The Invisible Employee: Is Your Next Hire Actually an AI Agent

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Ticketing looks clean on paper, you get the numbers, the cues, the dashboards.

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But in reality, that model only measures pain after the system has already failed.

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The expensive part of the problem usually starts much earlier.

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It starts when an employee loses 10 minutes, then 20 then eventually just gives up.

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They retry, they ask a coworker, they miss the meeting or share the wrong file

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because the right path stopped working.

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That's the part most support teams still don't see.

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The best internal support model today isn't about faster triage.

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It's about removing the need for triage entirely.

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Imagine an autonomous agent inside Microsoft 365 that watches context,

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spots friction early and acts before a person ever thinks about opening a portal.

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In this episode, I want to show you why the help desk model breaks,

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what actually replaces it, and how you can govern it without creating chaos.

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If this shift matters to your work, subscribe to M365 FM

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because one level deeper, the real problem isn't the cue, it's the model behind it.

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The death of the ticket.

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The old support flow is still everywhere, something breaks, a user notices it,

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the user tries to explain it, then IT translates that explanation into a category,

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a priority, and maybe an assignment group.

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The issue enters a cue, someone triages it, someone else asks for more detail.

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Another team gets involved, time keeps moving while the actual work stays stuck.

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That process feels normal because it's familiar,

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but familiar and effective are not the same thing.

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A ticket is not support, a ticket is evidence that support arrived late.

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By the time a ticket even exists, the employee has already hit friction and lost their context.

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They've already switched their attention away from the work they were trying to do.

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The cue isn't the service, the cue is a record of failure.

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In large Microsoft 365 estates, manual triage becomes the real choke point.

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This doesn't happen because support teams are lazy.

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It happens because humans are being asked to do something that no longer fits the scale of the environment.

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You have more users, more devices, more apps, more policies,

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you have more edge cases crossing teams, SharePoint, Exchange, Intune, and Entra at the exact same time.

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The volume doesn't just grow, the combinations grow, that's where the old model breaks.

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Most teams respond by adding more structure.

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They build another portal, another bot, or another routing rule.

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But in reality, that just adds one more layer between the employee and the resolution.

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The system is still waiting for the user to notice the problem, describe the problem,

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and ask for help in the right way.

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Work doesn't start in a support portal, it starts in teams, it starts in outlook,

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it starts in a document someone needs right now,

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or a meeting that begins in 30 seconds,

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that's where support demand actually begins,

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and the ticketing model is completely detached from that moment.

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The hidden cost is much bigger than your queue report shows.

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People context switch, duplicate incidents pile up under different labels.

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Knowledge articles go stale, analysts spend their entire day sorting,

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routing, and clarifying instead of actually fixing things.

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The time to resolution stretches out because every handoff adds a delay,

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and every delay strips away the original context.

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Research on a Genetic AI and IT operations points to the same structural issue.

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At least 80% of effort is still trapped in reactive ITIL style work.

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That number matters because it tells you exactly where the labor is going.

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It isn't going into resilience or prevention, it's going into reaction.

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Once you see that, the ticket starts to look less like an operating model,

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and more like a symptom, it's a symptom of a system that only responds after the damage is visible.

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So the next question isn't how to process tickets faster,

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it's what the system should do before a ticket is ever needed.

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The Invisible Employee Model.

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So what actually replaces the ticket?

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It isn't just another chatbot sitting on top of the same mess,

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and it certainly isn't a pretty front door to the same old queue.

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The shift goes much deeper than that because the Invisible Employee

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is an operational worker living inside your Microsoft 365 environment.

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This agent has its own identity, a defined scope of work,

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memory of what it did yesterday, and strict rules about what it can and cannot touch.

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That distinction matters.

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A chatbot waits for you to type a prompt,

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but an Invisible Employee watches for conditions to change.

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It starts from context instead of starting from a conversation.

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Because you can see sign-in states, device health,

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license status, and how files are being shared,

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it doesn't have to wait for friction to appear.

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Instead of asking how it can help after something breaks,

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it constantly asks a different question in the background.

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What is drifting away from the state this employee needs to do their job right now?

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That is where things change.

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The model is embedded directly inside the work itself.

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When someone joins a meeting and the audio-rooting fails,

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or a guest cannot open a file they were just sent,

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the old world creates a report and a handoff.

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In the new model, the agent sees the pattern immediately,

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checks if the condition matches a known issue,

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and takes the next safe action to fix it.

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Maybe it resets a path or triggers a policy check,

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or perhaps it reroutes a request and prompts for MFA

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before documenting the fix.

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The point is simple.

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The first response no longer requires a human to read a vague description

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and guess what happened.

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This feels invisible when it works well

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because there is no waiting room and no,

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please select a category form to fill out.

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Support shows up as restored flow,

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the employee just gets back to work,

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and in many cases they never even see the machinery running behind the scenes.

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You can already see how this changes your staffing logic.

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The old model hires people to absorb volume,

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sought through requests and clean up broken contacts between different teams.

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The new model needs fewer people doing triage

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and more people designing guardrails, defining desired states,

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and supervising how these agents behave over time.

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Humans stay in the system,

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but the entire shape of their daily work shifts.

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Research on a GENTIG AI in support keeps pointing in this direction.

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Routine support can move toward exception-based handling,

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which creates massive efficiency gains

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when the environment and policies are well-bounded.

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That last part is vital because this is not magic.

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If your data is weak or your process is messy,

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the agent will not become smart just because you decided to call it autonomous.

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This all clicked for me when I stopped thinking about the agent as a software feature

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and started viewing it as a non-human employee with a badge.

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A real worker needs an identity, a job description, permissions, and a manager to report to.

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The agent needs those exact same things

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because the value comes from reliable action inside a system,

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not from clever language in a chat window.

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The invisible employee is not a UI trick.

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It is a new support layer that watches for friction

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across the Microsoft 365 estate, acts within policy,

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and pushes human attention up to the exceptions that actually need judgment.

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Once that model is clear, the next question gets very practical.

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How does preemption actually work?

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The architecture of preemption.

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So how does preemption actually work inside Microsoft 365?

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The shift moves from request and responds to event reasoning and orchestration.

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That sounds technical, but the model is simple once you break it apart.

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First, something in the environment changes,

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then the system interprets if that change means risk or drift.

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And finally, it takes the approved action using the tools already in your stack.

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Start with the event layer.

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Microsoft 365 already produces signals everywhere,

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which means entracies sign in behavior

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while in tune tracks, device posture, and compliance.

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Defender watches for suspicious activity,

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and team shows you exactly when a meeting fails or call quality drops.

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SharePoint and Exchange expose access patterns and content activity,

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while service health signals tell you when the platform itself is the problem.

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When you add user actions on top of that,

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you have a constant stream of context.

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Most organizations collect those signals,

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but very few actually use them as a support system.

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Raw events alone do not help,

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because they need a reasoning layer to make sense of the noise.

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This is where the invisible employee stops being automation theater

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and starts acting like a real operator.

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The agent looks at the signal,

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compares the current state against the desired state,

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and decides what class of action is safe to take.

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A meeting audio issue is not the same as a risky sign in,

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and a broken sharing link is different from a license mismatch.

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The agent has to classify the problem first

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because taking action without classification just creates mistakes faster,

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then comes orchestration.

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Once the agent understands the issue,

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it can use the right mechanism to respond.

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That might be power-automate for workflows,

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co-pilot studio for logic,

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or Microsoft Graph for system actions and context.

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The tools matter, but the bigger point is how they connect to one another.

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You are not building one smart prompt,

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but rather a chain that can detect, decide, act, and verify.

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That last part gets missed all the time.

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A self-correcting system does not stop once the action is taken.

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It runs a loop where it detects the issue,

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tests the condition, applies the fix,

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and verifies the result before recording what happened.

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Research on self-correcting logic makes it clear that these systems work

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through feedback and correction patterns,

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rather than one isolated answer from a language model.

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If the first action fails,

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the system needs a way to assess why,

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so it can revise the next step while staying inside defined limits.

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You can picture this clearly in a few Microsoft 365 cases.

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When a user gets blocked because their license and policy scope do not match,

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the agent detects the mismatch and checks the intended role profile.

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It applies the allowed correction, confirms the user has access,

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and logs the change without anyone having to ask.

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If a risky sign-in appears in Entra,

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the agent sees the risk event and checks your conditional access rules immediately.

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It prompts for stronger verification or blocks access based on your policy,

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then records the outcome for a human to review later.

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When a "Stale Sharing" link keeps failing in SharePoint,

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the agent compares the current permission path with the expected sharing rules.

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It replaces the broken route with the approved one

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and sends the corrected link in context,

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which closes the loop before a desk analyst ever has to read a ticket titled "Axis Issue Urgent".

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Meeting support is another great example.

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If a room device fails or a joining path breaks seconds before a call starts,

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the agent can detect the condition and validate the fault.

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It tries a low-risk recovery step and notifies the organizer in teams

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with a status update right where the work is happening.

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That communication layer is a massive part of the design.

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Support has to live where the interruption happens.

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If your work happens in teams, outlook and the M365 workspace,

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then support should answer you there too.

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You shouldn't have to drag the user somewhere else

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when you can meet the event in context.

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None of this means full autonomy across every single task,

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because that would be reckless.

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Low-risk actions can run automatically,

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but high-impact changes still need approval from a person.

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That approval path has to be part of the architecture from the start,

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rather than being bolted on later.

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The agent should know when to act, when to ask,

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and exactly when to stop.

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And once you build that into the flow,

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the conversation stops being about automation features.

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It becomes about control, governance, trust, and agent identity.

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When support starts acting on its own,

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a difficult question surfaces immediately.

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Who is actually doing the work?

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If that answer is vague, the entire model falls apart.

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An agent cannot exist in your environment

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as some fuzzy helper with broad access and no clear owner.

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It needs a real identity and entry.

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It needs a named owner, a defined scope,

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and a life cycle that starts and ends for a specific reason.

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If that agent can read data, trigger workflows,

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or message your users,

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you have to govern it like an active participant in the tenant.

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You cannot treat it like a feature someone turned on

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and then forgot about.

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This is why agent identity is so critical to the system.

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You need to know exactly what the agent is,

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which systems it can touch,

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and who gets the call when things start to drift.

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Least privilege has to be the baseline here.

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You can't settle for broad convenience

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or tell yourself you will tighten the permissions later.

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Because the blast radius of a support agent

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depends entirely on its permissions,

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designing those permissions is a core part of designing support.

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Agent 365 serves as a necessary control layer in the setup

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because it brings the entire agent estate into clear view.

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It provides the registry, the approval workflows,

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and the policy templates that keep things running safely.

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While those pieces might sound like administrative chores,

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they solve a massive structural problem.

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Most organizations don't struggle because they lack AI features,

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but because they cannot see or control

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what is already running in their environment.

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The research on this point is blunt.

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Only a small minority of large enterprises

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can fully inventory every agent currently running in their system.

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Before leaders start talking about scaling autonomous support,

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they have to answer a much simpler question.

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Do we even know which agents exist

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and what they are doing right now?

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Oversight also has to match the level of risk involved.

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A personal productivity agent working in a narrow lane is one thing,

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but a collaborative agent touching shared content

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is something else entirely.

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Then you have enterprise agents taking action

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across sensitive workflows,

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which sits in a different category of risk.

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This is why the risk zone model, personal, collaborative,

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and enterprise matters so much.

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Your control model should rise alongside the possible impact.

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If you treat every agent the same,

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low-risk work gets buried in slow approvals

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while high-risk work slips through with weak scrutiny.

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At a minimum, your control stack in Microsoft 365

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should look familiar to your team.

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You use identity and entra, data boundaries,

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through purview, and monitoring through defender.

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You apply access conditions through conditional access

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and ensure auditability is baked in from the start.

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You need to be able to reconstruct exactly what happened

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without having to guess, after an incident occurs.

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Human oversight needs to be designed into the logic

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rather than improvised during a crisis.

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High impact actions should always pause for a human signature.

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Reasoning should be logged in a form

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that people can actually inspect and understand.

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You need rollback paths to exist

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before the very first autonomous action goes live.

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If an agent updates access or changes a routing policy,

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someone must have a way to trace that decision and undo it cleanly.

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There is another factor leaders often miss

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because the early demos look so good, the cost of rework.

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If an agent creates cleanup loops for your team,

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the projected savings disappear instantly.

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This is the rework tax.

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It is the time spent correcting bad output,

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reversing wrong actions,

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or chasing down unintended side effects.

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If your team saves 10 minutes on execution

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but burns 8 minutes cleaning up the mess,

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you didn't actually automate support.

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You just moved the friction to a different part of the process.

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Trust does not come from a polished policy deck

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or a big launch announcement.

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It comes from runtime evidence.

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People need to see what the agent saw,

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what it decided, and what actually changed in the system.

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That is how you build trust in autonomous support.

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The proof has to sit in the behavior of the tool

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rather than the branding of the project.

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Once that governance foundation is solid,

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the conversation can finally move away

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from security jargon and into executive language.

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What does this actually change for the bottom line?

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The new ROI of invisible support.

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Once leaders accept the control model,

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the financial questions start to shift.

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In the old model, support value is measured

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00:13:12,280 --> 00:13:14,120
by visible work like tickets closed,

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calls handled, and SLA performance.

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Those numbers still matter,

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but they only describe a reaction to a problem.

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They don't tell you how much interruption

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never reached the queue

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or how much employee time stayed intact

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because a problem was killed early.

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That is the core accounting problem.

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Invisible support creates value through absence.

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You see the value when there is no ticket, no escalation,

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and no analyst spending 20 minutes

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00:13:34,280 --> 00:13:36,760
reconstructing a problem from half-complete notes.

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If you only measure the visible effort of the support team,

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00:13:40,280 --> 00:13:42,200
you missed the outcome you actually wanted.

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You wanted stable work with fewer interruptions.

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Ticket volume is a weak signal when it stands on its own.

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A drop in tickets might mean demand fell,

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but it could also mean the environment got healthier

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and the support layer got smarter.

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Those are two very different stories.

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Executives need a KPI set that can tell the difference between them.

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You should start by looking at the autonomous resolution rate.

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This isn't about how many conversations the bot had,

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but how many support events were resolved

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00:14:05,400 --> 00:14:07,560
end to end without a human ever touching them.

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You also need to track the reduction in mean time to resolution.

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In AI-driven environments,

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00:14:11,720 --> 00:14:15,480
diagnosis heavy flows often see improvements in the 40-60% range.

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Then you can look at the cost per resolution

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00:14:17,400 --> 00:14:19,640
and the user productivity you've regained.

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That last metric is easy to skip

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00:14:21,320 --> 00:14:24,360
because it doesn't sit neatly in a standard service desk report.

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00:14:24,360 --> 00:14:26,520
However, that is often where the real value lives.

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00:14:26,520 --> 00:14:29,720
A person who is blocked for 15 minutes before they even open a ticket

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has already created a cost for the company.

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00:14:31,800 --> 00:14:35,080
When you multiply that by meeting interruptions and access friction

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across a large tenant, the hidden loss is much bigger than the queue suggests.

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This is why the cost of an action model is so important for the business case.

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You calculate the hours spent on manual effort

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before formal support even begins.

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Then you add the cost of errors and repeated work.

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Suddenly, the argument for preemption

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00:14:50,040 --> 00:14:51,560
stops sounding like a theory.

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00:14:51,560 --> 00:14:54,120
You aren't just funding a futuristic support layer.

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You are removing attacks that has been sitting inside your daily work for years.

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00:14:57,640 --> 00:15:00,200
The economics of the narrow case make this even clearer.

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00:15:00,200 --> 00:15:02,840
Research on Agenteic IT support puts human handling

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00:15:02,840 --> 00:15:05,320
at about $8.75 per issue.

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00:15:05,320 --> 00:15:10,680
In contrast, narrow Agenteic handling can fall below $0.50 per issue once the system is developed.

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00:15:10,680 --> 00:15:13,240
This doesn't mean every support action will be that cheap

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00:15:13,240 --> 00:15:15,560
and leaders should be careful with broad averages.

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00:15:15,560 --> 00:15:18,920
Telemetry quality and governance will always affect the final cost

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00:15:18,920 --> 00:15:21,320
but the direction of the trend is unmistakable.

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00:15:21,320 --> 00:15:24,600
This is the moment where IT moves out of the old cost center story.

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00:15:24,600 --> 00:15:25,960
It isn't because support disappears

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00:15:25,960 --> 00:15:28,440
but because support turns into a reliability layer

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00:15:28,440 --> 00:15:31,400
that protects employee throughput in the background.

365
00:15:31,400 --> 00:15:33,400
The business feels this as smoother work

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00:15:33,400 --> 00:15:35,400
rather than a dramatic support event.

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You get fewer interruptions, faster restoration

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00:15:37,640 --> 00:15:40,280
and less labor wasted on routing and clarification.

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00:15:40,280 --> 00:15:42,680
Still, bad measurement can easily fake progress.

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00:15:42,680 --> 00:15:46,920
If your telemetry is weak, the agent might just summarize a failure faster

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00:15:46,920 --> 00:15:48,280
instead of actually preventing it.

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00:15:48,280 --> 00:15:49,720
If rework stays off the dashboard,

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the ROI will look much cleaner than it really is.

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00:15:51,800 --> 00:15:55,240
If reclaimed time just leaks back into low-value activity,

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leaders will overstate the gain.

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The financial case must stay grounded in hard evidence

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00:15:59,400 --> 00:16:01,800
like prevented incidents in lower manual touches.

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00:16:01,800 --> 00:16:04,840
You have to measure the time truly returned to useful work.

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00:16:04,840 --> 00:16:06,120
Once you measure that properly,

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00:16:06,120 --> 00:16:09,160
the support function stops looking like overhead that needs to be cut.

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00:16:09,160 --> 00:16:12,360
It starts looking like operating capacity that the business finally gets back.

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00:16:12,360 --> 00:16:14,760
What this does to the support team,

383
00:16:14,760 --> 00:16:17,160
think about what this actually does to the team on the ground

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00:16:17,160 --> 00:16:19,560
because this shift isn't really about cutting headcount.

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00:16:19,560 --> 00:16:22,440
It's about changing what your people actually do with their day.

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00:16:22,440 --> 00:16:25,080
In the old setup, most of your support capacity gets eaten up

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00:16:25,080 --> 00:16:28,440
by password resets, ticket routing, and endless clarification loops.

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00:16:28,440 --> 00:16:31,320
You have people chasing updates and moving data between systems

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00:16:31,320 --> 00:16:33,080
that were never designed to talk to each other.

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00:16:33,080 --> 00:16:35,320
None of that work makes the environment more stable.

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00:16:35,320 --> 00:16:37,400
It just keeps the queue from overflowing.

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00:16:37,400 --> 00:16:40,120
When agents take over that predictable routine work,

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00:16:40,120 --> 00:16:42,280
human effort finally moves up a level.

394
00:16:42,280 --> 00:16:44,440
Support engineers start spending their time defining

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00:16:44,440 --> 00:16:45,880
what a healthy state looks like

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00:16:45,880 --> 00:16:47,880
and tuning the logic behind approvals.

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00:16:47,880 --> 00:16:49,560
They focus on improving knowledge sources,

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00:16:49,560 --> 00:16:51,080
testing how policies behave,

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00:16:51,080 --> 00:16:53,880
and making sure the agent stays within the boundaries it was given.

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00:16:53,880 --> 00:16:57,160
That is a completely different job from staring at subject lines all day

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00:16:57,160 --> 00:16:59,560
and trying to guess what a user actually wants.

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00:16:59,560 --> 00:17:02,120
This is exactly where most deployments store out.

403
00:17:02,120 --> 00:17:04,760
Organizations try to keep their old broken processes

404
00:17:04,760 --> 00:17:07,720
and just drop an agent on top while expecting a better result.

405
00:17:07,720 --> 00:17:10,600
But the process is still reactive, the handoffs are still messy,

406
00:17:10,600 --> 00:17:12,680
and the underlying knowledge is still weak.

407
00:17:12,680 --> 00:17:15,720
The agent just becomes another layer of paint on a crumbling wall.

408
00:17:15,720 --> 00:17:17,080
It might answer a question faster,

409
00:17:17,080 --> 00:17:19,800
but it can't fix a support model that was built around delay.

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00:17:19,800 --> 00:17:21,880
The better way is to redesign the work first.

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You have to decide which tasks stay human

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because they require judgment or sensitive communication.

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00:17:26,680 --> 00:17:29,080
Then you decide which work moves into autonomous flows

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00:17:29,080 --> 00:17:31,560
because the outcome is predictable and the actions are safe.

415
00:17:31,560 --> 00:17:34,280
This division is vital because support teams aren't going away.

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00:17:34,280 --> 00:17:36,440
They are just becoming the people who design the controls

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00:17:36,440 --> 00:17:37,400
and handle the exceptions.

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00:17:37,400 --> 00:17:40,040
That means the way we train people has to change too.

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00:17:40,040 --> 00:17:42,200
Teams need to learn how to oversee a system

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00:17:42,200 --> 00:17:44,360
rather than just executing a manual task.

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00:17:44,360 --> 00:17:47,160
They need to understand rollback logic, audit reviews,

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00:17:47,160 --> 00:17:49,400
and how to test an agent before it goes live.

423
00:17:49,400 --> 00:17:52,120
They also have to be able to spot when a model is failing

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00:17:52,120 --> 00:17:54,840
because an automated bad workflow is still a bad workflow.

425
00:17:54,840 --> 00:17:56,360
It just breaks things faster.

426
00:17:56,360 --> 00:17:57,800
If you are hiring for this future,

427
00:17:57,800 --> 00:18:00,120
you probably don't need another generalist to tree-arge tickets.

428
00:18:00,120 --> 00:18:03,880
You need someone who can orchestrate flows across the Microsoft 365 stack

429
00:18:03,880 --> 00:18:06,200
and define the controls that keep agents in check.

430
00:18:06,200 --> 00:18:08,120
You want people who can improve the environment

431
00:18:08,120 --> 00:18:11,880
so that support demand actually drops instead of just stacking up.

432
00:18:11,880 --> 00:18:13,800
So how do you actually start this without it turning

433
00:18:13,800 --> 00:18:16,120
into a massive multi-year transformation project?

434
00:18:16,120 --> 00:18:18,040
The best move is to pick one single flow

435
00:18:18,040 --> 00:18:21,560
that causes constant friction inside your Microsoft 365 environment.

436
00:18:21,560 --> 00:18:23,800
Access remediation is usually a great candidate,

437
00:18:23,800 --> 00:18:25,480
but you could also look at meeting support

438
00:18:25,480 --> 00:18:26,920
or device compliance handoffs.

439
00:18:26,920 --> 00:18:28,600
It doesn't matter which one you choose,

440
00:18:28,600 --> 00:18:30,440
as long as the pattern happens often

441
00:18:30,440 --> 00:18:33,160
and you know exactly what the successful outcome should look like.

442
00:18:33,160 --> 00:18:34,360
Once you have the target,

443
00:18:34,360 --> 00:18:37,000
you have to define the basics with real discipline.

444
00:18:37,000 --> 00:18:38,840
You need to know what healthy looks like

445
00:18:38,840 --> 00:18:40,440
and which specific events tell you

446
00:18:40,440 --> 00:18:42,040
that things are drifting off track.

447
00:18:42,040 --> 00:18:44,200
Decide which actions are safe to automate

448
00:18:44,200 --> 00:18:46,760
and exactly where the human escalation needs to begin.

449
00:18:46,760 --> 00:18:48,280
Before you let anything go live,

450
00:18:48,280 --> 00:18:50,280
make sure you have a baseline for your ticket counts,

451
00:18:50,280 --> 00:18:51,000
manual touches,

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00:18:51,000 --> 00:18:53,160
and how long users are currently waiting for a fix.

453
00:18:53,160 --> 00:18:55,560
You also have to put your governance in place on day one,

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00:18:55,560 --> 00:18:57,400
give the agent a specific identity,

455
00:18:57,400 --> 00:18:58,840
assign a human owner to it,

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00:18:58,840 --> 00:19:00,600
and set clear approval rules.

457
00:19:00,600 --> 00:19:03,160
Every single action the agent takes needs to be logged.

458
00:19:03,160 --> 00:19:04,280
You should also have a plan

459
00:19:04,280 --> 00:19:06,600
for when that agent gets retired or reassigned,

460
00:19:06,600 --> 00:19:09,160
so it doesn't just become ghost automation.

461
00:19:09,160 --> 00:19:10,680
When that first flow starts working,

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00:19:10,680 --> 00:19:12,760
your entire operating model begins to shift.

463
00:19:12,760 --> 00:19:14,520
Support stops acting like a front desk

464
00:19:14,520 --> 00:19:16,280
that just reacts to problems.

465
00:19:16,280 --> 00:19:19,000
Instead, it starts behaving like a reliability engine

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00:19:19,000 --> 00:19:21,240
that is built directly into the system.

467
00:19:21,240 --> 00:19:23,160
Support is shifting from a visible reaction

468
00:19:23,160 --> 00:19:24,440
to embedded prevention.

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00:19:24,440 --> 00:19:25,800
The ticket was never the service.

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It was proof the service showed up too late.

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Pick one support flow this quarter

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and redesign it for preemption instead of response.

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If you want more on M365,

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00:19:34,040 --> 00:19:37,240
copilot Azure, security, and AI operating models,

475
00:19:37,240 --> 00:19:40,040
subscribe to M365 FM and leave a review.

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00:19:40,040 --> 00:19:41,480
Connect with me, Mirko Peters,

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00:19:41,480 --> 00:19:43,880
on LinkedIn with the next topic you want unpacked.