AI Operations Assessment: A Workflow Scoring Checklist
Use an AI operations assessment checklist to inventory workflows, score value and risk, set human-review gates, and choose a measurable first automation pilot.
AI operations guides: decide what to change first
These AI operations guides help you move from an AI mandate to a workflow decision: what to eliminate, what to automate, what needs a person, and how to tell whether a pilot helped.
The short answer: assess the workflow before choosing a tool. Start with readiness and a baseline, sort the work, then test one bounded change. I write these notes from the operator's seat; use the path below to find the next decision, not another list of use cases.
Choose your first resource
| Your situation | First resource | Decision to make | Next step |
|---|---|---|---|
| You have an AI mandate but no clear starting point. | AI Operations Scorecard | Is the gap in data, measurement, workflow definition or ownership? | Gather the missing inputs before selecting a pilot. |
| Several workflows look promising; you need a defensible shortlist. | AI Operations Assessment | Which opportunities justify the effort and risk, and which should wait? | Bring volume, cost, cycle-time and error data to the assessment. |
| Recurring support work is consuming the team. | Eliminate, Automate, Too Late: How to Sort Support Work Before You Automate It | Remove the cause, automate resolution, or improve the human path? | Classify a representative set of requests and name an owner for each intervention. |
| A prototype runs, but its outputs are not useful enough. | Building a Lead Signal Engine with LLM Classification | Is the failure in problem framing, inputs or classification? | Review actual misses, separate failure modes and test a narrower change. |
| You know the target workflow and need a rollout plan. | AI Pilot to Production | What evidence earns rollout, and what must still escalate to a person? | Agree on baseline metrics, review gates and the exception path. |
Start here: assess the workflow
The AI Operations Scorecard is a quick readiness check, not a workflow-level business case. It asks about data, measurement, ownership and the path to a person when automation goes wrong. Use the result to identify what you need to learn next.
If you need a ranked opportunity list rather than a readiness signal, the AI Operations Assessment maps the work and scores candidates on volume, cost, cycle time, errors and risk. The output is a build, buy or wait decision and a sequenced plan. Before that work starts, name one process, its owner and the evidence you can actually pull.
For the leadership question behind the mandate, read How Non-Technical Leaders Should Think About AI. It separates whether AI can do something from whether it is the right tool for the problem.
Decide what to eliminate, automate or keep human
The Eliminate, Automate, Too Late: How to Sort Support Work Before You Automate It note gives you three different interventions. Eliminate means fixing the product, process or policy so the request never arises. Automate means using self-service or automation for work that still needs to happen. Too Late means accepting the need for human intervention and making that path work better.
Do not turn those categories into a target for removing people. A frequent request may deserve a product fix; a rare, high-exposure case may deserve a faster escalation. The note walks through normalizing complaints, classifying the pain and grouping patterns to inform priorities. Its public-complaint example is a demonstration, not evidence of results in your operation.
Set up a pilot and measure outcomes
Building a Lead Signal Engine with LLM Classification shows why a working pipeline is not the same as a useful one. I review noisy outputs, separate explicit demand from implicit signals and change the classification approach. Read it for the iteration method, not as a promised lead-generation result.
For an operations rollout, the AI Pilot to Production service describes the next step: baseline the workflow, build with human review, then roll out with training and escalation paths. Measure against the old process using cycle time, error rate, cost per case and team adoption. Decide in advance what would justify expanding the pilot and what would make you stop.
Looking for the product-leadership notes?
They are still in the directory below. Those notes cover intake, strategy, roadmaps and leadership decisions; they are separate from my current AI operations service scope. Use the category filters when the underlying problem is product direction or accountability, rather than an automation opportunity.
All Session Notes
13 notes found.
AI & operations notes
Eliminate, Automate, Too Late: How to Sort Support Work Before You Automate It
The EAT framework (Eliminate, Automate, Too Late) for deciding which customer support work to remove, which to automate with AI, and which to leave to people, plus how to classify customer pain at scale with an LLM.
How Non-Technical Leaders Should Think About AI
A decision framework for leaders who are not engineers: where AI fits, where it does not, and how to avoid the AI-first traps that waste a quarter.
Building a Lead Signal Engine with LLM Classification
A working case study: designing, prototyping, and iterating an AI classification system that surfaces high-signal opportunities, including what broke and what finally made it useful.
Product leadership, strategy & process
Product Intake Without Sales Hijacking It
Designing a product intake process that separates signal from noise, gives sales real influence without capture, and preserves trust on both sides.
What a Fractional Product Leader Actually Does
A practical explanation of fractional product leadership: what it is, when it works, and what outcomes to expect.
When Fractional Product Leadership Works Better Than a Full-Time VP
Why timing and company stage matter more than title, and when fractional leadership delivers better outcomes than hiring a full-time VP.
Designing Product Strategy When Requirements Keep Changing
Decision-making under uncertainty, and why locking plans too early backfires.
Why Teams Build the Wrong Thing Without Realizing It
Local optimization, false signals of success, and the slow drift that looks like progress until it is too late.
When Leadership Alignment Is the Real Product Problem
Hidden decision conflict, why product gets blamed for it, and how to force alignment without politics.
Do You Need a Product Leader or Better PMs?
How to tell a leadership gap from an execution gap, and what to hire for in each case.
Turning Customer Feedback Into Product Strategy
How to use customer feedback without letting the loudest voice win: decision filters over backlogs.
Why Most Product Roadmaps Don't Actually Guide Decisions
Most roadmaps exist to reduce anxiety, not to guide decisions. When they help, when they hurt, and what clarity actually looks like.
Bring the workflow, not the tool wishlist
If you need help choosing the first change, start with the process that eats the most time, the person who owns it and the data you have. The assessment scopes the work before a build commitment.
Scope an AI Operations Assessment