 –> 00:15:11,240
Not because people are irrational, but because they never built the same internal model of

322
00:15:11,240 –> 00:15:12,760
why the decision was made.

323
00:15:12,760 –> 00:15:15,080
They approved language, then discovered consequences.

324
00:15:15,080 –> 00:15:16,080
So what do you do?

325
00:15:16,080 –> 00:15:17,080
You don’t ban copilot in chat.

326
00:15:17,080 –> 00:15:19,680
You design for disagreement to survive polish.

327
00:15:19,680 –> 00:15:23,600
A simple norm, every proposal needs two alternatives, not because you love bureaucracy,

328
00:15:23,600 –> 00:15:28,200
because alternatives force the team to prove they considered option space, not just accepted

329
00:15:28,200 –> 00:15:29,720
the first coherent output.

330
00:15:29,720 –> 00:15:34,880
It also breaks authority bias by making it clear that the draft is a candidate, not a verdict.

331
00:15:34,880 –> 00:15:37,680
Another norm, separate generation from approval.

332
00:15:37,680 –> 00:15:41,320
If the person who generated the proposal is also the person asking for approval, you’ll

333
00:15:41,320 –> 00:15:43,320
get compliance, not critique.

334
00:15:43,320 –> 00:15:45,000
The structure should make critique cheap.

335
00:15:45,000 –> 00:15:48,360
Here’s the draft, here are the assumptions, here’s what would change my mind.

336
00:15:48,360 –> 00:15:50,880
And finally, force ownership back into the thread.

337
00:15:50,880 –> 00:15:53,600
Not thoughts, not please review.

338
00:15:53,600 –> 00:15:57,640
A named human owner who is accountable for the recommendation, including the parts copilot

339
00:15:57,640 –> 00:15:58,640
wrote.

340
00:15:58,640 –> 00:16:01,480
If nobody is willing to sign their name to it, it isn’t ready.

341
00:16:01,480 –> 00:16:04,520
Because chat is now where decisions get socialized at scale.

342
00:16:04,520 –> 00:16:08,400
And if chat becomes a confirmation engine, it will manufacture agreement faster than your

343
00:16:08,400 –> 00:16:10,640
organization can manufacture understanding.

344
00:16:10,640 –> 00:16:14,280
That’s not collaboration, that’s throughput disguised as consensus.

345
00:16:14,280 –> 00:16:17,880
Draft first collaboration becomes the default.

346
00:16:17,880 –> 00:16:22,400
Once chat becomes a confirmation layer, documents become the battlefield, because documents

347
00:16:22,400 –> 00:16:24,800
are where the organization pretends thinking happened.

348
00:16:24,800 –> 00:16:27,560
AI accelerates that pretence.

349
00:16:27,560 –> 00:16:30,480
The new default is draft first collaboration.

350
00:16:30,480 –> 00:16:35,440
A good enough version appears instantly and everyone moves into edit mode.

351
00:16:35,440 –> 00:16:38,200
Not because the team agreed the problem is understood.

352
00:16:38,200 –> 00:16:42,480
Because the artifact exists, and artifacts create gravity, the uncomfortable truth is that

353
00:16:42,480 –> 00:16:47,000
writing the first draft used to be the cognitive tax that paid for clarity.

354
00:16:47,000 –> 00:16:50,040
Someone had to decide what the problem was, what the structure should be, what evidence

355
00:16:50,040 –> 00:16:52,400
mattered, and what they were willing to claim.

356
00:16:52,400 –> 00:16:58,000
That process forced internal coherence before external polish copilot removes that tax.

357
00:16:58,000 –> 00:17:00,360
So the organization pays a different cost.

358
00:17:00,360 –> 00:17:03,400
It starts optimizing for refinement instead of reasoning.

359
00:17:03,400 –> 00:17:07,280
Editing is not thinking it’s post-processing, it can improve a document, it can also conceal

360
00:17:07,280 –> 00:17:10,160
that nobody actually formed a shared model of the problem.

361
00:17:10,160 –> 00:17:11,760
Here’s what it looks like in real life.

362
00:17:11,760 –> 00:17:15,960
A strategy deck shows up with a clean narrative arc, headings that sound authoritative, and

363
00:17:15,960 –> 00:17:18,480
a set of recommendations that feel complete.

364
00:17:18,480 –> 00:17:22,800
The team spends an hour rearranging bullets, tightening language, and adding a chart.

365
00:17:22,800 –> 00:17:24,320
The deck is done.

366
00:17:24,320 –> 00:17:27,200
But the team never argued about what the deck should be true about.

367
00:17:27,200 –> 00:17:28,920
They argued about how it should sound.

368
00:17:28,920 –> 00:17:31,760
This is where coherence drops while throughput rises.

369
00:17:31,760 –> 00:17:34,840
And leaders love it at first because the pipeline feels faster.

370
00:17:34,840 –> 00:17:37,520
Fewer meetings faster drafts cleaner outputs.

371
00:17:37,520 –> 00:17:40,880
But the system behavior is different, the work has moved upstream into framing, and the

372
00:17:40,880 –> 00:17:44,160
framing just got outsourced to a probabilistic narrator.

373
00:17:44,160 –> 00:17:48,080
That distinction matters because the first draft sets the constrained boundaries.

374
00:17:48,080 –> 00:17:52,200
It decides what gets included as a category, what gets labeled as a risk, what becomes an

375
00:17:52,200 –> 00:17:53,960
assumption, and what never gets mentioned.

376
00:17:53,960 –> 00:17:57,280
And once the first draft exists, most teams won’t rebuild it from scratch.

377
00:17:57,280 –> 00:17:58,520
They’ll patch it.

378
00:17:58,520 –> 00:17:59,840
Patching feels efficient.

379
00:17:59,840 –> 00:18:01,960
Patching is also how technical debt accumulates.

380
00:18:01,960 –> 00:18:05,480
Draft first collaboration produces cognitive debt the same way.

381
00:18:05,480 –> 00:18:09,320
Small omissions in problem framing that accumulate until you get a decision that looks

382
00:18:09,320 –> 00:18:12,440
rational but doesn’t survive contact with reality.

383
00:18:12,440 –> 00:18:17,280
There’s another structural side effect that leaders underestimate, learning loops shorten.

384
00:18:17,280 –> 00:18:20,280
When people struggle through creating a structure, they learn the domain.

385
00:18:20,280 –> 00:18:21,680
They internalize the constraints.

386
00:18:21,680 –> 00:18:23,040
They develop conviction.

387
00:18:23,040 –> 00:18:27,160
When the draft arrives instantly, the struggle disappears, and so does the durable learning.

388
00:18:27,160 –> 00:18:28,640
You get speed without understanding.

389
00:18:28,640 –> 00:18:30,840
And in knowledge work, understanding is the asset.

390
00:18:30,840 –> 00:18:35,240
So the team becomes dependent on the artifact, not as a record, as a substitute for cognition.

391
00:18:35,240 –> 00:18:40,200
This is how epistemic agency loss begins structurally before it becomes a cultural problem.

392
00:18:40,200 –> 00:18:44,480
The organization stops building internal models and starts consuming external ones.

393
00:18:44,480 –> 00:18:46,920
Copilot becomes the default mechanism for starting.

394
00:18:46,920 –> 00:18:48,800
Humans become the mechanism for finishing.

395
00:18:48,800 –> 00:18:51,960
That sounds harmless until you realize what finishing works elects for.

396
00:18:51,960 –> 00:18:54,080
Tone, formatting, and plausibility.

397
00:18:54,080 –> 00:18:56,480
Not truth, not trade-offs, not risk appetite.

398
00:18:56,480 –> 00:18:59,240
In other words, you can ship a beautifully edited hallucination.

399
00:18:59,240 –> 00:19:02,440
Now this isn’t a claim that copilot always produces wrong content.

400
00:19:02,440 –> 00:19:05,880
This is a claim about what happens when the organization treats the first draft as the

401
00:19:05,880 –> 00:19:07,240
real start of thinking.

402
00:19:07,240 –> 00:19:09,520
It converts exploration into optimization.

403
00:19:09,520 –> 00:19:13,040
An optimization is only valuable after you pick the right objective function.

404
00:19:13,040 –> 00:19:14,600
Most teams never explicitly pick it.

405
00:19:14,600 –> 00:19:16,120
They inherit it from the draft.

406
00:19:16,120 –> 00:19:18,960
So the design response is not ban AI drafts.

407
00:19:18,960 –> 00:19:22,480
The response is to enforce intentional friction at the framing stage.

408
00:19:22,480 –> 00:19:23,880
A simple rule that works.

409
00:19:23,880 –> 00:19:27,360
No editing until the problem statement is human authored and agreed.

410
00:19:27,360 –> 00:19:28,360
Not long.

411
00:19:28,360 –> 00:19:29,360
Two paragraphs.

412
00:19:29,360 –> 00:19:32,360
What decision is being made, what constraints are real, what would make the recommendation

413
00:19:32,360 –> 00:19:33,360
unacceptable?

414
00:19:33,360 –> 00:19:37,320
If humans can’t write that in plain language, the work isn’t ready for drafting.

415
00:19:37,320 –> 00:19:38,840
It’s ready for thinking.

416
00:19:38,840 –> 00:19:43,520
And then when the draft exists, force the team to surface the hidden structure, assumptions,

417
00:19:43,520 –> 00:19:45,080
alternatives, and risks.

418
00:19:45,080 –> 00:19:48,280
Because AI loves to give you a single coherent story.

419
00:19:48,280 –> 00:19:52,040
Excellence requires the discipline to ask what story did we not tell.

420
00:19:52,040 –> 00:19:55,560
That’s the transition into the cognitive layer where copilot stops being an assistant

421
00:19:55,560 –> 00:19:58,560
and becomes a co-author of how the organization thinks.

422
00:19:58,560 –> 00:20:02,240
Cognitive copilot is a cognitive co-author, not an assistant.

423
00:20:02,240 –> 00:20:06,720
Okay, so basically the moment copilot writes the first coherent version of your idea, it’s

424
00:20:06,720 –> 00:20:08,120
no longer helping you type.

425
00:20:08,120 –> 00:20:09,800
It is co-authoring your cognition.

426
00:20:09,800 –> 00:20:12,040
Most people hear that and assume it’s philosophical.

427
00:20:12,040 –> 00:20:13,040
It isn’t.

428
00:20:13,040 –> 00:20:14,760
It’s mechanical.

429
00:20:14,760 –> 00:20:17,320
The system changes the order of operations.

430
00:20:17,320 –> 00:20:19,640
First you struggle to form a model then you wrote.

431
00:20:19,640 –> 00:20:23,760
Now you generate a model shaped artifact, then you decide whether you agree with it.

432
00:20:23,760 –> 00:20:27,840
That’s what sounds harmless until you ask what your organization is optimizing for.

433
00:20:27,840 –> 00:20:29,320
It reduces search cost.

434
00:20:29,320 –> 00:20:33,960
It can pull relevant threads, summarize prior work, synthesize across documents, and propose

435
00:20:33,960 –> 00:20:37,000
structure faster than a human team can assemble it.

436
00:20:37,000 –> 00:20:42,000
In Microsoft’s ecosystem, this works best when the system has access to organizational context.

437
00:20:42,000 –> 00:20:44,480
People, files, meetings, and activity signals.

438
00:20:44,480 –> 00:20:49,280
That’s the work graph and work IQ story, access plus memory plus inference, and yes that context

439
00:20:49,280 –> 00:20:50,800
makes outputs more relevant.

440
00:20:50,800 –> 00:20:53,320
It also makes the output feel like it knows.

441
00:20:53,320 –> 00:20:54,480
That’s the weird part.

442
00:20:54,480 –> 00:20:58,320
This reads like truth.

443
00:20:58,320 –> 00:21:00,040
When co-pilot drafts a briefing that references last week’s meeting, the current project plan,

444
00:21:00,040 –> 00:21:03,400
and the stakeholder email chain, it feels grounded, it feels authoritative.

445
00:21:03,400 –> 00:21:08,560
But what it’s really doing is assembling a plausible narrative from available artifacts.

446
00:21:08,560 –> 00:21:09,880
It is not building conviction.

447
00:21:09,880 –> 00:21:11,960
It is not taking responsibility for omissions.

448
00:21:11,960 –> 00:21:13,720
It is not experiencing consequence.

449
00:21:13,720 –> 00:21:16,920
It is doing what a narrative engine does, compressing, and completing.

450
00:21:16,920 –> 00:21:20,160
So the cognitive risk isn’t that co-pilot gives bad answers.

451
00:21:20,160 –> 00:21:23,500
The risk is that it gives good sounding answers early enough that humans stop doing the

452
00:21:23,500 –> 00:21:25,460
cognitive labor that produces judgment.

453
00:21:25,460 –> 00:21:28,180
And in most organizations, judgment is the scarce resource.

454
00:21:28,180 –> 00:21:29,780
Here is the hidden roll swap.

455
00:21:29,780 –> 00:21:31,940
Humans move from creators to reviewers.

456
00:21:31,940 –> 00:21:34,980
Reviewers are essential, but reviewers are also vulnerable.

457
00:21:34,980 –> 00:21:37,420
Review optimizes for does this look reasonable?

458
00:21:37,420 –> 00:21:39,740
Not is this the right framing of the problem?

459
00:21:39,740 –> 00:21:42,900
And the hardest areas in knowledge work are almost never spelling mistakes.

460
00:21:42,900 –> 00:21:43,900
They’re framing mistakes.

461
00:21:43,900 –> 00:21:47,940
So you see a draft, you tweak the tone, you adjust the recommendation, you add a caveat,

462
00:21:47,940 –> 00:21:48,940
and you ship.

463
00:21:48,940 –> 00:21:52,220
Meanwhile, the underlying model, the assumptions, the trade-offs, the risk appetite,

464
00:21:52,220 –> 00:21:53,700
was imported, not constructed.

465
00:21:53,700 –> 00:21:56,580
That’s why co-pilot as assistant is a comforting myth.

466
00:21:56,580 –> 00:21:58,460
In reality, it is something else.

467
00:21:58,460 –> 00:22:02,380
A cognitive co-author that proposes the mental scaffolding your team will stand on.

468
00:22:02,380 –> 00:22:03,660
Now what remains human?

469
00:22:03,660 –> 00:22:07,940
This is where leaders need to stop talking about creativity in the abstract and start talking

470
00:22:07,940 –> 00:22:08,940
about governance.

471
00:22:08,940 –> 00:22:13,740
Humans own meaning, humans own trade-offs, humans own ethical boundaries, humans own risk

472
00:22:13,740 –> 00:22:18,180
tolerance, humans own the final decision, and the consequences when it goes wrong.

473
00:22:18,180 –> 00:22:20,700
That’s not inspirational, that’s liability.

474
00:22:20,700 –> 00:22:25,060
A pilot can produce options, it can suggest a structure, it can propose a narrative arc,

475
00:22:25,060 –> 00:22:28,140
it can make the document look complete, but it can’t decide what you’re willing to be

476
00:22:28,140 –> 00:22:29,220
wrong about.

477
00:22:29,220 –> 00:22:32,020
And it definitely can’t decide what you’re willing to be blamed for.

478
00:22:32,020 –> 00:22:36,500
So the value claim is precise, AI expands the option space only if humans keep steering.

479
00:22:36,500 –> 00:22:39,420
If humans stop steering, AI doesn’t amplify intelligence.

480
00:22:39,420 –> 00:22:41,020
It amplifies momentum.

481
00:22:41,020 –> 00:22:45,020
It makes the organization faster at moving in whatever direction the first coherent draft

482
00:22:45,020 –> 00:22:46,020
implied.

483
00:22:46,020 –> 00:22:48,220
That’s not excellence, that’s just velocity.

484
00:22:48,220 –> 00:22:53,700
This is why Satya Nadella’s decision frameworks point matters, even outside software development.

485
00:22:53,700 –> 00:22:55,940
What the system needs is meta-cognition.

486
00:22:55,940 –> 00:23:00,140
Tools that help humans think about thinking, not tools that replace thinking with output.

487
00:23:00,140 –> 00:23:04,420
Because in an AI-mediated environment, the high leverage work is not typing, it’s asking

488
00:23:04,420 –> 00:23:08,140
what are we optimizing, what are we assuming, and what would change our minds.

489
00:23:08,140 –> 00:23:11,780
If your teams don’t do that explicitly, co-pilot will do it implicitly.

490
00:23:11,780 –> 00:23:13,220
And implicit is where errors hide.

491
00:23:13,220 –> 00:23:15,940
So a practical executive lens is this.

492
00:23:15,940 –> 00:23:19,860
It’s a treat, co-pilot, like an authorization compiler for narratives.

493
00:23:19,860 –> 00:23:24,140
It takes inputs, your documents, meetings and chats, and produces an output that looks

494
00:23:24,140 –> 00:23:25,900
like a decision-ready artifact.

495
00:23:25,900 –> 00:23:28,020
But compilation is not comprehension.

496
00:23:28,020 –> 00:23:29,220
Compilation is not accountability.

497
00:23:29,220 –> 00:23:32,580
You still need humans to validate the model, not just the words.

498
00:23:32,580 –> 00:23:35,980
And the minute you accept that, the leadership job becomes clear.

499
00:23:35,980 –> 00:23:38,060
Protect cognition as a first class asset.

500
00:23:38,060 –> 00:23:42,220
Design workflows where AI drafts are welcome, but human reasoning remains visible.

501
00:23:42,220 –> 00:23:44,500
Because if reasoning disappears, you don’t get automation.

502
00:23:44,500 –> 00:23:47,340
You get unauditable organizational belief.

503
00:23:47,340 –> 00:23:49,580
And the next failure mode isn’t wrong answer.

504
00:23:49,580 –> 00:23:53,820
It’s that nobody can explain why the organization believed it in the first place.

505
00:23:53,820 –> 00:23:57,380
Cognitive authority bias, the AI suggested, becomes a veto.

506
00:23:57,380 –> 00:24:00,900
Here’s where the real damage starts, and it looks like efficiency.

507
00:24:00,900 –> 00:24:05,100
Authority bias is the human habit of trusting an output because it looks confident, complete

508
00:24:05,100 –> 00:24:06,100
and official.

509
00:24:06,100 –> 00:24:09,140
In the AI era, that bias gets a new uniform.

510
00:24:09,140 –> 00:24:12,580
Fluent language, clean structure, and the illusion of neutrality.

511
00:24:12,580 –> 00:24:15,180
We don’t even need someone to say Microsoft recommends.

512
00:24:15,180 –> 00:24:19,180
The phrase co-pilot says is enough to end a debate, because the output sounds like it already

513
00:24:19,180 –> 00:24:20,180
did the thinking.

514
00:24:20,180 –> 00:24:22,460
And humans are cognitively lazy in groups.

515
00:24:22,460 –> 00:24:26,100
They will accept a coherent narrative to avoid the cost of disagreement, especially when

516
00:24:26,100 –> 00:24:28,820
time is tight and everyone wants to look decisive.

517
00:24:28,820 –> 00:24:30,220
This is the uncomfortable truth.

518
00:24:30,220 –> 00:24:33,180
AI doesn’t have to be right to become authoritative.

519
00:24:33,180 –> 00:24:34,780
It just has to be readable.

520
00:24:34,780 –> 00:24:36,620
Fluency is interpreted as competence.

521
00:24:36,620 –> 00:24:38,180
Completness is interpreted as diligence.

522
00:24:38,180 –> 00:24:41,140
And a tidy summary is interpreted as consensus.

523
00:24:41,140 –> 00:24:45,180
That’s how the AI suggested becomes a veto, not through explicit policy but through social

524
00:24:45,180 –> 00:24:46,180
pressure.

525
00:24:46,180 –> 00:24:50,020
Disagreting now feels like arguing with the system, not critiquing a draft, so people

526
00:24:50,020 –> 00:24:51,260
don’t critique.

527
00:24:51,260 –> 00:24:52,260
They comply.

528
00:24:52,260 –> 00:24:55,700
There are two failure patterns that show up everywhere.

529
00:24:55,700 –> 00:24:57,140
Commission errors and omission errors.

530
00:24:57,140 –> 00:24:58,700
Commission errors are the obvious ones.

531
00:24:58,700 –> 00:25:02,540
The model produces a recommendation that is wrong or incomplete or miscalibrated, and

532
00:25:02,540 –> 00:25:04,380
the team adopts it because it looks finished.

533
00:25:04,380 –> 00:25:05,700
The danger isn’t the error.

534
00:25:05,700 –> 00:25:09,660
The danger is that nobody feels responsible for challenging it because the output arrived

535
00:25:09,660 –> 00:25:11,340
with the posture of certainty.

536
00:25:11,340 –> 00:25:14,740
The team says, well, it’s probably fine that that sentence has killed more projects than

537
00:25:14,740 –> 00:25:16,260
budget cuts ever will.

538
00:25:16,260 –> 00:25:18,180
Omission errors are more subtle and more common.

539
00:25:18,180 –> 00:25:20,220
The AI output doesn’t explicitly lie.

540
00:25:20,220 –> 00:25:23,820
It simply fails to include the alternatives, edge cases or risks that a human would have

541
00:25:23,820 –> 00:25:27,140
surfaced if they were forced to reason from first principles.

542
00:25:27,140 –> 00:25:30,780
And because the output looks comprehensive, the team assumes the missing pieces don’t

543
00:25:30,780 –> 00:25:32,020
exist, but they do exist.

544
00:25:32,020 –> 00:25:35,940
They just weren’t retrieved or weren’t prioritized or weren’t present in the artifacts

545
00:25:35,940 –> 00:25:36,940
the model could see.

546
00:25:36,940 –> 00:25:39,060
In other words, the model didn’t forget.

547
00:25:39,060 –> 00:25:40,220
It never knew.

548
00:25:40,220 –> 00:25:43,340
And the organization didn’t notice because the paragraph looked complete.

549
00:25:43,340 –> 00:25:46,380
This is why authority bias is so corrosive in AI-mediated work.

550
00:25:46,380 –> 00:25:48,420
It makes absence look like irrelevance.

551
00:25:48,420 –> 00:25:49,860
And that’s how debate dies.

552
00:25:49,860 –> 00:25:51,180
Nobody explains this, right?

553
00:25:51,180 –> 00:25:53,580
But what gets suppressed isn’t disagreement.

554
00:25:53,580 –> 00:25:54,740
It’s epistemic work.

555
00:25:54,740 –> 00:25:58,900
The act of surfacing why you believe something, what evidence you’re using, and what would

556
00:25:58,900 –> 00:25:59,900
falsify it.

557
00:25:59,900 –> 00:26:01,900
AI outputs skip that process by default.

558
00:26:01,900 –> 00:26:04,940
They deliver conclusions and leave the reasoning implicit.

559
00:26:04,940 –> 00:26:08,060
So the team stops asking the questions that create resilience.

560
00:26:08,060 –> 00:26:09,020
What are we assuming?

561
00:26:09,020 –> 00:26:10,020
What did we not consider?

562
00:26:10,020 –> 00:26:11,380
What would make this wrong?

563
00:26:11,380 –> 00:26:13,580
Instead, they ask a lower quality question.

564
00:26:13,580 –> 00:26:14,660
Does this look good?

565
00:26:14,660 –> 00:26:17,660
And looks good is a terrible standard for high stakes work.

566
00:26:17,660 –> 00:26:22,260
This bias compounds inside Microsoft 365 because co-pilot is not a random chatbot in a

567
00:26:22,260 –> 00:26:23,260
vacuum.

568
00:26:23,260 –> 00:26:25,340
It’s grounded in your organization’s artifacts.

569
00:26:25,340 –> 00:26:29,020
It references last week’s meeting and the current deck and the email chain so it feels

570
00:26:29,020 –> 00:26:31,140
like it has institutional authority.

571
00:26:31,140 –> 00:26:33,180
But this is the distinction that matters.

572
00:26:33,180 –> 00:26:35,500
Institutional memory is not institutional judgment.

573
00:26:35,500 –> 00:26:38,620
Work, graph and work IQ can give co-pilot more context.

574
00:26:38,620 –> 00:26:39,620
That improves relevance.

575
00:26:39,620 –> 00:26:40,940
It does not produce governance.

576
00:26:40,940 –> 00:26:42,940
It does not create accountability.

577
00:26:42,940 –> 00:26:44,420
It does not validate trade-offs.

578
00:26:44,420 –> 00:26:48,180
It just makes the narrative harder to challenge because now it feels like the organization

579
00:26:48,180 –> 00:26:49,180
said it.

580
00:26:49,180 –> 00:26:50,340
So you get a new social dynamic.

581
00:26:50,340 –> 00:26:53,180
The person challenging the output becomes the blocker.

582
00:26:53,180 –> 00:26:56,100
The person accepting it becomes the executor.

583
00:26:56,100 –> 00:26:59,460
In a culture that rewards speed, the executor wins.

584
00:26:59,460 –> 00:27:03,820
Over time, the organization trains itself to treat AI outputs as defaults and human critique

585
00:27:03,820 –> 00:27:04,820
as friction.

586
00:27:04,820 –> 00:27:05,820
That’s backwards.

587
00:27:05,820 –> 00:27:10,300
Let it run, your decision quality becomes probabilistic, not because the models are probabilistic because

588
00:27:10,300 –> 00:27:14,460
your human stopped behaving like reviewers of truth and started behaving like editors of

589
00:27:14,460 –> 00:27:15,460
plausibility.

590
00:27:15,460 –> 00:27:16,460
So what’s the design move?

591
00:27:16,460 –> 00:27:19,460
You make authority explicit and you downgrade it on purpose.

592
00:27:19,460 –> 00:27:20,460
A simple norm.

593
00:27:20,460 –> 00:27:22,820
AI suggested is never a justification.

594
00:27:22,820 –> 00:27:23,820
It’s a starting point.

595
00:27:23,820 –> 00:27:27,180
If the recommendation matters, the human owner must restate the reasoning in their own

596
00:27:27,180 –> 00:27:31,100
words with assumptions and trade-offs and be willing to sign their name to it.

597
00:27:31,100 –> 00:27:32,940
Another norm, force alternatives, not ten.

598
00:27:32,940 –> 00:27:36,940
Two, if the team can’t articulate two plausible parts, they didn’t understand the problem.

599
00:27:36,940 –> 00:27:39,780
They just accepted the first narrative that compiled cleanly.

600
00:27:39,780 –> 00:27:44,140
And finally, you normalize critique of AI output as critique of a tool, not a person.

601
00:27:44,140 –> 00:27:47,700
Because if critique feels personal, the organization will choose silence.

602
00:27:47,700 –> 00:27:50,580
And silence is where authority bias becomes permanent.

603
00:27:50,580 –> 00:27:55,020
Once the AI suggested ends the conversation, you don’t have augmented intelligence.

604
00:27:55,020 –> 00:27:56,740
You have automated consensus.

605
00:27:56,740 –> 00:28:01,660
And automated consensus is how smart organizations make dumb decisions faster.

606
00:28:01,660 –> 00:28:02,660
They can’t do that.

607
00:28:02,660 –> 00:28:04,660
And the idea is that the system is not the same.

608
00:28:04,660 –> 00:28:06,660
And the idea is that the system is not the same.

609
00:28:06,660 –> 00:28:07,660
And the idea is that the system is not the same.

610
00:28:07,660 –> 00:28:08,660
And the idea is that the system is not the same.

611
00:28:08,660 –> 00:28:09,660
And the idea is that the system is not the same.

612
00:28:09,660 –> 00:28:10,660
And the idea is that the system is not the same.

613
00:28:10,660 –> 00:28:11,660
And the idea is that the system is not the same.

614
00:28:11,660 –> 00:28:12,660
And the idea is that the system is not the same.

615
00:28:12,660 –> 00:28:13,660
And the idea is that the system is not the same.

616
00:28:13,660 –> 00:28:14,660
And the idea is that the system is not the same.

617
00:28:14,660 –> 00:28:15,660
And the idea is that the system is not the same.

618
00:28:15,660 –> 00:28:16,660
And the idea is that the system is not the same.

619
00:28:16,660 –> 00:28:17,660
And the idea is that the system is not the same.

620
00:28:17,660 –> 00:28:18,660
And the idea is that the system is not the same.

621
00:28:18,660 –> 00:28:19,660
And the idea is that the system is not the same.

622
00:28:19,660 –> 00:28:31,660
And the idea is that the system is not the same.

623
00:28:31,660 –> 00:28:32,660
And the idea is that the system is not the same.

624
00:28:32,660 –> 00:28:33,660
And the idea is that the system is not the same.

625
00:28:33,660 –> 00:28:34,660
And the idea is that the system is not the same.

626
00:28:34,660 –> 00:28:35,660
And the idea is that the system is not the same.

627
00:28:35,660 –> 00:28:36,660
And the idea is that the system is not the same.

628
00:28:36,660 –> 00:28:37,660
And the idea is that the system is not the same.

629
00:28:37,660 –> 00:28:38,660
And the idea is that the system is not the same.

630
00:28:38,660 –> 00:28:39,660
And the idea is that the system is not the same.

631
00:28:39,660 –> 00:28:40,660
And the idea is that the system is not the same.

632
00:28:40,660 –> 00:28:41,660
And the idea is that the system is not the same.

633
00:28:41,660 –> 00:28:42,660
And the idea is that the system is not the same.

634
00:28:42,660 –> 00:28:43,660
And the idea is that the system is not the same.

635
00:28:43,660 –> 00:28:44,660
And the idea is that the system is not the same.

636
00:28:44,660 –> 00:28:45,660
And the idea is that the system is not the same.

637
00:28:45,660 –> 00:28:50,660
And the answer is a paraphrase of a copilot summary, not malicious, not lazy, just normal.

638
00:28:50,660 –> 00:28:55,660
The person doesn’t remember the original debate because they never formed a durable internal model of it.

639
00:28:55,660 –> 00:28:57,660
They remember the artifact, they remember the narrative.

640
00:28:57,660 –> 00:29:00,660
So the organization becomes a distributed recall system.

641
00:29:00,660 –> 00:29:02,660
And the humans become retrieval clients.

642
00:29:02,660 –> 00:29:05,660
That distinction matters because human judgment isn’t just making a choice.

643
00:29:05,660 –> 00:29:07,660
It’s holding an internal model of the world.

644
00:29:07,660 –> 00:29:13,660
Constraints, dependencies, risks, incentives, and the messy parts that never made it into the summary.

645
00:29:13,660 –> 00:29:16,660
If humans stop constructing those models, they can still approve decisions.

646
00:29:16,660 –> 00:29:19,660
But they can’t anticipate second-order effects.

647
00:29:19,660 –> 00:29:22,660
They become fast and fragile.

648
00:29:22,660 –> 00:29:27,660
The counterintuitive part is that this happens even when the AI output is accurate.

649
00:29:27,660 –> 00:29:29,660
Because the problem isn’t correctness.

650
00:29:29,660 –> 00:29:30,660
The problem is cognitive ownership.

651
00:29:30,660 –> 00:29:35,660
When a team repeatedly starts with a generated draft, repeatedly accepts a generated recap,

652
00:29:35,660 –> 00:29:37,660
repeatedly uses AI to catch up.

653
00:29:37,660 –> 00:29:40,660
They stop practicing the mental labor that turns information into understanding.

654
00:29:40,660 –> 00:29:42,660
Working memory gets outsourced.

655
00:29:42,660 –> 00:29:44,660
Synthesis gets outsourced.

656
00:29:44,660 –> 00:29:48,660
Even curiosity gets outsourced because the machine is always ready with a plausible next step.

657
00:29:48,660 –> 00:29:51,660
Over time, the organization loses what it thinks it gained.

658
00:29:51,660 –> 00:29:52,660
Speed.

659
00:29:52,660 –> 00:29:55,660
Because speed without internal models produces rework.

660
00:29:55,660 –> 00:29:57,660
And rework is just delayed cognition.

661
00:29:57,660 –> 00:30:00,660
This is the productivity paradox showing up at the cognitive layer.

662
00:30:00,660 –> 00:30:03,660
Efficiency rises, but coherence drops.

663
00:30:03,660 –> 00:30:07,660
The organization produces more artifacts per week, but fewer shared mental models per quarter.

664
00:30:07,660 –> 00:30:10,660
It feels like progress because motion is measurable.

665
00:30:10,660 –> 00:30:11,660
Understanding isn’t.

666
00:30:11,660 –> 00:30:13,660
Now put this inside Microsoft 365.

667
00:30:13,660 –> 00:30:19,660
The work graph is effectively the organization’s memory of people, meetings, files, and activity signals.

668
00:30:19,660 –> 00:30:21,660
Work IQ builds an intelligence layer on top.

669
00:30:21,660 –> 00:30:23,660
Access, memory, inference.

670
00:30:23,660 –> 00:30:28,660
That’s what allows co-pilot to generate a briefing that feels like it’s inside your business, not outside it.

671
00:30:28,660 –> 00:30:30,660
And when that works, well, it’s intoxicating.

672
00:30:30,660 –> 00:30:32,660
You stop searching. You stop asking people.

673
00:30:32,660 –> 00:30:35,660
You stop reading the raw sources. You stop redriving context.

674
00:30:35,660 –> 00:30:38,660
Except the synthesis, that’s where epistemic agency dies.

675
00:30:38,660 –> 00:30:41,660
The organization no longer insists on primary artifacts.

676
00:30:41,660 –> 00:30:42,660
It insists on summaries.

677
00:30:42,660 –> 00:30:45,660
And summaries are not knowledge. They are a claim about knowledge.

678
00:30:45,660 –> 00:30:49,660
So the risk becomes specific, dependency on an unorditable narrator.

679
00:30:49,660 –> 00:30:52,660
Not unauditable because Microsoft didn’t build logging.

680
00:30:52,660 –> 00:30:57,660
Unauditable because the human workflow stops preserving the reasoning traces that made the conclusion defensible.

681
00:30:57,660 –> 00:31:00,660
The system can show you what it cited. It can show you what it wrote.

682
00:31:00,660 –> 00:31:04,660
It cannot show you what the team believed, what the team rejected, and why.

683
00:31:04,660 –> 00:31:08,660
What the team did to those things live in human cognition, or they evaporate.

684
00:31:08,660 –> 00:31:11,660
This is why the leadership obligation is not teach better prompts.

685
00:31:11,660 –> 00:31:14,660
The obligation is to preserve epistemic agency as a design requirement.

686
00:31:14,660 –> 00:31:18,660
You have to build workflows where humans still do three things explicitly.

687
00:31:18,660 –> 00:31:22,660
First, direct inquiry. What are we trying to find out and what would count as evidence?

688
00:31:22,660 –> 00:31:24,660
Second, test claims.

689
00:31:24,660 –> 00:31:27,660
What assumptions are embedded in this narrative and what would falsify them?

690
00:31:27,660 –> 00:31:29,660
Third, own conclusions.

691
00:31:29,660 –> 00:31:34,660
Who is accountable for this decision and can they restate the reasoning without leaning on the AI suggested?

692
00:31:34,660 –> 00:31:39,660
If your organization can’t do those three things, it’s not an AI problem. It’s a governance failure.

693
00:31:39,660 –> 00:31:44,660
And it gets worse in groups because groups will always default to the path of least resistance.

694
00:31:44,660 –> 00:31:48,660
The least resistance path is accepting a clean narrative that reduces tension.

695
00:31:48,660 –> 00:31:53,660
It avoids conflict, it avoids embarrassment, it avoids the cost of being the person who says, “I don’t think we know that.”

696
00:31:53,660 –> 00:31:55,660
But that cost is the price of excellence.

697
00:31:55,660 –> 00:32:01,660
So here’s the line you want to remember. Copilot can accelerate knowledge work. It cannot replace epistemic responsibility.

698
00:32:01,660 –> 00:32:05,660
If you allow your organization to outsource how we know, you don’t become modern.

699
00:32:05,660 –> 00:32:08,660
You become a high throughput rumor mill with better formatting.

700
00:32:08,660 –> 00:32:14,660
And once that happens, every other control you think you have, policy, training, culture becomes cosmetic.

701
00:32:14,660 –> 00:32:18,660
Because the organization’s truth pipeline is now optimized for plausibility not for understanding.

702
00:32:18,660 –> 00:32:19,660
That’s the real risk.

703
00:32:19,660 –> 00:32:23,660
Cognitive, work, graph, work IQ. Context becomes the product.

704
00:32:23,660 –> 00:32:27,660
Once epistemic agency starts eroding, leaders reach for a fix that sounds sensible.

705
00:32:27,660 –> 00:32:29,660
Fine, we’ll improve the context.

706
00:32:29,660 –> 00:32:32,660
That’s where work, graph, and work IQ enter the story.

707
00:32:32,660 –> 00:32:39,660
And yes, in Microsoft terms, graph is the organizational memory, people meetings, chats, documents, relationships, and activity signals.

708
00:32:39,660 –> 00:32:44,660
Work IQ is the intelligence layer that sits on top. Access plus memory plus inference.

709
00:32:44,660 –> 00:32:48,660
It’s what makes Copilot stop sounding generic and start sounding like it attended your meetings.

710
00:32:48,660 –> 00:32:50,660
But here’s the architectural reality.

711
00:32:50,660 –> 00:32:57,660
Once context becomes machine readable, context becomes the product, not the spreadsheet, not the deck, not the transcript, the context.

712
00:32:57,660 –> 00:33:01,660
Because the value of an AI system in the enterprise isn’t it can write.

713
00:33:01,660 –> 00:33:10,660
Any model can write. The differentiator is whether it can retrieve the right internal state, assemble the right narrative, and root the right next action without you spelling it out.

714
00:33:10,660 –> 00:33:14,660
That’s what work IQ is designed to do. And it changes what organizations fight over.

715
00:33:14,660 –> 00:33:21,660
In a pre-AI workplace teams fought over outputs whose deck, whose notes, whose decision memo, whose version is current.

716
00:33:21,660 –> 00:33:34,660
In an AI mediated workplace, teams start fighting over the underlying context, which artifacts are visible, which are authoritative, which are tagged, which are discoverable, and which are buried in private chats and personal one drives.

717
00:33:34,660 –> 00:33:36,660
Because the machine can only summarize what it can see.

718
00:33:36,660 –> 00:33:40,660
So the organization quietly shifts from right better to curate better.

719
00:33:40,660 –> 00:33:46,660
From produce to index, from share to shape. That distinction matters because curating context is power.

720
00:33:46,660 –> 00:33:51,660
If your projects artifacts are clean, well linked, and consistently located, co-pilot will make your project look coherent.

721
00:33:51,660 –> 00:34:01,660
If your work is fragmented, ambiguous, or scattered across informal channels, co-pilot will make your project look incoherent, not because reality changed, because visibility changed.

722
00:34:01,660 –> 00:34:06,660
So the work graph becomes a narrative engine, and work IQ becomes a narrative accelerator.

723
00:34:06,660 –> 00:34:16,660
Which sounds fine until you remember, narrative is not truth. Work IQ will infer. It will rank. It will prioritize. It will compress. It will act like a helpful librarian with a sense of urgency.

724
00:34:16,660 –> 00:34:23,660
And the organization will treat that ranking as an objective description of what matters, because it surfaced inside the flow of work.

725
00:34:23,660 –> 00:34:28,660
This is the danger. Infer truth becomes operational truth. Someone asks co-pilot, what’s the status?

726
00:34:28,660 –> 00:34:33,660
And it responds with a crisp summary grounded in the latest email thread and the most recent doc edit.

727
00:34:33,660 –> 00:34:43,660
That answer will get forwarded. It will become the update. It will drive next steps, even if it missed the real blocker, because the blocker lived in a hallway conversation, a side chat, or a meeting where nobody saved the decision logs.

728
00:34:43,660 –> 00:34:52,660
So leaders think they are buying intelligence. They are buying a new dependency. The organization’s ability to know becomes constrained by what the graph contains.

729
00:34:52,660 –> 00:34:54,660
Which means the hard work is not deploying co-pilot.

730
00:34:54,660 –> 00:35:05,660
It’s engineering the organization’s memory, so it doesn’t lie by omission. And that brings us back to why humans become irreplaceable. Humans are the only part of the system that can interpret context. Not just retrieve it.

731
00:35:05,660 –> 00:35:13,660
A graph can tell you that two people met. It can’t tell you that one of them left unconvinced. It can tell you a document was edited. It can’t tell you the edit was political, not technical.

732
00:35:13,660 –> 00:35:25,660
It can show you that a decision was made. It can’t tell you the decision was made under fatigue or under pressure or under false consensus created by a tidy summary. Work IQ gives you a relevance engine. Humans provide the meaning engine.

733
00:35:25,660 –> 00:35:29,660
So the executive move is to treat work graph like a control plane for collaboration.

734
00:35:29,660 –> 00:35:36,660
If you don’t intentionally design where knowledge lives, how it’s linked and what counts as authoritative, the AI will still operate.

735
00:35:36,660 –> 00:35:45,660
It will just operate on accidental context and accidental context produces conditional chaos, fast, plausible outputs that root the organization into the wrong decisions.

736
00:35:45,660 –> 00:35:54,660
This is why context engineering isn’t a cute term. It’s governance. You are deciding what the organization will remember, what it will forget and what it will treat as reality.

737
00:35:54,660 –> 00:36:00,660
And there’s one more uncomfortable consequence. Once context becomes the product, people will start optimizing for the graph.

738
00:36:00,660 –> 00:36:12,660
And if you want to create for retrieval, they will perform clarity. They will over document to be visible. They will shape artifacts to influence how co-pilot summarizes them. That’s not paranoia. That’s rational behavior in the system where summaries move power.

739
00:36:12,660 –> 00:36:18,660
So if you want excellence, you don’t just ask is co-pilot accurate. You ask who controls the context that co-pilot treats as truth.

740
00:36:18,660 –> 00:36:25,660
Because when AI is the narrator, whoever curates the narrative, shapes the decision. And that is the next layer of erosion.

741
00:36:25,660 –> 00:36:32,660
Whoever curates the narrative shapes the decision. So now the real game emerges and it isn’t technology. It’s power.

742
00:36:32,660 –> 00:36:40,660
Not the theatrical kind, the quiet kind, who gets believed, who gets cited and whose version becomes the starting point for the next decision.

743
00:36:40,660 –> 00:36:48,660
In AI-mediated collaboration, power shifts toward whoever curates the narrative. Not necessarily who knows the most. Not necessarily who’s highest in the org chart.

744
00:36:48,660 –> 00:36:56,660
The person who controls what gets captured, summarized, forwarded and framed. Because the organization doesn’t run on truth. It runs on the story that feels true enough to act on.

745
00:36:56,660 –> 00:37:08,660
Before AI, narrative control was expensive. You needed time to write the update. You needed status meetings to socialize it. You needed someone to take notes. You needed the political capital to tell the story. And keep it consistent across conversations.

746
00:37:08,660 –> 00:37:20,660
Now narrative control is cheap. A recap can be generated in seconds. A status update can be synthesized from 10 threads. A leadership brief can be created from a folder of documents. And because those artifacts look complete, they get treated like facts.

747
00:37:20,660 –> 00:37:35,660
This is the structural consequence of AI as participant. Information flow, flattens and hardens at the same time. It flattens because a junior employee can generate a brief that would have taken a manager two hours. They can publish clarity on demand. They can root context across functions without waiting for hierarchy to package it.

748
00:37:35,660 –> 00:37:49,660
But it also hardens because once the artifact exists, it becomes the official story. It becomes searchable. It becomes quotable. It becomes the input to the next co-pilot query. And the organization starts using it as a substitute for asking people what they meant.

749
00:37:49,660 –> 00:38:02,660
So you get new gatekeepers, not keepers in the old sense of withholding access, but in the new sense of selecting what counts as relevant. Prompt competence becomes influence. Curation competence becomes authority. Publishing competence becomes power. And it’s not malicious. It’s mechanical.

750
00:38:02,660 –> 00:38:16,660
If a person consistently produces clean summaries, leadership will rely on them. If a team consistently maintains a well-structured sharepoint site or a single canonical plan, co-pilot will make them look coherent. Coherence becomes reputation. And reputation becomes influence.

751
00:38:16,660 –> 00:38:29,660
Meanwhile, the teams whose work is messy, ambiguous or distributed across chat will look disorganized in the AI layer even if the underlying work is strong. That’s not fair. It’s just how retrieval works. This is where leaders get surprised. They assume AI democratizes work.

752
00:38:29,660 –> 00:38:37,660
It does, but it also centralizes the narrative layer because the people who can shape the inputs and the artifacts that get treated as authoritative shape the outputs.

753
00:38:37,660 –> 00:38:44,660
And the outputs are what executives see, what gets actioned and what becomes the organizational memory. So decision-making becomes more like media.

754
00:38:44,660 –> 00:38:51,660
Agenda control shifts to whoever decides what gets summarized and what gets ignored. A meeting recap can highlight risk or it can highlight progress.

755
00:38:51,660 –> 00:39:03,660
A team’s thread summary can include descent or it can compress descent into a polite. Some concerns were raised. A project update can surface dependencies or it can bury them under next steps.

756
00:39:03,660 –> 00:39:09,660
Same meeting, same facts, different narrative and the organization will act on the narrative, not on the raw transcript that nobody reads.

757
00:39:09,660 –> 00:39:14,660
This is why recap is input, not truth, wasn’t acute norm. It was a governance requirement.

758
00:39:14,660 –> 00:39:27,660
Because once AI accelerates narrative production, the cost of rewriting reality drops and the incentives to do it rise. Now add the psychological piece. People feel this shift before they can describe it. They’ll say things like decisions are being made without us.

759
00:39:27,660 –> 00:39:33,660
Or I don’t recognize our work in that summary or we keep getting surprised by what leadership thinks is happening.

760
00:39:33,660 –> 00:39:36,660
That’s not drama. That’s the lived experience of narrative drift.

761
00:39:36,660 –> 00:39:45,660
So the leadership risk is specific, power shifts without governance and the organization confuses that shift for progress because the artifacts look better.

762
00:39:45,660 –> 00:39:54,660
Excellence requires you to make narrative power visible. Who owns the recap? Who owns the decision log? Where is the canonical source of truth? What is the escalation path when the summary is wrong?

763
00:39:54,660 –> 00:40:02,660
And critically, who is accountable for the interpretation not just the transcript? Because AI can capture, AI can synthesize, AI can draft.

764
00:40:02,660 –> 00:40:12,660
Accountability still lives with humans even when the humans pretend it doesn’t. If you don’t assign narrative ownership intentionally, it will self assign to whoever is fastest, most fluent, or most proximate to leadership.

765
00:40:12,660 –> 00:40:21,660
And then you’ll get a shadow hierarchy, not based on role, but on who controls the story. And once that exists, your culture problems won’t announce themselves as power dynamics.

766
00:40:21,660 –> 00:40:31,660
They’ll show up as human experience issues, psychological safety drops, ownership gets weird, and people disengage while output looks fine. Which is exactly where this goes next.

767
00:40:31,660 –> 00:40:40,660
Experiential, psychological safety under AI, speak up changes shape. Once narrative control shifts, the first thing that breaks is not a process, its voice.

768
00:40:40,660 –> 00:40:57,660
Psychological safety is the system property that determines whether people will say that summary is wrong, or that framing is dangerous, or we are missing the real constraint. In an AI-mediated workplace, speak up doesn’t disappear. It mutates. It becomes more costly, more ambiguous, and easier to punish without anyone admitting they punished it.

769
00:40:57,660 –> 00:41:07,660
Before AI, challenging a person was socially risky, but at least the target was clear. You could disagree with Mark’s interpretation of the meeting. You could argue with Sarah’s proposal. You could ask for evidence.

770
00:41:07,660 –> 00:41:18,660
And the room understood the game, humans disagree, humans negotiate. Now the challenge often targets an artifact that looks official, a recap, a synthesized thread, a recommended next step.

771
00:41:18,660 –> 00:41:23,660
The output arrives with a calm, neutral tone, and that tone changes the social meaning of descent.

772
00:41:23,660 –> 00:41:29,660
Critiquing it feels less like, “I have a different view.” And more like, “I’m disputing reality.” That distinction matters.

773
00:41:29,660 –> 00:41:42,660
Because teams don’t experience AI output as someone’s draft, they experience it as the system speaking, and people have been trained for decades to treat systems as objective. So the psychological cost of disagreement rises even when the disagreement is correct.

774
00:41:42,660 –> 00:41:52,660
This is where the fear shifts, it’s no longer only fear of being wrong in front of peers, it’s fear of being wrong versus the machine, fear of looking slow, fear of looking like the person who doesn’t get it.

775
00:41:52,660 –> 00:42:02,660
And fear of challenging an artifact that leadership might already have read and forwarded, so what happens? People stop challenging outputs directly, they root concern sideways, they DM a colleague.

776
00:42:02,660 –> 00:42:15,660
Is it just me or is that recap missing the main issue? They rewrite a sentence quietly instead of raising the underlying disagreement. They accept the summary but try to fix it later in the document. They delay because delay is safer than open descent.

777
00:42:15,660 –> 00:42:25,660
This is how errors become durable, because the one thing that prevents narrative drift is visible descent at the moment the narrative is formed. When descent goes private, the organization loses the corrective mechanism.

778
00:42:25,660 –> 00:42:30,660
The artifact propagates and the people who know it’s wrong assume someone else will handle it, nobody does.

779
00:42:30,660 –> 00:42:37,660
Now add the identity threat, in draft first environments, critique already feels personal because the artifact represents someone’s competence.

780
00:42:37,660 –> 00:42:47,660
AI intensifies that because the person who posted it may have invested status into it, I produce the clean version. Challenging it isn’t critiquing the tool, it’s critiquing my contribution.

781
00:42:47,660 –> 00:42:57,660
So defensive behavior shows up fast, teams defend outputs instead of exploring ideas. They argue about wording, not assumptions, they treat objections as delays, not as risk discovery.

782
00:42:57,660 –> 00:43:07,660
And because AI outputs look complete early, the organization perceives exploration as inefficiencies, that’s how debate suppression happens without anyone saying stop debating. It’s not censorship, it’s cost engineering.

783
00:43:07,660 –> 00:43:13,660
Make descent feel expensive and people will self-sensor. This is also why psychological safety can’t be handled with slogans.

784
00:43:13,660 –> 00:43:24,660
The system has changed, the social incentives have changed, so leaders need to do something unfashionable. Design explicit permission to challenge the machine, not be brave, not speak up, design it.

785
00:43:24,660 –> 00:43:32,660
A simple move, normalize the phrase, the summary is a draft, not a verdict. Say it every time, say it until it sounds boring.

786
00:43:32,660 –> 00:43:38,660
Because boredom is the point you’re trying to remove the social drama from disagreeing with AI output, you’re trying to make critique procedural.

787
00:43:38,660 –> 00:43:48,660
Another move, require a descent slot in the artifact itself, not a side conversation. In the recap, open questions, risks, minority view, assumptions.

788
00:43:48,660 –> 00:44:02,660
When descent has a named container, speaking up stops being an act of rebellion and becomes part of the workflow, and the leadership behavior has to match the structure. If someone challenges a copilot summary, and the leader responds with irritation, the lesson is permanent. Don’t do that again.

789
00:44:02,660 –> 00:44:17,660
If the leader responds with good catch, revise the artifact, then the lesson is equally permanent. The system is editable and reality is negotiated by humans. That’s what psychological safety is in architectural terms, the ability of the system to accept corrections without punishing the correction mechanism.

790
00:44:17,660 –> 00:44:25,660
Because AI will be wrong, sometimes subtly, sometimes socially, sometimes strategically. The only question is whether your people will tell you while it still matters.

791
00:44:25,660 –> 00:44:34,660
Experiential, psychological ownership, productivity up, pride ambiguous. One psychological safety gets shaky, the next failure looks less dramatic and more efficient.

792
00:44:34,660 –> 00:44:38,660
Productivity goes up. Pride gets weird.

793
00:44:38,660 –> 00:44:54,660
Psychological ownership is the sense that this is mine, not legally, not politically, but cognitively. It’s the invisible bond between a person and a piece of work that makes them care about details nobody asked for, defend quality without being told, and feel responsible for outcomes they can’t fully control.

794
00:44:54,660 –> 00:45:08,660
Organizations depend on that bond more than they admit, because most quality isn’t enforced by policy. It’s enforced by people who feel ownership. Traditionally, psychological ownership comes from three pathways. Control, intimate knowledge, and self-investment.

795
00:45:08,660 –> 00:45:15,660
Control means the person had real agency, they chose the approach, negotiated constraints, and made decisions.

796
00:45:15,660 –> 00:45:26,660
Intimate knowledge means they understand the work deeply enough to see what’s missing. Self-investment means they paid a cognitive price, time, effort, and the discomfort of not knowing until they figured it out.

797
00:45:26,660 –> 00:45:35,660
AI shifts all three at once. Draft first workflows reduce self-investment. You don’t struggle through a first draft, you curate one. You don’t wrestle with structure, you accept structure and adjust it.

798
00:45:35,660 –> 00:45:49,660
The emotional arc changes from “I build this, I improve this.” That seems fine. But over time it produces a specific pattern, throughput rises and attachment declines, and attachment is what makes people do the last 10% that prevents the next incident review.

799
00:45:49,660 –> 00:45:56,660
There’s also a quieter problem, authorship becomes socially ambiguous. In a pre-AI world, a document had an author, a reviewer, maybe an editor.

800
00:45:56,660 –> 00:46:10,660
You could argue about quality because you knew who owned the thinking. In an AI-mediated world, you get mixed roles, the prompt holder, the human editor, the approver, and the person who gets blamed when it fails. Those roles rarely align, so pride becomes diluted.

801
00:46:10,660 –> 00:46:16,660
If a person can’t confidently say, “I wrote this and I stand behind it,” they also won’t say, “This is wrong and I’ll fix it.”

802
00:46:16,660 –> 00:46:23,660
They’ll say, “It’s good enough, and ship it because good enough is the only defensible stance when authorship is distributed and accountability is unclear.”

803
00:46:23,660 –> 00:46:32,660
This is not a personality issue. Its system design AI also changes the social reward function. Fast drafts get praised, clean summaries get forwarded, polish decks get celebrated.

804
00:46:32,660 –> 00:46:41,660
The organization starts rewarding artifact quality, not reasoning quality. And people adapt. They optimize for what gets noticed, output volume and presentational coherence.

805
00:46:41,660 –> 00:46:49,660
Meanwhile, the work that builds real ownership, deep understanding, uncomfortable trade-offs, explicit reasoning, gets compressed out of the visible workflow.

806
00:46:49,660 –> 00:46:56,660
That produces a new kind of disengagement that’s hard for leaders to spot. People look busy. Artifacts keep shipping.

807
00:46:56,660 –> 00:47:06,660
But the internal posture shifts from, “I own this outcome to, I process this request.” That is how excellence erodes, not through sabotage, but through steady replacement of authorship with participation.

808
00:47:06,660 –> 00:47:14,660
Here’s the counter-intuitive part. AI can increase perceived productivity while decreasing craftsmanship, because craftsmanship requires friction.

809
00:47:14,660 –> 00:47:25,660
It requires the person to feel the weight of consequences and the intimacy of details. When the work arrives, pre-shaped and pre-worded, the person becomes a custodian, not a creator.

810
00:47:25,660 –> 00:47:30,660
Custodians keep things moving, creators build conviction, and conviction is what holds under pressure.

811
00:47:30,660 –> 00:47:37,660
So what does leadership do without turning this into a moral lecture about human pride? They make ownership explicit in the places that matter.

812
00:47:37,660 –> 00:47:46,660
High-impact artifacts need an accountable owner, visibly named, who is responsible for the recommendation and its reasoning even if the draft was co-authored by AI.

813
00:47:46,660 –> 00:47:57,660
That single design move restores the ownership pathway of control. Someone is steering, and everyone knows who it is. Second, preserve self-investment where it pays off. Not everywhere, not on low stakes work.

814
00:47:57,660 –> 00:48:10,660
But on decisions that shape policy, risk, posture, people outcomes, and strategic direction, those artifacts should force humans to write the problem statement and the decision rationale in plain language because that’s where ownership is formed.

815
00:48:10,660 –> 00:48:17,660
Third, treat AI contribution as normal, but not invisible. Invisible co-authorship destroys pride and accountability simultaneously.

816
00:48:17,660 –> 00:48:31,660
If the organization normalizes copilot-wrote it as a shameful secret, people will hide it and then nobody can calibrate trust. If the organization normalizes it as disclosed tooling like using Excel, then people can still own the outcome without pretending they typed every word.

817
00:48:31,660 –> 00:48:37,660
That’s the practical truth. AI doesn’t remove the need for human ownership. It removes the default conditions that used to generate it.

818
00:48:37,660 –> 00:48:51,660
And if leaders don’t rebuild those conditions intentionally, they won’t get a workforce of empowered super workers. They’ll get a workforce of high-speed editors who don’t feel the work is theirs. Right up until something breaks and everyone asks, “Who approved this?” Viva Insights.

819
00:48:51,660 –> 00:49:03,660
Signals as inside, not surveillance. Now, leadership typically responds to all of this with the same reflex. Measure it. And yes, measurement matters, but in the AI era, measurement is also a weapon.

820
00:49:03,660 –> 00:49:13,660
So if you don’t understand what you’re measuring and how people will experience it, you’ll turn insight into surveillance in about three weeks. That distinction matters. Viva Insights sits right on that fault line.

821
00:49:13,660 –> 00:49:21,660
It analyzes collaboration patterns from Microsoft 365 signals, meetings, email chats, calls, focus time after hours activity.

822
00:49:21,660 –> 00:49:33,660
The consensus trends at the individual level, the manager level and the organizational level, usually with privacy protections like aggregation and the identification. The intent is reasonable. Show you where collaboration is degrading performance and well-being.

823
00:49:33,660 –> 00:49:45,660
But the system behavior is predictable. If leaders treat the dashboard as a control panel for people, people will treat the system as an adversary. So the first principle is simple. Viva Insights is not a productivity scoreboard. It’s a drift detector.

824
00:49:45,660 –> 00:50:01,660
It detects collaboration, debt, overload, fragmentation, shallow async, meetings, brawl, after hours, creep. And in an AI-mediated workplace, those patterns can get worse while output volume looks better, because co-pilot can keep shipping drafts even when humans are exhausted. That’s the whole problem.

825
00:50:01,660 –> 00:50:15,660
Throughput can mask strain. Signals make the hidden cost visible, but signals don’t explain causality. If a team has high meeting hours, the dashboard can show it. It cannot tell you whether those meetings are waste, governance, crisis, response, onboarding or actual decision work.

826
00:50:15,660 –> 00:50:24,660
If after hours work rises, Viva can flag it. It cannot tell you if it’s a temporary surge for a launch or a structural failure in staffing or a cultural addiction to responsiveness.

827
00:50:24,660 –> 00:50:37,660
So the executive job is interpretation. Then interpretation is human. This is where the privacy boundary is not a legal detail. It’s the product. Leaders must be explicit. The purpose of these signals is to improve the system, not to punish individuals.

828
00:50:37,660 –> 00:50:51,660
Because the moment employees believe these metrics affect performance evaluation, you’ve created two outcomes. People will hide their work and your data will get worse. The system will still show improvement. It will be lying. And this is where AI changes the stakes again.

829
00:50:51,660 –> 00:51:06,660
Once co-pilot becomes a co-author, you can’t manage collaboration by counting artifacts. You have to manage it by watching the conditions that produce coherent thinking, protected focus time, real debate, clear decision structures and norms that keep accountability visible.

830
00:51:06,660 –> 00:51:16,660
Viva insights can help you see whether those conditions exist. For example, our meetings shrinking, but chat volume exploding. That often means decisions moved into async without the structure to support them.

831
00:51:16,660 –> 00:51:30,660
Our employees in constant interruption patterns, meetings, notifications, context switching every few minutes. That tells you the cognitive environment is hostile to deep work. And AI will simply paper over the damage by generating good enough outputs while understanding declines.

832
00:51:30,660 –> 00:51:37,660
Our managers spending their week in meetings, leaving no time for coaching. That’s not a wellness problem. That’s a leadership throughput problem.

833
00:51:37,660 –> 00:51:53,660
The most dangerous leadership move is to treat these dashboards as proof that a co-pilot rollout worked. Adoption and impact are not the same thing. High usage can mean value. It can also mean dependency. It can also mean people are using AI to survive the collaboration chaos you refuse to fix.

834
00:51:53,660 –> 00:51:57,660
So the correct posture is signals are prompts for inquiry, not verdicts.

835
00:51:57,660 –> 00:52:08,660
A productive pattern is to use Viva insights to ask better questions in leadership reviews, not who is overworking instead, what in our operating model is producing after hours work and what will we remove?

836
00:52:08,660 –> 00:52:20,660
Not why is this team meeting so much? Instead, which decisions are unclear enough that we keep relitigating them? Not why are they fragmented? Instead, where did we remove friction that was actually protecting coherence?

837
00:52:20,660 –> 00:52:34,660
And then there’s the AI-specific layer. If the system starts generating more recaps, more summaries, more drafts, you should expect the organization’s sense of progress to rise even if decision quality is decaying, Viva’s value is that it can show the mismatch.

838
00:52:34,660 –> 00:52:43,660
Efficiency rising, but focus time collapsing. Meeting countdown but network fragmentation up. Outputs up but burnout signals up.

839
00:52:43,660 –> 00:52:55,660
That’s your early warning that you’re not building excellence, you’re building conditional chaos at scale. So if you’re going to deploy Viva insights in an AI era, you need one non-negotiable governance rule. Treat the data as feedback, not control.

840
00:52:55,660 –> 00:53:05,660
Use it to redesign work, not to police workers. Because the second you weaponize signals, people will stop being honest and the platform will faithfully optimize you into a false sense of stability.

841
00:53:05,660 –> 00:53:10,660
And that’s the fastest way to lose the one thing AI cannot replace, humans willing to tell you the truth.

842
00:53:10,660 –> 00:53:21,660
The productivity paradox. Efficiency rises while coherence drops. Now connect the dots because this is where leaders get fooled by their own dashboards. The productivity paradox in AI-mediated work is simple.

843
00:53:21,660 –> 00:53:32,660
You can remove friction and still make the organization worse at deciding, you see it as faster. But what actually happened is you traded sense-making for output, shorter meetings can mean better discipline.

844
00:53:32,660 –> 00:53:42,660
Or they can mean the decision just moved somewhere else, usually into chat, usually without a decision structure, and usually without anyone’s surfacing assumptions. You didn’t eliminate the work, you displaced it.

845
00:53:42,660 –> 00:53:54,660
Faster drafts can mean less time spent formatting and more time spent thinking. Or it can mean nobody did the thinking because the artifact arrived pre-shaped and the team jumped straight to editing. Again, you didn’t eliminate the work, you postponed it.

846
00:53:54,660 –> 00:54:04,660
Less friction can mean less bureaucracy. Or it can mean less debate, less dissent, and fewer people willing to say, “This is the wrong frame, that’s not agility, that’s fragility.”

847
00:54:04,660 –> 00:54:14,660
Here’s the uncomfortable truth. AI makes organizations feel coherent because it can generate coherence on demand. It writes the recap, it writes the plan, it writes the update, it writes the risks, it writes the next steps.

848
00:54:14,660 –> 00:54:28,660
And every one of those artifacts looks like progress. But coherence isn’t a document property, it’s a shared mental model property. Coherence is when 10 people can predict what the other nine will do next and why, because they actually understand the constraints and the trade-offs.

849
00:54:28,660 –> 00:54:33,660
AI can generate a clean narrative without generating that shared understanding. That’s the paradox.

850
00:54:33,660 –> 00:54:43,660
Your output quality goes up while your collective cognition gets thinner. You see the symptoms later, not immediately. You see rework. Not because people are incompetent, but because they never aligned on meaning.

851
00:54:43,660 –> 00:54:52,660
You see decision reversals, not because leadership is fickle, but because the first decision was made under a false sense of consensus created by tidy summaries and polished drafts.

852
00:54:52,660 –> 00:55:01,660
You see alignment language everywhere, were aligned, were on the same page, and then two weeks later you realize nobody was. They were aligned on text, not on intent.

853
00:55:01,660 –> 00:55:20,660
Also why AI adoption is a useless success metric by itself, how usage can mean value, it can also mean the organization is using AI to survive its own fragmentation. Co-pilot becomes the duct tape that keeps the artifact pipeline moving while the underlying system drifts and drift is the real enemy here, missing policies create obvious gaps, drifting collaboration creates ambiguity.

854
00:55:20,660 –> 00:55:24,660
Ambiguity is where the organization starts paying in hidden ways.

855
00:55:24,660 –> 00:55:42,660
Escalations, duplicated work, passive resistance and quiet risk accumulation. Viva Insights will show you some of the signals, constant interruptions, shrinking focus time after hours creep, meeting overload that should have declined but didn’t. Those are not employee problems, those are system problems.

856
00:55:42,660 –> 00:55:57,660
But the more dangerous signal is when you won’t see directly, the loss of shared conviction, people can’t explain why the work is true, they can’t defend the trade-offs, they can’t tell you what would change their mind, they can only forward the artifact that is not operational excellence, that is administrative motion.

857
00:55:57,660 –> 00:56:06,660
So the executive interpretation needs to be precise, efficiency is not the same thing as effectiveness, efficiency is, we produce the artifact faster.

858
00:56:06,660 –> 00:56:19,660
Effectiveness is we made a better decision and reduced future rework, AI boosts efficiency by default. It only boosts effectiveness if you deliberately preserve the human work that creates coherence, framing, debate and accountability.

859
00:56:19,660 –> 00:56:26,660
This is why the leadership job isn’t to ask, how do we use co-pilot more? It’s to ask, where does speed create illusions in our operating model?

860
00:56:26,660 –> 00:56:38,660
Because if you don’t answer that, you’ll do the normal corporate thing, celebrate the visible wins and ignore the invisible losses until they show up as an incident, a failed initiative or a workforce that’s technically productive and spiritually disengaged.

861
00:56:38,660 –> 00:56:41,660
And then you’ll say, we need better change management, no.

862
00:56:41,660 –> 00:56:46,660
You need intentional friction in the right places, on purpose as a design feature.

863
00:56:46,660 –> 00:56:57,660
Design principle, intentional friction keeps humans irreplaceable, so here’s the design move that stops all of this from becoming a slow organizational collapse disguised as AI transformation.

864
00:56:57,660 –> 00:57:06,660
Intentional friction, not bureaucracy, not committees, not more templates. Friction as a deliberate checkpoint that forces human judgment to show up where it matters.

865
00:57:06,660 –> 00:57:17,660
Because the reality is simple, if you remove all friction from knowledge work, you don’t get excellence. You get autopilot and autopilot is fine until the environment changes, the data, drifts or the stakes spike.

866
00:57:17,660 –> 00:57:30,660
Then you discover what you actually built, a fast system with no steering. Intentional friction is the steering. It’s the set of points where the system refuses to proceed without a human doing the hard cognitive work, framing, trade-offs and ownership.

867
00:57:30,660 –> 00:57:43,660
This is where leaders need to stop romanticizing speed and start treating it as a risk factor. Speed compresses time for disagreement. Speed compresses time for sense making. Speed compresses the space where someone can say, wait, are we sure we’re solving the right problem?

868
00:57:43,660 –> 00:57:51,660
And in AI mediated workflows, that space is the only thing preventing a coherent draft from becoming a coherent mistake. So friction has a definition in this context.

869
00:57:51,660 –> 00:58:05,660
A purposeful delay inserted at decision-critical moments to preserve epistemic agency, that means friction is not evenly distributed. You don’t add it everywhere, you don’t slow down low stakes work, you don’t make people defend every email, that’s how you turn a good idea into performative governance.

870
00:58:05,660 –> 00:58:16,660
You reserve friction for high impact work, policy, strategy, risk posture, people decisions, external messaging and anything that becomes president, everything else stays fast. Because the point isn’t to protect humans from work.

871
00:58:16,660 –> 00:58:26,660
The point is to protect the organization from unowned decisions. Here’s what most people miss. The irreplaceable human isn’t the person who can write. It’s the person who can be held accountable for a claim.

872
00:58:26,660 –> 00:58:38,660
AI can generate an answer, only a human can own the consequences. So your friction points should line up with consequence. There are three friction mechanisms that work because they’re structural, not motivational. First, the framing gate.

873
00:58:38,660 –> 00:58:49,660
No AI draft becomes the working artifact until the problem statement exists in human language. Two paragraphs. What decision is being made, what constraints are real and what failure looks like, not what success looks like, failure.

874
00:58:49,660 –> 00:58:57,660
Because that forces risk appetite into the open. If the team can’t write that, they don’t understand the work yet. Second, the alternatives requirement.

875
00:58:57,660 –> 00:59:13,660
Every consequential recommendation must include at least two plausible alternatives with a sentence on why each was rejected, not ten alternatives. Two, the goal is to prevent first coherent story winds from becoming the operating system. This is how you keep debate alive without turning every meeting into philosophy.

876
00:59:13,660 –> 00:59:21,660
Third, the reasoning trace. Not chain of thought from the model. Human reasoning trace assumptions, inputs and the one thing that would change the decision.

877
00:59:21,660 –> 00:59:30,660
If this goes wrong, the post-mortem shouldn’t be the AI hallucinated. It should be we accepted a claim without validating the assumptions. That’s the accountability loop.

878
00:59:30,660 –> 00:59:40,660
Now notice what this friction does. It doesn’t block AI, it disciplines AI. It preserves the human contribution that AI cannot replicate. Epistemic responsibility.

879
00:59:40,660 –> 00:59:48,660
And that’s why it keeps humans irreplaceable because the organization becomes explicit about where the human role is mandatory, not as a vibe, as a control.

880
00:59:48,660 –> 01:00:04,660
This is also where Viva insights and work IQ become complementary rather than distracting. Work IQ improves relevance. Great, that makes the drafts better. Viva insights shows you where the system is overloading humans so badly that they’ll accept anything that looks finished. That’s the environment your friction has to protect against.

881
01:00:04,660 –> 01:00:13,660
If people are interrupted constantly, they will skip the friction unless it’s enforced by workflow. So the leadership job is to insert friction where it can’t be socially bypassed.

882
01:00:13,660 –> 01:00:24,660
Put it in the artifact, not in the meeting. Make it a required section in the decision memo. Make it a required field in the project template. Make it the norm that a recap without assumptions is incomplete.

883
01:00:24,660 –> 01:00:29,660
Because humans don’t consistently do hard things due to inspiration. They do them because the system expects it.

884
01:00:29,660 –> 01:00:39,660
And if you design it correctly, something counterintuitive happens. You get faster over time. Not because you moved faster in the moment, but because you cut rework decision reversals and downstream confusion.

885
01:00:39,660 –> 01:01:00,660
You trade a small intentional delay upfront for a large reduction in entropy later. That is what excellence looks like in an AI-mediated workplace. Not more output. More owned decisions. Case study. Productivity went up. Confidence went sideways. A case makes this real. Because otherwise it sounds like philosophy. And executives don’t fund philosophy.

886
01:01:00,660 –> 01:01:11,660
This was a knowledge-heavy team. Strategy, policy, stakeholder comms, the kind of work where the output looks like words. But the real product is judgment. They adopted co-pilot in the predictable way.

887
01:01:11,660 –> 01:01:22,660
Meeting recaps, drafting, summarizing long threads, and accelerating first versions of briefs. Nobody tried to automate the business. They just tried to save time that. And they did. Time to first draft, dropped sharply.

888
01:01:22,660 –> 01:01:38,660
Work that used to take half a day to get into a presentable shape now landed in minutes as something editable. Meetings got shorter because everyone assumed the recap would catch what they missed. People felt relief. Leaders saw movement. The artifact pipeline looked cleaner. Then the side effects showed up quietly as downstream cost.

889
01:01:38,660 –> 01:01:52,660
The first symptom wasn’t errors. It was weaker debate. The team stopped arguing early because the AI output arrived early. And once a coherent narrative exists, the social pressure is to edit, not to reframe. So conversations shifted from, “What are we really trying to decide?”

890
01:01:52,660 –> 01:02:03,660
And to, “Can we tighten this language from what’s the risk we’re accepting? To does this read well?” That’s where confidence went sideways. Not confidence in co-pilot. Confidence in the work. People started shipping things they couldn’t fully defend.

891
01:02:03,660 –> 01:02:19,660
When asked, “Why are we recommending this?” The answer became a paraphrase of the draft. Not a reconstruction of the reasoning. Nobody was lying. Nobody was being lazy. They were behaving exactly how a draft first system trains humans to behave. Except, “Polish forward.”

892
01:02:19,660 –> 01:02:31,660
The second symptom was summary dependence. Leaders started reading recaps instead of attending, which is fine, until the recap becomes the official memory. The recap got forwarded. It became what we decided even when it wasn’t.

893
01:02:31,660 –> 01:02:42,660
And because the recap looked neutral and complete, challenging it felt like challenging reality. So, descent moved to side chats. People tried to fix misunderstandings later. In private. After momentum already moved.

894
01:02:42,660 –> 01:02:58,660
The third symptom was ownership ambiguity. In the old workflow, one person wrote the memo and everyone knew where the thinking lived. In the new workflow, the memo started as AI got edited by two people approved by a third and then shipped. When it was good credit dispersed. When it was wrong, accountability became a group shrug.

895
01:02:58,660 –> 01:03:12,660
Not because anyone avoided responsibility, but because the system diluted authorship so effectively that nobody could point to the moment where the organization actually decided. That dilution matters because high quality work depends on someone feeling “This is mine.”

896
01:03:12,660 –> 01:03:18,660
Over a few weeks, the team’s output volume went up, but their shared mental model got thinner. They began to see rework.

897
01:03:18,660 –> 01:03:30,660
Stakeholders asked the same questions repeatedly. Decisions got revisited, not because the plan changed, but because the original rationale never existed in a way the team could reproduce under pressure. So, they intervened, not by banning co-pilot.

898
01:03:30,660 –> 01:03:44,660
By installing friction in exactly three places. First, they separated generation from approval. Co-pilot could draft anything. But nothing moved forward until a named human owner rewrote the problem statement and the decision rationale in plain language from scratch.

899
01:03:44,660 –> 01:03:52,660
Two short paragraphs. If the owner couldn’t do that, they weren’t ready to ship. This wasn’t punishment. It was a diagnostic comprehension check before momentum.

900
01:03:52,660 –> 01:04:00,660
Second, they required alternatives. Every recommendation needed two plausible paths, with explicit trade-offs. Not a long list.

901
01:04:00,660 –> 01:04:20,660
Two, this forced the team to demonstrate they understood the decision space instead of accepting the first narrative that compiled cleanly. Third, they created a lightweight decision log. Not meeting minutes, decisions. What was decided by whom, what assumptions it depended on and what would change it? That last part mattered more than people expected, because it reintroduced epistemic agency.

902
01:04:20,660 –> 01:04:38,660
The result wasn’t slower work and it was fewer reversals. The team still used co-pilot heavily. They just stopped treating AI outputs as defaults. Draft stayed cheap. Judgment became visible again. And once judgment was visible, confidence came back because people could explain their own decisions without leaning on the machine as a shield.

903
01:04:38,660 –> 01:04:50,660
That’s the lesson. Productivity gains are real. But unless you redesign for ownership, you’ll trade time-save for coherence lost and you’ll pay it back with interest. Leadership rule, make AI visible where accountability matters.

904
01:04:50,660 –> 01:05:00,660
So the case study lands on the real lesson. You don’t need less AI. You need less invisible AI because invisible co-authorship creates the perfect corporate escape hatch.

905
01:05:00,660 –> 01:05:14,660
Output with no author, decisions with no owner, and a trail of the system said when someone asks what happened. That’s why the leadership rule is blunt. Make AI visible where accountability matters. Not in every trivial email, not in every internal chat.

906
01:05:14,660 –> 01:05:24,660
But in the artifacts that carry consequence, strategy decks, policy changes, customer facing statements, performance messaging, incident communications, risk decisions, and anything that becomes precedent.

907
01:05:24,660 –> 01:05:36,660
If the organization can’t see where AI participated, it can’t calibrate trust, it can’t audit reasoning, and it can’t assign responsibility without turning every post-mortem into a blame sayons. Visibility is governance, it’s not etiquette.

908
01:05:36,660 –> 01:05:52,660
Start with the first ban. Band the invisible co-author. If a key artifact was materially generated by co-pilot or another model, the artifact needs a simple disclosure line. Not performative, not apologetic, just factual. Draft generated with AI assistance, reviewed and approved by human owner.

909
01:05:52,660 –> 01:06:05,660
That single sentence does two things at once. It removes shame and it fixes accountability. Because now everyone knows who owns the decision. Next, separate generation from approval, AI can generate. Humans approve.

910
01:06:05,660 –> 01:06:15,660
That sounds obvious until you look at how most teams behave. They treat the first coherent draft as the starting point, and then they treat editing as the same thing as thinking. It isn’t.

911
01:06:15,660 –> 01:06:28,660
Editing is post-processing, approval is responsibility. Leaders need to make that separation explicit in the workflow. A simple pattern, one person can use AI to generate options, but a different person or a different role must sign off.

912
01:06:28,660 –> 01:06:42,660
That creates distance from the fluency effect. It reintroduces skepticism. It makes it harder for the AI suggested to become a veto. And if you’re in a smaller team where separation isn’t possible, then enforce role separation in time. Draft today approved tomorrow.

913
01:06:42,660 –> 01:06:48,660
Sleep is an underrated governance control. Decision fatigue creates automation bias. Teams accept what looks finished when they’re tired.

914
01:06:48,660 –> 01:06:55,660
Then, preserve reasoning, not just conclusions. The artifact that gets forwarded around your company is rarely the raw evidence.

915
01:06:55,660 –> 01:07:06,660
It’s the summary. So if you let the summary omit the reasoning, you will eventually lose the ability to defend decisions under pressure. And pressure is where your controls get tested. The fix isn’t more documentation. It’s structured documentation.

916
01:07:06,660 –> 01:07:20,660
For high stakes work require three fields in the artifact itself. What we believe, what we’re assuming, what would change our mind. That last one is the agency preservative. It forces humans to articulate falsifiers. It prevents the organization from turning AI output into scripture.

917
01:07:20,660 –> 01:07:33,660
And it gives you a real audit trail later. Not just what the machine wrote, but what the humans committed to. Now the weird part, visibility also protects psychological safety. When AI contribution is hidden, challenging the output becomes personal.

918
01:07:33,660 –> 01:07:48,660
The person who posted the draft feels attacked. The team avoids conflict. Errors persist. But when AI contribution is explicit, critique becomes critique of a tool. The social temperature drops. People will say, “This synthesis missed the risk without implying you’re incompetent.” That distinction keeps speak up alive.

919
01:07:48,660 –> 01:07:57,660
And it also fixes ownership. Humans don’t need to pretend they typed every word to feel pride. They need to know the work is theirs in outcome and reasoning. Visibility makes that possible.

920
01:07:57,660 –> 01:08:18,660
Yes, the draft was co-authored. The decision is still mine. Finally, define the accountable owner upfront. Every consequential artifact needs one person whose name is attached to the decision. Not just the document. Not the team, not we, a human. This isn’t about hero culture. It’s about avoiding the default state of modern organizations. Distributed responsibility concentrated blame.

921
01:08:18,660 –> 01:08:36,660
If you don’t assign ownership, the system will assign it later during an incident and it will be unfair. So the leadership rule can be summarized like a control plane principle. If accountability matters, opacity is an outage. Make AI contribution visible. Make human approval explicit. Make reasoning durable. And then you can let the machine run fast.

922
01:08:36,660 –> 01:08:46,660
Without turning your organization into a high throughput generator of plausible unknown decisions. Because AI doesn’t remove accountability. It just gives people new ways to hide from it.

923
01:08:46,660 –> 01:09:04,660
Weekly decision framework. What must remain human versus co-authored? So now you have the rule. Make AI visible where accountability matters. But rules decay unless you operationalize them. This is where leaders usually reach for policy. And policies are great at creating PDFs. They’re not great at changing behavior inside teams at 4.47 pm.

924
01:09:04,660 –> 01:09:21,660
What works is a weekly decision framework, a simple lens that leaders apply out loud, repeatedly until the organization starts using it as reflex. Because the real failure mode isn’t AI misuse. It’s that everything becomes co-authored by default, including the work that should never be co-authored without visible human ownership.

925
01:09:21,660 –> 01:09:29,660
So here’s the framework. Three questions asked in this order every week in leadership staff and project reviews in people decisions in incident reviews.

926
01:09:29,660 –> 01:09:48,660
First question. Does this require judgment and accountability? Not does it require intelligence? Judgment. Trade-offs. Risk tolerance. Accepting consequences. If the answer is yes, AI can assist, but the human must be named. And the human must be able to restate the reasoning without leaning on co-pilot said.

927
01:09:48,660 –> 01:10:03,660
Because the system can generate recommendations. It cannot carry liability. It cannot take the call from legal. It cannot look someone in the eye after a decision lands badly. Second question. Does this shape trust, culture or power dynamics? This is the one leaders skip because it feels soft. It isn’t.

928
01:10:03,660 –> 01:10:22,660
Anything that changes how people feel about fairness, voice recognition or belonging is a high stakes decision, even if it looks like just communication, a performance message, a re-organancement, a policy clarification, a quick summary after a tense meeting. If the outcome changes social reality, you don’t let the machine become the narrator without explicit human intent.

929
01:10:22,660 –> 01:10:31,660
So again, AI assists, humans decide, humans own the narrative. Third question. Would removing human authorship reduce learning or debate? This is the epistemic agency test.

930
01:10:31,660 –> 01:10:45,660
If the process becomes draft arrives, edit, ship, and nobody had to build a model of the problem, you will get speed in the moment and fragility later. That is not a trade you want on strategy, on risk or on anything that will be repeated as precedent.

931
01:10:45,660 –> 01:10:55,660
So if yes, you force a human to do the cognitive work that produces understanding, frame the problem, propose alternatives and write a reasoning trace. Now translate this into a decision.

932
01:10:55,660 –> 01:11:09,660
If any answer is yes, the work is in the human required lane, not human only, human required AI can do the first draft, AI can summarize inputs, AI can generate options. But you enforce three controls, visible AI contribution, named accountable owner and preserved reasoning.

933
01:11:09,660 –> 01:11:22,660
If all three answers are no, then you let it be co-authored aggressively because you should not waste human cognition on low stakes artifacts, let the machine compress it, let it write it, let it schedule it, let it summarize it, let it move the work forward. That’s the point of AI.

934
01:11:22,660 –> 01:11:33,660
Offload the trivial so the human can be concentrated where consequences live. Now make it practical. Run this framework once a week against the work your leadership team is about to ship strategy decks. Yes.

935
01:11:33,660 –> 01:11:36,660
Judgment and power dynamics.

936
01:11:36,660 –> 01:11:43,660
Human required. Policy changes. Yes. Trust and precedent. Human required. Performance messaging. Yes.

937
01:11:43,660 –> 01:11:58,660
Culture and psychological safety. Human required. Incident communications. Yes. Risk and accountability. Human required. Project status emails. About low risk delivery work. Probably no. Co-authored. Meeting recap for an informational sink. Probably no. Co-authored.

938
01:11:58,660 –> 01:12:04,660
But here’s the twist. The framework doesn’t just classify tasks. It forces you to see where your operating model is leaking accountability.

939
01:12:04,660 –> 01:12:18,660
If your leaders can’t answer these questions quickly, you don’t have an AI adoption problem. You have a decision architecture problem because high performing organizations already know which work is consequential. They already know where debate matters. They already know which narratives shape culture.

940
01:12:18,660 –> 01:12:33,660
AI just makes the cost of ignoring that truth visible. So the weekly ritual is simple. Pick five artifacts, run the three questions and adjust the workflow accordingly and do it publicly. Say this one stays fast. Say this one requires a decision log. Say this one needs named ownership.

941
01:12:33,660 –> 01:12:48,660
Say this one needs alternatives. You’re teaching the organization that excellence is designed not hoped for. And over time the organization stops treating co-pilot as a feature set. It starts treating it as a participant that must be governed. That’s the whole shift. Not tool adoption.

942
01:12:48,660 –> 01:13:00,660
Decision hygiene under acceleration. Collaboration norms for the AI era. Guardrails without bureaucracy. Now take the framework and turn it into something the organization can actually live with.

943
01:13:00,660 –> 01:13:12,660
Because norms are the only thing that scale faster than entropy. And no, this doesn’t mean a 27 page co-pilot policy that nobody reads. It means a few guardrails that show up in the work itself. Meetings, chat and documents.

944
01:13:12,660 –> 01:13:19,660
The surface is where drift happens. Start with meetings because AI has turned meetings into content factories. The meeting recap is not truth. It’s input.

945
01:13:19,660 –> 01:13:34,660
So the norm is every recap must point to a decision log entry or explicitly state no decisions made. That sounds pedantic until you realize how often we talked about it gets mistaken for we decided. If you want coherence decisions must be named owned and retrievable.

946
01:13:34,660 –> 01:13:42,660
Second meeting norm. The assumptions line. If a recap contains recommendations, it must contain at least one stated assumption, not five.

947
01:13:42,660 –> 01:13:58,660
One, the goal is to remind everyone that every clean summary sits on hidden premises, bring one premise into daylight and you keep the organization from treating a synthesis as a verdict. Now chat because chat is where debate goes to die. The AI era pushes teams threads toward confirmation.

948
01:13:58,660 –> 01:14:08,660
Looks good, approved, ship it. You don’t fix that by begging people to ask more questions. You fix it by designing space for descent. So the chat norm is a descent slot.

949
01:14:08,660 –> 01:14:22,660
One message early in the thread that invites counter views. Something as simple as before we align what’s the strongest reason this could be wrong. If nobody answers fine but the prompt exists and that changes the social cost of speaking up.

950
01:14:22,660 –> 01:14:32,660
Then add a second chat norm, the two alternatives rule for proposals. If someone posts a recommendation that will drive action, they must include two plausible options even if one is do nothing.

951
01:14:32,660 –> 01:14:44,660
It blocks the first coherent draft from winning by default. It also forces the poster to demonstrate that they explored the space rather than adopting the first AI-shaped narrative. Now documents, because documents are where accountability lives or dies.

952
01:14:44,660 –> 01:14:56,660
The dog norm is visible authorship and visible ownership. If AI materially contributed, disclose it in one line. If the dog carries consequence, name the accountable human owner at the top. And then enforce the one section that keeps humans irreplaceable.

953
01:14:56,660 –> 01:15:14,660
Replaceable. Reasoning. A standard decision appendix. What we decided, why assumptions and what would change our mind. Not a novel, a few bullets. You’re not documenting for compliance. You’re documenting so the organization can reconstruct its own thinking later, under pressure, without relying on copilot’s memory as the source of truth.

954
01:15:14,660 –> 01:15:23,660
And for work graph, that will work IQ specifically. Treat discoverability as a governance surface, not an IT optimization. Canon matters. Location matters. Linking matters.

955
01:15:23,660 –> 01:15:35,660
If work lives in 10 places, the graph will tell 10 inconsistent stories. So the norm is one canonical location for the plan, one canonical location for the decision log, and one canonical place for risks.

956
01:15:35,660 –> 01:15:43,660
Not because SharePoint is holy, because retrieval systems punish fragmentation. Now the last norm is leadership behavior because everything else collapses without it.

957
01:15:43,660 –> 01:16:00,660
Not performative contrarianism. Intelligent disagreement. Alternative framing. Surface risks. Explicit trade-offs. And punish silence by default. Not by shaming people, but by refusing to accept artifacts that contain no assumptions, no alternatives, and no accountable owner.

958
01:16:00,660 –> 01:16:09,660
That’s what designing for excellence actually means. The system refuses to move forward without visible human judgment where it matters. These norms aren’t bureaucracy, they’re entropy controls.

959
01:16:09,660 –> 01:16:28,660
Because the platform will keep accelerating, copilot will keep drafting, work IQ will keep ranking, Viva Insights will keep surfacing signals. None of that slows down. So you either install small intentional constraints now, or you accept conditional chaos later, fast outputs, plausible summaries, and unowned decisions that drift until something breaks.

960
01:16:28,660 –> 01:16:38,660
Conclusion. The question leaders can’t outsource. AI doesn’t make humans obsolete. It makes human judgment, context interpretation and accountability, the only scarce resource that still matters.

961
01:16:38,660 –> 01:16:48,660
If you want this to work at scale, subscribe to CTS and become a member on speaker to get the podcast ad free and keep going with the full episode where we break down the operating model in detail.

962
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Because the real leadership question isn’t, how do we deploy copilot? It’s this. What kind of collaborators are you designing your organization to produce?