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Before You Spend Part 2 of 10
By Kishan Thankey 5 min read StrategyDecision MakingAdoption

Where to Start With AI in a Clinic: How to Pick Your First Workflow

The hardest part of AI in a practice is not the model, it is choosing where to begin. A simple way to pick a first workflow that is high-value, low-risk, and proves itself fast, and the three candidates a clinic should never start with.

An inbox tray opening into several diverging paths, one of them marked. Cover art for Where to Start With AI in a Clinic: How to Pick Your First Workflow. An inbox tray opening into several diverging paths, one of them marked. Cover art for Where to Start With AI in a Clinic: How to Pick Your First Workflow.
In this article

The hardest part of adding AI to a practice is not the model, the budget, or the technology. It is the blank page. You know it could help somewhere, but where do you actually begin? Pick wrong and you burn your most valuable asset: the first win. Pick right and that first result funds and de-risks everything after it.

So treat the first workflow as a strategic choice, not a coin flip. Here is how we help a practice make it.

What a good first workflow looks like

The best place to start is rarely the most impressive idea. It is the one that is safe to get occasionally wrong and easy to prove. Look for a workflow that is:

  • Frequent and genuinely painful, so a win is felt every day, not once a quarter. Intake, recall, referral letters and claim rework all qualify.
  • Tolerant of an occasional wrong answer, so a mistake is cheap to catch and correct. A mis-drafted recall message costs a phone call. A mis-drafted dose costs something else entirely.
  • Grounded in records you already have, so the AI has something real to work from rather than generating plausible text.
  • Measurable by one clear metric, with bounded scope, so you can prove it moved. Notes closed same day, chairs filled, claims accepted first time.

If a candidate has all four, it is a strong first bet. If it is missing grounding or a metric, keep looking.

Scorecard

Score your first workflow

Check every criterion that is true of this candidate.

0 of 4 checked

Score your candidates

When you have a few ideas, do not argue about them, score them. Rate each on two axes: value (how painful times how frequent) and feasibility (do you have the data, is the scope clear, is the risk low).

A value versus feasibility matrix: high value and high feasibility is where to start; other quadrants are later, skip, or risky.A value versus feasibility matrix: high value and high feasibility is where to start; other quadrants are later, skip, or risky.

The idea that lands high on both is where you start. High value but low feasibility goes on the “later” list. Easy but low value is a distraction. This one picture ends most debates in about ten minutes.

Avoid the three traps

Most bad first projects are bad in a predictable way.

  • The flashiest idea. The one everyone is excited about is often the one with the least of your own data to ground it. Excitement is not feasibility.
  • The zero-error process. Anything where a wrong answer reaches a patient or a chart is the wrong place to learn. In a clinic that rules out triage, dosing, and anything diagnostic as a first project, and it rules them out on purpose rather than out of timidity. Those come after the evals exist and the practice has watched the thing behave for a year.
  • The one with no data. If there is nothing to point the model at, expect generic output. No grounding, no first win.

Green-light criteria for a good first AI workflow on the left, and red-light traps to avoid on the right.Green-light criteria for a good first AI workflow on the left, and red-light traps to avoid on the right.

Pick a win people can see

There is one more filter that matters more than it looks: visibility. A first workflow whose success is obvious to the people around it spreads on its own. If the front desk leaves on time for the first month in a year, every clinician in the building notices. A quiet win in the billing office teaches you something, but it does not build the momentum you need for the next project.

Start narrow, then climb

The goal of the first workflow is not to transform the practice. It is to prove, cheaply and fast, that intelligence changes one outcome that matters. Pick the narrow, high-value, low-risk candidate, run it as a short proof of value, and let the result decide the next step.

Start where a win is likely and visible. Everything else you want to do with AI gets easier once you have one real result behind you.

From blank page to first win

  1. 1

    List your candidates Start here

    Every workflow where intelligence might help. Collect, do not argue yet.

  2. 2

    Score them

    Two axes: value, pain times frequency, and feasibility, data, scope, risk.

  3. 3

    Drop the traps

    The flashy idea, anything that reaches a patient or a chart, the one with no records to ground it.

  4. 4

    Pick a visible win

    A front desk that leaves on time is a result the whole clinic can see.

  5. 5

    Prove it fast

    A short proof of value with one clear metric. Let the result decide the next step.

The steps light up as you scroll, the same way the choice narrows: each one shrinks the field until the one workflow worth proving is left.

Staring at a blank page wondering where AI fits in your practice? That first choice is the one worth getting right. Book a free consult and we will score your candidates with you and pick a first workflow worth proving. See more on the Practice Audit.

Frequently asked questions

What is the best first workflow to add AI to?

One that is frequent and genuinely painful, can tolerate an occasional wrong answer, has records to ground it, and has a single clear metric you can move. In most practices that is intake, recall, or the letter and form backlog, and it is almost never anything clinical. Bounded scope beats ambition. The best first workflow is the one that proves value fast and safely, not the most impressive one.

How do we choose between several AI ideas?

Score each candidate on two axes: value (how painful and how frequent the task is) and feasibility (do you have the records, is the scope clear, is the risk to a patient low). The idea that scores high on both is where you start. The flashy idea that scores low on feasibility can wait, and in a clinic the flashy idea is usually the clinical one.

Should our first AI project be something ambitious?

No. Ambition is the most common first-project mistake in a practice, and it usually looks like starting with something clinical because that is what feels important. A narrow, provable win in the admin layer earns the trust and the budget for harder work later. Start where a clear result is likely in weeks, prove it, then climb.

What first workflows should we avoid?

Avoid three traps: the flashiest idea that everyone is excited about but nobody can ground in your own records, any step where a wrong answer reaches a patient or a chart, and anything with no data to point the AI at. In a clinic the second trap is the one that catches people, because triage, dosing and anything diagnostic all feel like the obvious place to start and are all exactly the wrong one.

Before You Spend

All 10 decisions

The decisions an owner or CEO makes before the first dollar of AI budget leaves the building. One decision per article, each ending in a page you can put in front of your leadership team.

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