Julia Broberg · governed AI and decision systems

AI can already run your planning.
When it gets one wrong, who has to answer for it?

Plenty of teams have wired an AI into something this year. Almost none of them can still tell you, six months on, what it did, who approved it, or how to undo it. Doing it so it holds is the hard part, and it is the one nobody is selling. I get the definitions to agree first, prove it on one real decision, and leave you a record of what happened and who said yes.

Here to check the engineering? See the work →

A meeting you have already sat in

Somebody asks a question that sounds simple. How much do we actually spend with this supplier?

Three people answer. Procurement has one number. Finance has a different one. The site that receives the material has a third. Nobody is lying. Every number is correct inside the system it came out of.

It ends the way it always ends, with somebody agreeing to go and check. Next month it happens again. That is not a data problem. It is a definition problem, patched every month by one person who knows where the bodies are.

Now automate on top of it.

The disagreement does not go away. It gets faster, it stops being visible because nobody is patching it by hand any more, and it arrives sounding completely certain.

You do not get a faster company. You get the same wrong answer at machine speed, in more places, with the one person who used to catch it now out of the loop. That is worse than where you started, and it will fail you confidently.

It's the same wrong answer, but this time at machine speed.
Accountability, not capability, is the scarce thing now.

So where do you start

You already know which process it is.

Everybody has one. The thing that eats a week every month, or the approval that goes missing, or the number the sites stopped believing two years ago. You do not need an AI strategy to begin. You need two weeks and one honest look at the worst of them.

The month-end that takes nine daysThe approval nobody can findThe forecast the sites ignoreThe report one person can produceThe invoice that sat for three weeks
Start here

Find out what is actually broken

Two weeks

Which decisions are costing you, what data they lean on, where the definitions disagree, and what is worth automating and what is not. You get a plan for the next 90 days instead of a general worry about AI.

Then you choose: pilot or not. With me or with anybody else.

If it is worth it

Run one decision in shadow

Six to eight weeks

One decision, one bounded scope, running alongside how you do it today so we can compare. A gate on anything consequential, and a record of every call it makes.

Then you choose: go or stop. If the numbers do not hold up, I will tell you to stop.

If it works

Hand it to your team

Until you do not need me

Widen what the pilot proved, a wave at a time. Monitoring, training, written procedures, and a handover your people can run without me in the room.

Then it is yours.
The point is that I leave.
Send me yoursEvery engagement starts small on purpose. Prices belong in the conversation, because the scope is what sets them.

What those two weeks are actually for

Same tools. Same company. Two sequences.

Nobody sets out to build the top row. It happens because the tool is available today and agreeing on definitions is slow, political work that nobody gets promoted for. That is exactly why it is the step that gets skipped, and exactly why skipping it is expensive.

The usual order

Automate first, sort it out later

StartSystems that disagreeThree answers to one question, patched by hand every month.
Step 1Bolt the AI on topFast to stand up. Sits beside the process instead of inside it.
Step 2Answers arrive quickerEverybody is pleased for about a quarter.
Step 3Nobody can explain oneThe person who used to catch it is out of the loop.
Worse than where you started, and confident about it
The order that holds

Agree, prove, then automate

StartSystems that disagreeSame starting point. Nothing about the mess has changed yet.
Step 1Agree what the numbers meanMapped with the people who do the work, not at them. They own the definition afterwards.
Step 2Prove it on one decisionRun in shadow beside the current process. Cheap place to be wrong.
Step 3Automate only that partWith a gate on anything consequential and a record of every call.
A decision somebody can still answer for in six months

Step one is the one everybody wants to skip. It is also the one that decides whether the other two hold, and it is why I do it with your team in the room rather than in a document I hand over at the end.

Two things you have to believe before any of this matters

That I have run the thing, and that I will not hand it to a machine and walk away.

Separate claims. Most people selling AI governance can only make the second one, because they have never carried a number. Open any line to see how it was checked.

One. What I have built and had to answer for

I have been the one who gets the call at five in the morning when the number is wrong. Most people selling this have not.

the one who gets the call at five in the morning when the number is wrong

Two. How I build so somebody can still answer for it

An agent with no evaluation suite is not a production system. It is a demo that has been left switched on.