Customers Don’t Know Their Own Process
Welcome back to Founder Mode!
There is a question we ask every customer at Pretty Good AI.
“How does this work today?”
And almost every time, we get a clean answer.
Clear steps. Logical flow. Everything sounds organized.
Then we turn the system on.
And within a few days, that clean story falls apart.
The Story vs The System
When people explain their workflow, they describe the ideal version.
It is how things should work.
It is how they believe things work.
It is how they want others to think it works.
But the system tells a different story.
Calls get routed in unexpected ways.
Appointments are booked differently depending on who answers.
Edge cases become the norm.
The gap between the story and the system is where most problems live.
At Pretty Good AI, we stopped trusting the explanation. We started trusting the data.
One Workflow, Five Versions
In one deployment, we mapped out what looked like a simple scheduling process.
The team described one clear workflow.
When we looked at real calls and transcripts, we found five different versions of that same process.
Each staff member handled it slightly differently.
None of them was wrong. But none of them were consistent.
Humans are good at adapting in real time. They adjust based on context. They fill in gaps.
AI does not adapt like that. It follows the rules it is given.
So when those rules are unclear or inconsistent, the system reflects that.
The Ideal Scheduling Example
One team told us exactly how scheduling worked.
Step one: Verify the patient.
Step two: check availability.
Step three: Book the appointment.
Simple.
But when we listened to the calls, it was different.
Some staff skipped verification.
Some prioritized certain patients.
Some changed appointment types on the fly.
Some routed calls based on habit instead of rules.
The “official” workflow was not wrong. It just was not real.
The transcript showed the truth.
Humans Simplify, Systems Expose
This is a natural human tendency.
We simplify complex systems when we explain them.
We remove edge cases.
We ignore exceptions.
We compress reality into something easier to understand.
That works in conversation.
It does not work in automation.
AI forces you to be precise. It forces you to define what actually happens, not what should happen.
That is where things get uncomfortable.
Because once you see the real system, you cannot ignore it.
Observability Changes Everything
At Pretty Good AI, one of the biggest shifts happens when teams start watching the data.
Not reports. Not summaries.
Raw interactions.
Transcripts.
Call logs.
Routing decisions.
The moment a team starts reviewing real interactions, everything changes.
They see inconsistencies they never noticed.
They hear how staff actually respond.
They catch edge cases in real time.
The system becomes visible.
And once it is visible, it becomes fixable.
Design for Data, Not Narrative
This is the lesson we keep coming back to.
Do not design your system based on how people describe it.
Design it based on how it actually behaves.
The narrative is useful for context. The data is useful for the truth.
If you build for the narrative, you will miss the edge cases.
If you build for the data, you will handle reality.
At Pretty Good AI, we treat every transcript like a source of truth.
Not to criticize the team, but to understand the system.
Why This Matters
Most AI projects fail quietly.
Not because the model is wrong.
Not because the technology is weak.
But because the system it is plugged into was never clearly defined.
When the AI starts operating, it surfaces every inconsistency.
That can feel like failure.
In reality, it is clarity.
5 Key Takeaways
- People describe ideal workflows, not real ones.
- Multiple versions of the same process often exist inside one team.
- Transcripts reveal the truth faster than meetings do.
- Observability is more valuable than assumptions.
- Design systems based on behavior, not explanation.
Final Thoughts
Building Pretty Good AI has changed how I think about systems.
I used to believe the hardest part was building the technology.
Now I know the harder part is understanding how things actually work.
AI does not create problems.
It reveals them.
If your system feels inconsistent after adding AI, that is not a sign to stop.
It is a signal to look deeper.
Listen to the calls.
Watch the data.
Trust what the system shows you.
Do not design for the story.
Design for the truth.
See you on Friday!
-kevin
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