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Prompt engineering is a 2024 solution to a 2026 problem. For the past year, organizations have been told that success with AI comes down to phrasing—finding the perfect prompt. The promise is simple: say the right words, and suddenly your AI behaves like a senior consultant. But that promise doesn’t hold up in real-world environments. A prompt is not intelligence. It’s just a surface-level request hitting a deeply disorganized system. Right now, many organizations treat Copilot like a magic wand. They rely on tricks like “think step-by-step” or curated prompt cheat sheets. But these are band-aids, not strategies. If your data environment is chaotic—unmapped files, duplicate content, conflicting sources—no amount of clever wording will fix the outcome. You’re not guiding a genius.
You’re asking a genius to search through a dumpster. We are moving out of the era of improvisation. Prompt hacks don’t scale across teams, departments, or enterprises. The future is not about how well individuals talk to AI—it’s about how well organizations architect the system behind it. We are entering the era of orchestration.
THE STRUCTURAL ROT: WHY CONTEXT COLLAPSES
What looks like AI failure is often something else entirely: structural rot. You’ve likely seen polished demos where Copilot delivers perfect summaries. But in production environments, results are inconsistent—missing context, pulling outdated data, or contradicting itself. This isn’t randomness. It’s architecture.
CONTEXT COLLAPSE
The first failure mode is context collapse. Work today is fragmented:
- Conversations in Teams
- Ideas in Loop
- Documents in SharePoint
- Ask the same question twice → get different answers
- Chat says one thing → document says another
- No hierarchy → no reconciliation
MIS-SCOPED POLICY
The second failure is trust erosion through poor governance. Two extremes dominate: Over-restrictive environments
- Everything locked down with Purview
- AI cannot access enough data
- Outputs become empty or useless
- Legacy “open to everyone” links
- Sensitive data exposed unintentionally
- AI surfaces what should have stayed hidden
- Too locked → AI is useless
- Too open → AI becomes dangerous
ORPHANED KNOWLEDGE
The third—and most dangerous—issue is orphaned knowledge. Every organization has it:
- Draft_v1
- Draft_Final
- Draft_Final_v2_REAL
- Old data ≈ New data
- Stale strategy ≈ Current truth
BEYOND PROMPTS: THE SHIFT TO ARCHITECTURE
We’ve built systems for humans navigating folders. But AI doesn’t navigate. It retrieves. And retrieval requires:
- Clean data
- Structured relationships
- Governed access
- Defined context
REPLACING THE PROMPT WITH THE DECISION LATTICE
The real shift is this: From conversation → to system design A prompt is a request.
A business runs on systems. Enter the Decision Lattice. A structured framework where outputs are:
- grounded
- repeatable
- auditable
THE FOUR LAYERS OF THE DECISION LATTICE
SIGNALS (RAW INPUTS)
These are the incoming streams:
- Emails
- Meetings
- Transactions
- Logs
2. CONTEXT (CURATED TRUTH)
This is where most organizations fail. Instead of “search everything,” you define:
- curated SharePoint libraries
- scoped datasets
- Graph connectors for external systems
3. DECISION NODE (LOGIC ENGINE)
This is where Copilot operates—but not freely. Here you embed:
- business rules
- SOPs
- risk logic
- structured
- repeatable
- embedded in the system
The result is:
- auditable
- traceable
- consistent
- source signal
- applied logic
- governing rules
ANCHORING THE ARCHITECTURE: BEYOND THE INTERFACE
Copilot is not the system. It’s the front door. The real architecture lives underneath:
CORE COMPONENTS
- Microsoft Graph → the nervous system (relationships + context)
- Graph Connectors → bridge to external systems
- Microsoft Purview → governance + safety boundaries
- Entra ID → identity-driven context
- Microsoft Fabric / OneLake → structured data layer
- Copilot Studio → orchestration + logic design
- AI becomes inconsistent
- outputs become risky
- trust collapses
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Prompt Engineering is a 2024 solution to a 2026 problem.
2
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We have been told that if we just find the right words,
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the AI will suddenly perform like a senior partner.
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But in reality, a prompt is just a surface level request,
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hitting a deep level mess.
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Most organizations are treating co-pilot like a magic wand right now.
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They think the thinking step-by-step trick is a real strategy,
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but it is actually just a band-aid.
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Because if your data is a chaotic wreck of un-mapped files,
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no amount of clever phrasing will save the output.
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You are essentially asking a genius to search through a dumpster.
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We are moving away from the era of improvisation.
13
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The days of individual prompt hacks are ending
14
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because they simply do not scale.
15
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The future isn't about how well you talk to the machine,
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but rather how well you architect the system behind it.
17
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We are entering the era of orchestration.
18
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The structural rot, why context collapses?
19
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You've likely seen the demo where co-pilot summarizes a meeting
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perfectly.
21
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It looks like magic in the video.
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But when you try it in your own tenant,
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it misses the point entirely, or it pulls data
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from three years ago.
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This isn't an AI failure.
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It is structural rot.
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The first failure mode is what I call context collapse.
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It happens because of teams and SharePoints brawl.
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In most organizations, work is scattered across too many places.
30
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You have a chat in Teams, a loop component for brainstorming,
31
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and a SharePoint site for the final policy.
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But the moment those three things diverge,
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the AI loses the source of truth.
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You ask the same question twice and get two different answers
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because the context is fragmented.
36
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Co-pilot is essentially guessing which version of truth
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you want today.
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If the chat says one thing and the document says another,
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the system breaks.
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It cannot reconcile the conflict because there is no hierarchy
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in your data.
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The second failure is the mis-scoped policy.
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This is where trust goes to die.
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I see two extremes here.
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First, there is the over-restrictive approach.
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IT gets nervous and they lock everything down.
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They apply per view labels to every single folder.
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And then the coworker becomes useless.
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You ask co-pilot for a summary of the project,
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and it says it can't find any files.
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You've successfully secured your data,
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but you've also lobotomized your AI.
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It has no RAM to work with.
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The other extreme is under-restrictive.
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You have opened to everyone links floating around from 2019.
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This leads to catastrophic data leaks.
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Not because a hacker got in, but because the AI was too helpful.
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It found a spreadsheet of executive salaries
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that someone left in a public folder five years ago.
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The moment that happens, trust collapses.
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And once trust is gone, adoption stops.
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The third failure is orphaned knowledge.
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This is the most dangerous one for your decision making.
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Think about how many duplicate files live in your one drive.
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Draft V1, draft final and draft final V2 Mirko.
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To a human, we know the date modified matters,
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but to a large language model,
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stale data is often treated with the same authority
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as yesterday's meeting notes.
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Co-pilot doesn't know that the 2022 strategy is obsolete.
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If it's still sitting in a high traffic sharepoint site,
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it sees the keywords and the relevance,
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so it includes it in the summary.
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This makes the AI a hallucination amplifier.
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It isn't making things up out of thin air.
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It is just pulling from a reality that no longer exists.
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Stale data is actually worse than no data.
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If the AI finds nothing, you go look for it yourself.
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But if the AI finds the wrong thing and presents it confidently,
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you make a bad decision.
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We have built hierarchies for a world that no longer exists.
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We built them for people who navigate folders,
83
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but co-pilot doesn't navigate.
84
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It retrieves.
85
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And retrieval requires a different kind of integrity.
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It requires a substrate that is clean, mapped and governed.
87
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If you don't fix the rot, the prompt doesn't matter.
88
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You are essentially trying to build a skyscraper on a swamp.
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You can buy the most expensive glass for the windows,
90
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but the building is still going to sink.
91
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Most organizations are currently obsessed with the glass.
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They are training people on how to write better prompts
93
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and making cheat sheets for the interface.
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But the interface is just the tip of the iceberg.
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The real work, the hard work is underneath.
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It is about moving from a pile of un-mapped files
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to a rigid semantic index.
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It is about ensuring that when the AI reaches for a fact,
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it finds a single, high-fidelity signal.
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Because in reality, work doesn't start with navigation anymore.
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It starts with context.
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And if that context is broken, your AI co-worker
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is just another source of noise in an already loud room.
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We need a new model.
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We need to move beyond the conversation.
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We need to start building the decision lattice,
107
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replacing the prompt with the decision lattice.
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We have to stop thinking about prompts
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as the primary lever for AI's success,
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because a prompt is just a conversation.
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It is essentially a request for a favor,
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but you don't run a multi-billion dollar enterprise
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on favors and casual chats.
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You run it on systems.
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That is why we are moving away from the chat box mentality
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and toward what I call the decision lattice.
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A lattice isn't a conversation,
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but rather a structured and multi-dimensional framework,
119
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where every single output is supported
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by defined and verified inputs.
121
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It marks the move from individual improvisation
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to corporate orchestration.
123
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In the old model, you simply hope
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the employee knew how to ask the right question.
125
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In the lattice model, the system is designed,
126
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so the right answer is the only logical outcome.
127
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Think of this lattice in four distinct layers
128
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and keep in mind that if you miss even one,
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the whole structure of trust falls apart.
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Layer one is the signals.
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These are the raw feeds.
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We are talking about the emails, the meeting transcripts
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and the continuous data streams
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that flow through your tenant every second.
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Most people think this is the data.
136
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But in reality, it is actually just noise
137
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until it is cold.
138
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In a decision lattice, you don't just let these signals float
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around.
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You identify which ones are high fidelity
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and you decide which streams actually matter
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for the business outcome you are trying to reach.
143
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Layer two is where the magic happens
144
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and we call this the context layer.
145
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This isn't just all of SharePoint,
146
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which is a guaranteed recipe for a hallucination.
147
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This is about curated libraries
148
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and specifically configured graph connectors.
149
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You are creating a digital boundary
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and telling the AI to look here and only here
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when you ask about the Q3 budget.
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You are essentially creating a world garden of truth.
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By using graph connectors to bring an external data
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from your CRM or a legacy SQL database,
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you are grounding the AI in a reality
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that spans far beyond just a few word documents.
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Layer three is the decision node
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and this is where co-pilot actually sits.
159
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But in this model, co-pilot isn't just thinking
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on its own.
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It is applying a specific set of logic
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and rules to the refined context you provided in Layer two.
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This is where you embed your corporate SOPs.
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This is where the prompt actually lives,
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but it is no longer a standalone sentence.
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It is a set of instructions
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that tells the system to take the signals from Layer one,
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filter them by the context in Layer two
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and apply your standard risk assessment logic
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to generate a result.
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The decision node is the engine,
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but the lattice is the fuel system and the steering.
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Layer four is the action, which is the final
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and governed output.
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Because you have controlled the inputs in the logic,
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you can actually trust what comes out of the machine.
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It might be a report, a recommendation,
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or a drafted email to a client.
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But unlike a random chat response,
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this action is fully auditable.
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You can trace it back through the lattice
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to see exactly which signal and which context piece
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created that specific sentence.
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The shift here is fundamental because prompting is ad hoc
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and individual.
186
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It relies on the person sitting at the desk
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having a good day and remembering
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to include the right details in their message.
189
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The decision lattice is structured and shared.
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It is a piece of organizational infrastructure.
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When you build a lattice for an executive decision brief,
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it doesn't matter who triggers the request
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because the logic is the same, the sources are the same,
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and the level of trust is the same.
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We are essentially building a machine for thinking.
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We are moving from a world where we use AI
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to a world where we architect AI.
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This is how you scale.
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You cannot scale 1,000 different people
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writing 1,000 different prompts.
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You can, however, scale 10 well-designed
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lattices that handle 10 core business processes.
203
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Because at the end of the day, your board
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doesn't care if your employees are good at prompting.
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They care if the decisions being made are accurate,
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fast, and secure.
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The lattice gives you that certainty.
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It turns the black box of AI into a transparent and governed
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pipeline.
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You aren't just talking to a machine anymore.
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You are designing the way your company thinks,
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and that starts by realizing the prompt
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is just the smallest part of the system.
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Anchoring the architecture beyond the interface.
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If you want to understand why your current AI pilot feels
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like a series of disconnected toys,
217
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you have to look at the interface.
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Co-pilot is a beautiful front door.
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It is designed to be inviting, and it is built to make
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you feel like you are having a human conversation.
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But a front door is not a house.
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If you focus only on the chat bubble,
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you are missing the entire foundation
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that actually makes the machine work.
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The real work, which is the heavy lifting of enterprise intelligence,
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happens in the substrate.
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You aren't just using an app when you trigger a request.
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You are orchestrating a massive and interconnected stack
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of Microsoft services that must fire in perfect synchronicity
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to give you a single accurate sentence.
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Think of Microsoft Graph and its connectors
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as the central nervous system of this architecture.
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They are the pipes.
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Without them, the AI is effectively blind.
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It is just a model with a high IQ but zero local memory.
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When you ask a question, the graph doesn't just search for a file.
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It maps the relationship between you, your manager,
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the three meetings you had this morning,
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and the specific version of the project plan
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you edited at midnight.
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Graph connectors are the bridge to your external world.
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They pull in data from Gera Salesforce
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or your custom SQL servers,
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so the AI stops guessing and starts knowing.
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If these pipes are clogged or unmapped,
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the co-worker is just a stranger in your office
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with no access to the filing cabinet.
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Then we have Microsoft PerView, which serves as the filter.
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In the old world, security was about who could open a specific folder.
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In the AI world, security is about what the model is allowed to learn from.
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PerView is the silent governor that defines the boundaries of the AI's imagination.
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It tells co-pilot what it is allowed to know,
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and, more importantly, what it must absolutely ignore.
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When you apply a sensitivity label,
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you aren't just tagging a document for compliance.
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You are essentially creating a no-fly zone for the LLM.
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If your PerView labels are a mess,
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your AI's internal map is a mess.
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You cannot have a trustable co-worker
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if the system doesn't have a rigid set of ethical and legal guardrails
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built directly into the retrieval flow.
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But even with the right data and the right filters,
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the system needs to know who is asking.
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That is Microsoft EntraID.
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Identity is the ultimate context.
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A CEO asking for a summary of personnel changes
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should get a vastly different response
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than a junior manager asking the same thing.
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EntraID provides the role-based reality that shapes the output.
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The AI doesn't just see a prompt.
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It sees a set of permissions, a department, and a history of access.
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If your identity management is sloppy
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and people have privileged creep from three jobs ago,
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co-pilot will inadvertently become
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the world's most efficient internal spy.
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It will surface exactly what those people shouldn't see
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because you told the system they were allowed to see it.
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To prevent the chaos of raw files from poisoning the model,
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we use Microsoft, Fabric, and OneLake.
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This is your structured data layer.
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Most companies are trying to feed co-pilot
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a diet of messy Excel trackers and random CSV exports.
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That is how you get hallucinations.
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OneLake access the single and unified logical lake
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for all your data.
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It organizes the Excel chaos into a format
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the AI can actually digest without getting confused
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by formatting errors or broken links.
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It turns your raw numbers into a refined product.
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Finally, there is co-pilot studio.
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This is the workbench.
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This is where you actually build the decision boundaries.
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We don't just hope the AI follows your rules.
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You embed the logic here.
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You define the specific topics, the external actions,
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and the multi-step workflows that move beyond a simple chat.
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You are designing the brain of the specific co-worker
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you want to hire.
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When you realize that co-pilot is just the interface
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for this entire stack,
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your strategy changes.
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You stop training people on how to type
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and start training your architects on how to build.
304
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You aren't buying a tool.
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00:10:39,880 --> 00:10:41,880
You are designing a digital employee.
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And that employee is only as smart as the architecture you provide.
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The concrete walkthrough, the executive decision brief.
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To move this from a conceptual framework
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into a tangible reality.
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Let's walk through a specific high-stakes scenario.
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We are going to build an automated executive decision brief.
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This is the moment where theory meets the ground.
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In most companies today, preparing for a board meeting
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00:11:01,120 --> 00:11:04,000
or a major pivot involves a week of manual labor.
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00:11:04,000 --> 00:11:06,640
You have analysts hunting for the latest sales figures.
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00:11:06,640 --> 00:11:08,240
Managers digging through email threads
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00:11:08,240 --> 00:11:10,800
to find the real reason a project stalled.
318
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And executives trying to reconcile
319
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three different versions of a slide deck.
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00:11:14,040 --> 00:11:16,120
It is a process defined by friction.
321
00:11:16,120 --> 00:11:18,360
But when we apply the architecture we've discussed,
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the prompt becomes the smallest
323
00:11:19,720 --> 00:11:22,080
and least important part of the entire operation.
324
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Phase one is the input.
325
00:11:23,160 --> 00:11:24,520
We aren't just opening a chat box
326
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and asking what happened this month.
327
00:11:26,400 --> 00:11:29,320
That is a low fidelity request that invites a low fidelity answer.
328
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Instead, we point our decision lattice
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at specific graph-connected sources.
330
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We define the boundary.
331
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We tell the system to pull the raw financial data
332
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from Microsoft Fabric, the project milestones
333
00:11:38,760 --> 00:11:40,600
from a specific SharePoint library.
334
00:11:40,600 --> 00:11:42,920
And the sentiment analysis from the last four leadership
335
00:11:42,920 --> 00:11:45,000
town halls via Teams transcripts.
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We are essentially giving the AI a specific curated map
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00:11:47,880 --> 00:11:48,760
of the territory.
338
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We aren't asking it to find the truth.
339
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We are providing the truth and asking it to synthesize it.
340
00:11:53,480 --> 00:11:54,840
Next comes the orchestration.
341
00:11:54,840 --> 00:11:56,800
This happens inside Copilot Studio.
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00:11:56,800 --> 00:11:58,600
This is where you move from a conversation
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to a transformation.
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We define the logic.
345
00:12:00,840 --> 00:12:03,280
We aren't relying on the AI's general intelligence
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to decide what an executive needs to know.
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00:12:05,480 --> 00:12:07,240
We build a workflow that says,
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00:12:07,240 --> 00:12:10,720
"Identify any project where the spend is 10% over budget.
349
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Cross-reference that with the risk register
350
00:12:12,440 --> 00:12:14,320
in our external SQL database
351
00:12:14,320 --> 00:12:16,600
and highlight the specific manager responsible."
352
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This isn't a prompt.
353
00:12:17,880 --> 00:12:19,960
It is a set of rigid architectural instructions.
354
00:12:19,960 --> 00:12:22,640
The AI is acting as a processor, not a creator.
355
00:12:22,640 --> 00:12:23,920
It is taking the raw signals
356
00:12:23,920 --> 00:12:25,720
and running them through your corporate filter
357
00:12:25,720 --> 00:12:27,240
to create a recommendation.
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But the most critical step is the validation.
359
00:12:29,200 --> 00:12:31,960
Before a single word reaches the executive screen.
360
00:12:31,960 --> 00:12:34,200
The system performs an automated compliance check.
361
00:12:34,200 --> 00:12:36,360
It uses Microsoft purview to scan the draft
362
00:12:36,360 --> 00:12:38,160
for any sensitive data that doesn't belong
363
00:12:38,160 --> 00:12:39,400
in this specific brief.
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00:12:39,400 --> 00:12:40,560
If a project name is mentioned,
365
00:12:40,560 --> 00:12:42,840
that is still under a strict NDA.
366
00:12:42,840 --> 00:12:45,200
Or if a specific salary figure was accidentally pulled
367
00:12:45,200 --> 00:12:46,360
from a raw spreadsheet.
368
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The system flags it.
369
00:12:47,640 --> 00:12:49,040
It doesn't just generate content.
370
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It governs it in real time.
371
00:12:50,760 --> 00:12:52,480
This is how you solve the trust gap.
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00:12:52,480 --> 00:12:54,280
You aren't hoping the AI is safe.
373
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You are ensuring it is safe
374
00:12:55,560 --> 00:12:58,120
through a series of programmatic checks and balances.
375
00:12:58,120 --> 00:13:00,040
The final output is a high fidelity report.
376
00:13:00,040 --> 00:13:01,760
It isn't a generic summary that requires
377
00:13:01,760 --> 00:13:04,040
a three hour human rewrite to make it useful
378
00:13:04,040 --> 00:13:06,560
because you control the inputs and define the logic.
379
00:13:06,560 --> 00:13:08,560
The result is a precise actionable document.
380
00:13:08,560 --> 00:13:10,400
It looks like it was written by a senior partner
381
00:13:10,400 --> 00:13:12,080
who has been with the company for a decade.
382
00:13:12,080 --> 00:13:13,360
It understands the context.
383
00:13:13,360 --> 00:13:14,880
It respects the boundaries.
384
00:13:14,880 --> 00:13:16,400
And it focuses on the metrics
385
00:13:16,400 --> 00:13:18,360
that actually drive the business forward.
386
00:13:18,360 --> 00:13:20,240
This is the difference between a cool demo
387
00:13:20,240 --> 00:13:22,120
and an enterprise grade coworker.
388
00:13:22,120 --> 00:13:24,400
In the demo, you ask a question and get a paragraph
389
00:13:24,400 --> 00:13:25,560
in the enterprise architecture.
390
00:13:25,560 --> 00:13:27,480
You design a system and get a decision.
391
00:13:27,480 --> 00:13:28,840
When you look at this walkthrough,
392
00:13:28,840 --> 00:13:30,720
you realize that the person triggering the brief
393
00:13:30,720 --> 00:13:32,680
didn't need to be a prompt engineer.
394
00:13:32,680 --> 00:13:34,720
They didn't need a list of magic keywords.
395
00:13:34,720 --> 00:13:36,000
They just needed to click a button
396
00:13:36,000 --> 00:13:37,680
that triggered a pre-designed lattice.
397
00:13:37,680 --> 00:13:38,920
The architecture did the work.
398
00:13:38,920 --> 00:13:40,120
The graph provided the memory.
399
00:13:40,120 --> 00:13:41,440
Preview provided the safety.
400
00:13:41,440 --> 00:13:42,880
Fabric provided the structure.
401
00:13:42,880 --> 00:13:44,280
And Copilot provided the voice.
402
00:13:44,280 --> 00:13:46,200
This is how you move beyond the hype.
403
00:13:46,200 --> 00:13:48,400
You stop treating AI as a search engine
404
00:13:48,400 --> 00:13:50,240
and start treating it as a specialized factory
405
00:13:50,240 --> 00:13:51,280
for intelligence.
406
00:13:51,280 --> 00:13:53,360
You are no longer just chatting with a bot.
407
00:13:53,360 --> 00:13:55,520
You are running a sophisticated automated pipeline
408
00:13:55,520 --> 00:13:57,200
that turns raw organizational noise
409
00:13:57,200 --> 00:13:58,680
into clear executive signal.
410
00:13:58,680 --> 00:14:00,880
And that shift only happens when you stop obsessing
411
00:14:00,880 --> 00:14:03,680
over the words in the box and start designing the pipes
412
00:14:03,680 --> 00:14:04,680
behind the wall.
413
00:14:04,680 --> 00:14:06,280
This is the coworker architecture.
414
00:14:06,280 --> 00:14:08,320
This is how work actually changes.
415
00:14:08,320 --> 00:14:10,720
Measuring success, decision-confident speed.
416
00:14:10,720 --> 00:14:12,800
How do you actually prove that any of this is working?
417
00:14:12,800 --> 00:14:15,520
If you go to your board and talk about improved AI engagement
418
00:14:15,520 --> 00:14:18,400
or total number of prompts, then you are going to lose them.
419
00:14:18,400 --> 00:14:20,480
They have heard about AI potential for three years.
420
00:14:20,480 --> 00:14:23,320
They are tired of hearing that the technology is revolutionary.
421
00:14:23,320 --> 00:14:25,480
They want to know if the investment is actually
422
00:14:25,480 --> 00:14:27,320
moving the needle on the bottom line.
423
00:14:27,320 --> 00:14:29,240
To answer that, we have to stop measuring
424
00:14:29,240 --> 00:14:31,240
how people talk to the machine and start
425
00:14:31,240 --> 00:14:32,960
measuring the velocity of the outcome.
426
00:14:32,960 --> 00:14:33,920
We need a new metric.
427
00:14:33,920 --> 00:14:35,480
I call it decision-confident speed.
428
00:14:35,480 --> 00:14:38,720
This is the only number that truly matters in a post-prompting world.
429
00:14:38,720 --> 00:14:40,840
It is the measurement of the time it takes for a team
430
00:14:40,840 --> 00:14:42,640
to reach a committed, final action
431
00:14:42,640 --> 00:14:44,080
that they do not need to revisit.
432
00:14:44,080 --> 00:14:46,880
In most organizations, speed is an illusion.
433
00:14:46,880 --> 00:14:48,640
You might get a first draft in 30 seconds,
434
00:14:48,640 --> 00:14:51,160
but if that draft requires four hours of human fact checking
435
00:14:51,160 --> 00:14:53,280
and three meetings to fix the hallucinations,
436
00:14:53,280 --> 00:14:54,840
you haven't actually gained anything.
437
00:14:54,840 --> 00:14:57,320
You've just shifted the labor from writing to editing.
438
00:14:57,320 --> 00:14:59,760
Decision-confident speed tracks the entire journey
439
00:14:59,760 --> 00:15:01,880
from the initial signal to the final sign-off.
440
00:15:01,880 --> 00:15:05,520
To track this effectively, we look at three specific pillars.
441
00:15:05,520 --> 00:15:07,240
The first is time to decision.
442
00:15:07,240 --> 00:15:09,240
This is the raw duration between the moment
443
00:15:09,240 --> 00:15:11,000
a business need is identified
444
00:15:11,000 --> 00:15:14,080
and the moment a final approved action is taken.
445
00:15:14,080 --> 00:15:16,120
In the old model of un-mapped data,
446
00:15:16,120 --> 00:15:18,240
this process is played by research.
447
00:15:18,240 --> 00:15:19,920
People spend half their time just trying
448
00:15:19,920 --> 00:15:21,760
to find the right version of a file.
449
00:15:21,760 --> 00:15:23,280
In a structured decision letters,
450
00:15:23,280 --> 00:15:25,000
that search time drops to zero.
451
00:15:25,000 --> 00:15:27,360
The system already knows where the truth is.
452
00:15:27,360 --> 00:15:29,840
We aren't measuring how fast the AI types.
453
00:15:29,840 --> 00:15:32,480
We are measuring how much of the human middle we can eliminate.
454
00:15:32,480 --> 00:15:34,160
If your architecture is sound,
455
00:15:34,160 --> 00:15:36,440
you should see the gap between a request and a resolution
456
00:15:36,440 --> 00:15:39,680
shrink by half because the trust building phase is automated.
457
00:15:39,680 --> 00:15:41,640
The second pillar is the rework rate.
458
00:15:41,640 --> 00:15:43,360
This is the percentage of AI outputs
459
00:15:43,360 --> 00:15:44,840
that require a human to intervene
460
00:15:44,840 --> 00:15:46,880
and fix a factual error or a logic flaw.
461
00:15:46,880 --> 00:15:49,320
This is the ultimate test of your semantic index.
462
00:15:49,320 --> 00:15:51,880
If your rework rate is high, your architecture is failing.
463
00:15:51,880 --> 00:15:54,560
It means your context is too broad, your data is stale,
464
00:15:54,560 --> 00:15:57,240
or your per-view labels are letting noise into the signal.
465
00:15:57,240 --> 00:15:59,880
A successful implementation doesn't just make people faster,
466
00:15:59,880 --> 00:16:01,440
it makes them more accurate.
467
00:16:01,440 --> 00:16:03,120
We want to see a downward trend here.
468
00:16:03,120 --> 00:16:04,800
When the rework rate hits near zero,
469
00:16:04,800 --> 00:16:07,200
you have achieved an enterprise grade coworker.
470
00:16:07,200 --> 00:16:10,960
You've moved from AI as a toy to AI as a reliable infrastructure.
471
00:16:10,960 --> 00:16:12,720
The third pillar is the exception count.
472
00:16:12,720 --> 00:16:14,600
This is the most technical of the three,
473
00:16:14,600 --> 00:16:16,920
but it is vital for IT and compliance.
474
00:16:16,920 --> 00:16:20,120
We track how often the AI tries to access data it shouldn't,
475
00:16:20,120 --> 00:16:21,280
which per-view blocks,
476
00:16:21,280 --> 00:16:23,320
or how often it fails to find the data it needs
477
00:16:23,320 --> 00:16:25,600
because of a broken graph connector.
478
00:16:25,600 --> 00:16:28,600
High exception counts indicate a friction-heavy architecture.
479
00:16:28,600 --> 00:16:31,160
It means your digital boundaries are poorly defined.
480
00:16:31,160 --> 00:16:32,400
By monitoring these exceptions,
481
00:16:32,400 --> 00:16:34,520
you can fine-tune your lattice in real time.
482
00:16:34,520 --> 00:16:37,040
You are essentially debugging your company's intelligence.
483
00:16:37,040 --> 00:16:40,040
When you present these metrics, you change the conversation.
484
00:16:40,040 --> 00:16:42,520
You are no longer talking about cool tools.
485
00:16:42,520 --> 00:16:44,720
You are talking about operational efficiency.
486
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A successful architecture usually yields a 20-40% reduction
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00:16:47,840 --> 00:16:49,800
in total decision time within the first month
488
00:16:49,800 --> 00:16:51,560
that isn't just a productivity gain,
489
00:16:51,560 --> 00:16:53,200
it is a competitive advantage.
490
00:16:53,200 --> 00:16:55,280
It means your company can pivot faster,
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00:16:55,280 --> 00:16:57,320
respond to market changes quicker,
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00:16:57,320 --> 00:17:00,080
and execute with more certainty than anyone else.
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00:17:00,080 --> 00:17:03,560
Efficiency isn't about typing more words into a chat box.
494
00:17:03,560 --> 00:17:05,440
It is about needing to type less often
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00:17:05,440 --> 00:17:07,840
because the system already understands the goal.
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00:17:07,840 --> 00:17:09,120
The 30-day proof plan.
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00:17:09,120 --> 00:17:10,480
If this feels overwhelming,
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00:17:10,480 --> 00:17:13,400
remember that you don't have to boil the ocean on day one.
499
00:17:13,400 --> 00:17:15,520
Architecture is built in stages.
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00:17:15,520 --> 00:17:17,880
You can prove this model in exactly 30 days
501
00:17:17,880 --> 00:17:19,960
if you stay disciplined about the scope.
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00:17:19,960 --> 00:17:23,200
During weeks one and two, your only job is to baseline the chaos.
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00:17:23,200 --> 00:17:25,200
Don't change anything yet.
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00:17:25,200 --> 00:17:27,160
Pick one messy frequent workflow,
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00:17:27,160 --> 00:17:29,280
something like a weekly project status report
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00:17:29,280 --> 00:17:30,840
or a vendor risk assessment.
507
00:17:30,840 --> 00:17:33,800
Measure the current time to decision, count the rework loops,
508
00:17:33,800 --> 00:17:35,560
document every single time someone asks
509
00:17:35,560 --> 00:17:37,360
if they have the right version of the spreadsheet.
510
00:17:37,360 --> 00:17:39,040
This data is your leverage
511
00:17:39,040 --> 00:17:42,080
because it shows the board exactly what your architectural debt costs
512
00:17:42,080 --> 00:17:43,560
in hours and frustration.
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00:17:43,560 --> 00:17:46,000
In week three, you introduce the decision letters.
514
00:17:46,000 --> 00:17:47,280
You don't do it for the whole company.
515
00:17:47,280 --> 00:17:49,000
You do it for that one specific workflow.
516
00:17:49,000 --> 00:17:51,360
Define your approved data sources in SharePoint.
517
00:17:51,360 --> 00:17:52,920
Clear out the stale duplicates
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00:17:52,920 --> 00:17:55,280
and map your external signals through a graph connector.
519
00:17:55,280 --> 00:17:57,000
Set the logic in Copilot Studio.
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00:17:57,000 --> 00:17:59,560
You are creating a part of truth for this one task.
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00:17:59,560 --> 00:18:01,560
By week four, you run the comparison.
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00:18:01,560 --> 00:18:04,520
You let the team use the lattice instead of the raw chat box.
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00:18:04,520 --> 00:18:07,000
And that's when you'll watch the "Can we trust this?"
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00:18:07,000 --> 00:18:09,160
Conversation simply disappear.
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00:18:09,160 --> 00:18:10,480
The rework rate will plummet
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00:18:10,480 --> 00:18:12,200
because the AI is finally grounded
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00:18:12,200 --> 00:18:13,920
in a rigid verified reality.
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00:18:13,920 --> 00:18:15,840
At the end of the month, you take those three metrics,
529
00:18:15,840 --> 00:18:18,680
time to decision, rework rate and exception count.
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00:18:18,680 --> 00:18:20,000
And you show the board the difference.
531
00:18:20,000 --> 00:18:23,720
Starts more, but always build with the final architecture in mind.
532
00:18:23,720 --> 00:18:26,000
Every library you clean and every connector you map
533
00:18:26,000 --> 00:18:28,720
is a brick in the foundation of your future agency.
534
00:18:28,720 --> 00:18:30,720
If this shift from prompting to architecture
535
00:18:30,720 --> 00:18:32,160
changed how you think about AI,
536
00:18:32,160 --> 00:18:33,760
follow me, Mirko Peters, on LinkedIn.
537
00:18:33,760 --> 00:18:35,480
I spend my time diagnosing these systems
538
00:18:35,480 --> 00:18:37,440
so you can stop guessing and start building.
539
00:18:37,440 --> 00:18:40,040
Subscribe to the M365FM podcast
540
00:18:40,040 --> 00:18:42,400
for more deep dives into the structural reality
541
00:18:42,400 --> 00:18:43,560
of the modern workplace.
542
00:18:43,560 --> 00:18:45,160
Share this with your team, especially
543
00:18:45,160 --> 00:18:48,080
if you're currently drowning in the perfect prompt trap.
544
00:18:48,080 --> 00:18:51,320
Once you fix the foundation, your AI stops being a chatbot
545
00:18:51,320 --> 00:18:52,880
and starts being a coworker.
546
00:18:52,880 --> 00:18:54,840
For the advanced walkthrough and graph connectors,
547
00:18:54,840 --> 00:18:56,240
check out this video next.
548
00:18:56,240 --> 00:18:57,480
Subscribe for more.
00:00:00,000 --> 00:00:03,040
Prompt Engineering is a 2024 solution to a 2026 problem.
2
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We have been told that if we just find the right words,
3
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the AI will suddenly perform like a senior partner.
4
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But in reality, a prompt is just a surface level request,
5
00:00:11,200 --> 00:00:13,120
hitting a deep level mess.
6
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Most organizations are treating co-pilot like a magic wand right now.
7
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They think the thinking step-by-step trick is a real strategy,
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but it is actually just a band-aid.
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Because if your data is a chaotic wreck of un-mapped files,
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no amount of clever phrasing will save the output.
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You are essentially asking a genius to search through a dumpster.
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We are moving away from the era of improvisation.
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The days of individual prompt hacks are ending
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because they simply do not scale.
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The future isn't about how well you talk to the machine,
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00:00:39,640 --> 00:00:42,720
but rather how well you architect the system behind it.
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We are entering the era of orchestration.
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The structural rot, why context collapses?
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You've likely seen the demo where co-pilot summarizes a meeting
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00:00:50,120 --> 00:00:50,720
perfectly.
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It looks like magic in the video.
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But when you try it in your own tenant,
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it misses the point entirely, or it pulls data
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from three years ago.
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This isn't an AI failure.
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It is structural rot.
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The first failure mode is what I call context collapse.
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It happens because of teams and SharePoints brawl.
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In most organizations, work is scattered across too many places.
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You have a chat in Teams, a loop component for brainstorming,
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and a SharePoint site for the final policy.
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But the moment those three things diverge,
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the AI loses the source of truth.
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You ask the same question twice and get two different answers
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because the context is fragmented.
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Co-pilot is essentially guessing which version of truth
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you want today.
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If the chat says one thing and the document says another,
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the system breaks.
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It cannot reconcile the conflict because there is no hierarchy
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in your data.
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The second failure is the mis-scoped policy.
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This is where trust goes to die.
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I see two extremes here.
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First, there is the over-restrictive approach.
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IT gets nervous and they lock everything down.
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They apply per view labels to every single folder.
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And then the coworker becomes useless.
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You ask co-pilot for a summary of the project,
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and it says it can't find any files.
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You've successfully secured your data,
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but you've also lobotomized your AI.
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It has no RAM to work with.
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The other extreme is under-restrictive.
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You have opened to everyone links floating around from 2019.
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This leads to catastrophic data leaks.
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Not because a hacker got in, but because the AI was too helpful.
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It found a spreadsheet of executive salaries
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that someone left in a public folder five years ago.
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The moment that happens, trust collapses.
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And once trust is gone, adoption stops.
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The third failure is orphaned knowledge.
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This is the most dangerous one for your decision making.
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Think about how many duplicate files live in your one drive.
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Draft V1, draft final and draft final V2 Mirko.
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To a human, we know the date modified matters,
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but to a large language model,
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stale data is often treated with the same authority
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as yesterday's meeting notes.
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Co-pilot doesn't know that the 2022 strategy is obsolete.
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If it's still sitting in a high traffic sharepoint site,
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it sees the keywords and the relevance,
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so it includes it in the summary.
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This makes the AI a hallucination amplifier.
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It isn't making things up out of thin air.
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It is just pulling from a reality that no longer exists.
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Stale data is actually worse than no data.
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If the AI finds nothing, you go look for it yourself.
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But if the AI finds the wrong thing and presents it confidently,
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you make a bad decision.
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We have built hierarchies for a world that no longer exists.
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We built them for people who navigate folders,
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but co-pilot doesn't navigate.
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It retrieves.
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And retrieval requires a different kind of integrity.
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It requires a substrate that is clean, mapped and governed.
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If you don't fix the rot, the prompt doesn't matter.
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You are essentially trying to build a skyscraper on a swamp.
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You can buy the most expensive glass for the windows,
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but the building is still going to sink.
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Most organizations are currently obsessed with the glass.
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They are training people on how to write better prompts
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and making cheat sheets for the interface.
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But the interface is just the tip of the iceberg.
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The real work, the hard work is underneath.
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It is about moving from a pile of un-mapped files
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to a rigid semantic index.
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It is about ensuring that when the AI reaches for a fact,
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it finds a single, high-fidelity signal.
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Because in reality, work doesn't start with navigation anymore.
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It starts with context.
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And if that context is broken, your AI co-worker
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is just another source of noise in an already loud room.
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We need a new model.
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We need to move beyond the conversation.
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We need to start building the decision lattice,
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replacing the prompt with the decision lattice.
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We have to stop thinking about prompts
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as the primary lever for AI's success,
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because a prompt is just a conversation.
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It is essentially a request for a favor,
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but you don't run a multi-billion dollar enterprise
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on favors and casual chats.
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You run it on systems.
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That is why we are moving away from the chat box mentality
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and toward what I call the decision lattice.
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A lattice isn't a conversation,
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but rather a structured and multi-dimensional framework,
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where every single output is supported
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by defined and verified inputs.
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It marks the move from individual improvisation
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to corporate orchestration.
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In the old model, you simply hope
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the employee knew how to ask the right question.
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In the lattice model, the system is designed,
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so the right answer is the only logical outcome.
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Think of this lattice in four distinct layers
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and keep in mind that if you miss even one,
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the whole structure of trust falls apart.
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Layer one is the signals.
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These are the raw feeds.
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We are talking about the emails, the meeting transcripts
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and the continuous data streams
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that flow through your tenant every second.
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Most people think this is the data.
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But in reality, it is actually just noise
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until it is cold.
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In a decision lattice, you don't just let these signals float
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around.
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You identify which ones are high fidelity
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and you decide which streams actually matter
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for the business outcome you are trying to reach.
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Layer two is where the magic happens
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and we call this the context layer.
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This isn't just all of SharePoint,
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which is a guaranteed recipe for a hallucination.
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This is about curated libraries
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and specifically configured graph connectors.
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You are creating a digital boundary
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and telling the AI to look here and only here
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when you ask about the Q3 budget.
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You are essentially creating a world garden of truth.
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By using graph connectors to bring an external data
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from your CRM or a legacy SQL database,
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you are grounding the AI in a reality
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that spans far beyond just a few word documents.
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Layer three is the decision node
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and this is where co-pilot actually sits.
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But in this model, co-pilot isn't just thinking
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on its own.
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It is applying a specific set of logic
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and rules to the refined context you provided in Layer two.
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This is where you embed your corporate SOPs.
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This is where the prompt actually lives,
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but it is no longer a standalone sentence.
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It is a set of instructions
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that tells the system to take the signals from Layer one,
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filter them by the context in Layer two
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and apply your standard risk assessment logic
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to generate a result.
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The decision node is the engine,
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but the lattice is the fuel system and the steering.
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Layer four is the action, which is the final
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and governed output.
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Because you have controlled the inputs in the logic,
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you can actually trust what comes out of the machine.
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It might be a report, a recommendation,
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or a drafted email to a client.
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But unlike a random chat response,
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this action is fully auditable.
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You can trace it back through the lattice
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to see exactly which signal and which context piece
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created that specific sentence.
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The shift here is fundamental because prompting is ad hoc
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and individual.
186
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It relies on the person sitting at the desk
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having a good day and remembering
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to include the right details in their message.
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The decision lattice is structured and shared.
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It is a piece of organizational infrastructure.
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When you build a lattice for an executive decision brief,
192
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it doesn't matter who triggers the request
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because the logic is the same, the sources are the same,
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and the level of trust is the same.
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We are essentially building a machine for thinking.
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We are moving from a world where we use AI
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to a world where we architect AI.
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This is how you scale.
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You cannot scale 1,000 different people
200
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writing 1,000 different prompts.
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You can, however, scale 10 well-designed
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lattices that handle 10 core business processes.
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Because at the end of the day, your board
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doesn't care if your employees are good at prompting.
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They care if the decisions being made are accurate,
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fast, and secure.
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The lattice gives you that certainty.
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It turns the black box of AI into a transparent and governed
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pipeline.
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You aren't just talking to a machine anymore.
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You are designing the way your company thinks,
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and that starts by realizing the prompt
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is just the smallest part of the system.
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Anchoring the architecture beyond the interface.
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If you want to understand why your current AI pilot feels
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like a series of disconnected toys,
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you have to look at the interface.
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Co-pilot is a beautiful front door.
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It is designed to be inviting, and it is built to make
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you feel like you are having a human conversation.
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But a front door is not a house.
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If you focus only on the chat bubble,
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you are missing the entire foundation
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that actually makes the machine work.
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The real work, which is the heavy lifting of enterprise intelligence,
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happens in the substrate.
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You aren't just using an app when you trigger a request.
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You are orchestrating a massive and interconnected stack
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of Microsoft services that must fire in perfect synchronicity
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to give you a single accurate sentence.
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Think of Microsoft Graph and its connectors
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as the central nervous system of this architecture.
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They are the pipes.
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Without them, the AI is effectively blind.
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It is just a model with a high IQ but zero local memory.
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When you ask a question, the graph doesn't just search for a file.
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It maps the relationship between you, your manager,
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the three meetings you had this morning,
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and the specific version of the project plan
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you edited at midnight.
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Graph connectors are the bridge to your external world.
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They pull in data from Gera Salesforce
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or your custom SQL servers,
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so the AI stops guessing and starts knowing.
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If these pipes are clogged or unmapped,
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the co-worker is just a stranger in your office
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with no access to the filing cabinet.
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Then we have Microsoft PerView, which serves as the filter.
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In the old world, security was about who could open a specific folder.
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In the AI world, security is about what the model is allowed to learn from.
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PerView is the silent governor that defines the boundaries of the AI's imagination.
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It tells co-pilot what it is allowed to know,
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and, more importantly, what it must absolutely ignore.
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When you apply a sensitivity label,
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you aren't just tagging a document for compliance.
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You are essentially creating a no-fly zone for the LLM.
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If your PerView labels are a mess,
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your AI's internal map is a mess.
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You cannot have a trustable co-worker
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if the system doesn't have a rigid set of ethical and legal guardrails
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built directly into the retrieval flow.
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But even with the right data and the right filters,
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the system needs to know who is asking.
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That is Microsoft EntraID.
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Identity is the ultimate context.
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A CEO asking for a summary of personnel changes
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should get a vastly different response
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than a junior manager asking the same thing.
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EntraID provides the role-based reality that shapes the output.
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The AI doesn't just see a prompt.
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It sees a set of permissions, a department, and a history of access.
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If your identity management is sloppy
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and people have privileged creep from three jobs ago,
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co-pilot will inadvertently become
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the world's most efficient internal spy.
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It will surface exactly what those people shouldn't see
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because you told the system they were allowed to see it.
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To prevent the chaos of raw files from poisoning the model,
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we use Microsoft, Fabric, and OneLake.
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This is your structured data layer.
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Most companies are trying to feed co-pilot
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a diet of messy Excel trackers and random CSV exports.
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That is how you get hallucinations.
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OneLake access the single and unified logical lake
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for all your data.
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It organizes the Excel chaos into a format
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the AI can actually digest without getting confused
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by formatting errors or broken links.
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It turns your raw numbers into a refined product.
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Finally, there is co-pilot studio.
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This is the workbench.
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This is where you actually build the decision boundaries.
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We don't just hope the AI follows your rules.
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You embed the logic here.
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You define the specific topics, the external actions,
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and the multi-step workflows that move beyond a simple chat.
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You are designing the brain of the specific co-worker
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you want to hire.
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When you realize that co-pilot is just the interface
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for this entire stack,
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your strategy changes.
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You stop training people on how to type
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and start training your architects on how to build.
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You aren't buying a tool.
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You are designing a digital employee.
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And that employee is only as smart as the architecture you provide.
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The concrete walkthrough, the executive decision brief.
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To move this from a conceptual framework
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into a tangible reality.
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Let's walk through a specific high-stakes scenario.
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We are going to build an automated executive decision brief.
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This is the moment where theory meets the ground.
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In most companies today, preparing for a board meeting
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or a major pivot involves a week of manual labor.
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You have analysts hunting for the latest sales figures.
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Managers digging through email threads
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to find the real reason a project stalled.
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And executives trying to reconcile
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three different versions of a slide deck.
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It is a process defined by friction.
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But when we apply the architecture we've discussed,
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the prompt becomes the smallest
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and least important part of the entire operation.
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Phase one is the input.
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We aren't just opening a chat box
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and asking what happened this month.
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That is a low fidelity request that invites a low fidelity answer.
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Instead, we point our decision lattice
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at specific graph-connected sources.
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We define the boundary.
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We tell the system to pull the raw financial data
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from Microsoft Fabric, the project milestones
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from a specific SharePoint library.
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And the sentiment analysis from the last four leadership
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town halls via Teams transcripts.
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We are essentially giving the AI a specific curated map
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of the territory.
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We aren't asking it to find the truth.
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We are providing the truth and asking it to synthesize it.
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Next comes the orchestration.
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This happens inside Copilot Studio.
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This is where you move from a conversation
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to a transformation.
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We define the logic.
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We aren't relying on the AI's general intelligence
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to decide what an executive needs to know.
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We build a workflow that says,
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"Identify any project where the spend is 10% over budget.
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Cross-reference that with the risk register
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in our external SQL database
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and highlight the specific manager responsible."
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This isn't a prompt.
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It is a set of rigid architectural instructions.
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The AI is acting as a processor, not a creator.
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It is taking the raw signals
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and running them through your corporate filter
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to create a recommendation.
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But the most critical step is the validation.
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Before a single word reaches the executive screen.
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The system performs an automated compliance check.
361
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It uses Microsoft purview to scan the draft
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for any sensitive data that doesn't belong
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in this specific brief.
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If a project name is mentioned,
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that is still under a strict NDA.
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Or if a specific salary figure was accidentally pulled
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from a raw spreadsheet.
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The system flags it.
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It doesn't just generate content.
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It governs it in real time.
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This is how you solve the trust gap.
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You aren't hoping the AI is safe.
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You are ensuring it is safe
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through a series of programmatic checks and balances.
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The final output is a high fidelity report.
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It isn't a generic summary that requires
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a three hour human rewrite to make it useful
378
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because you control the inputs and define the logic.
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The result is a precise actionable document.
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It looks like it was written by a senior partner
381
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who has been with the company for a decade.
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It understands the context.
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It respects the boundaries.
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And it focuses on the metrics
385
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that actually drive the business forward.
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This is the difference between a cool demo
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and an enterprise grade coworker.
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In the demo, you ask a question and get a paragraph
389
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in the enterprise architecture.
390
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You design a system and get a decision.
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When you look at this walkthrough,
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you realize that the person triggering the brief
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didn't need to be a prompt engineer.
394
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They didn't need a list of magic keywords.
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They just needed to click a button
396
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that triggered a pre-designed lattice.
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The architecture did the work.
398
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The graph provided the memory.
399
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Preview provided the safety.
400
00:13:41,440 --> 00:13:42,880
Fabric provided the structure.
401
00:13:42,880 --> 00:13:44,280
And Copilot provided the voice.
402
00:13:44,280 --> 00:13:46,200
This is how you move beyond the hype.
403
00:13:46,200 --> 00:13:48,400
You stop treating AI as a search engine
404
00:13:48,400 --> 00:13:50,240
and start treating it as a specialized factory
405
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for intelligence.
406
00:13:51,280 --> 00:13:53,360
You are no longer just chatting with a bot.
407
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You are running a sophisticated automated pipeline
408
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that turns raw organizational noise
409
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into clear executive signal.
410
00:13:58,680 --> 00:14:00,880
And that shift only happens when you stop obsessing
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over the words in the box and start designing the pipes
412
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behind the wall.
413
00:14:04,680 --> 00:14:06,280
This is the coworker architecture.
414
00:14:06,280 --> 00:14:08,320
This is how work actually changes.
415
00:14:08,320 --> 00:14:10,720
Measuring success, decision-confident speed.
416
00:14:10,720 --> 00:14:12,800
How do you actually prove that any of this is working?
417
00:14:12,800 --> 00:14:15,520
If you go to your board and talk about improved AI engagement
418
00:14:15,520 --> 00:14:18,400
or total number of prompts, then you are going to lose them.
419
00:14:18,400 --> 00:14:20,480
They have heard about AI potential for three years.
420
00:14:20,480 --> 00:14:23,320
They are tired of hearing that the technology is revolutionary.
421
00:14:23,320 --> 00:14:25,480
They want to know if the investment is actually
422
00:14:25,480 --> 00:14:27,320
moving the needle on the bottom line.
423
00:14:27,320 --> 00:14:29,240
To answer that, we have to stop measuring
424
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how people talk to the machine and start
425
00:14:31,240 --> 00:14:32,960
measuring the velocity of the outcome.
426
00:14:32,960 --> 00:14:33,920
We need a new metric.
427
00:14:33,920 --> 00:14:35,480
I call it decision-confident speed.
428
00:14:35,480 --> 00:14:38,720
This is the only number that truly matters in a post-prompting world.
429
00:14:38,720 --> 00:14:40,840
It is the measurement of the time it takes for a team
430
00:14:40,840 --> 00:14:42,640
to reach a committed, final action
431
00:14:42,640 --> 00:14:44,080
that they do not need to revisit.
432
00:14:44,080 --> 00:14:46,880
In most organizations, speed is an illusion.
433
00:14:46,880 --> 00:14:48,640
You might get a first draft in 30 seconds,
434
00:14:48,640 --> 00:14:51,160
but if that draft requires four hours of human fact checking
435
00:14:51,160 --> 00:14:53,280
and three meetings to fix the hallucinations,
436
00:14:53,280 --> 00:14:54,840
you haven't actually gained anything.
437
00:14:54,840 --> 00:14:57,320
You've just shifted the labor from writing to editing.
438
00:14:57,320 --> 00:14:59,760
Decision-confident speed tracks the entire journey
439
00:14:59,760 --> 00:15:01,880
from the initial signal to the final sign-off.
440
00:15:01,880 --> 00:15:05,520
To track this effectively, we look at three specific pillars.
441
00:15:05,520 --> 00:15:07,240
The first is time to decision.
442
00:15:07,240 --> 00:15:09,240
This is the raw duration between the moment
443
00:15:09,240 --> 00:15:11,000
a business need is identified
444
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and the moment a final approved action is taken.
445
00:15:14,080 --> 00:15:16,120
In the old model of un-mapped data,
446
00:15:16,120 --> 00:15:18,240
this process is played by research.
447
00:15:18,240 --> 00:15:19,920
People spend half their time just trying
448
00:15:19,920 --> 00:15:21,760
to find the right version of a file.
449
00:15:21,760 --> 00:15:23,280
In a structured decision letters,
450
00:15:23,280 --> 00:15:25,000
that search time drops to zero.
451
00:15:25,000 --> 00:15:27,360
The system already knows where the truth is.
452
00:15:27,360 --> 00:15:29,840
We aren't measuring how fast the AI types.
453
00:15:29,840 --> 00:15:32,480
We are measuring how much of the human middle we can eliminate.
454
00:15:32,480 --> 00:15:34,160
If your architecture is sound,
455
00:15:34,160 --> 00:15:36,440
you should see the gap between a request and a resolution
456
00:15:36,440 --> 00:15:39,680
shrink by half because the trust building phase is automated.
457
00:15:39,680 --> 00:15:41,640
The second pillar is the rework rate.
458
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This is the percentage of AI outputs
459
00:15:43,360 --> 00:15:44,840
that require a human to intervene
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00:15:44,840 --> 00:15:46,880
and fix a factual error or a logic flaw.
461
00:15:46,880 --> 00:15:49,320
This is the ultimate test of your semantic index.
462
00:15:49,320 --> 00:15:51,880
If your rework rate is high, your architecture is failing.
463
00:15:51,880 --> 00:15:54,560
It means your context is too broad, your data is stale,
464
00:15:54,560 --> 00:15:57,240
or your per-view labels are letting noise into the signal.
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A successful implementation doesn't just make people faster,
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it makes them more accurate.
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We want to see a downward trend here.
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When the rework rate hits near zero,
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you have achieved an enterprise grade coworker.
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You've moved from AI as a toy to AI as a reliable infrastructure.
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The third pillar is the exception count.
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This is the most technical of the three,
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but it is vital for IT and compliance.
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We track how often the AI tries to access data it shouldn't,
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which per-view blocks,
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or how often it fails to find the data it needs
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because of a broken graph connector.
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High exception counts indicate a friction-heavy architecture.
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It means your digital boundaries are poorly defined.
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By monitoring these exceptions,
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you can fine-tune your lattice in real time.
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You are essentially debugging your company's intelligence.
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When you present these metrics, you change the conversation.
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You are no longer talking about cool tools.
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You are talking about operational efficiency.
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A successful architecture usually yields a 20-40% reduction
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in total decision time within the first month
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that isn't just a productivity gain,
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it is a competitive advantage.
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It means your company can pivot faster,
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respond to market changes quicker,
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and execute with more certainty than anyone else.
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Efficiency isn't about typing more words into a chat box.
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It is about needing to type less often
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because the system already understands the goal.
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The 30-day proof plan.
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If this feels overwhelming,
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remember that you don't have to boil the ocean on day one.
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Architecture is built in stages.
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You can prove this model in exactly 30 days
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if you stay disciplined about the scope.
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During weeks one and two, your only job is to baseline the chaos.
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Don't change anything yet.
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Pick one messy frequent workflow,
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something like a weekly project status report
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or a vendor risk assessment.
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Measure the current time to decision, count the rework loops,
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document every single time someone asks
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if they have the right version of the spreadsheet.
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This data is your leverage
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because it shows the board exactly what your architectural debt costs
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in hours and frustration.
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In week three, you introduce the decision letters.
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You don't do it for the whole company.
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00:17:47,280 --> 00:17:49,000
You do it for that one specific workflow.
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Define your approved data sources in SharePoint.
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Clear out the stale duplicates
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and map your external signals through a graph connector.
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00:17:55,280 --> 00:17:57,000
Set the logic in Copilot Studio.
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You are creating a part of truth for this one task.
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By week four, you run the comparison.
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You let the team use the lattice instead of the raw chat box.
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00:18:04,520 --> 00:18:07,000
And that's when you'll watch the "Can we trust this?"
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Conversation simply disappear.
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The rework rate will plummet
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00:18:10,480 --> 00:18:12,200
because the AI is finally grounded
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in a rigid verified reality.
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00:18:13,920 --> 00:18:15,840
At the end of the month, you take those three metrics,
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time to decision, rework rate and exception count.
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And you show the board the difference.
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Starts more, but always build with the final architecture in mind.
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00:18:23,720 --> 00:18:26,000
Every library you clean and every connector you map
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is a brick in the foundation of your future agency.
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00:18:28,720 --> 00:18:30,720
If this shift from prompting to architecture
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00:18:30,720 --> 00:18:32,160
changed how you think about AI,
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00:18:32,160 --> 00:18:33,760
follow me, Mirko Peters, on LinkedIn.
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00:18:33,760 --> 00:18:35,480
I spend my time diagnosing these systems
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00:18:35,480 --> 00:18:37,440
so you can stop guessing and start building.
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00:18:37,440 --> 00:18:40,040
Subscribe to the M365FM podcast
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00:18:40,040 --> 00:18:42,400
for more deep dives into the structural reality
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00:18:42,400 --> 00:18:43,560
of the modern workplace.
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00:18:43,560 --> 00:18:45,160
Share this with your team, especially
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00:18:45,160 --> 00:18:48,080
if you're currently drowning in the perfect prompt trap.
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00:18:48,080 --> 00:18:51,320
Once you fix the foundation, your AI stops being a chatbot
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00:18:51,320 --> 00:18:52,880
and starts being a coworker.
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00:18:52,880 --> 00:18:54,840
For the advanced walkthrough and graph connectors,
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00:18:54,840 --> 00:18:56,240
check out this video next.
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00:18:56,240 --> 00:18:57,480
Subscribe for more.