tgroenwals shared this post · Jul 1

John Wernfeldt

Jun 29 · archived Jul 1

This is the version I now print and walk into AI strategy meetings with.

Last week, forty minutes into a diagnostic call, a CDO stopped me.

She told me her entire customer analytics pipeline depended on one Excel file maintained by one person.

This kind of happens on almost every call I have run this year.

The team always wants to start at the AI layer. I keep pulling the conversation down the stack, and we usually do not make it past Operational Reality.

Excel files, Slack threads, one person who knows how it works.

That is where AI in production fails, not at the model layer.

Oluoma Ilobah, FIMC John Wernfeldt strengthening the weak layers before building AI models is so key. It will only amplify chaos if the foundational pillars are weak.

John Wernfeldt Author Oluoma Ilobah, FIMC yeah, usually strengthening the layer needs someone to own it rather than another tooling project

tgroenwals shared this post · Jun 19

John Wernfeldt

Jun 18 · archived Jun 19

Most companies I work with have one of these four quadrants and call it data governance.

The most common version has artifacts. A catalog, a few metric cards, a policy document somewhere on SharePoint. Other companies build out roles instead. A CDO with no peer at her level, a Gov Council that meets quarterly, a DPO who is mostly there for the regulator. A few do concepts well, with decision rights on a slide and named ownership on another, both signed off by someone who left the company. Almost everyone has use cases queued up: AI pilots running on data the team does not trust.

Vipul Patel Most teams treat governance as something you finish before AI runs. The harder realization is that governance has to run alongside AI, enforcing what data an agent can access, what decisions require a human in the loop, and what gets logged as evidence. That is where the sequencing breaks in practice. You can have clean ownership and a solid data catalog, but if an AI agent can query sensitive data outside its approved scope at runtime, the four-quadrant model did not protect you. The point about AI pilots running on data the team does not trust is the symptom. The structural shift is moving governance from a documentation layer into an execution layer.

Vaidehi Muradi The phrase that stood out to me: “You cannot buy the outer ring without building every ring inside it first.”

Too many organizations try to solve governance with catalogs, tools, or AI platforms. In practice, sustainable AI adoption usually depends on much less glamorous capabilities: clear ownership, trusted data, defined decision rights, and escalation paths.

tgroenwals shared this post · Jun 16

John Wernfeldt

Jun 15 · archived Jun 16

Most governance business cases die in the rewrite, before the meeting even happens.

The pitch gets softened in the 48 hours before the meeting, the meeting happens anyway, and the funding question gets pushed to next quarter.

I have watched €2.4M in concrete GDPR exposure get rewritten as "significant regulatory risk" in that window. The pitch did not get worse exactly, but the teeth got pulled.

The image has five rows that decide whether a governance program gets funded or filed. Every row needs a number and a name attached.

Adegoroye Adeyeye This is very real. I’ve seen the same. Once the numbers and clear ownership get stripped out, the whole thing turns into something that sounds safe but is impossible to act on.
The “surviving the rewrite” point is key. If risk isn’t framed in concrete exposure with names attached, it almost always gets deferred rather than funded.

Rizwan Tufail The R A I S structure offers a clear way to shape governance conversations around measurable elements, John. It brings discipline into funding discussions through simple clarity in each layer.

tgroenwals shared this post · Jun 3

John Wernfeldt

Jun 3 · archived Jun 3

I’ve sat in enough AI pitch meetings to know what the left-hand picture looks like in the deck. Model on top, data on the bottom, an arrow between them, a target date in the bottom right.

The project starts, and the team finds the right-hand picture.

Each of those layers is real work that has been deferred for years, because there was never a reason to fund it that the board cared about. Now there is one, complete with a deadline and a sponsor who built the timeline by counting to two.

Raʼed Awdeh, PhD Thanks for this. And this is just the data stack. AI readiness also needs process discipline, adoption muscle, risk judgment, and leaders willing to slow the slide deck down. Jun 3

Nudrat N. Organizations frequently underestimate the invisible infrastructure held together by one person who knows where everything lives John. Jun 3

tgroenwals shared this post · Jun 1

John Wernfeldt

May 31 · archived Jun 1

Most data leaders I work with are convinced their board doesn’t get governance. The board gets it. They’re being sold the wrong thing.

When you walk in with “we need better metadata, our lineage is weak, our maturity score is low,” you’re describing your work. The CFO hears operational hygiene, the same kind of ask they get from every function with a tooling shortlist. It’s going to be funded last, if at all.

The version that gets funded sounds different. You walk in with a quantified risk in money, a value lever tied to a real business number, and a 90-day outcome you can be held to.

Reeves Smith Put it in money terms and keep it tied to one area the business actually cares about. Jun 1

Adegoroye Adeyeye Strong perspective, I’d add that governance doesn’t struggle because boards don’t understand it, but because it’s often presented as cost without consequence. As soon as the risk is quantified in financial or strategic terms, the conversation changes immediately. Jun 1

tgroenwals shared this post · May 15

John Wernfeldt

May 15 · archived May 15

Governance exists to protect decisions, not data.

That line changes the whole conversation with the board. The moment you stop talking about data quality and start talking about decision quality, the room leans in.

I drew this framework after getting the same question from three different CDOs in the same month: “We have governance on our roadmap but nobody agrees on what it actually includes.”

So here are the layers, bottom to top:

Layer 1: Foundational data management. Data modelling, master data, integration, storage, security. Without foundations, every layer above is unstable.

Wayne Conrad The reframe from data quality to decision quality is the right one. Most boards do not fund what they cannot connect to outcomes they own.

The layer that collapses most often is Layer 4. Ownership gets assigned on paper during a governance workshop. Then the data steward leaves, the domain lead changes roles, and nobody updates the registry. Twelve months later the council exists but the owners do not. Policies sit in a document that nobody enforces because enforcement requires someone with authority and time. Most stewards have neither.

The AI output problem compounds this fast. Organizations are building AI on top of metrics that have no named owner at the business level. The model performs. The output gets used. The decision gets made. When the output is wrong, the accountability chain has no end point. The governance framework documented the layers. It did not survive the first reorg.

Layer 5 is where AI governance actually lives, and most frameworks treat it as consumption, not control. If the output has no owner, the decision below it has no check.

Governance without enforcement is just a diagram. May 15 2 likes

Dr. Alexander Borek A lot of governance discussions stay too abstract. Connecting it to decision quality makes it operational very quickly. May 15

tgroenwals shared this post · Apr 29

John Wernfeldt

Apr 29 · archived Apr 29

I asked a client last month how many decisions their AI agent makes per day.

They said “maybe a few.”

We counted. It was over 600.

Which data source to query. How to interpret the metric. What context to include in the answer. Whether the output was confident enough to show a customer.

Every one of those is a decision. Every one of them had zero governance around it.

Nobody owned any of them. No documentation, no named reviewer, no exception process.

So I drew the levels of decision rights an AI agent actually has, and what humans need to own at each level.

Rajesh Ramaswami True, John. AI agents move fast until nobody owns the decisions they make. Work gets risky fast when agents trigger actions and exceptions have no owner. Apr 29 1 like

Alex Barády Important point, John. Governance gaps at the Act level can cause real risk. Apr 29 1 like

tgroenwals shared this post · Apr 17

John Wernfeldt

Apr 16 · archived Apr 17

Every governance program needs data owners. Almost nobody writes down what a data owner actually does.

So you end up with a name on a slide and no real change.

I've been pulled into too many governance reviews where the data owner role exists in theory and disappears in practice. Nobody knows what they're accountable for. Nobody knows what they decide. Nobody knows when to call them.

Here's the job description that actually works:

Accountable for:

→ One named metric
→ Its written definition
→ Its calculation logic

Decisions they make:

→ Approves definition changes
→ Signs off…

Edris Yaghob The disconnect between accountability on paper and accountability in practice shows up everywhere, and your breakdown of specific decisions makes the difference between a RACI chart that sits in a drawer versus one that actually gets used. Apr 16 1 like

Clare Kitching Well said John, defining ownership at the metric level makes accountability real instead of just something that exists on slides. Apr 16 2 likes

Author posts

This is the version I now print and walk into AI strategy meetings with.

Most companies I work with have one of these four quadrants and call it data governance.

Most governance business cases die in the rewrite, before the meeting even happens.