Operations · a method
Define the process. Then decide what AI should do.
The AI Operator role and DRIVE method turn one business process into a testable implementation with an owner, evals, and a rollout plan.
Start with the process, not the model. Document the inputs, decisions, tools, exceptions, owner, and acceptable output before choosing where AI belongs.
This process-first approach gives the implementation a measurable target. It also makes clear which decisions need a person and which steps can be automated.
A process is ready for AI only when its owner can explain how the work succeeds and fails.
The working thesis
The first deliverable is therefore a process specification and eval set. The software follows from those decisions.
01 · The role
The AI Operator owns the process result.
The AI Operator translates a business process into requirements, acceptance criteria, and a production feedback loop. The role can sit in operations, product, or program management.
Process definition
Designs how agents operate
Maps the current process with the people who perform it. Writes the operating specification, exceptions, and handoff rules the system follows.
Operational mindset
Thinks in steps and workflows
Breaks a goal into documented inputs, decisions, actions, and outputs. Project managers and operations leads often already do this work.
Context design
Writes testable instructions
Structures instructions, tool descriptions, and process context so the system has the information needed for a decision. Coding helps, but clear questions and acceptance criteria matter first.
Subject expertise
Interviews the process owners
Works with subject matter experts to capture steps, edge cases, judgment calls, and the evidence needed to approve an output.
Eval owner
Knows when it's working
Defines what "good" looks like and how it's measured. Runs the feedback loop that keeps AI output trustworthy as models, tools, and processes change.
Agent orchestrator
Composes the workflow
Decides which parts of a process are handled by agents, which use copilots, and which stay human. Assigns explicit boundaries and handoffs.
02 · Three hats
Three owners for one AI process.
Each implementation needs an owner for business priority, technical delivery, and process results. One person can hold more than one role, but each decision still needs a name beside it.
The AI Visionary
Purpose: Selects the business problem, assigns budget, names the owner, and removes organizational blockers.
Profile: An executive such as the CEO, COO, or responsible vice president. This person approves the target and stop condition without managing every task.
I provide 1:1 planning and implementation review for this role. See the AI for CEOs offering.
The AI Implementer
Purpose: Owns architecture, integrations, tools, deployment, monitoring, and operational maintenance.
Profile: A software engineering or IT background with context engineering, API integration, and production review skills. The Implementer may direct AI coding agents, but remains accountable for the code and system behavior.
A caution: If one person is both Implementer and Operator, make the two sets of acceptance criteria explicit. Technical completion is not the same as a successful process result.
How I help: I can serve as your AI Implementer, guide your technical team, or provide the strategic technical advice needed to build and manage AI solutions effectively.
The AI Operator
Owns the process specification, acceptance criteria, rollout, and feedback loop.
This person may already sit in operations or program management. Name the role before the build begins.
03 · Mindset
Two useful operating models.
analogy #1
AI is a new digital colleague.
Think about interacting with AI the way you'd manage a capable team member. People need clear instructions, training, task design, strategy. So does AI.
Give the system complete instructions, the tools it may use, representative examples, and an eval set. Keep judgment, accountability, and consequence-bearing with a named person. Humans approve the decision. Agents do not.
analogy #2
AI changes the cost of repetition.
A tested automated step can run more often without adding the same amount of human labor. Compute cost, review time, exceptions, and maintenance still remain.
Use the saved time where it has a named destination, such as reviewing exceptions, speaking with customers, or improving the process. If no work is removed or improved, the automation has not created value.
04 · The framework
The DRIVE implementation cycle.
DRIVE is a five-step method for one business process. Define the work, roadmap the first release, implement it, validate it, then evolve it from production evidence.
Define
the process and the AI outcomeGoal: document the current process and the exact result the first release must produce.
Activities: in-depth SME interviews, workflow mapping, pain-point identification, step-by-step documentation. Live transcription of process walkthroughs captures nuance.
Roadmap
an MVP and a phased planGoal: select the smallest release that can prove or disprove the approach.
Activities: rank features by expected value and feasibility, choose the first AI interaction, and define success and stop conditions.
Implement
the AI solutionGoal: build the MVP chunk.
Activities: write a focused model call, design an agentic workflow, connect tools through MCP, compose an n8n workflow, or build a custom integration. Choose the least complex design that meets the acceptance criteria.
Validate
test, gather feedback, verifyGoal: test performance against the acceptance criteria before broader rollout.
Activities: build an eval set with representative inputs and expected outputs. Run it on every change. Separate incorrect answers from format or tone problems, then revise instructions, context, tools, or process structure.
Evolve & Expand
roll out, iterate, scaleGoal: integrate the AI-powered process into daily operations and plan for wider application.
Activities: plan the rollout, train the team, monitor cost and quality, and collect feedback. Expand only after the first production group meets the acceptance criteria.
DRIVE is a continuous loop, not a one-time pass. Your integrations evolve alongside your business and the models.
05 · Why
Why this matters for your business.
The Operator role and DRIVE method create four concrete controls.
- Name the person accountable for the process result.
- Test model output against representative inputs before rollout.
- Measure cost per completed task instead of total model spend alone.
- Use production failures and feedback to decide the next change.
I apply this method to your process and help your team produce the specification, working system, eval set, cost controls, and handoff.
06 · FAQ