AI Operating Model Insights

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Everyone is racing to adopt AI.

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Very few are ready to operate it.

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That’s why the same pattern keeps repeating.

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Impressive demos, then untrusted outputs,

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then a sudden cost spike, then security and compliance panic,

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and finally a pilot that pauses and never comes back.

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The system didn’t collapse because the model was weak.

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It collapsed because the enterprise

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had no operating discipline behind it.

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In this episode, the focus is a three to five year playbook,

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who owns truth, who absorbs risk, who pays,

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and what gets enforced.

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Stop asking what AI can do.

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Start asking what your enterprise can safely absorb.

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The foundational misunderstanding, AI is not the transformation.

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Most organizations treat the AI platform as the transformation

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that they are wrong.

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Architecturally, AI is an accelerator.

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It does not create structure.

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It magnifies whatever structure already exists,

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your data quality, your identity boundaries,

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your decision rights, your exception culture,

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your cost discipline.

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If those are coherent, AI makes the enterprise faster.

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If they’re not, AI makes the enterprise loud and expensive.

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This is the foundational misunderstanding.

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Leaders think the transformation is adopting AI,

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meaning licensing models, standing up a platform,

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hiring a few specialists and launching pilots.

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In reality, the transformation target is the operating model

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that sits underneath decisions.

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Because AI isn’t a tool that sits on the edge of the business.

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It becomes part of the decision loop.

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And the decision loop is where enterprises either create value

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or create incidents.

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Here’s why pilots look so good.

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The pilot is a small controlled experiment.

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It runs on a narrow slice of data.

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It has a friendly audience.

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It uses a curated document set.

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It usually has an unofficial exception stack.

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Temporary access granted just for the demo,

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missing classification because we’ll fix it later,

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relaxed policies because it’s not production

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and a lot of manual cleanup that nobody writes down.

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That’s not innovation.

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That’s conditional chaos.

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And it works briefly because you’re operating

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outside the real system.

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Then you try to industrialize it.

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Production is where scale forces every hidden assumption

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to become explicit.

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Suddenly, the model is exposed to conflicting meanings,

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missing owners drift in access and data

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that has never been held to a consistent semantic standard.

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The same question gets two correct answers,

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depending on which dataset or document the system retrieved.

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The model doesn’t know it’s inconsistent.

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It just synthesizes confidently.

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And this is where executives misdiagnose the failure.

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They blame the model.

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They blame hallucinations.

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They blame the platform.

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But the system did exactly what you built.

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It produced outputs from ambiguous inputs

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under undefined accountability.

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AI doesn’t leak data accidentally.

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It leaks it correctly under bad identity design.

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AI doesn’t create wrong answers randomly.

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It produces wrong answers deterministically

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when your enterprise cannot agree on truth.

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That distinction matters.

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So the useful split is not AI tools versus no AI tools.

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The useful split is the innovation stack

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versus the operating system.

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The innovation stack is what most organizations

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already know how to fund.

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Experiments, pilots, proof of concepts, hackathons, labs,

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it’s optional.

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It’s exciting.

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It’s also disposable when it fails you shrug and move on.

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The operating system is different.

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It’s durable.

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It’s owned.

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It has guardrails.

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It has budgets.

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It has accountability.

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It has enforcement.

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It’s boring on purpose.

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And AI belongs to the operating system category.

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Once AI participates in decisions,

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you’re no longer deploying a feature.

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You’re deploying a decision engine that

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will run continuously at scale across the organization

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with a failure mode that looks like trust collapse.

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That means the first executive decision

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is not which model are we using.

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But the first executive decision is who owns truth.

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Second, who approves access for AI and for how long.

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Third, who carries the cost when usage spikes.

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Because those are not technical questions.

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Those are funding risk and accountability decisions.

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And they last three to five years,

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regardless of which vendor wins the model race next quarter.

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This clicked for a lot of platform leaders

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when they watched the same pattern happen in cloud.

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Cloud didn’t fail because the services weren’t capable.

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Cloud failed when organizations treated it

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like a procurement event instead of an operating model

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shift.

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They bought capacity, migrated workloads,

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and assumed governance would arrive later.

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Then drift happened.

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Exceptions accumulated.

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Costs surprised finance.

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Security found gaps after the fact.

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The platform team became an incident response unit.

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AI is the same failure pattern but faster.

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Because AI is probabilistic output sitting

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on top of deterministic controls.

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If you don’t enforce the deterministic layer identity data

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governance semantic contracts cost constraints,

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you will get probabilistic enterprise behavior.

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Nobody can explain it.

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Nobody can predict it.

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Everyone will blame someone else.

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Now here’s the pivot.

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If the transformation target is decisions,

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then data becomes the control surface.

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Not dashboards, not warehouses, not lake migrations.

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Data as the control surface definitions, lineage, access,

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quality, and cost attribution all tied to a decision

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that someone is accountable for.

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Once you see that, the platform stops being a tool set.

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It becomes your operating model.

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And if you’re a CIO, CTO, or CDO, this is the uncomfortable

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truth.

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The only way AI scaled safely is if you

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make those operating model decisions

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before the pilot goes viral.

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From digital transformation to decision transformation.

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Most leaders still think in digital transformation terms,

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take a process, remove friction, automate steps,

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make throughput higher, and ideally reduce

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headcount pressure.

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That was a rational goal for a decade.

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But AI doesn’t mainly optimize throughput.

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AI optimizes decisions.

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That distinction matters because enterprises

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don’t fail from slow processes as often

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as they fail from inconsistent decisions.

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The different teams making different calls

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with different definitions using different data

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and the different risk tolerances.

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That’s not inefficiency.

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That’s entropy.

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Digital transformation asks, can we do the same work faster?

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Decision transformation asks, can we make the same decision

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better, faster, and consistently?

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And better has a real meaning here.

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It means the decision is based on trusted inputs,

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the semantics are understood, and the accountability is explicit.

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It also means the decision has a feedback loop

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so the enterprise can learn when it was wrong.

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Because AI will make the wrong decision sometimes.

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That’s not a scandal.

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That’s mathematics.

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The scandal is when nobody can explain why it was wrong.

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Nobody owns the correction and the organization

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keeps acting on it anyway.

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So if the unit of value is now the decision,

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every AI initiative has to answer four decision requirements

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upfront.

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First, trusted inputs.

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Not we have data.

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Trusted inputs mean you know the origin,

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you know the transformations, and you can defend the quality.

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You don’t need perfect data.

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You need data with known failure modes.

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Second, define semantics.

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The thing most people miss is that data quality problems

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are often semantic problems wearing a technical disguise.

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Two systems can both be accurate and still disagree

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because they mean different things by the same word.

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Customer.

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Revenue.

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Active.

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Closed.

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Incident.

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Risk.

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Those are political nouns with budgets attached.

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AI will not resolve that ambiguity.

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It will learn it.

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And then it will scale it.

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Third, accountability.

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Every decision needs an owner, not as an abstract governance

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concept, but as an operational fact.

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When the output is wrong, who is accountable

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for the correction?

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Who owns the business rule?

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Who owns the data product?

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Who owns the access policy?

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If the answer is the platform team, you’ve already lost.

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They can’t own your business reality.

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Fourth, feedback loops.

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Decisions without feedback loops are just outputs.

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Outputs don’t improve.

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Outputs drift.

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Feedback loops are how you turn AI from a demo

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into a controllable system.

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Capture exceptions.

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Measure outcomes.

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Correct data.

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Refine prompts.

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Retrain models when necessary.

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And update policies when reality changes.

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Now here’s the part executives’ underway.

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Decision errors compound faster than process errors.

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A process error might waste time.

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A decision error creates downstream decisions

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that are now built on the wrong premise.

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It infects other systems, pricing, inventory, compliance,

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customer experience, risk.

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You don’t just get one wrong answer.

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You get a chain reaction.

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That’s why AI raises the cost of poor data design.

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It doesn’t hide it.

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In the old world, bad data slowed reporting.

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In the AI world, bad data drives action.

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The error becomes operational.

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And operations don’t tolerate ambiguity for long.

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This is where Azure and the Microsoft ecosystem become

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relevant in a non-broker way.

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Azure AI, fabric, one-leg, purview, foundry,

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entra, these are not services you can turn on.

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They are surfaces where decision transformation

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either becomes governable or becomes conditional chaos

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at scale.

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If your enterprise treats them as an innovation stack,

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you’ll get impressive pilots that can’t be defended.

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If your enterprise treats them as an operating model,

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you’ll get decision systems you can scale.

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So the executive framing has to shift.

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Stop funding AI pilots.

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Fund decision improvements with named owners

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and measurable outcomes.

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Pick one decision that matters.

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Case triage, ford review, contract risk assessment,

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supply chain exception handling, customer entitlement validation.

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Then force the question, what data powers this decision?

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Who owns it?

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What does correct mean?

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And how do we measure error?

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Once decisions become the unit of value,

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the platform becomes the product, not a procurement event.

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A product with a roadmap, SLOs, governance, and economics.

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And that’s why the next part matters.

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The data platform isn’t just where you store things.

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It’s the system that makes decisions safe.

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The data platform is the real product.

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This is where most enterprise strategies go to die.

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They treat the data platform like a tooling migration.

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They pick a destination.

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Lake warehouse, lake house, streaming, they starve a project.

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00:08:48,520 –> 00:08:50,160
They measure progress by terabytes,

266
00:08:50,160 –> 00:08:52,240
moved and dashboards rebuilt.

267
00:08:52,240 –> 00:08:55,040
And then three quarters later, they announced we modernized.

268
00:08:55,040 –> 00:08:57,120
But nothing is modernized if the enterprise still

269
00:08:57,120 –> 00:08:59,480
can’t agree on definitions, can’t trace data lineage

270
00:08:59,480 –> 00:09:02,480
end to end and can’t explain why a number changed.

271
00:09:02,480 –> 00:09:03,840
That distinction matters.

272
00:09:03,840 –> 00:09:06,480
A data platform is not a place you store things.

273
00:09:06,480 –> 00:09:08,040
It is a capability you operate.

274
00:09:08,040 –> 00:09:10,920
And capabilities have owners, service levels, guardrails,

275
00:09:10,920 –> 00:09:12,000
and economics.

276
00:09:12,000 –> 00:09:14,880
If you don’t design it that way, it becomes the familiar pattern,

277
00:09:14,880 –> 00:09:16,920
a shared utility that everyone blames,

278
00:09:16,920 –> 00:09:18,240
and nobody finds properly.

279
00:09:18,240 –> 00:09:20,120
Here’s the thing most leaders miss.

280
00:09:20,120 –> 00:09:22,520
The enterprise already treats other shared capabilities

281
00:09:22,520 –> 00:09:25,080
as products, even if it doesn’t use that language.

282
00:09:25,080 –> 00:09:27,040
Identities are products, networks are products,

283
00:09:27,040 –> 00:09:28,480
endpoint management is a product.

284
00:09:28,480 –> 00:09:29,720
Collaboration is a product.

285
00:09:29,720 –> 00:09:32,160
If you want teams to work, you don’t migrate to teams

286
00:09:32,160 –> 00:09:32,840
and walk away.

287
00:09:32,840 –> 00:09:35,080
You operate it, you patch it, you govern it,

288
00:09:35,080 –> 00:09:37,520
you measure adoption and incidents, you assign owners,

289
00:09:37,520 –> 00:09:40,120
you budget it every year, data is no different.

290
00:09:40,120 –> 00:09:42,400
If you want AI to be reliable, the data platform

291
00:09:42,400 –> 00:09:43,880
has to be operated like a product,

292
00:09:43,880 –> 00:09:46,600
because AI consumes it the way every other system does,

293
00:09:46,600 –> 00:09:47,760
as a dependency.

294
00:09:47,760 –> 00:09:50,080
And dependencies don’t tolerate ambiguity.

295
00:09:50,080 –> 00:09:53,080
So what makes a data platform a product in enterprise terms?

296
00:09:53,080 –> 00:09:55,720
First, it has a roadmap, not a one-time migration.

297
00:09:55,720 –> 00:09:57,560
A roadmap with capabilities you’ll add,

298
00:09:57,560 –> 00:10:00,520
standards you’ll enforce, and legacy behaviors you’ll retire.

299
00:10:00,520 –> 00:10:02,800
Second, it has SLOs, not vague prompt.

300
00:10:02,800 –> 00:10:06,120
Real operational expectations, freshness,

301
00:10:06,120 –> 00:10:08,240
availability of critical pipelines,

302
00:10:08,240 –> 00:10:10,480
time to fix for quality defects, latency

303
00:10:10,480 –> 00:10:12,120
for key decision data sets.

304
00:10:12,120 –> 00:10:14,880
If it’s not measurable, it’s not governable.

305
00:10:14,880 –> 00:10:16,880
Third, it has governance built into the delivery,

306
00:10:16,880 –> 00:10:18,240
not bolted on after.

307
00:10:18,240 –> 00:10:20,040
The platform doesn’t just move data,

308
00:10:20,040 –> 00:10:22,040
it enforces how data can be published,

309
00:10:22,040 –> 00:10:24,040
discovered, accessed, and reused.

310
00:10:24,040 –> 00:10:26,400
Fourth, it has a cost model that maps consumption

311
00:10:26,400 –> 00:10:27,760
to accountability.

312
00:10:27,760 –> 00:10:30,440
If you can’t show who consumed what, and why,

313
00:10:30,440 –> 00:10:31,920
you’re building a finance incident.

314
00:10:31,920 –> 00:10:33,680
Now here’s the organizational failure pattern

315
00:10:33,680 –> 00:10:34,960
that shows up every time.

316
00:10:34,960 –> 00:10:37,400
A centralized data team builds a powerful platform.

317
00:10:37,400 –> 00:10:39,680
They do it with good intent, consistency, security,

318
00:10:39,680 –> 00:10:43,960
shared standards, and at first, it works, then demand scales.

319
00:10:43,960 –> 00:10:45,680
Every domain wants their own integration,

320
00:10:45,680 –> 00:10:47,520
their own semantics, their own dashboards,

321
00:10:47,520 –> 00:10:49,280
their own urgent exception.

322
00:10:49,280 –> 00:10:50,800
The central team becomes the bottleneck,

323
00:10:50,800 –> 00:10:52,680
they get blamed for being slow,

324
00:10:52,680 –> 00:10:55,920
they respond by opening the gates, self-service.

325
00:10:55,920 –> 00:10:57,640
And now you get the opposite failure.

326
00:10:57,640 –> 00:11:00,880
Decentralized teams move fast, but they become entropy engines.

327
00:11:00,880 –> 00:11:02,480
Everyone builds their own pipelines,

328
00:11:02,480 –> 00:11:04,000
everyone defines customer locally,

329
00:11:04,000 –> 00:11:06,160
everyone creates their own gold layer,

330
00:11:06,160 –> 00:11:08,840
and the platform becomes a catalog of competing truths.

331
00:11:08,840 –> 00:11:10,520
Both models fail for the same reason,

332
00:11:10,520 –> 00:11:12,360
they never establish decision rights.

333
00:11:12,360 –> 00:11:13,880
So define the roles cleanly.

334
00:11:13,880 –> 00:11:16,360
The platform team owns the platform capability,

335
00:11:16,360 –> 00:11:18,040
the shared services, the guardrails,

336
00:11:18,040 –> 00:11:21,120
the governance services, and the operational reliability.

337
00:11:21,120 –> 00:11:23,680
Domain teams own data products,

338
00:11:23,680 –> 00:11:27,080
the data sets and contracts that represent business concepts,

339
00:11:27,080 –> 00:11:30,400
with named owners, explicit consumers, and clear definitions.

340
00:11:30,400 –> 00:11:32,080
And you need both because centralization

341
00:11:32,080 –> 00:11:33,880
without domains creates bottlenecks,

342
00:11:33,880 –> 00:11:35,560
and decentralization without standards

343
00:11:35,560 –> 00:11:37,280
creates scalable ambiguity.

344
00:11:37,280 –> 00:11:40,160
This is where a lot of data mesh conversations go off the rails.

345
00:11:40,160 –> 00:11:42,560
People hear domain ownership and assume it means

346
00:11:42,560 –> 00:11:44,920
domain autonomy without constraint.

347
00:11:44,920 –> 00:11:47,680
It does not, that’s not autonomy, that’s drift.

348
00:11:47,680 –> 00:11:49,560
A functional mesh has federated governance,

349
00:11:49,560 –> 00:11:52,240
centralized standards with decentralized execution,

350
00:11:52,240 –> 00:11:54,040
which means the enterprise must be explicit

351
00:11:54,040 –> 00:11:57,320
about what domains can decide and what they cannot,

352
00:11:57,320 –> 00:11:58,720
and the non-negotiables are boring,

353
00:11:58,720 –> 00:12:00,520
which is why they get skipped.

354
00:12:00,520 –> 00:12:03,120
Quality decision rights who sets the acceptable failure mode

355
00:12:03,120 –> 00:12:04,520
and who funds the fix.

356
00:12:04,520 –> 00:12:06,560
Semantic decision rights who arbitrates

357
00:12:06,560 –> 00:12:09,520
when two domains disagree about what a metric means.

358
00:12:09,520 –> 00:12:11,640
Access decision rights, who can approve

359
00:12:11,640 –> 00:12:14,480
that an AI system can read a data set in for how long,

360
00:12:14,480 –> 00:12:16,560
cost decision rights, who pays for consumption

361
00:12:16,560 –> 00:12:18,600
and what happens when usage spikes.

362
00:12:18,600 –> 00:12:20,840
If you can’t answer those in one sentence each,

363
00:12:20,840 –> 00:12:22,600
you don’t have a platform product,

364
00:12:22,600 –> 00:12:24,960
you have a shared storage account with better marketing.

365
00:12:24,960 –> 00:12:26,600
Now connect this back to the thesis.

366
00:12:26,600 –> 00:12:28,200
If decisions are the unit of value,

367
00:12:28,200 –> 00:12:30,240
then data products are the unit of control.

368
00:12:30,240 –> 00:12:32,000
And the platform exists to make those products

369
00:12:32,000 –> 00:12:33,680
publishable, discoverable, governable,

370
00:12:33,680 –> 00:12:35,080
and economically sustainable.

371
00:12:35,080 –> 00:12:36,800
That’s why the next section matters.

372
00:12:36,800 –> 00:12:38,280
Azure Stack is not the point,

373
00:12:38,280 –> 00:12:40,640
what matters is which layers you make deterministic

374
00:12:40,640 –> 00:12:43,200
because AI will make everything else probabilistic.

375
00:12:43,200 –> 00:12:46,600
Azure’s data and AI stack, what actually matters.

376
00:12:46,600 –> 00:12:48,040
Now the uncomfortable part,

377
00:12:48,040 –> 00:12:50,840
Azure’s advantage isn’t that it has more services,

378
00:12:50,840 –> 00:12:53,520
every vendor has a brochure with an infinite scroll bar.

379
00:12:53,520 –> 00:12:56,160
Azure’s advantage is integration, shared identity,

380
00:12:56,160 –> 00:12:58,320
shared policy surfaces, shared governance

381
00:12:58,320 –> 00:13:00,240
and a relatively coherent control plane.

382
00:13:00,240 –> 00:13:01,600
That distinction matters because AI

383
00:13:01,600 –> 00:13:03,240
doesn’t fail at the model layer first.

384
00:13:03,240 –> 00:13:04,800
It fails at the seams.

385
00:13:04,800 –> 00:13:07,800
The handoffs between identity, data, analytics,

386
00:13:07,800 –> 00:13:08,920
and deployment.

387
00:13:08,920 –> 00:13:11,840
So instead of naming tools, think in strategic layers.

388
00:13:11,840 –> 00:13:13,720
Layers are where you make design choices

389
00:13:13,720 –> 00:13:17,240
that either hold under scale or decay into exception culture.

390
00:13:17,240 –> 00:13:18,880
Start with ingestion and integration.

391
00:13:18,880 –> 00:13:21,840
This is where most organizations still behave like it’s 2015.

392
00:13:21,840 –> 00:13:23,840
They copy everything, they replicate everything

393
00:13:23,840 –> 00:13:26,400
and then they wonder why costs and consistency drift.

394
00:13:26,400 –> 00:13:28,400
In the Microsoft world, you’ve got a spectrum,

395
00:13:28,400 –> 00:13:32,000
data factory style orchestration, streaming and event ingestion

396
00:13:32,000 –> 00:13:35,120
and zero-ish ETL patterns like mirroring and shortcuts.

397
00:13:35,120 –> 00:13:36,760
The point is not which connector you use.

398
00:13:36,760 –> 00:13:38,120
The point is whether you’ve designed

399
00:13:38,120 –> 00:13:40,960
for one authoritative copy of data per decision domain

400
00:13:40,960 –> 00:13:43,840
or whether you’ve designed for institutionalized duplication.

401
00:13:43,840 –> 00:13:45,440
Duplication isn’t just storage cost.

402
00:13:45,440 –> 00:13:47,960
Duplication is semantic divergence on a timer.

403
00:13:47,960 –> 00:13:49,560
Next is storage and analytics.

404
00:13:49,560 –> 00:13:51,120
This is where fabric and one leg matter,

405
00:13:51,120 –> 00:13:52,360
but not because they’re shinied.

406
00:13:52,360 –> 00:13:53,680
They matter because they push you

407
00:13:53,680 –> 00:13:55,560
toward a unified lake house pattern.

408
00:13:55,560 –> 00:13:58,160
One logical lake, open formats like delta

409
00:13:58,160 –> 00:14:01,320
and multiple engines reading and writing the same foundation.

410
00:14:01,320 –> 00:14:03,440
That’s valuable because it removes data movement

411
00:14:03,440 –> 00:14:06,440
as the default behavior, but it also removes excuses

412
00:14:06,440 –> 00:14:08,120
when everything can be accessed everywhere.

413
00:14:08,120 –> 00:14:10,720
Your governance gaps become instantly scalable.

414
00:14:10,720 –> 00:14:12,840
The unified platform reduces friction,

415
00:14:12,840 –> 00:14:15,280
therefore it amplifies weak standards faster

416
00:14:15,280 –> 00:14:16,600
then you need a semantic layer.

417
00:14:16,600 –> 00:14:18,840
This is where many data strategies quietly collapse.

418
00:14:18,840 –> 00:14:20,200
Raw tables are not truth.

419
00:14:20,200 –> 00:14:21,800
Tables are options.

420
00:14:21,800 –> 00:14:24,480
Truth in an enterprise is a governed semantic contract.

421
00:14:24,480 –> 00:14:27,320
Matrix, definitions, relationships and the rules for change.

422
00:14:27,320 –> 00:14:29,880
In the Microsoft ecosystem that often materializes

423
00:14:29,880 –> 00:14:32,800
as Power BI semantic models endorse data sets,

424
00:14:32,800 –> 00:14:35,440
certified definitions and controlled modeling practices.

425
00:14:35,440 –> 00:14:37,560
If you let every team invent their own measures

426
00:14:37,560 –> 00:14:39,760
and definitions, you don’t have self-service.

427
00:14:39,760 –> 00:14:41,600
You have self-inflicted inconsistency

428
00:14:41,600 –> 00:14:44,120
and AI will happily learn that inconsistency.

429
00:14:44,120 –> 00:14:46,080
Now we get to the AI lifecycle layer.

430
00:14:46,080 –> 00:14:48,560
This is where Azure AI Foundry matters again,

431
00:14:48,560 –> 00:14:50,120
not as a place to click deploy,

432
00:14:50,120 –> 00:14:52,560
but as a way to standardize how models and agents

433
00:14:52,560 –> 00:14:56,280
get selected, evaluated, deployed, observed and governed.

434
00:14:56,280 –> 00:14:59,560
The reason this works architecturally is simple.

435
00:14:59,560 –> 00:15:01,880
AI systems are not single components.

436
00:15:01,880 –> 00:15:05,560
They are dependency graphs, models, tools, retrieval,

437
00:15:05,560 –> 00:15:08,960
prompts, policies, data sources and identity.

438
00:15:08,960 –> 00:15:12,000
A unified AI platform helps you control the graph.

439
00:15:12,000 –> 00:15:13,800
But only if you treat it as a governed system,

440
00:15:13,800 –> 00:15:15,000
not as a playground.

441
00:15:15,000 –> 00:15:17,560
Foundry’s model catalog, evaluation, tracing

442
00:15:17,560 –> 00:15:19,640
and safety controls are all useful,

443
00:15:19,640 –> 00:15:21,720
but they don’t replace your enterprise decisions.

444
00:15:21,720 –> 00:15:24,040
They operationalize them, they make enforcement possible,

445
00:15:24,040 –> 00:15:26,520
what models are allowed, what data sources are allowed,

446
00:15:26,520 –> 00:15:29,640
what logging is required, what safety filters are enforced

447
00:15:29,640 –> 00:15:32,120
and what observability is non-negotiable,

448
00:15:32,120 –> 00:15:33,720
which brings us to the governance plane.

449
00:15:33,720 –> 00:15:36,760
This is the layer most executive still treat like paperwork.

450
00:15:36,760 –> 00:15:38,480
It is not.

451
00:15:38,480 –> 00:15:41,400
Governance in Azure and Microsoft’s ecosystem

452
00:15:41,400 –> 00:15:43,600
is a set of enforcement surfaces.

453
00:15:43,600 –> 00:15:45,320
Entra for identity and access,

454
00:15:45,320 –> 00:15:47,560
purview for classification and lineage,

455
00:15:47,560 –> 00:15:49,320
Azure policy for resource constraints,

456
00:15:49,320 –> 00:15:52,440
defender and monitoring systems for posture and detection

457
00:15:52,440 –> 00:15:55,320
and the audit trails that let you survive scrutiny.

458
00:15:55,320 –> 00:15:56,760
If you can’t trace data into end,

459
00:15:56,760 –> 00:15:59,000
you can’t defend AI outputs under pressure.

460
00:15:59,000 –> 00:16:00,400
And pressure is not hypothetical.

461
00:16:00,400 –> 00:16:03,120
It arrives the first time the output affects a customer,

462
00:16:03,120 –> 00:16:05,400
a regulator, a contract or a clinical decision.

463
00:16:05,400 –> 00:16:07,200
So here’s the architectural punch line.

464
00:16:07,200 –> 00:16:09,640
When you ask what Azure services should we use,

465
00:16:09,640 –> 00:16:11,080
you are asking the wrong question.

466
00:16:11,080 –> 00:16:13,440
The real question is which layers are deterministic

467
00:16:13,440 –> 00:16:15,760
and which layers are allowed to be probabilistic.

468
00:16:15,760 –> 00:16:17,320
Identity must be deterministic.

469
00:16:17,320 –> 00:16:20,040
Data classification and lineage must be deterministic.

470
00:16:20,040 –> 00:16:22,040
Semantic contracts must be deterministic.

471
00:16:22,040 –> 00:16:25,000
Cost controls and accountability must be deterministic.

472
00:16:25,000 –> 00:16:28,200
Then and only then you can afford probabilistic components

473
00:16:28,200 –> 00:16:30,680
in the decision loop because you’ve bounded the blast radius.

474
00:16:30,680 –> 00:16:32,680
If you don’t, you’re building conditional chaos

475
00:16:32,680 –> 00:16:34,040
with better infrastructure.

476
00:16:34,040 –> 00:16:37,440
And this is where unified platforms like fabric are double edged.

477
00:16:37,440 –> 00:16:39,240
They remove operational friction,

478
00:16:39,240 –> 00:16:41,160
which means teams can deliver faster.

479
00:16:41,160 –> 00:16:41,840
Good.

480
00:16:41,840 –> 00:16:43,480
But without standards and contracts,

481
00:16:43,480 –> 00:16:45,800
faster means you accumulate entropy faster.

482
00:16:45,800 –> 00:16:49,280
So the recommendation is not adopt fabric or adopt foundry.

483
00:16:49,280 –> 00:16:51,560
The recommendation is adopt an operating model

484
00:16:51,560 –> 00:16:53,760
that makes those platforms survivable.

485
00:16:53,760 –> 00:16:56,320
Because once the platform becomes easy to use,

486
00:16:56,320 –> 00:16:59,000
the only thing stopping chaos is enforcement.

487
00:16:59,000 –> 00:17:01,000
Now if this sounds abstract, good.

488
00:17:01,000 –> 00:17:02,560
It means you’re seeing the system.

489
00:17:02,560 –> 00:17:04,000
And the next section makes it concrete.

490
00:17:04,000 –> 00:17:06,080
Governance isn’t a value statement.

491
00:17:06,080 –> 00:17:08,040
It’s a set of non-negotiable guardrails

492
00:17:08,040 –> 00:17:10,520
you design into identity trust and semantics.

493
00:17:10,520 –> 00:17:12,600
Non-negotiable guardrail one.

494
00:17:12,600 –> 00:17:15,000
Identity and access as the root constraint.

495
00:17:15,000 –> 00:17:18,480
If governance is the plane, identity is the root constraint.

496
00:17:18,480 –> 00:17:19,920
Not because identity is exciting,

497
00:17:19,920 –> 00:17:21,800
but because identity is where the enterprise

498
00:17:21,800 –> 00:17:23,360
decides what is allowed to happen.

499
00:17:23,360 –> 00:17:25,240
Everything else is downstream theater.

500
00:17:25,240 –> 00:17:28,120
Most organizations still frame AI workloads as tools,

501
00:17:28,120 –> 00:17:30,480
a copilot, a chat interface, a model endpoint,

502
00:17:30,480 –> 00:17:31,320
a clever workflow.

503
00:17:31,320 –> 00:17:32,440
That framing is comfortable.

504
00:17:32,440 –> 00:17:33,880
It is also wrong.

505
00:17:33,880 –> 00:17:36,680
An AI workload is a high-privileged actor operating

506
00:17:36,680 –> 00:17:37,800
at machine speed.

507
00:17:37,800 –> 00:17:40,160
It reads broadly, summarizes confidently,

508
00:17:40,160 –> 00:17:41,480
and can be wired into actions.

509
00:17:41,480 –> 00:17:43,800
That means you aren’t deploying AI.

510
00:17:43,800 –> 00:17:45,720
You are introducing a new class of principle

511
00:17:45,720 –> 00:17:47,240
into your authorization graph.

512
00:17:47,240 –> 00:17:48,480
That distinction matters.

513
00:17:48,480 –> 00:17:50,280
If your identity model is loose,

514
00:17:50,280 –> 00:17:52,840
your AI system won’t accidentally leak data.

515
00:17:52,840 –> 00:17:53,840
It will leak it correctly.

516
00:17:53,840 –> 00:17:56,240
It will retrieve exactly what it is permitted to retrieve.

517
00:17:56,240 –> 00:17:58,840
It will synthesize exactly what it is permitted to see.

518
00:17:58,840 –> 00:18:00,920
And when that output lands in the wrong place,

519
00:18:00,920 –> 00:18:02,840
everyone will call it an AI failure.

520
00:18:02,840 –> 00:18:03,600
It won’t be.

521
00:18:03,600 –> 00:18:06,240
It will be an identity failure that finally became visible.

522
00:18:06,240 –> 00:18:08,720
So the first non-negotiable guardrail is simple.

523
00:18:08,720 –> 00:18:11,880
Treat AI as a privileged identity problem,

524
00:18:11,880 –> 00:18:13,240
not an application feature.

525
00:18:13,240 –> 00:18:15,440
In the Microsoft ecosystem, Microsoft Enter ID

526
00:18:15,440 –> 00:18:18,360
is the boundary where this either works or collapses.

527
00:18:18,360 –> 00:18:20,200
A lot of enterprises have a tenant strategy

528
00:18:20,200 –> 00:18:22,520
that can be summarized as, we have one tenant.

529
00:18:22,520 –> 00:18:23,120
It exists.

530
00:18:23,120 –> 00:18:23,760
Good luck.

531
00:18:23,760 –> 00:18:24,880
That is not a strategy.

532
00:18:24,880 –> 00:18:26,320
That is an eventual incident.

533
00:18:26,320 –> 00:18:28,640
A tenant strategy for AI-era operating models

534
00:18:28,640 –> 00:18:30,640
means you decide where experimentation lives,

535
00:18:30,640 –> 00:18:32,600
where production lives, and how you prevent

536
00:18:32,600 –> 00:18:34,320
the experimental permissions from bleeding

537
00:18:34,320 –> 00:18:35,920
into the operational estate.

538
00:18:35,920 –> 00:18:38,360
Because permission drift is not a theoretical concept,

539
00:18:38,360 –> 00:18:40,640
it is the default state of every large environment.

540
00:18:40,640 –> 00:18:42,920
Once you accept that, role design stops

541
00:18:42,920 –> 00:18:45,840
being a compliance exercise and becomes entropy management.

542
00:18:45,840 –> 00:18:50,160
Every broad role assignment, every standing privileged account,

543
00:18:50,160 –> 00:18:53,840
every temporary access grant that never expires

544
00:18:53,840 –> 00:18:55,160
is an entropy generator.

545
00:18:55,160 –> 00:18:58,880
These pathways accumulate, and AI will traverse them

546
00:18:58,880 –> 00:19:01,280
faster than any human ever could.

547
00:19:01,280 –> 00:19:03,120
So what does non-negotiable look like here?

548
00:19:03,120 –> 00:19:05,080
First, you isolate privileged access.

549
00:19:05,080 –> 00:19:07,560
If AI systems can reach sensitive data sets,

550
00:19:07,560 –> 00:19:09,560
then the identities that configure, approve,

551
00:19:09,560 –> 00:19:11,200
and operate those systems are effectively

552
00:19:11,200 –> 00:19:13,360
controlling sensitive access at scale.

553
00:19:13,360 –> 00:19:15,480
That means you need privileged access patterns

554
00:19:15,480 –> 00:19:18,400
that can survive audit scrutiny and survive staff turnover.

555
00:19:18,400 –> 00:19:21,320
Second, you design roles for intent, not convenience.

556
00:19:21,320 –> 00:19:23,080
Most enterprises build roles by asking,

557
00:19:23,080 –> 00:19:24,440
what does the team need to do?

558
00:19:24,440 –> 00:19:27,000
And then granting a bundle that seems to work over time,

559
00:19:27,000 –> 00:19:29,200
those bundles expand because something broke

560
00:19:29,200 –> 00:19:30,440
and someone needed access.

561
00:19:30,440 –> 00:19:32,680
That is how the authorization surface inflates.

562
00:19:32,680 –> 00:19:35,640
AI multiplies the blast radius of that inflation.

563
00:19:35,640 –> 00:19:38,120
Third, you establish an executive decision

564
00:19:38,120 –> 00:19:40,120
that almost nobody wants to make.

565
00:19:40,120 –> 00:19:42,960
Who can authorize data access for AI and for how long?

566
00:19:42,960 –> 00:19:45,240
This is where governance meetings go to die

567
00:19:45,240 –> 00:19:47,640
because it forces an explicit ownership decision.

568
00:19:47,640 –> 00:19:49,800
If no one is accountable for authorizing access,

569
00:19:49,800 –> 00:19:52,040
then access becomes platform default.

570
00:19:52,040 –> 00:19:54,400
And platform default access is always broader than business

571
00:19:54,400 –> 00:19:54,920
intent.

572
00:19:54,920 –> 00:19:56,640
That means the operating model must define

573
00:19:56,640 –> 00:19:59,680
an approval authority for AI, data access,

574
00:19:59,680 –> 00:20:01,200
with explicit time limits.

575
00:20:01,200 –> 00:20:02,880
Because forever is not a duration.

576
00:20:02,880 –> 00:20:04,160
It is abandonment.

577
00:20:04,160 –> 00:20:06,120
Now, here’s the operational consequence.

578
00:20:06,120 –> 00:20:07,480
If you don’t enforce these boundaries,

579
00:20:07,480 –> 00:20:09,080
your platform leaders will spend their lives

580
00:20:09,080 –> 00:20:10,640
cleaning up access drift.

581
00:20:10,640 –> 00:20:12,000
Not because they’re incompetent,

582
00:20:12,000 –> 00:20:14,680
because the system will do what systems always do.

583
00:20:14,680 –> 00:20:16,960
Accumulate exceptions until the policy no longer

584
00:20:16,960 –> 00:20:17,960
describes reality.

585
00:20:17,960 –> 00:20:21,080
You will see it as pilots that need just a bit more access,

586
00:20:21,080 –> 00:20:23,160
service principles with broad permissions,

587
00:20:23,160 –> 00:20:25,320
workspaces shared across domains,

588
00:20:25,320 –> 00:20:28,800
and eventually an AI agent that can read something it shouldn’t.

589
00:20:28,800 –> 00:20:31,760
And it will read it reliably at scale.

590
00:20:31,760 –> 00:20:33,120
This is the uncomfortable truth.

591
00:20:33,120 –> 00:20:34,840
Identity is not guardrail number one

592
00:20:34,840 –> 00:20:36,720
because it prevents bad outcomes.

593
00:20:36,720 –> 00:20:38,800
It’s guardrail number one because it makes outcomes

594
00:20:38,800 –> 00:20:39,960
attributable.

595
00:20:39,960 –> 00:20:42,880
If you can’t answer which identity access to what,

596
00:20:42,880 –> 00:20:45,160
under which policy approved by whom,

597
00:20:45,160 –> 00:20:47,320
you don’t have control, you have hope.

598
00:20:47,320 –> 00:20:48,880
And hope is not an operating model.

599
00:20:48,880 –> 00:20:50,440
So the executive level reframe is this.

600
00:20:50,440 –> 00:20:51,880
You aren’t approving an AI pilot.

601
00:20:51,880 –> 00:20:53,640
You are authorizing a new class of actor

602
00:20:53,640 –> 00:20:55,600
that actor will amplify whatever access model

603
00:20:55,600 –> 00:20:56,520
you already have.

604
00:20:56,520 –> 00:20:59,440
Make it deterministic now, while it’s still cheap.

605
00:20:59,440 –> 00:21:02,440
Because once the AI system is embedded into workflows,

606
00:21:02,440 –> 00:21:04,760
identity redesign stops being governance work

607
00:21:04,760 –> 00:21:06,640
and becomes a business interruption.

608
00:21:06,640 –> 00:21:07,960
And that’s the transition.

609
00:21:07,960 –> 00:21:10,280
Identity gates access, but it doesn’t create trust.

610
00:21:10,280 –> 00:21:11,400
Trust comes from governance.

611
00:21:11,400 –> 00:21:14,000
You can inspect, audit and defend.

612
00:21:14,000 –> 00:21:17,360
Non-negotiable guardrail two, data trust and governance

613
00:21:17,360 –> 00:21:18,560
that can be audited.

614
00:21:18,560 –> 00:21:20,400
Trust is not a policy you publish.

615
00:21:20,400 –> 00:21:22,680
Trust is an operating behavior you can prove.

616
00:21:22,680 –> 00:21:25,320
That distinction matters because every enterprise says,

617
00:21:25,320 –> 00:21:26,920
we care about data quality,

618
00:21:26,920 –> 00:21:28,960
right up until they need to ship something.

619
00:21:28,960 –> 00:21:30,760
Then quality becomes a future task.

620
00:21:30,760 –> 00:21:33,520
Governance becomes a document and the platform becomes a rumor.

621
00:21:33,520 –> 00:21:35,120
AI doesn’t tolerate rumors.

622
00:21:35,120 –> 00:21:38,200
AI consumes whatever is available at machine speed

623
00:21:38,200 –> 00:21:40,520
and it produces outputs with a confidence level

624
00:21:40,520 –> 00:21:42,400
that humans instinctively over trust.

625
00:21:42,400 –> 00:21:45,200
If you can’t defend the inputs, you can’t defend the outputs.

626
00:21:45,200 –> 00:21:47,360
And when someone asks you to defend the outputs,

627
00:21:47,360 –> 00:21:49,680
they are not asking for your value statement.

628
00:21:49,680 –> 00:21:50,840
But they are asking for evidence.

629
00:21:50,840 –> 00:21:52,920
So this guardrail is simple in wording

630
00:21:52,920 –> 00:21:54,600
and brutal in execution.

631
00:21:54,600 –> 00:21:56,840
Your data trust and governance must be auditable.

632
00:21:56,840 –> 00:21:58,120
Not we think it’s fine.

633
00:21:58,120 –> 00:21:59,680
Not the team reviewed it.

634
00:21:59,680 –> 00:22:01,480
Auditable means you can answer the questions

635
00:22:01,480 –> 00:22:03,160
that always arrive at scale.

636
00:22:03,160 –> 00:22:04,760
What data do the system use?

637
00:22:04,760 –> 00:22:05,720
Where did it come from?

638
00:22:05,720 –> 00:22:07,040
Who approved it for this use?

639
00:22:07,040 –> 00:22:08,400
Who can access it and why?

640
00:22:08,400 –> 00:22:09,480
How did it move?

641
00:22:09,480 –> 00:22:11,200
What transformations touched it?

642
00:22:11,200 –> 00:22:14,000
And what version was active when the decision was made?

643
00:22:14,000 –> 00:22:15,760
If you can’t answer those quickly,

644
00:22:15,760 –> 00:22:17,440
you’re not operating a data platform.

645
00:22:17,440 –> 00:22:18,920
You are operating a liability.

646
00:22:18,920 –> 00:22:20,520
This is where Microsoft PerView fits,

647
00:22:20,520 –> 00:22:22,480
but again, not as a box you check.

648
00:22:22,480 –> 00:22:24,280
PerView is a governance surface,

649
00:22:24,280 –> 00:22:26,960
classification, lineage and discoverability.

650
00:22:26,960 –> 00:22:29,600
Those three things sound like hygiene in practice.

651
00:22:29,600 –> 00:22:31,880
They are prerequisites for operating AI

652
00:22:31,880 –> 00:22:34,120
without ending up in a shutdown meeting.

653
00:22:34,120 –> 00:22:37,000
Classification matters because AI doesn’t distinguish sensitive

654
00:22:37,000 –> 00:22:38,240
from interesting.

655
00:22:38,240 –> 00:22:41,400
It distinguishes allowed from blocked.

656
00:22:41,400 –> 00:22:43,000
If you haven’t labeled data,

657
00:22:43,000 –> 00:22:45,880
you can’t enforce consistent controls across the estate.

658
00:22:45,880 –> 00:22:47,840
And if you can’t enforce consistent controls,

659
00:22:47,840 –> 00:22:50,680
you will eventually ship a system that uses data it shouldn’t.

660
00:22:50,680 –> 00:22:53,160
Not maliciously, correctly.

661
00:22:53,160 –> 00:22:55,760
Lineage matters because you will eventually get the question,

662
00:22:55,760 –> 00:22:57,160
why did this answer change?

663
00:22:57,160 –> 00:22:59,080
In an AI system, answers change

664
00:22:59,080 –> 00:23:00,920
because the grounding data changed,

665
00:23:00,920 –> 00:23:02,720
the retrieval path changed,

666
00:23:02,720 –> 00:23:05,880
the semantic meaning drifted or the prompt logic changed.

667
00:23:05,880 –> 00:23:07,720
If you can’t trace data end to end,

668
00:23:07,720 –> 00:23:10,160
you can’t isolate which of those happen, you can’t fix it.

669
00:23:10,160 –> 00:23:11,280
You can only argue about it.

670
00:23:11,280 –> 00:23:14,440
Discoverability matters because when people can’t find trusted data,

671
00:23:14,440 –> 00:23:15,400
they create their own.

672
00:23:15,400 –> 00:23:17,000
Shadow data sets are not a user problem.

673
00:23:17,000 –> 00:23:18,840
They are a platform failure mode.

674
00:23:18,840 –> 00:23:21,600
They are what happens when governance is experienced

675
00:23:21,600 –> 00:23:23,360
as friction instead of safety.

676
00:23:23,360 –> 00:23:26,160
Now, here’s the governance timing law that keeps showing up.

677
00:23:26,160 –> 00:23:28,920
If governance arrives after deployment, it arrives as a shutdown.

678
00:23:28,920 –> 00:23:30,720
Because the first serious audit question,

679
00:23:30,720 –> 00:23:34,440
the first legal escalation or the first customer impacting incident forces

680
00:23:34,440 –> 00:23:38,000
the organization to stop the system until it can prove control.

681
00:23:38,000 –> 00:23:40,280
Executives don’t do this because they hate innovation.

682
00:23:40,280 –> 00:23:43,040
They do it because they can’t sign their name under uncertainty.

683
00:23:43,040 –> 00:23:45,800
So the executive job is not to ask, do we have governance?

684
00:23:45,800 –> 00:23:49,320
The executive job is to ask, is governance default behavior?

685
00:23:49,320 –> 00:23:52,880
Default behavior means the system generates evidence without heroics.

686
00:23:52,880 –> 00:23:56,120
The lineage is captured because pipelines and platforms emitted.

687
00:23:56,120 –> 00:23:59,360
The classifications exist because ingestion and publishing require them.

688
00:23:59,360 –> 00:24:02,560
Access policies are consistent because identity and data governance

689
00:24:02,560 –> 00:24:04,840
are integrated, not negotiated.

690
00:24:04,840 –> 00:24:10,120
And the thing most enterprises miss is that trust is not just about whether the data is correct.

691
00:24:10,120 –> 00:24:12,920
Trust is also about whether the data can be used under scrutiny.

692
00:24:12,920 –> 00:24:16,800
You can have perfectly accurate data and still be unable to use it for AI

693
00:24:16,800 –> 00:24:19,760
because you cannot prove how it was obtained, how it was transformed,

694
00:24:19,760 –> 00:24:20,920
and who approved its use.

695
00:24:20,920 –> 00:24:23,120
In regulated environments, that’s not a detail.

696
00:24:23,120 –> 00:24:25,240
That’s the difference between operating and pausing.

697
00:24:25,240 –> 00:24:28,520
Now, you might be thinking this becomes a bureaucratic nightmare.

698
00:24:28,520 –> 00:24:32,480
It does if you treat governance like documentation, but governance isn’t documentation.

699
00:24:32,480 –> 00:24:37,160
Governance is enforcement and enforcement becomes manageable when you define the question set

700
00:24:37,160 –> 00:24:40,400
that every AI use case must answer before it gets promoted.

701
00:24:40,400 –> 00:24:41,480
What data is in scope?

702
00:24:41,480 –> 00:24:42,680
Who owns it? Who approved it?

703
00:24:42,680 –> 00:24:43,760
Who can see it? How does it move?

704
00:24:43,760 –> 00:24:45,280
Where is it logged? What’s the retention rule?

705
00:24:45,280 –> 00:24:46,440
And what happens when it’s wrong?

706
00:24:46,440 –> 00:24:47,520
This isn’t for auditors.

707
00:24:47,520 –> 00:24:51,280
This is for operating reality because AI outputs will be challenged.

708
00:24:51,280 –> 00:24:55,000
The question is whether you can respond with evidence or with vibes.

709
00:24:55,000 –> 00:24:57,280
So here’s the transition into the next guardrail.

710
00:24:57,280 –> 00:24:59,400
Identity tells you who can access data.

711
00:24:59,400 –> 00:25:02,840
Governance tells you what that data means, where it came from,

712
00:25:02,840 –> 00:25:04,320
and whether you can defend using it.

713
00:25:04,320 –> 00:25:08,720
But governance without a semantic layer still fails because truth is not raw data.

714
00:25:08,720 –> 00:25:11,680
Truth is the contract that makes raw data coherent.

715
00:25:11,680 –> 00:25:16,680
Non-negotiable guardrail three, semantic contracts, not everyone builds their own.

716
00:25:16,680 –> 00:25:20,000
Here’s where the enterprise finally meets its oldest enemy, semantics,

717
00:25:20,000 –> 00:25:24,120
not data volume, not tooling, not even governance paperwork, meaning

718
00:25:24,120 –> 00:25:27,160
semantic chaos is simple to describe and painful to live with.

719
00:25:27,160 –> 00:25:31,160
The same concept gets defined five different ways, all of them correct locally

720
00:25:31,160 –> 00:25:33,080
and all of them wrong globally.

721
00:25:33,080 –> 00:25:37,560
Customer, active user, revenue, incident, SLA, risk, resolved.

722
00:25:37,560 –> 00:25:40,800
Everyone has a definition, everyone has a dashboard, none of them reconcile.

723
00:25:40,800 –> 00:25:44,560
And then you add AI on top and act surprised when the outputs disagree.

724
00:25:44,560 –> 00:25:48,120
AI doesn’t arbitrate, meaning it amplifies it, the model can learn patterns,

725
00:25:48,120 –> 00:25:50,600
it can summarize, it can rank, it can generate,

726
00:25:50,600 –> 00:25:54,760
but it cannot decide which department’s definition of customer is the enterprise definition.

727
00:25:54,760 –> 00:25:58,920
That’s not a technical problem, that’s a governance problem wearing a metric name tag.

728
00:25:58,920 –> 00:26:01,800
This is the part where leaders often reach for a comfortable phrase,

729
00:26:01,800 –> 00:26:05,280
“We’ll let teams innovate” and they do, they innovate definitions.

730
00:26:05,280 –> 00:26:09,560
Now you have a platform that can answer any question, but can’t answer it consistently.

731
00:26:09,560 –> 00:26:13,080
That distinction matters because consistency is what turns outputs into decisions.

732
00:26:13,080 –> 00:26:16,720
If two executives get two different truths from two different co-pilates,

733
00:26:16,720 –> 00:26:18,320
the enterprise doesn’t get faster.

734
00:26:18,320 –> 00:26:23,800
It gets suspicious, adoption collapses, then every AI project gets relabelled as not ready.

735
00:26:23,800 –> 00:26:26,440
It is ready, your semantics are not.

736
00:26:26,440 –> 00:26:29,480
So the third non-negotiable guardrail is semantic contracts,

737
00:26:29,480 –> 00:26:31,680
not guidance, not best practice contracts.

738
00:26:31,680 –> 00:26:35,320
A semantic contract is a published, endorsed definition of a business concept

739
00:26:35,320 –> 00:26:39,760
that includes the meaning, the calculation logic, the grain, the allowed joins,

740
00:26:39,760 –> 00:26:41,600
and the rules for change.

741
00:26:41,600 –> 00:26:43,480
It’s not just a table, it’s a promise.

742
00:26:43,480 –> 00:26:45,880
If you build on this, you inherit stable meaning.

743
00:26:45,880 –> 00:26:49,160
This is where a semantic layer becomes an operating model component,

744
00:26:49,160 –> 00:26:50,600
not an analytics preference.

745
00:26:50,600 –> 00:26:55,000
In the Microsoft ecosystem, semantic models, endorsed data sets, certified definitions,

746
00:26:55,000 –> 00:26:57,800
whatever your implementation looks like, are the mechanism.

747
00:26:57,800 –> 00:27:00,560
The important part is the governance behavior behind them.

748
00:27:00,560 –> 00:27:03,960
Because without govern semantics, you create a perverse incentive structure.

749
00:27:03,960 –> 00:27:07,480
Every domain team optimizes locally, they ship quickly, they define metrics

750
00:27:07,480 –> 00:27:09,280
that make sense inside their boundary,

751
00:27:09,280 –> 00:27:13,440
and then the enterprise tries to combine those metrics and discovers they’re incompatible.

752
00:27:13,440 –> 00:27:15,800
That incompatibility is the real integration tax.

753
00:27:15,800 –> 00:27:19,640
AI makes that tax visible immediately because it cross references, it blends,

754
00:27:19,640 –> 00:27:23,760
it retrieves, it generalizes, it will happily stitch together conflicting meanings

755
00:27:23,760 –> 00:27:26,440
and present the output as coherent.

756
00:27:26,440 –> 00:27:29,440
Confident wrong answers are the natural product of ungoverned semantics.

757
00:27:29,440 –> 00:27:31,200
So what does enforcement actually look like?

758
00:27:31,200 –> 00:27:34,600
First, data products don’t just publish tables, they publish contracts.

759
00:27:34,600 –> 00:27:38,160
If a domain publishes customer, the contract specifies what customer means,

760
00:27:38,160 –> 00:27:40,880
what active means, what de-duplication rules exist,

761
00:27:40,880 –> 00:27:45,200
which source systems are authoritative, and what the expected failure modes are.

762
00:27:45,200 –> 00:27:50,200
If that sounds heavy, good, it should be heavy because you are publishing meaning at enterprise scale.

763
00:27:50,200 –> 00:27:54,200
Second, semantic models are governed artifacts with controlled change.

764
00:27:54,200 –> 00:27:59,200
If the definition changes, it is versioned, communicated, and validated against downstream impacts.

765
00:27:59,200 –> 00:28:02,600
This is where most organizations accidentally create chaos.

766
00:28:02,600 –> 00:28:06,600
Someone fixes a measure and half the board deck changes next morning.

767
00:28:06,600 –> 00:28:10,000
That isn’t agility, that’s uncontrolled change in the decision layer.

768
00:28:10,000 –> 00:28:12,200
Third, you establish an arbitration function.

769
00:28:12,200 –> 00:28:15,600
This is the part executives avoid because semantic disputes are political.

770
00:28:15,600 –> 00:28:18,200
They are budget disputes with nicer vocabulary.

771
00:28:18,200 –> 00:28:22,800
But the enterprise needs an explicit authority that can resolve which definition wins and why.

772
00:28:22,800 –> 00:28:25,600
If you don’t assign an arbitrator, the system will assign one for you.

773
00:28:25,600 –> 00:28:27,200
It will be whichever team shipped last.

774
00:28:27,200 –> 00:28:29,400
Now, there’s a common mistake platform leaders make here.

775
00:28:29,400 –> 00:28:33,400
They try to solve semantics with a central team that defines everything upfront.

776
00:28:33,400 –> 00:28:36,000
That fails too because the center doesn’t own domain reality.

777
00:28:36,000 –> 00:28:40,000
They create beautiful definitions nobody uses, then teams root around them.

778
00:28:40,000 –> 00:28:41,800
The correct model is federated.

779
00:28:41,800 –> 00:28:45,200
Domains own their concepts, but they publish them through shared standards

780
00:28:45,200 –> 00:28:47,000
and they enterprise governs the overlaps.

781
00:28:47,000 –> 00:28:49,400
You don’t need one team to define everything.

782
00:28:49,400 –> 00:28:52,800
You need one system that makes definitions enforceable and reusable.

783
00:28:52,800 –> 00:28:54,600
And yes, this feels like slowing down.

784
00:28:54,600 –> 00:28:58,200
It is on purpose because the alternative is accelerating ambiguity.

785
00:28:58,200 –> 00:29:00,400
And AI is a perfect ambiguity accelerator.

786
00:29:00,400 –> 00:29:01,800
So here is the transition.

787
00:29:01,800 –> 00:29:04,600
If you lock identity, you control who can see data.

788
00:29:04,600 –> 00:29:08,600
If you can audit governance, you can defend where data came from and how it moved.

789
00:29:08,600 –> 00:29:12,000
But if you don’t lock semantics, you can’t defend what the data means.

790
00:29:12,000 –> 00:29:14,800
And the first time an AI output becomes a real business decision,

791
00:29:14,800 –> 00:29:16,800
meaning is what you’ll be asked to justify.

792
00:29:16,800 –> 00:29:20,400
Failure scenario A, the geni-pilot that went viral.

793
00:29:20,400 –> 00:29:23,200
Now, let’s make this concrete with a failure pattern that keeps repeating

794
00:29:23,200 –> 00:29:26,400
because it feels like success right up until it becomes real.

795
00:29:26,400 –> 00:29:28,400
A geni-pilot goes viral internally.

796
00:29:28,400 –> 00:29:30,400
It starts as the cleanest demo you can build.

797
00:29:30,400 –> 00:29:33,800
Retrieval augmented generation over enterprise documents.

798
00:29:33,800 –> 00:29:37,800
A curated SharePoint library, a handful of approved PDFs, some policies,

799
00:29:37,800 –> 00:29:39,000
a nice chat interface.

800
00:29:39,000 –> 00:29:41,600
People ask questions and the system answers in seconds.

801
00:29:41,600 –> 00:29:45,000
Leadership sees the adoption curve and decides this is finally the breakthrough.

802
00:29:45,000 –> 00:29:46,800
And at the pilot stage, they’re not wrong.

803
00:29:46,800 –> 00:29:47,800
It looks impressive.

804
00:29:47,800 –> 00:29:48,800
The answers are fast.

805
00:29:48,800 –> 00:29:50,800
The citations make it feel responsible.

806
00:29:50,800 –> 00:29:52,200
The UX feels modern.

807
00:29:52,200 –> 00:29:55,400
And because the corpus is narrow, the system stays mostly coherent.

808
00:29:55,400 –> 00:30:00,000
It even feels safer than reality because the system is consistent inside its small sandbox.

809
00:30:00,000 –> 00:30:01,400
Then the adoption happens.

810
00:30:01,400 –> 00:30:03,600
The link gets shared, a team’s message, an email forward,

811
00:30:03,600 –> 00:30:05,000
“Hey, you have to try this.”

812
00:30:05,000 –> 00:30:06,800
Suddenly, the pilot isn’t a pilot anymore.

813
00:30:06,800 –> 00:30:09,600
It’s a shadow production system with executive attention.

814
00:30:09,600 –> 00:30:12,600
This is where the enterprise usually makes its first design omission.

815
00:30:12,600 –> 00:30:14,600
The document corpus has no named owner.

816
00:30:14,600 –> 00:30:18,600
Not IT owns SharePoint, not the platform team runs the connector.

817
00:30:18,600 –> 00:30:22,200
A real owner, the person who can say what is in scope, what is out of scope,

818
00:30:22,200 –> 00:30:25,200
what correct means, and what happens when something is wrong

819
00:30:25,200 –> 00:30:26,800
because documents aren’t data.

820
00:30:26,800 –> 00:30:27,800
They are claims.

821
00:30:27,800 –> 00:30:30,600
A policy document says one thing, a procedure says another.

822
00:30:30,600 –> 00:30:32,200
A contract says something else.

823
00:30:32,200 –> 00:30:35,000
A five-year-old slide deck says something completely different

824
00:30:35,000 –> 00:30:37,800
and it is still discoverable because nobody wanted to delete it.

825
00:30:37,800 –> 00:30:39,200
So the system retrieves.

826
00:30:39,200 –> 00:30:41,800
It synthesizes, it answers correctly.

827
00:30:41,800 –> 00:30:44,400
Under the assumptions you accidentally encoded,

828
00:30:44,400 –> 00:30:46,000
and then the first conflict lands,

829
00:30:46,000 –> 00:30:48,400
an employee asks a simple question that matters.

830
00:30:48,400 –> 00:30:50,400
What’s the approved approach for X?

831
00:30:50,400 –> 00:30:53,600
The assistant answers with confidence and cites a document.

832
00:30:53,600 –> 00:30:57,400
A second employee asks the same question the next day and gets a different answer,

833
00:30:57,400 –> 00:30:58,800
citing a different document.

834
00:30:58,800 –> 00:31:00,200
Both answers are plausible.

835
00:31:00,200 –> 00:31:02,000
Both answers are supported.

836
00:31:02,000 –> 00:31:05,200
And now you’ve created the most dangerous class of enterprise output,

837
00:31:05,200 –> 00:31:06,800
authoritative inconsistency.

838
00:31:06,800 –> 00:31:08,200
This is where the escalation starts,

839
00:31:08,200 –> 00:31:09,600
not because people hate AI,

840
00:31:09,600 –> 00:31:11,400
because people hate being wrong in public.

841
00:31:11,400 –> 00:31:14,800
A manager sees an answer that conflicts with what they’ve been enforcing.

842
00:31:14,800 –> 00:31:17,400
They forward it to legal, legal asks compliance,

843
00:31:17,400 –> 00:31:21,000
compliance asks security, security asks the platform team.

844
00:31:21,000 –> 00:31:23,800
And the platform team is now in the middle of a dispute they cannot solve

845
00:31:23,800 –> 00:31:25,800
because it isn’t the platform problem.

846
00:31:25,800 –> 00:31:27,400
It’s a truth problem.

847
00:31:27,400 –> 00:31:30,600
The enterprise never decided who owns truth for this corpus.

848
00:31:30,600 –> 00:31:31,800
So the pilot freezes.

849
00:31:31,800 –> 00:31:34,000
Not in a dramatic way in the enterprise way,

850
00:31:34,000 –> 00:31:37,000
until we can review it, until we can validate the content,

851
00:31:37,000 –> 00:31:39,000
until we can ensure the right controls,

852
00:31:39,000 –> 00:31:40,400
and you’ll notice what happens next.

853
00:31:40,400 –> 00:31:42,400
The pilot doesn’t get improved.

854
00:31:42,400 –> 00:31:44,600
It gets paused, the budget gets redirected,

855
00:31:44,600 –> 00:31:47,400
the energy moves on to the next exciting prototype.

856
00:31:47,400 –> 00:31:50,000
Leadership quietly concludes that Geni isn’t ready,

857
00:31:50,000 –> 00:31:51,000
but the model didn’t fail.

858
00:31:51,000 –> 00:31:53,600
The enterprise refused to decide what correct means,

859
00:31:53,600 –> 00:31:56,400
and who gets to arbitrate when two documents disagree.

860
00:31:56,400 –> 00:31:58,000
Now here’s the part that stings.

861
00:31:58,000 –> 00:31:59,800
The viral pilot didn’t create the risk.

862
00:31:59,800 –> 00:32:02,000
It exposed the risk that already existed.

863
00:32:02,000 –> 00:32:04,000
The organization has conflicting instructions,

864
00:32:04,000 –> 00:32:07,000
conflicting definitions and conflicting policies living side by side.

865
00:32:07,000 –> 00:32:11,000
Humans cope with that by relying on tribal knowledge and escalation chains.

866
00:32:11,000 –> 00:32:15,400
The assistant removed the tribal knowledge layer and returned the raw contradiction.

867
00:32:15,400 –> 00:32:18,000
And because it did it quickly at scale and with confidence,

868
00:32:18,000 –> 00:32:19,600
everyone treated it like a new threat.

869
00:32:19,600 –> 00:32:22,000
So what’s the executive move that prevents this?

870
00:32:22,000 –> 00:32:24,400
Treat the document corpus as a governed data product.

871
00:32:24,400 –> 00:32:27,800
That means a named owner, a defined scope, a life cycle,

872
00:32:27,800 –> 00:32:30,800
what gets added, what gets retired, what gets flagged as outdated,

873
00:32:30,800 –> 00:32:32,600
what gets marked as authoritative.

874
00:32:32,600 –> 00:32:35,400
It means classification rules that follow the content

875
00:32:35,400 –> 00:32:37,800
and access rules that match the sensitivity.

876
00:32:37,800 –> 00:32:39,400
And it means a semantic decision.

877
00:32:39,400 –> 00:32:41,600
What questions this corpus is allowed to answer

878
00:32:41,600 –> 00:32:43,200
and what questions it must refuse.

879
00:32:43,200 –> 00:32:47,200
Because not every enterprise question is answerable from documents alone

880
00:32:47,200 –> 00:32:51,600
and pretending otherwise is how you turn helpful assistant into liability generator.

881
00:32:51,600 –> 00:32:52,800
So the lesson is simple.

882
00:32:52,800 –> 00:32:54,600
If you can’t name the owner of truth,

883
00:32:54,600 –> 00:32:57,200
the system will stall the first time truth gets challenged.

884
00:32:57,200 –> 00:32:58,400
And it will be challenged.

885
00:32:58,400 –> 00:33:01,000
That’s not pessimism. That’s how enterprises behave

886
00:33:01,000 –> 00:33:03,800
when outputs start affecting real decisions.

887
00:33:03,800 –> 00:33:05,800
Now governance failure stop pilots,

888
00:33:05,800 –> 00:33:07,200
but economics failure stop platforms.

889
00:33:07,200 –> 00:33:09,000
That’s next failure scenario B.

890
00:33:09,000 –> 00:33:12,000
Analytics modernization becomes an AI bill crisis.

891
00:33:12,000 –> 00:33:14,800
The second failure pattern looks nothing like a governance panic.

892
00:33:14,800 –> 00:33:15,800
It looks like success.

893
00:33:15,800 –> 00:33:17,800
An organization modernizes analytics.

894
00:33:17,800 –> 00:33:19,200
They consolidate tools.

895
00:33:19,200 –> 00:33:21,000
They standardize workspaces.

896
00:33:21,000 –> 00:33:23,400
They move toward a unified lake house pattern

897
00:33:23,400 –> 00:33:25,600
often with a fabric style experience.

898
00:33:25,600 –> 00:33:28,200
One place to engineer model and report.

899
00:33:28,200 –> 00:33:29,600
They turn on self service.

900
00:33:29,600 –> 00:33:30,800
They enable AI features.

901
00:33:30,800 –> 00:33:34,200
They celebrate because the friction is gone and the backlog starts shrinking.

902
00:33:34,200 –> 00:33:35,800
And for a while it is real progress.

903
00:33:35,800 –> 00:33:37,800
Because unification does remove waste.

904
00:33:37,800 –> 00:33:40,800
Fewer copies, fewer pipelines, fewer bespoke environments.

905
00:33:40,800 –> 00:33:43,400
Teams stop spending weeks negotiating access to data.

906
00:33:43,400 –> 00:33:44,800
Reports light up faster.

907
00:33:44,800 –> 00:33:47,400
The executive dashboard actually refreshes on time.

908
00:33:47,400 –> 00:33:50,200
Everyone feels like they finally fixed data.

909
00:33:50,200 –> 00:33:52,200
Then the bill arrives.

910
00:33:52,200 –> 00:33:54,800
Not as a gradual increase as a cliff.

911
00:33:54,800 –> 00:33:57,000
Suddenly compute consumption spikes.

912
00:33:57,000 –> 00:33:58,600
Capacity is saturated.

913
00:33:58,600 –> 00:34:00,400
Interactive performance degrades.

914
00:34:00,400 –> 00:34:01,400
Queries queue.

915
00:34:01,400 –> 00:34:03,600
Background work competes with user workloads.

916
00:34:03,600 –> 00:34:06,400
Finance gets a number that doesn’t map to a business outcome

917
00:34:06,400 –> 00:34:09,400
and they do what finance always does when the system can’t explain itself.

918
00:34:09,400 –> 00:34:10,200
They intervene.

919
00:34:10,200 –> 00:34:12,600
This is the moment the modernization story flips.

920
00:34:12,600 –> 00:34:15,000
The platform team gets asked why is this so expensive?

921
00:34:15,000 –> 00:34:17,200
And the platform team answers with technical truths

922
00:34:17,200 –> 00:34:19,200
that aren’t useful at the executive layer.

923
00:34:19,200 –> 00:34:22,600
Concurrency, workloads, burst behavior, and a shared capacity model.

924
00:34:22,600 –> 00:34:23,600
All true.

925
00:34:23,600 –> 00:34:25,800
None of it is the reason the enterprise is angry.

926
00:34:25,800 –> 00:34:26,800
The real reason is simpler.

927
00:34:26,800 –> 00:34:28,600
Nobody can connect cost to accountability.

928
00:34:28,600 –> 00:34:29,800
No unit economics.

929
00:34:29,800 –> 00:34:31,400
No cost owner per outcome.

930
00:34:31,400 –> 00:34:33,200
No line of sight from consumption.

931
00:34:33,200 –> 00:34:34,400
Back to decisions.

932
00:34:34,400 –> 00:34:38,000
So the only available governance mechanism becomes the blunt instrument.

933
00:34:38,000 –> 00:34:40,000
Throttle, disable, or restrict.

934
00:34:40,000 –> 00:34:41,800
In the Microsoft fabric model,

935
00:34:41,800 –> 00:34:43,800
everything draws from shared capacity units.

936
00:34:43,800 –> 00:34:45,200
When demand rises,

937
00:34:45,200 –> 00:34:48,000
throttling becomes the platform’s way of preserving stability.

938
00:34:48,000 –> 00:34:50,000
And from an executive perspective,

939
00:34:50,000 –> 00:34:52,200
throttling feels like the platform is unreliable.

940
00:34:52,200 –> 00:34:53,000
It isn’t.

941
00:34:53,000 –> 00:34:55,800
It’s doing exactly what the architecture was designed to do

942
00:34:55,800 –> 00:34:57,400
when demand exceeds intent.

943
00:34:57,400 –> 00:34:59,000
But intent was never enforced.

944
00:34:59,000 –> 00:35:01,200
Here’s how this failure sequence usually plays out.

945
00:35:01,200 –> 00:35:03,800
First, self-service expands faster than governance.

946
00:35:03,800 –> 00:35:05,200
Teams create more artifacts.

947
00:35:05,200 –> 00:35:06,200
More pipelines run.

948
00:35:06,200 –> 00:35:07,400
More notebooks execute.

949
00:35:07,400 –> 00:35:08,800
More reports hit the system.

950
00:35:08,800 –> 00:35:10,200
None of this is inherently wrong.

951
00:35:10,200 –> 00:35:12,800
It’s the point of democratized analytics.

952
00:35:12,800 –> 00:35:15,000
Second, AI features amplify usage patterns.

953
00:35:15,000 –> 00:35:16,000
People iterate more.

954
00:35:16,000 –> 00:35:17,200
They ask more questions.

955
00:35:17,200 –> 00:35:18,200
They run heavier queries.

956
00:35:18,200 –> 00:35:19,200
They experiment.

957
00:35:19,200 –> 00:35:21,700
And experimentation is expensive by definition

958
00:35:21,700 –> 00:35:23,900
because it trades certainty for exploration.

959
00:35:23,900 –> 00:35:26,200
Third, costs become visible to finance

960
00:35:26,200 –> 00:35:28,400
before they become understandable to leadership.

961
00:35:28,400 –> 00:35:30,200
The bill shows spend, not value.

962
00:35:30,200 –> 00:35:31,700
It shows compute, not decisions.

963
00:35:31,700 –> 00:35:33,700
It shows capacity usage, not outcomes.

964
00:35:33,700 –> 00:35:36,600
So finance escalates.

965
00:35:36,600 –> 00:35:39,300
Then comes the executive directive that kills trust.

966
00:35:39,300 –> 00:35:41,400
Turn it off until we understand it.

967
00:35:41,400 –> 00:35:42,800
And now the platform is stuck

968
00:35:42,800 –> 00:35:44,300
because you can’t build adoption

969
00:35:44,300 –> 00:35:47,200
and then remove it without creating organizational backlash.

970
00:35:47,200 –> 00:35:48,700
Teams stop trusting the platform.

971
00:35:48,700 –> 00:35:49,700
They root around it.

972
00:35:49,700 –> 00:35:51,300
Shadow tools reappear.

973
00:35:51,300 –> 00:35:53,200
The modernization effort starts to unravel

974
00:35:53,200 –> 00:35:55,700
into the same fragmentation you were trying to escape.

975
00:35:55,700 –> 00:35:58,400
The lesson is not unified platforms are expensive.

976
00:35:58,400 –> 00:36:00,500
The lesson is without unit economics,

977
00:36:00,500 –> 00:36:02,800
unified platforms are uncontrollable.

978
00:36:02,800 –> 00:36:05,700
If the organization can’t describe cost per decision

979
00:36:05,700 –> 00:36:07,200
or cost per insight,

980
00:36:07,200 –> 00:36:09,500
then every cost discussion becomes political.

981
00:36:09,500 –> 00:36:11,700
One team claims they’re doing valuable work.

982
00:36:11,700 –> 00:36:14,600
Another team claims they’re paying for someone else’s experiments.

983
00:36:14,600 –> 00:36:17,300
Nobody has a shared measurement system to arbitrate.

984
00:36:17,300 –> 00:36:19,800
And because AI workloads are bursting and variable,

985
00:36:19,800 –> 00:36:22,000
the bill will never be stable enough to ignore.

986
00:36:22,000 –> 00:36:24,000
Cost surprises are architecture signals,

987
00:36:24,000 –> 00:36:25,600
not finance failures.

988
00:36:25,600 –> 00:36:28,000
So they tell you the system has missing boundaries.

989
00:36:28,000 –> 00:36:30,500
So what is the executive move that prevents this?

990
00:36:30,500 –> 00:36:32,800
Make cost a first-class governance surface,

991
00:36:32,800 –> 00:36:34,400
not a quarterly surprise.

992
00:36:34,400 –> 00:36:36,200
That means every AI enabled workload

993
00:36:36,200 –> 00:36:38,700
needs a cost owner, not eat owns the bill.

994
00:36:38,700 –> 00:36:40,300
A named owner tied to an outcome,

995
00:36:40,300 –> 00:36:41,900
case resolution, fraud review,

996
00:36:41,900 –> 00:36:44,400
customer support deflection contract analysis.

997
00:36:44,400 –> 00:36:46,100
If there’s no outcome, there’s no owner.

998
00:36:46,100 –> 00:36:48,200
If there’s no owner, it’s a lab experiment,

999
00:36:48,200 –> 00:36:49,200
treated like one.

1000
00:36:49,200 –> 00:36:51,400
Then define one unit metric per use case

1001
00:36:51,400 –> 00:36:54,600
that survives vendor change, not cost per token.

1002
00:36:54,600 –> 00:36:56,500
Tokens are implementation detail.

1003
00:36:56,500 –> 00:36:58,200
The metric is cost per decision,

1004
00:36:58,200 –> 00:37:00,800
cost per insight or cost per automated workflow.

1005
00:37:00,800 –> 00:37:02,100
Something leadership can govern

1006
00:37:02,100 –> 00:37:03,800
without learning model internals.

1007
00:37:03,800 –> 00:37:05,600
When leaders can see the unit economics,

1008
00:37:05,600 –> 00:37:07,100
the conversation changes.

1009
00:37:07,100 –> 00:37:08,900
You stop arguing about platform spend,

1010
00:37:08,900 –> 00:37:10,900
you start managing decision economics.

1011
00:37:10,900 –> 00:37:13,200
And once you do that, the platform becomes fundable

1012
00:37:13,200 –> 00:37:15,400
because the enterprise can decide deliberately

1013
00:37:15,400 –> 00:37:16,900
what it is willing to pay for.

1014
00:37:16,900 –> 00:37:19,800
Without that, the platform will always hit the same end point,

1015
00:37:19,800 –> 00:37:22,000
finance intervention, throttling,

1016
00:37:22,000 –> 00:37:23,400
and a slow collapse of trust.

1017
00:37:23,400 –> 00:37:24,400
And once trust collapses,

1018
00:37:24,400 –> 00:37:26,500
the next instinct is decentralization,

1019
00:37:26,500 –> 00:37:29,500
which solves bottlenecks and then creates semantic chaos.

1020
00:37:29,500 –> 00:37:30,300
That’s next.

1021
00:37:30,300 –> 00:37:31,800
Failure scenario C.

1022
00:37:31,800 –> 00:37:35,000
Data mesh meets AI and produces confident wrong answers.

1023
00:37:35,000 –> 00:37:37,600
The third failure pattern is the one that hurts the most

1024
00:37:37,600 –> 00:37:41,100
because it starts as the correct organizational move.

1025
00:37:41,100 –> 00:37:42,800
The centralized data team was a bottleneck,

1026
00:37:42,800 –> 00:37:44,900
so leadership embraces domain ownership,

1027
00:37:44,900 –> 00:37:46,300
teams publish data products.

1028
00:37:46,300 –> 00:37:48,200
They document things, they set up domains,

1029
00:37:48,200 –> 00:37:50,200
everyone says the right words, federated governance,

1030
00:37:50,200 –> 00:37:52,900
data as a product, self-serve platform.

1031
00:37:52,900 –> 00:37:54,900
And for a while, it looks like maturity,

1032
00:37:54,900 –> 00:37:57,600
domains ship faster because they’re closer to the work

1033
00:37:57,600 –> 00:37:59,900
but they know their systems, they know their edge cases,

1034
00:37:59,900 –> 00:38:01,700
they can iterate without waiting three months

1035
00:38:01,700 –> 00:38:03,700
for the central backlog to move.

1036
00:38:03,700 –> 00:38:05,800
Then AI arrives and asks the question,

1037
00:38:05,800 –> 00:38:08,700
“That data mesh alone doesn’t force you to answer,

1038
00:38:08,700 –> 00:38:10,300
“are your meanings compatible?”

1039
00:38:10,300 –> 00:38:12,500
Because AI doesn’t stay inside a domain boundary.

1040
00:38:12,500 –> 00:38:15,400
The whole point of AI is cross-cutting synthesis.

1041
00:38:15,400 –> 00:38:17,160
Customer support questions, touch product,

1042
00:38:17,160 –> 00:38:19,800
billing, identity, compliance and entitlement,

1043
00:38:19,800 –> 00:38:22,100
fraud, touches, transactions, device signals

1044
00:38:22,100 –> 00:38:23,400
and customer history.

1045
00:38:23,400 –> 00:38:25,800
Supply chain touches, inventory, orders, logistics,

1046
00:38:25,800 –> 00:38:26,800
and finance.

1047
00:38:26,800 –> 00:38:27,800
The model will traverse domains

1048
00:38:27,800 –> 00:38:29,700
because the decision traverses domains

1049
00:38:29,700 –> 00:38:33,000
and this is where the system produces confident wrong answers.

1050
00:38:33,000 –> 00:38:34,600
Not because the model hallucinated

1051
00:38:34,600 –> 00:38:36,900
because the enterprise published conflicting semantics

1052
00:38:36,900 –> 00:38:37,800
at scale.

1053
00:38:37,800 –> 00:38:41,000
Here’s what it looks like, domain A publishes customer

1054
00:38:41,000 –> 00:38:44,600
and means an entity with an active contract in system A.

1055
00:38:45,400 –> 00:38:48,100
Domain B publishes customer and means an entity

1056
00:38:48,100 –> 00:38:50,500
with a billing relationship in system B.

1057
00:38:50,500 –> 00:38:53,700
Domain C publishes customer and means any person

1058
00:38:53,700 –> 00:38:56,800
who created an account regardless of contract or billing.

1059
00:38:56,800 –> 00:39:00,100
All three definitions are defensible inside their own boundary

1060
00:39:00,100 –> 00:39:01,500
and all three are incompatible

1061
00:39:01,500 –> 00:39:03,500
when you build cross-domain decisions.

1062
00:39:03,500 –> 00:39:05,400
Now add AI, you build a retrieval layer

1063
00:39:05,400 –> 00:39:07,500
over these data products, you train or ground

1064
00:39:07,500 –> 00:39:08,800
the model across them.

1065
00:39:08,800 –> 00:39:10,600
You build an assistant that can answer questions

1066
00:39:10,600 –> 00:39:12,700
like how many active customers do we have,

1067
00:39:12,700 –> 00:39:14,500
or which customers are eligible for X

1068
00:39:14,500 –> 00:39:16,000
or what’s our churn risk.

1069
00:39:16,000 –> 00:39:17,400
The model sees multiple patterns,

1070
00:39:17,400 –> 00:39:18,600
it sees multiple meanings,

1071
00:39:18,600 –> 00:39:20,100
it doesn’t resolve the conflict,

1072
00:39:20,100 –> 00:39:21,300
it learns the distribution.

1073
00:39:21,300 –> 00:39:23,500
So you get an output that sounds coherent,

1074
00:39:23,500 –> 00:39:25,500
sites, sources and is still wrong.

1075
00:39:25,500 –> 00:39:27,100
Not because the sources are wrong

1076
00:39:27,100 –> 00:39:29,800
because the synthesis assumes the enterprise has one definition

1077
00:39:29,800 –> 00:39:30,900
when it has three.

1078
00:39:30,900 –> 00:39:33,200
This is the most dangerous failure mode in AI,

1079
00:39:33,200 –> 00:39:34,400
correctness theater.

1080
00:39:34,400 –> 00:39:35,800
The output looks professional,

1081
00:39:35,800 –> 00:39:36,400
it’s fast,

1082
00:39:36,400 –> 00:39:39,300
it might even be numerically consistent with one data set,

1083
00:39:39,300 –> 00:39:40,700
but it is semantically wrong

1084
00:39:40,700 –> 00:39:43,000
for the decision the business thinks it’s making

1085
00:39:43,000 –> 00:39:44,500
and the business will detect it quickly

1086
00:39:44,500 –> 00:39:46,400
because the business lives in consequences.

1087
00:39:46,400 –> 00:39:48,300
The number doesn’t match what finance reports.

1088
00:39:48,300 –> 00:39:51,600
The eligibility list doesn’t match what operations sees.

1089
00:39:51,600 –> 00:39:53,800
The assistant tells the support agent one thing

1090
00:39:53,800 –> 00:39:55,600
and the billing system enforces another,

1091
00:39:55,600 –> 00:39:56,800
people stop trusting it.

1092
00:39:56,800 –> 00:39:58,200
And the platform gets blamed,

1093
00:39:58,200 –> 00:40:00,400
this is where the narrative becomes predictable.

1094
00:40:00,400 –> 00:40:02,100
Leaders say data mesh didn’t work,

1095
00:40:02,100 –> 00:40:04,400
or AI isn’t reliable,

1096
00:40:04,400 –> 00:40:06,000
or we need a better model.

1097
00:40:06,000 –> 00:40:08,200
No, you need semantic governance.

1098
00:40:08,200 –> 00:40:10,200
Decentralization solves the delivery bottleneck,

1099
00:40:10,200 –> 00:40:11,400
but it decentralizes meaning

1100
00:40:11,400 –> 00:40:14,000
and meaning cannot be decentralized without contracts

1101
00:40:14,000 –> 00:40:16,800
because the enterprise is not a set of independent startups.

1102
00:40:16,800 –> 00:40:19,000
It is one legal entity with one balance sheet.

1103
00:40:19,000 –> 00:40:19,700
At some point,

1104
00:40:19,700 –> 00:40:21,000
someone must be able to say,

1105
00:40:21,000 –> 00:40:22,600
this is the enterprise definition.

1106
00:40:22,600 –> 00:40:25,200
This is why semantic disputes are executive work.

1107
00:40:25,200 –> 00:40:26,600
They are not technical disagreements.

1108
00:40:26,600 –> 00:40:27,900
They are boundary disputes.

1109
00:40:27,900 –> 00:40:30,900
They affect reporting, incentives and accountability.

1110
00:40:30,900 –> 00:40:33,600
If you leave them to teams, teams will optimize locally.

1111
00:40:33,600 –> 00:40:35,600
If you leave them to the platform team,

1112
00:40:35,600 –> 00:40:38,600
the platform team becomes the political referee for the business.

1113
00:40:38,600 –> 00:40:40,200
That’s not scalable and it’s not fair.

1114
00:40:40,200 –> 00:40:42,600
So the fix is not go back to centralization.

1115
00:40:42,600 –> 00:40:44,200
The fix is federated governance

1116
00:40:44,200 –> 00:40:45,600
that standardizes semantics

1117
00:40:45,600 –> 00:40:47,300
while preserving domain autonomy.

1118
00:40:47,300 –> 00:40:49,300
Domains can own their data products,

1119
00:40:49,300 –> 00:40:51,300
but they must publish semantic contracts

1120
00:40:51,300 –> 00:40:52,800
that meet enterprise standards.

1121
00:40:52,800 –> 00:40:55,800
The enterprise must endorse and certify shared definitions.

1122
00:40:55,800 –> 00:40:57,300
And when two domains disagree,

1123
00:40:57,300 –> 00:40:58,600
you need an arbitration pathway

1124
00:40:58,600 –> 00:41:00,600
that resolves the conflict deliberately

1125
00:41:00,600 –> 00:41:02,800
with a decision record and control change.

1126
00:41:02,800 –> 00:41:04,200
Because once AI is in the loop,

1127
00:41:04,200 –> 00:41:06,400
ambiguity becomes operational risk

1128
00:41:06,400 –> 00:41:08,800
and the executive move is simple to say and hard to do.

1129
00:41:08,800 –> 00:41:11,200
Do not allow everyone builds their own semantics,

1130
00:41:11,200 –> 00:41:13,000
allow domains to build their own pipelines,

1131
00:41:13,000 –> 00:41:14,700
allow them to own their own products,

1132
00:41:14,700 –> 00:41:16,200
allow them to move fast,

1133
00:41:16,200 –> 00:41:19,000
but in forced shared meaning for shared decisions.

1134
00:41:19,000 –> 00:41:21,000
Otherwise you will scale ambiguity

1135
00:41:21,000 –> 00:41:24,100
and AI will do it politely, confidently and at machine speed.

1136
00:41:24,100 –> 00:41:26,500
Now you might be thinking this sounds like governance overhead.

1137
00:41:26,500 –> 00:41:27,200
It is overhead.

1138
00:41:27,200 –> 00:41:30,700
It’s the overhead that replaces rework, distrust and incident reviews.

1139
00:41:30,700 –> 00:41:33,200
Because the alternative is spending that same effort

1140
00:41:33,200 –> 00:41:35,000
later under pressure

1141
00:41:35,000 –> 00:41:36,500
when the business already lost faith.

1142
00:41:36,500 –> 00:41:38,600
So the lesson from this failure scenario

1143
00:41:38,600 –> 00:41:39,600
is blunt.

1144
00:41:39,600 –> 00:41:41,000
Data mesh without semantic contracts

1145
00:41:41,000 –> 00:41:42,200
doesn’t create agility.

1146
00:41:42,200 –> 00:41:43,900
It creates scalable confusion

1147
00:41:43,900 –> 00:41:47,000
and AI turns scalable confusion into automated decision damage.

1148
00:41:47,000 –> 00:41:47,900
Once you’ve seen that,

1149
00:41:47,900 –> 00:41:49,800
you can predict the next break.

1150
00:41:49,800 –> 00:41:51,500
Governance failures, stop pilots,

1151
00:41:51,500 –> 00:41:53,000
economics failures, stop platforms,

1152
00:41:53,000 –> 00:41:54,900
semantic failures, stop adoption

1153
00:41:54,900 –> 00:41:57,700
and all three happen faster when AI is involved.

1154
00:41:57,700 –> 00:42:00,800
Economics of AI cost as an architecture signal.

1155
00:42:00,800 –> 00:42:02,700
Now the part everyone pretends is boring

1156
00:42:02,700 –> 00:42:04,800
until it becomes urgent economics.

1157
00:42:04,800 –> 00:42:06,200
AI workloads are variable,

1158
00:42:06,200 –> 00:42:08,300
bursty and expensive by nature.

1159
00:42:08,300 –> 00:42:09,600
That isn’t a vendor problem.

1160
00:42:09,600 –> 00:42:12,400
That’s the physics of running probabilistic systems at scale.

1161
00:42:12,400 –> 00:42:13,400
You pay for compute,

1162
00:42:13,400 –> 00:42:14,500
you pay for retrieval,

1163
00:42:14,500 –> 00:42:15,800
you pay for storage and movement,

1164
00:42:15,800 –> 00:42:17,300
you pay for evaluation,

1165
00:42:17,300 –> 00:42:19,300
and you pay again when you iterate.

1166
00:42:19,300 –> 00:42:20,700
And iteration is the whole point.

1167
00:42:20,700 –> 00:42:24,200
So if your organization treats costs spikes as a finance surprise,

1168
00:42:24,200 –> 00:42:25,800
you’ve already misframed the problem.

1169
00:42:25,800 –> 00:42:28,000
Cost surprises are architecture signals.

1170
00:42:28,000 –> 00:42:30,800
They tell you where the operating model is missing boundaries

1171
00:42:30,800 –> 00:42:32,500
where usage is unconstrained,

1172
00:42:32,500 –> 00:42:33,900
where ownership is undefined

1173
00:42:33,900 –> 00:42:36,700
and where self-service became unpriced consumption.

1174
00:42:36,700 –> 00:42:39,700
That distinction matters because enterprises don’t shut down platforms

1175
00:42:39,700 –> 00:42:40,600
when they’re expensive.

1176
00:42:40,600 –> 00:42:42,200
They shut them down when they’re unpredictable.

1177
00:42:42,200 –> 00:42:44,900
Unpredictable spend is interpreted as a lack of control.

1178
00:42:44,900 –> 00:42:47,100
And control is what executives are paid to provide.

1179
00:42:47,100 –> 00:42:50,400
This is why unified platforms change the cost conversation

1180
00:42:50,400 –> 00:42:51,900
in uncomfortable ways.

1181
00:42:51,900 –> 00:42:53,300
In Microsoft fabric, for example,

1182
00:42:53,300 –> 00:42:55,600
you’re operating a shared capacity pool.

1183
00:42:55,600 –> 00:42:56,800
Everything draws from it,

1184
00:42:56,800 –> 00:43:00,200
engineering, warehousing, notebooks, pipelines reporting,

1185
00:43:00,200 –> 00:43:02,600
and the AI adjacent workloads that write on top.

1186
00:43:02,600 –> 00:43:05,800
That shared pool is a feature because it reduces fragmentation.

1187
00:43:05,800 –> 00:43:07,400
But it also forces prioritization,

1188
00:43:07,400 –> 00:43:10,400
which means you either design cost governance up front

1189
00:43:10,400 –> 00:43:12,300
or the platform will impose it later

1190
00:43:12,300 –> 00:43:14,800
through throttling backlog and internal conflict.

1191
00:43:14,800 –> 00:43:16,600
The platform doesn’t care about your org chart.

1192
00:43:16,600 –> 00:43:17,900
It cares about contention.

1193
00:43:17,900 –> 00:43:19,200
So here’s the reframe.

1194
00:43:19,200 –> 00:43:20,900
Leaders need to internalize.

1195
00:43:20,900 –> 00:43:22,000
When your AI builds spikes,

1196
00:43:22,000 –> 00:43:23,600
don’t ask who ran up the bill.

1197
00:43:23,600 –> 00:43:26,100
Ask what design omission allowed this to happen

1198
00:43:26,100 –> 00:43:27,600
without a conscious decision

1199
00:43:27,600 –> 00:43:29,100
because there is always an omission,

1200
00:43:29,100 –> 00:43:31,100
missing ownership, missing quotas,

1201
00:43:31,100 –> 00:43:32,600
missing prioritization,

1202
00:43:32,600 –> 00:43:34,100
missing unit economics,

1203
00:43:34,100 –> 00:43:35,500
missing enforcement

1204
00:43:35,500 –> 00:43:36,600
and it’s not just compute.

1205
00:43:36,600 –> 00:43:38,900
AI costs arrive through multiple pathways

1206
00:43:38,900 –> 00:43:40,800
that enterprises underestimate.

1207
00:43:40,800 –> 00:43:41,900
One bursty usage,

1208
00:43:41,900 –> 00:43:43,000
a pilot becomes popular

1209
00:43:43,000 –> 00:43:45,100
and suddenly the concurrency profile changes.

1210
00:43:45,100 –> 00:43:47,200
10 people tested, then a hundred,

1211
00:43:47,200 –> 00:43:48,000
then a thousand.

1212
00:43:48,000 –> 00:43:49,300
Costs don’t scale linearly

1213
00:43:49,300 –> 00:43:51,300
because demand doesn’t scale linearly.

1214
00:43:51,300 –> 00:43:54,300
Demand spikes, two, hidden background work,

1215
00:43:54,300 –> 00:43:56,500
platforms do useful maintenance tasks,

1216
00:43:56,500 –> 00:43:59,100
optimization, refresh, indexing, catching.

1217
00:43:59,100 –> 00:44:00,300
Those are real workloads.

1218
00:44:00,300 –> 00:44:01,300
If you don’t see them,

1219
00:44:01,300 –> 00:44:02,500
you can’t account for them.

1220
00:44:02,500 –> 00:44:03,700
And if you can’t account for them,

1221
00:44:03,700 –> 00:44:04,900
you’ll blame the wrong thing

1222
00:44:04,900 –> 00:44:06,300
when the numbers move.

1223
00:44:06,300 –> 00:44:08,000
3. Experimentation.

1224
00:44:08,000 –> 00:44:10,100
AI work is not a steady state factory line.

1225
00:44:10,100 –> 00:44:11,900
Teams test prompts, models,

1226
00:44:11,900 –> 00:44:14,500
retrieval strategies and evaluation runs.

1227
00:44:14,500 –> 00:44:16,400
If you treat experimentation as free

1228
00:44:16,400 –> 00:44:17,700
because it’s innovation,

1229
00:44:17,700 –> 00:44:18,900
the enterprise will pay for it

1230
00:44:18,900 –> 00:44:20,600
in the least controlled way possible,

1231
00:44:20,600 –> 00:44:22,200
uncontrolled consumption.

1232
00:44:22,200 –> 00:44:24,300
So the hard requirement becomes convergence.

1233
00:44:24,300 –> 00:44:25,900
Finops, data ops and MLOPS

1234
00:44:25,900 –> 00:44:27,400
cannot stay separate disciplines.

1235
00:44:27,400 –> 00:44:28,900
If FinOPS only sees invoices,

1236
00:44:28,900 –> 00:44:31,000
it arrives late and with a blunt instrument.

1237
00:44:31,000 –> 00:44:32,300
If data ops only sees pipelines,

1238
00:44:32,300 –> 00:44:34,300
it optimizes throughput but not economics.

1239
00:44:34,300 –> 00:44:35,900
If MLOPS only sees models,

1240
00:44:35,900 –> 00:44:38,700
it optimizes quality but not sustainability.

1241
00:44:38,700 –> 00:44:41,700
They must converge into a single operating discipline,

1242
00:44:41,700 –> 00:44:44,200
the ability to ship AI-driven decision systems

1243
00:44:44,200 –> 00:44:46,700
with predictable cost, observable quality

1244
00:44:46,700 –> 00:44:48,300
and enforceable governance.

1245
00:44:48,300 –> 00:44:49,700
And this is where cost visibility

1246
00:44:49,700 –> 00:44:51,500
becomes part of platform trust.

1247
00:44:51,500 –> 00:44:53,900
And if teams can’t predict what a feature will cost to run,

1248
00:44:53,900 –> 00:44:54,800
they will stop shipping,

1249
00:44:54,800 –> 00:44:56,100
not because they’re lazy.

1250
00:44:56,100 –> 00:44:58,500
Because every deployment becomes a budget risk

1251
00:44:58,500 –> 00:45:00,100
and nobody wants to be the person

1252
00:45:00,100 –> 00:45:01,900
who caused the finance escalation.

1253
00:45:01,900 –> 00:45:03,800
So the platform has to provide a cost model

1254
00:45:03,800 –> 00:45:06,800
that is legible, not CUs and tokens

1255
00:45:06,800 –> 00:45:09,000
and capacity utilization graphs,

1256
00:45:09,000 –> 00:45:11,000
although those matter for engineers.

1257
00:45:11,000 –> 00:45:13,900
Legible to leadership means the cost aligns to outcomes.

1258
00:45:13,900 –> 00:45:15,500
Because the only sustainable funding model

1259
00:45:15,500 –> 00:45:17,800
for AI is outcome-based accountability.

1260
00:45:17,800 –> 00:45:19,700
If you can’t tie spent to outcomes,

1261
00:45:19,700 –> 00:45:21,100
you will either overspend silently

1262
00:45:21,100 –> 00:45:22,500
or get shut down loudly.

1263
00:45:22,500 –> 00:45:23,700
Now, there’s a trap here.

1264
00:45:23,700 –> 00:45:27,000
Some leaders respond by trying to centralize all AI usage,

1265
00:45:27,000 –> 00:45:29,300
thinking control equals central approval.

1266
00:45:29,300 –> 00:45:31,500
That creates a different failure, bottlenecks

1267
00:45:31,500 –> 00:45:32,400
and shadow usage.

1268
00:45:32,400 –> 00:45:35,100
People root around controls when controls prevent work.

1269
00:45:35,100 –> 00:45:37,900
Governance erodes, exceptions accumulate,

1270
00:45:37,900 –> 00:45:41,200
costs still rise just in less visible places.

1271
00:45:41,200 –> 00:45:45,300
So the correct pattern is not centralize its price and govern.

1272
00:45:45,300 –> 00:45:47,900
You won’t use it to be easy, but not free.

1273
00:45:47,900 –> 00:45:49,900
Easy with guardrails, quotas, tagging,

1274
00:45:49,900 –> 00:45:51,900
workload separation, prioritization

1275
00:45:51,900 –> 00:45:54,700
and explicit ownership of the cost of a decision loop.

1276
00:45:54,700 –> 00:45:56,700
If a workload cannot name a cost owner,

1277
00:45:56,700 –> 00:45:57,700
it isn’t production.

1278
00:45:57,700 –> 00:45:59,500
It’s a lab, treated like a lab.

1279
00:45:59,500 –> 00:46:00,900
And that’s the executive insight.

1280
00:46:00,900 –> 00:46:02,600
Cost is not a metric you look at after.

1281
00:46:02,600 –> 00:46:03,900
Cost is a design input.

1282
00:46:03,900 –> 00:46:06,400
It tells you how you must shape architecture.

1283
00:46:06,400 –> 00:46:09,500
Caching versus live retrieval, batch versus real time,

1284
00:46:09,500 –> 00:46:12,000
shared capacity versus isolated pools

1285
00:46:12,000 –> 00:46:14,300
and how you measure the value you’re buying.

1286
00:46:14,300 –> 00:46:16,200
AI is not expensive because it’s new.

1287
00:46:16,200 –> 00:46:18,700
AI is expensive because it accelerates demand

1288
00:46:18,700 –> 00:46:20,800
and demand without boundaries becomes dead.

1289
00:46:20,800 –> 00:46:23,000
So the next move is to make those boundaries visible

1290
00:46:23,000 –> 00:46:24,500
in a way leadership can govern

1291
00:46:24,500 –> 00:46:26,300
without becoming model experts.

1292
00:46:26,300 –> 00:46:29,100
That means unit economics that survive vendor change.

1293
00:46:29,100 –> 00:46:31,300
So now the enterprise needs a cost language

1294
00:46:31,300 –> 00:46:34,300
that doesn’t require everyone to become a cloud billing expert.

1295
00:46:34,300 –> 00:46:37,300
Because if the cost story is CU’s went up

1296
00:46:37,300 –> 00:46:39,500
or token spend spiked, you’ve already lost the room.

1297
00:46:39,500 –> 00:46:41,100
Those are implementation details.

1298
00:46:41,100 –> 00:46:44,100
They matter to operators, but they don’t survive platform evolution,

1299
00:46:44,100 –> 00:46:47,100
vendor negotiations, or even your next architecture effector.

1300
00:46:47,100 –> 00:46:49,500
Executives need unit economics that are stable.

1301
00:46:49,500 –> 00:46:51,500
Stable means you can change models,

1302
00:46:51,500 –> 00:46:54,900
change tooling, change platforms and still measure value the same way.

1303
00:46:54,900 –> 00:46:57,100
And the simplest move is to stop talking about

1304
00:46:57,100 –> 00:47:00,100
AI spent and start talking about cost per outcome,

1305
00:47:00,100 –> 00:47:03,100
cost per decision, cost per insight, cost per automated workflow.

1306
00:47:03,100 –> 00:47:05,900
That distinction matters because the enterprise doesn’t buy models.

1307
00:47:05,900 –> 00:47:08,500
It buys behavior at scale, shorter cycle times,

1308
00:47:08,500 –> 00:47:12,100
higher consistency, lower error rates, fewer escalations,

1309
00:47:12,100 –> 00:47:16,900
fewer manual reviews, faster resolution, lower cost to serve.

1310
00:47:16,900 –> 00:47:19,100
So here’s the test for your unit metric.

1311
00:47:19,100 –> 00:47:21,900
If you can’t explain it to finance, you can’t govern it.

1312
00:47:21,900 –> 00:47:23,900
If you can’t explain it to the business owner,

1313
00:47:23,900 –> 00:47:24,900
you can’t fund it.

1314
00:47:24,900 –> 00:47:27,700
If you can’t explain it to the business owner, you can’t fund it.

1315
00:47:27,700 –> 00:47:30,700
If you can’t explain it to the platform team, you can’t operate it.

1316
00:47:30,700 –> 00:47:34,900
The thing most organizations miss is that unit economics is not a dashboard.

1317
00:47:34,900 –> 00:47:36,300
It’s a contract.

1318
00:47:36,300 –> 00:47:41,300
It defines what success costs and who absorbs variability when reality changes.

1319
00:47:41,300 –> 00:47:43,300
Now let’s anchor this in a concrete example.

1320
00:47:43,300 –> 00:47:45,100
AI assisted case resolution.

1321
00:47:45,100 –> 00:47:48,900
The enterprise spends $120,000 per month on the AI enabled workflow.

1322
00:47:48,900 –> 00:47:51,700
That spend can include model inference retrieval, platform compute,

1323
00:47:51,700 –> 00:47:54,700
observability and the plumbing you never put on the PowerPoint slide.

1324
00:47:54,700 –> 00:47:57,500
The system produces 60,000 decisions per month.

1325
00:47:57,500 –> 00:47:58,500
Decisions not tickets.

1326
00:47:58,500 –> 00:48:01,500
A decision here is a classification, a routing, a recommendation,

1327
00:48:01,500 –> 00:48:03,900
or an eligibility outcome that drives action.

1328
00:48:03,900 –> 00:48:05,500
So your unit economics are simple.

1329
00:48:05,500 –> 00:48:10,900
$120,000 divided by 60,000 decisions equals $2 per decision.

1330
00:48:10,900 –> 00:48:15,500
Now, the first reaction from a lot of leaders is to argue about what counts as a decision.

1331
00:48:15,500 –> 00:48:16,500
Good.

1332
00:48:16,500 –> 00:48:18,100
That argument is the beginning of governance.

1333
00:48:18,100 –> 00:48:21,100
Because if the organization can’t agree on what the unit of work is,

1334
00:48:21,100 –> 00:48:22,900
it can’t agree on value either.

1335
00:48:22,900 –> 00:48:26,300
And AI investments without a unit of work are always justified with vibes.

1336
00:48:26,300 –> 00:48:28,300
Now compare that to a human-only baseline.

1337
00:48:28,300 –> 00:48:31,100
Let’s say the human process costs $18 per case

1338
00:48:31,100 –> 00:48:33,700
with a 24-hour average resolution time.

1339
00:48:33,700 –> 00:48:36,700
That cost includes labor, rework, escalations,

1340
00:48:36,700 –> 00:48:41,900
and the hidden operational overhead that never shows up in the AI business case deck.

1341
00:48:41,900 –> 00:48:44,900
With AI assisted resolution, the decision cost is $2.

1342
00:48:44,900 –> 00:48:46,900
The resolution time drops to five minutes

1343
00:48:46,900 –> 00:48:50,900
and humans review 10% of cases for oversight and exception handling.

1344
00:48:50,900 –> 00:48:52,900
This is the executive framing that matters.

1345
00:48:52,900 –> 00:48:57,100
You didn’t buy AI, you bought cheaper, faster decisions with human oversight.

1346
00:48:57,100 –> 00:49:01,500
And that framing survives vendor change because it doesn’t depend on which model or which feature.

1347
00:49:01,500 –> 00:49:03,700
If you change from one model provider to another,

1348
00:49:03,700 –> 00:49:05,500
your unit metric stays the same.

1349
00:49:05,500 –> 00:49:09,700
Cost per decision at an acceptable error rate with a defined review pathway.

1350
00:49:09,700 –> 00:49:13,100
Now, there are two non-negotiables when you adopt unit economics.

1351
00:49:13,100 –> 00:49:15,500
First, you need to attach an owner to the unit.

1352
00:49:15,500 –> 00:49:18,700
Someone must own cost per decision for that workflow,

1353
00:49:18,700 –> 00:49:21,500
not IT, not the data team, not the platform.

1354
00:49:21,500 –> 00:49:24,300
The business owner who benefits from the decision throughput is the owner

1355
00:49:24,300 –> 00:49:26,700
because they control demand and accept the risk.

1356
00:49:26,700 –> 00:49:29,900
If the business owner refuses ownership, the use case is not real.

1357
00:49:29,900 –> 00:49:31,500
It’s tourism.

1358
00:49:31,500 –> 00:49:34,500
Second, you need to include the cost of governance and trust.

1359
00:49:34,500 –> 00:49:38,500
Most AI ROI stories cheat by ignoring the cost of controls,

1360
00:49:38,500 –> 00:49:41,300
evaluation runs, logging, prompt versioning,

1361
00:49:41,300 –> 00:49:44,300
access reviews, red team testing, incident response,

1362
00:49:44,300 –> 00:49:48,300
and the inevitable remediation work when a decision loop drifts.

1363
00:49:48,300 –> 00:49:50,100
Those costs are not optional overhead.

1364
00:49:50,100 –> 00:49:54,500
They are the price of making probabilistic systems safe enough to operate in an enterprise.

1365
00:49:54,500 –> 00:49:55,500
So don’t hide them.

1366
00:49:55,500 –> 00:49:56,500
Priced them.

1367
00:49:56,500 –> 00:49:59,500
Because anything you cannot price eventually gets shut down.

1368
00:49:59,500 –> 00:50:00,900
Now, a quick warning.

1369
00:50:00,900 –> 00:50:04,100
Unit economics does not mean you optimize for the cheapest decision.

1370
00:50:04,100 –> 00:50:05,900
That’s how you get unsafe automation.

1371
00:50:05,900 –> 00:50:09,100
Unit economics means you optimize for an acceptable decision.

1372
00:50:09,100 –> 00:50:11,700
Cost, speed, and quality bounded by governance.

1373
00:50:11,700 –> 00:50:13,500
It’s a trade space, not a race to zero.

1374
00:50:13,500 –> 00:50:16,300
And once you have that unit metric, you can do real architecture.

1375
00:50:16,300 –> 00:50:19,500
You can decide where caching belongs, where retrieval belongs,

1376
00:50:19,500 –> 00:50:22,900
where batch scoring belongs, where real time inference belongs,

1377
00:50:22,900 –> 00:50:25,700
where you need isolation versus shared capacity.

1378
00:50:25,700 –> 00:50:29,100
You can justify guardrails without sounding like a compliance committee.

1379
00:50:29,100 –> 00:50:31,500
Because now every guardrail has an economic purpose.

1380
00:50:31,500 –> 00:50:34,300
It protects unit economics from turning into an outage,

1381
00:50:34,300 –> 00:50:36,300
a cost spike or a trust collapse.

1382
00:50:36,300 –> 00:50:37,900
This is where leaders get leverage.

1383
00:50:37,900 –> 00:50:41,100
If you remember nothing else, don’t govern AI by platform spend.

1384
00:50:41,100 –> 00:50:43,700
Govern AI by unit economics, because platforms change.

1385
00:50:43,700 –> 00:50:45,300
Operating models must survive.

1386
00:50:45,300 –> 00:50:49,100
Operating model design, decision rights enforcement and exception pathways.

1387
00:50:49,100 –> 00:50:52,500
Now, we get to the part everyone wants to skip because it sounds like process.

1388
00:50:52,500 –> 00:50:53,500
It isn’t.

1389
00:50:53,500 –> 00:50:55,500
It’s the control plane of your enterprise.

1390
00:50:55,500 –> 00:50:59,500
The foundational mistake is thinking intent becomes reality because someone wrote it down.

1391
00:50:59,500 –> 00:51:01,100
Intent is not configuration.

1392
00:51:01,100 –> 00:51:02,500
Configuration is not enforcement.

1393
00:51:02,500 –> 00:51:04,900
An enforcement is the only thing that survives scale.

1394
00:51:04,900 –> 00:51:07,500
Over time, policies drift away from intent,

1395
00:51:07,500 –> 00:51:09,700
because the enterprise optimizes for shipping.

1396
00:51:09,700 –> 00:51:12,700
Every temporary access grant, every unowned data set,

1397
00:51:12,700 –> 00:51:17,300
every unofficial metric definition, every just this once exception becomes an entropy generator.

1398
00:51:17,300 –> 00:51:18,300
They accumulate.

1399
00:51:18,300 –> 00:51:21,900
Then AI arrives and turns that accumulated drift into real-time decisions.

1400
00:51:21,900 –> 00:51:24,500
So if you want this to survive three to five years,

1401
00:51:24,500 –> 00:51:28,100
you need an operating model that treats drift as inevitable and designs for it.

1402
00:51:28,100 –> 00:51:29,500
Start with decision rights.

1403
00:51:29,500 –> 00:51:31,900
Not who does the work.

1404
00:51:31,900 –> 00:51:36,300
Who has the authority to decide and who is accountable when reality doesn’t match the decision?

1405
00:51:36,300 –> 00:51:39,100
You need a map, one page, brutally explicit.

1406
00:51:39,100 –> 00:51:42,900
Here are the decision rights that matter and if you leave any of these undefined,

1407
00:51:42,900 –> 00:51:44,900
the system will pick an owner for you.

1408
00:51:44,900 –> 00:51:48,700
It will pick the person who answers the escalation call at 2AM quality owner,

1409
00:51:48,700 –> 00:51:51,900
the person who sets acceptable failure modes for a data product

1410
00:51:51,900 –> 00:51:53,900
and funds the fix when quality drops.

1411
00:51:53,900 –> 00:51:57,500
Not the platform team, the domain owner who benefits from the decision.

1412
00:51:57,500 –> 00:51:59,500
Semantic owner, the authority for meaning,

1413
00:51:59,500 –> 00:52:02,300
the person who can say this is what active customer means

1414
00:52:02,300 –> 00:52:07,300
and can approve changes without turning the enterprise into a weekly reconciliation meeting.

1415
00:52:07,300 –> 00:52:10,300
Access owner, the person who approves who can read what,

1416
00:52:10,300 –> 00:52:12,500
for which purpose and for how long.

1417
00:52:12,500 –> 00:52:15,900
This is where the enterprise either designs deterministic access

1418
00:52:15,900 –> 00:52:17,700
or accepts conditional chaos.

1419
00:52:17,700 –> 00:52:20,700
Cost owner, the person who is accountable for unit economics.

1420
00:52:20,700 –> 00:52:23,700
If cost per decision doubles, this person owns the response.

1421
00:52:23,700 –> 00:52:26,900
Not finance, not IT, the outcome owner.

1422
00:52:26,900 –> 00:52:29,900
Exceptional authority, the person who can approve exceptions

1423
00:52:29,900 –> 00:52:33,900
with a time limit and can be held accountable for the risk they just accepted.

1424
00:52:33,900 –> 00:52:34,900
That’s the map.

1425
00:52:34,900 –> 00:52:39,500
Now the part that separates functional operating models from PowerPoint enforcement mechanisms.

1426
00:52:39,500 –> 00:52:43,500
Most enterprises create policies and then outsource enforcement to human discipline.

1427
00:52:43,500 –> 00:52:44,500
That is a fantasy.

1428
00:52:44,500 –> 00:52:46,500
Enforcement must be mechanized.

1429
00:52:46,500 –> 00:52:49,900
Identity gates, entrabased access patterns that force least privilege

1430
00:52:49,900 –> 00:52:51,900
and make access grants expire by default

1431
00:52:51,900 –> 00:52:55,700
because we’ll clean it up later is how you create permanent drift.

1432
00:52:55,700 –> 00:52:58,900
Classification and lineage, governance surfaces like purview

1433
00:52:58,900 –> 00:53:00,500
that make data traceable by default,

1434
00:53:00,500 –> 00:53:03,100
so audits our evidence retrieval, not archaeology.

1435
00:53:03,100 –> 00:53:04,500
Semantic certification,

1436
00:53:04,500 –> 00:53:06,900
a mechanism to publish endorsed definitions

1437
00:53:06,900 –> 00:53:10,300
and prevent everyone builds their own from becoming the default behavior.

1438
00:53:10,300 –> 00:53:13,100
If it isn’t endorsed, it isn’t used for enterprise decisions.

1439
00:53:13,100 –> 00:53:15,900
Cost guardrails, tagging, quotas, capacity boundaries

1440
00:53:15,900 –> 00:53:19,300
and visibility that prevent spend from becoming an after the fact argument.

1441
00:53:19,300 –> 00:53:21,100
If you can’t see it, you can’t govern it.

1442
00:53:21,100 –> 00:53:22,300
And here’s the uncomfortable truth.

1443
00:53:22,300 –> 00:53:24,500
You don’t get to decide whether exceptions exist.

1444
00:53:24,500 –> 00:53:26,100
Exceptions are inevitable.

1445
00:53:26,100 –> 00:53:28,700
You decide whether exceptions are controlled or invisible.

1446
00:53:28,700 –> 00:53:30,900
An exception pathway is not bureaucracy.

1447
00:53:30,900 –> 00:53:32,300
It’s damage containment.

1448
00:53:32,300 –> 00:53:33,700
Without an exception pathway,

1449
00:53:33,700 –> 00:53:35,300
people will still get exceptions.

1450
00:53:35,300 –> 00:53:37,100
They’ll just do it through informal channels.

1451
00:53:37,100 –> 00:53:40,500
Someone knows someone, a role gets assigned temporarily,

1452
00:53:40,500 –> 00:53:43,100
a workspace gets shared, a dataset gets copied,

1453
00:53:43,100 –> 00:53:46,100
and now the exception is permanent because nobody recorded it.

1454
00:53:46,100 –> 00:53:47,900
So design the pathway deliberately.

1455
00:53:47,900 –> 00:53:49,700
Every exception needs four attributes,

1456
00:53:49,700 –> 00:53:51,300
one, who approved it,

1457
00:53:51,300 –> 00:53:52,900
two, what it grants,

1458
00:53:52,900 –> 00:53:54,300
three, why it exists,

1459
00:53:54,300 –> 00:53:56,100
four, when it expires.

1460
00:53:56,100 –> 00:53:58,300
And if you want to be serious, add a fifth.

1461
00:53:58,300 –> 00:54:01,500
What compensating control exists while the exception is active.

1462
00:54:01,500 –> 00:54:04,500
Logging additional reviews, reduced scope, explicit monitoring,

1463
00:54:04,500 –> 00:54:05,300
something.

1464
00:54:05,300 –> 00:54:07,900
This is where executives and platform leaders usually miss a line.

1465
00:54:07,900 –> 00:54:10,900
The executives want speed, platform leaders want safety,

1466
00:54:10,900 –> 00:54:12,300
both are rational.

1467
00:54:12,300 –> 00:54:15,300
The operating model reconciles them by making exceptions

1468
00:54:15,300 –> 00:54:16,900
a first-class capability,

1469
00:54:16,900 –> 00:54:19,100
fast-when justified, bounded by time,

1470
00:54:19,100 –> 00:54:21,300
and visible to the people who carry the risk.

1471
00:54:21,300 –> 00:54:23,500
Here’s a simple operational signal that tells you

1472
00:54:23,500 –> 00:54:25,100
whether you built this correctly.

1473
00:54:25,100 –> 00:54:27,700
If an incident happens, can you point to the owner in seconds?

1474
00:54:27,700 –> 00:54:29,700
If not, the system will stall in hours.

1475
00:54:29,700 –> 00:54:31,900
Because every escalation becomes a meeting,

1476
00:54:31,900 –> 00:54:33,100
every meeting becomes a debate,

1477
00:54:33,100 –> 00:54:34,900
and every debate becomes delay.

1478
00:54:34,900 –> 00:54:37,300
Then the enterprise concludes the platform is slow.

1479
00:54:37,300 –> 00:54:39,100
It isn’t, your decision rights are missing.

1480
00:54:39,100 –> 00:54:40,500
So the transition is straightforward.

1481
00:54:40,500 –> 00:54:43,100
If you can define decision rights, enforce them mechanically,

1482
00:54:43,100 –> 00:54:45,100
and treat exceptions as govern pathways,

1483
00:54:45,100 –> 00:54:46,300
you now have an operating model

1484
00:54:46,300 –> 00:54:49,100
that can absorb AI without breaking trust or budgets.

1485
00:54:49,100 –> 00:54:51,100
And that is what future ready actually means.

1486
00:54:51,100 –> 00:54:53,500
What future ready actually means?

1487
00:54:53,500 –> 00:54:56,500
Most enterprises use future ready as a comforting synonym

1488
00:54:56,500 –> 00:54:57,700
for, we pick the right vendor,

1489
00:54:57,700 –> 00:54:59,300
or we bet on the right model.

1490
00:54:59,300 –> 00:55:00,300
They are wrong.

1491
00:55:00,300 –> 00:55:02,100
Future ready is not predicting the next model.

1492
00:55:02,100 –> 00:55:04,100
It is absorbing change without breaking trust,

1493
00:55:04,100 –> 00:55:06,100
budgets, or accountability.

1494
00:55:06,100 –> 00:55:07,500
That distinction matters,

1495
00:55:07,500 –> 00:55:09,500
because AI progress is not linear.

1496
00:55:09,500 –> 00:55:11,100
It arrives as discontinuities,

1497
00:55:11,100 –> 00:55:13,700
a new model class, a new regulatory interpretation,

1498
00:55:13,700 –> 00:55:16,100
a new attack pattern, a new business demand,

1499
00:55:16,100 –> 00:55:17,100
a new cost curve.

1500
00:55:17,100 –> 00:55:19,700
If your operating model can’t absorb discontinuities,

1501
00:55:19,700 –> 00:55:22,300
your strategy is just a slide deck with a shelf life.

1502
00:55:22,300 –> 00:55:25,100
So what does future ready look like in operating terms?

1503
00:55:25,100 –> 00:55:27,100
First, clear ownership.

1504
00:55:27,100 –> 00:55:30,500
Not we have a team named humans attached to decisions,

1505
00:55:30,500 –> 00:55:31,900
who owns data quality,

1506
00:55:31,900 –> 00:55:33,300
who owns semantic meaning,

1507
00:55:33,300 –> 00:55:34,700
who owns access approvals,

1508
00:55:34,700 –> 00:55:36,300
who owns unit economics,

1509
00:55:36,300 –> 00:55:37,300
who owns exceptions.

1510
00:55:37,300 –> 00:55:38,900
If you can’t name the owner in seconds,

1511
00:55:38,900 –> 00:55:40,300
you don’t have an operating model,

1512
00:55:40,300 –> 00:55:41,500
you have an escalation loop,

1513
00:55:41,500 –> 00:55:43,500
second platform is product.

1514
00:55:43,500 –> 00:55:45,700
The data and AI platform isn’t a migration.

1515
00:55:45,700 –> 00:55:47,900
It’s a durable capability with a roadmap,

1516
00:55:47,900 –> 00:55:50,100
service levels, and an explicit cost model.

1517
00:55:50,100 –> 00:55:51,700
The platform team is not a help desk.

1518
00:55:51,700 –> 00:55:53,700
They are the owners of the shared system

1519
00:55:53,700 –> 00:55:55,300
that every domain depends on.

1520
00:55:55,300 –> 00:55:57,900
That means they need authority, not just responsibility.

1521
00:55:57,900 –> 00:55:59,900
Third, govern data products,

1522
00:55:59,900 –> 00:56:02,500
not raw storage, not a lake house with tables.

1523
00:56:02,500 –> 00:56:04,700
Data products with owners, consumers,

1524
00:56:04,700 –> 00:56:06,500
semantic contracts, quality signals,

1525
00:56:06,500 –> 00:56:08,500
and access policies that are enforceable.

1526
00:56:08,500 –> 00:56:10,500
AI doesn’t consume your storage layer.

1527
00:56:10,500 –> 00:56:13,300
It consumes whatever you let your organization treat as truth.

1528
00:56:13,300 –> 00:56:15,500
If truth is unknown, AI will expose it.

1529
00:56:15,500 –> 00:56:17,700
Fourth, observability as default behavior.

1530
00:56:17,700 –> 00:56:19,500
If you can’t see what data was used,

1531
00:56:19,500 –> 00:56:21,500
what changed, which model version ran,

1532
00:56:21,500 –> 00:56:22,900
what prompts were active,

1533
00:56:22,900 –> 00:56:24,300
what retrieval sources were hit,

1534
00:56:24,300 –> 00:56:25,500
what filters were applied,

1535
00:56:25,500 –> 00:56:27,500
and what it cost per unit of work.

1536
00:56:27,500 –> 00:56:29,100
You are operating blind.

1537
00:56:29,100 –> 00:56:30,500
And blind systems don’t scale.

1538
00:56:30,500 –> 00:56:33,500
They just accumulate mystery until someone turns them off.

1539
00:56:33,500 –> 00:56:35,300
Fifth, continuous learning loops,

1540
00:56:35,300 –> 00:56:36,700
not in the motivational sense.

1541
00:56:36,700 –> 00:56:40,100
In the mechanical sense, business outcomes feed back into data products.

1542
00:56:40,100 –> 00:56:42,100
Data products feed back into the platform.

1543
00:56:42,100 –> 00:56:44,500
Platform telemetry feeds back into governance.

1544
00:56:44,500 –> 00:56:47,100
AI outputs feedback into evaluation and tuning.

1545
00:56:47,100 –> 00:56:49,100
That loop is what keeps a probabilistic system

1546
00:56:49,100 –> 00:56:50,900
from drifting into confident wrongness.

1547
00:56:50,900 –> 00:56:52,700
And here’s the core executive takeaway.

1548
00:56:52,700 –> 00:56:54,900
Future ready means every missing boundary

1549
00:56:54,900 –> 00:56:56,500
becomes an incident later.

1550
00:56:56,500 –> 00:56:57,700
If you don’t define ownership,

1551
00:56:57,700 –> 00:56:59,300
you’ll get escalation paralysis.

1552
00:56:59,300 –> 00:57:00,900
If you don’t define semantics,

1553
00:57:00,900 –> 00:57:02,300
you’ll get inconsistent decisions.

1554
00:57:02,300 –> 00:57:05,300
If you don’t define access, you’ll get data exposure.

1555
00:57:05,300 –> 00:57:06,500
If you don’t define cost,

1556
00:57:06,500 –> 00:57:07,900
you’ll get finance intervention.

1557
00:57:07,900 –> 00:57:09,500
If you don’t define exceptions,

1558
00:57:09,500 –> 00:57:11,100
you’ll get invisible drift.

1559
00:57:11,100 –> 00:57:13,300
So future ready is not a maturity score.

1560
00:57:13,300 –> 00:57:14,700
It’s an absorptive system.

1561
00:57:14,700 –> 00:57:16,100
The enterprise that wins

1562
00:57:16,100 –> 00:57:17,900
is the one that can adopt new models,

1563
00:57:17,900 –> 00:57:20,500
new capabilities, new tooling and new workflows

1564
00:57:20,500 –> 00:57:23,100
without really degrading trust every quarter.

1565
00:57:23,100 –> 00:57:25,700
Because trust is the bottleneck, not model quality.

1566
00:57:25,700 –> 00:57:28,300
And that’s why the most valuable design work

1567
00:57:28,300 –> 00:57:29,500
isn’t choosing services.

1568
00:57:29,500 –> 00:57:31,000
It’s designing the operating system,

1569
00:57:31,000 –> 00:57:32,900
those services run inside.

1570
00:57:32,900 –> 00:57:35,300
Closing reflection plus seven day action,

1571
00:57:35,300 –> 00:57:38,600
Azure AI amplifies whatever operating model you already have.

1572
00:57:38,600 –> 00:57:41,600
So fix the model first or AI will expose it.

1573
00:57:41,600 –> 00:57:42,900
In the next seven days,

1574
00:57:42,900 –> 00:57:44,800
run a 90 minute readiness workshop

1575
00:57:44,800 –> 00:57:46,600
and produce three artifacts,

1576
00:57:46,600 –> 00:57:48,700
a one page decision rights map,

1577
00:57:48,700 –> 00:57:50,900
decision owner enforcement,

1578
00:57:50,900 –> 00:57:52,400
one governed data product

1579
00:57:52,400 –> 00:57:54,800
with a named owner and semantic contract

1580
00:57:54,800 –> 00:57:58,500
and one baseline unit metric like cost per decision.

1581
00:57:58,500 –> 00:57:59,600
If you want the follow on,

1582
00:57:59,600 –> 00:58:02,400
the next episode is operating AI at scale,

1583
00:58:02,400 –> 00:58:05,100
lifecycle governance automation and cost control.