Professional story

Seven years with Kindie changed how I see a product.

Kindie is a product I have worked with for 7 years. That is long enough to see that a product is not simply built and finished. It has to change with its users, its workflows and the situations that only appear when a system is operated every day.

Products such as OmniSpa, SavvyBot and Intake are other examples of the same way of thinking: start with the work as it happens, make ownership clear and only then decide where software or AI should participate.

One lesson I had to learn is that automating an unclear process does not make the team clearer. It only moves ambiguity faster and makes it harder to trace.

How I work

I need to understand the work before delegating it to a machine.

  1. 01Operational signal
  2. 02Process and ownership
  3. 03Standardize or build
  4. 04Bounded automation
  5. 05Observable outcome
A conceptual model, not system architecture.

Before asking where a business can use AI, I usually clarify five questions:

  1. What signal shows that the process has a problem?
  2. Where is the work blocked?
  3. Who needs to keep the decision?
  4. Which part can software or AI support safely?
  5. What evidence would show that the result is actually better?

Only then do I work with the team to decide whether to standardize the process, use an existing product, build software or automate a carefully bounded part. Decisions affecting money, data, access or reputation always need a responsible person.

Public learning

I share what is working and what has to be rebuilt.

I do not write to prove that every system succeeds on the first attempt. Operator Notes, case studies and demos are how I record the decisions made, the limits accepted and the evidence available.

To make the work inspectable, I separate production from experiments, describe my role apart from the work of the team and use measurements only when the definition, period and source are clear.

I share simplified diagrams to explain the reasoning. Internal architecture, customer data and private information remain outside the public record.

If a real process is costing your team time

We can begin by clarifying whether the problem is in the workflow, data, ownership or tooling; only then decide what to standardize, build or automate.