Who Should Your First AI Workflow Be For?
The best first AI workflow belongs to the person closest to useful feedback, customer pressure, and a clear business outcome.
You do not need the perfect AI roadmap to choose the first workflow. You need the right first user.
The risky move is picking a tool, naming a broad process, and hoping someone adopts it later. Start the other way around. Name the person first. Then design the workflow around the work they already do.
That is not just a softer way to sell the project. It is a cleaner way to lower risk. NIST's AI Risk Management Framework treats AI risk as context dependent, and the UK Service Manual tells teams to learn user needs before choosing or building a solution. (NIST AI RMF, UK Service Manual)
The short answer
Your first AI workflow should be for the person who can answer these questions plainly:
- What work keeps showing up?
- What does a good output look like?
- What does a bad output look like?
- Who reviews the work before it affects a customer, record, or decision?
- What changes if the workflow is useful?
If nobody can answer those, the workflow is not ready. You may have an interesting theme, but you do not yet have a useful first build.
Use business pressure, not novelty
The first workflow should be close to something the business already needs to do better.
Early stage businesses tend to work on a short list of things: making the product or service better, telling people about the paid offering, taking care of first paying customers, learning to ask for money, setting up basic business systems, and managing money carefully.
For an AI workflow, I would translate that into a simple filter: favor work tied to customer care, revenue follow-up, product or service quality, basic operating systems, or money visibility.
That does not mean the first workflow has to be large. It means the workflow should matter before AI enters the room.
Good first users are close to feedback
A good first user can tell you quickly whether the workflow helped.
That person might be the one handling inbound buyer questions, preparing customer handoffs, reviewing intake notes, sorting support requests, drafting follow-ups, or turning messy notes into a clean next action.
The specific role matters less than the feedback loop. If the user sees the work every day, knows what good looks like, and has permission to adjust the process, you can learn fast. If the user only sees the work in a monthly report, the first build will probably drift.
Warm outreach works the same way. An engaged reply is the highest priority thing in the queue. In an AI workflow, that points toward a practical rule: if a real person has already raised a hand, asked a question, or created a request, work near that response before you work on something abstract.
Do not start with the person who only has authority
An executive can sponsor the work. That does not make the executive the first user.
The first user should be close enough to the work to notice friction that would not show up in a slide. They should know which inputs are usually missing, which outputs are dangerous to trust, and which handoffs create rework.
Keep the executive in the loop. Just do not make the executive the only judge of whether the workflow works.
Do not start where nobody can review the output
A first AI workflow should have a human review point.
That review point does not need to be dramatic. It can be as simple as a person reading a draft before it is sent, checking a summary against the source material, or confirming a next step before a record changes.
This matters because AI risk depends on the use, context, and environment around the system, not just the model by itself. (NIST AI RMF) A workflow with review gives you a place to catch bad output before it becomes a customer problem, reporting problem, or operating problem.
Do not start with a promise you cannot defend
If the first workflow needs a big claim to sound worthwhile, it is probably the wrong first workflow.
Do not promise that AI will replace a role. Do not promise a performance lift you have not measured. Do not claim the workflow is safe just because it has a human somewhere near it.
The FTC warns businesses not to exaggerate what AI products can do or make unsupported performance claims. (FTC business guidance) The safer move is to make a smaller promise you can test: this workflow drafts, summarizes, flags, routes, or prepares work for a named person to review.
A simple workflow card
Before building, write the workflow on one page.
Use this structure:
- User: the person who will use it
- Trigger: the event that starts the workflow
- Input: the material the workflow reads or receives
- Output: the thing AI produces
- Review: the person who checks the output
- Decision: the action a human takes after review
- Success signal: what would make the user ask to keep it
- Stop signal: what would make you pause or remove it
If the card is hard to fill out, the idea is probably too vague. Do not solve that with a longer plan. Solve it by choosing a clearer user.
A generic example
User: the person handling inbound buyer questions.
Trigger: a new message from someone asking about the offer.
Input: the message, known account context, and any approved service notes.
Output: a short summary, missing details to ask for, and a draft response.
Review: the human sender edits before anything goes out.
Business reason: early stage work is mostly about telling people about the paid offering and taking care of first paying customers, and an engaged reply is the highest priority thing in the queue.
That example is not glamorous. That is the point. It has a user, a trigger, an output, a review step, and a business reason.
Give value before asking for change
AI adoption often fails when the first ask is too big: change your process, trust a new system, and explain the edge cases while doing your normal job.
Giving value before making an ask is the older principle underneath all of this. For internal AI work, I would use the same principle. Give the first user something useful before asking them to become a champion.
That might mean showing them a draft they can edit, a summary they can correct, or a queue they can sort faster. Let usefulness earn attention.
The final rule
Your first AI workflow should be for the person closest to useful feedback, business pressure, and safe review.
Not the loudest requester.
Not the most senior sponsor.
Not the flashiest demo.
Pick the person who owns real work, feels the pain often, can judge the output, and can keep a human review step in place. If you cannot name that person, wait. You are not delaying the AI rollout. You are preventing the first workflow from becoming theater.