Skip to content

The hardest call in AI automation is what not to automate.

AI where a step needs judgment. Fixed rules everywhere else. A human signing off where a mistake would be costly and irreversible.

That's how I plan, build and govern automation, backed by eleven years managing projects and leading Agile teams.

How I build automation

As AI makes building easier, the harder question is who can manage the build, the delivery and the promise of an automation. I give the planning and safeguards as much attention as the build, so the automation does what it promised.

  1. Step 1: Decide what to automate

    Find what's worth automating. Observe how the business actually runs: interviews and shadowing first, then process and task mining to verify it. Score each step by volume, handling time, error cost and security risk to find the highest-leverage automations. Decide where AI belongs and where a fixed rule is enough.

  2. Step 2: Plan

    Write the charter, lock the scope and set the guardrails. Confirm where AI is used. Set the human and security gates, a hard cost budget and a rollback plan before building.

  3. Step 3: Manage the build

    Direct the AI agents doing the build, and approve every change. Record every decision, and score the result in a separate session from the one that built it.

  4. Step 4: Test and soft-launch

    Freeze the test sets and pass rules before the first scored run. Run the evals, then soft-launch with a person approving every output until the results hold.

  5. Step 5: Learn, launch and monitor

    Feed real-world results back into the build, then launch officially. Hand over the documentation and monitor quality, drift and cost after launch.

Read the full method

Roles where my experience and knowledge excel

Case study
Read the case study

Apex Climate Solutions is a simulated heating and cooling business with three service zones and six technicians. The system has 27 Make.com workflows and three business level components.

Time freed
30 hrs /month
staff hours freed for a five-technician shop (modeled)
Saved
$12,420 /year
saved after paying its own running cost (modeled)
Cost to run
$59 to $121 /month
for a five-technician shop (estimate)
  • The problem

    Requests arrive by phone, web form and WhatsApp. A person reads each one, filters out the spam and creates the job by hand, so a no-heat emergency can sit behind a routine maintenance request.

    Our answer

    Intake and triage gathers requests from WhatsApp, a web form and an AI voice agent, filters out spam, rates urgency and creates the job. It posts to Slack, where a person does the final triage.

  • The problem

    Refrigerant procedures, city permit rules and warranty terms live in people’s heads. Technicians phone the office or guess.

    Our answer

    The knowledge base answers technicians’ questions in Slack from the company’s own manuals and documents, citing the source, so they stop guessing or phoning the office.

  • The problem

    Overdue jobs, empty truck stock and permits stuck in pending come to light only after they cost a job, when it is too late to fix.

    Our answer

    The watchdog and review monitor flags overdue jobs, low truck stock and stalled permits, and drafts replies to customer reviews for a person to approve.

  • 27 Make.com workflows
  • 3 business level components
  • 8 AI steps
  • 3 critical AI steps went through formal evals

About

My foundation
Eleven years of managing projects and leading cross-functional Agile teams of up to 17 in a global enterprise.
My edge
Fifteen years as a web designer, with an eye for intuitive interfaces and user-centered design (UI and UX).
My focus
Owning the full AI automation lifecycle, from finding what's worth automating to governing it after launch.
Location
  • Canadian citizen based in Panama City, Panama
  • Remote: Contractor (W-8BEN) or employer of record (EOR)
  • Flexible hours: UK to US West Coast