A year ago, I didn't want to spend months watching AI evolve from the sidelines while making avoidable mistakes along the way.
The technical build turned out to be the straightforward part; capturing the reasoning behind the work was not.
A year ago, I didn't want to spend months watching AI evolve from the sidelines while making avoidable mistakes along the way. So I took a class and learned to build an agent myself.
We put 25 years of how I think into a box and called her Nancy.
What surprised me was not what the agent could do. It was what the process revealed about me.
To teach Nancy to write a proposal or prepare discovery questions the way I would, I first had to explain how I actually make decisions. And that was harder than I expected.
Much of what I rely on was never documented. It lived in experience, instinct, and years of recognizing patterns without consciously thinking about them.
The obstacle was rarely the technology. It was articulating the judgment an experienced practitioner applies without thinking about it.
The hard part was never the technology.
The hard part was explaining the judgment behind the work.
For years, I assumed our consultants' greatest value was their knowledge of NetSuite. What I came to appreciate is that their real value lies in understanding what is actually broken in a business versus what appears to be broken.
That distinction matters.
Building Nancy, and later a growing workforce of agents, forced us to make that thinking visible. We had to describe how we approach problems, what questions we ask, and how we arrive at conclusions.
In the process, we captured knowledge that previously existed only in people's heads.
New team members can now start from our best thinking instead of starting from scratch.
Not because the technology is extraordinary, but because it pushed us to become more explicit about how we work.
A great deal of organisational expertise stays undocumented, essential to daily operations yet recorded nowhere.
I wonder how much expertise inside organizations remains trapped this way.
Critical knowledge. Daily judgment. Years of experience.
Essential to the business, yet largely undocumented.
AI may be the catalyst, but people are still the ones who see patterns, exercise judgment, and make decisions.
What I didn't expect was that building these agents would sharpen my own thinking too.
Explaining my reasoning to a machine forced me to understand it better myself.
AI is not a product you buy once. It is a capability you develop over time.
And sometimes, the process of building that capability changes the builder as much as the thing being built.
A year ago, I didn't want to spend months watching AI evolve from the sidelines while making avoidable mistakes along the way. So I took a class and learned to build an agent myself. We put 25 years of how I think into a box and called her Nancy.
The hard part was never the technology. The hard part was explaining the judgment behind the work. For years, I assumed our consultants' greatest value was their knowledge of NetSuite.
I wonder how much expertise inside organizations remains trapped this way. Essential to the business, yet largely undocumented. AI may be the catalyst, but people are still the ones who see patterns, exercise judgment, and make decisions.
These published sources cover the detail behind the points above:
Softype is an Oracle NetSuite solution provider with more than 25 years of experience and over 600 implementations. Our team sets NetSuite up around how your business already runs, then stays on to tune it as you grow. To see what that looks like for your own numbers, book a meeting with our team.
Practitioner note: in implementation work we see the same pattern repeatedly. The knowledge that determines whether a process succeeds rarely exists in a procedure document. It resides with the individuals who have performed the work long enough to recognise exceptions instinctively.
That concentration creates operational fragility. When an experienced colleague changes role or leaves the organisation, the reasoning departs with them, and the remaining team inherits the process without the judgment that made it dependable.
Automation reproduces the documented steps of a process, not the discretion applied around them. Where that discretion is undocumented, an automated workflow will handle routine cases competently and mishandle the exceptions that experienced staff previously absorbed.
The practical response is to capture the decision logic before automating: which exceptions occur, what signals indicate them, and what an experienced practitioner does in response. In live projects, this documentation stage consistently determines whether an automation initiative delivers its intended benefit.
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