AI Automation · 5 MIN
Which workflows should you automate with AI first?
Pick your first AI automation by volume, repeatability, error cost and data access. A scoring method and a short list of workflows that tend to work.
Automate first the workflow that is high in volume, repeated in a similar way every time, cheap to get wrong, and backed by data you can already access. That combination gives you fast evidence that the automation works and little risk if it does not. This post gives you a scoring method to rank candidates and a list of workflow types that tend to make good first projects.
- Rank candidates on five criteria: volume, repeatability, cost of error, data readiness and a clear owner.
- Start where mistakes are reversible. Earn trust before touching money, legal or safety decisions.
- Prefer workflows with a measurable baseline, so you can show before and after.
- Read-and-classify tasks are usually safer starting points than tasks that take actions.
- Nactore runs this prioritization at the start of an engagement, then builds the winner with evals, scoped to each team.
What makes a workflow a good first candidate?
A strong first project is boring in the best sense. It happens often, looks similar each time, and has an obvious definition of "done correctly". The five criteria below turn that intuition into something you can discuss with your team.
- Volume. Enough items that automating saves meaningful effort and gives you data to test on.
- Repeatability. Inputs and outputs follow a pattern. High variation is fine, as long as a person could write down the rules they follow.
- Cost of error. A wrong output is cheap or easily reversed. Lower is better for a first project.
- Data readiness. The inputs are digital, accessible through an API or export, and you have past examples with known correct answers.
- Ownership. One named person owns the workflow, can judge quality and will use the result.
How do we score and rank candidates?
Rate each candidate from 1 to 5 on each criterion, where 5 is the better answer for automation (for cost of error, 5 means errors are cheap). Add the totals, then sanity-check the top three with the people who do the work.
| Candidate | Volume | Repeatable | Error cost | Data ready | Owner | Total |
|---|---|---|---|---|---|---|
| Support ticket triage | 5 | 4 | 4 | 4 | 4 | 21 |
| Invoice data capture | 4 | 4 | 3 | 4 | 4 | 19 |
| Contract clause review | 2 | 2 | 1 | 3 | 3 | 11 |
Treat the numbers above as an illustration of the method, not a benchmark. Your scores will differ, and the discussion they trigger is the useful part. A low total on "error cost" is a reason to add human review, not necessarily to drop the idea.
Which workflow types usually work well first?
These categories tend to score well because they turn unstructured input into structured output and keep a person in control.
- Classification and routing. Support tickets, inbound leads, internal requests. See AI support ticket triage and AI lead routing for sales teams.
- Document extraction. Invoices, forms, applications, receipts. See document extraction with LLMs.
- Summarization for handoffs. Call notes, case histories, account briefings that a person reads before acting.
- Drafting for review. First drafts of replies, reports or proposals that a person edits and sends.
- Search over internal knowledge. Answering staff questions from your own documents, with citations to the source.
Which workflows should wait?
Hold these for a later phase, after you have evals and review habits in place.
- Anything that moves money or changes records without review. Payments, refunds, pricing changes.
- Legal, medical or safety judgments. These need qualified people at the decision point.
- Open-ended multi-step tasks with many tools. These are agent territory, which adds complexity and risk. Anthropic's guide to building effective agents advises finding the simplest solution possible and adding complexity only when it demonstrably improves outcomes. Our view is in AI agents vs workflows.
- Workflows nobody owns. If no one can say what a good output looks like, you cannot test it.
Interview the people who do the work, not just their managers. Ask what they do when the standard process does not fit. Those exceptions decide how much review the automation needs.
How do we confirm a candidate before building?
Spend a few days on a feasibility check.
- Collect real examples. A few hundred past items with the correct outcome.
- Try a quick prototype. Run a model on a sample and read the errors by hand.
- Define success. Pick the metrics and the threshold, for example per-category accuracy and share handled without review.
- Check the plumbing. Confirm you can read the inputs and write the outputs through an API.
- Decide the fallback. Know exactly how the manual process resumes if the automation is paused.
If the prototype looks promising and you can define success clearly, you have a pilot. If not, you learned that cheaply. For measuring the payoff, see measuring the ROI of AI automation.
What does the first month look like?
A fixed-scope pilot keeps the first project honest. In week one you rank candidates, pick one and build the labeled test set. In weeks two and three you build and measure, including a shadow run. In week four you roll out the parts that cleared the bar and write the results. The point is a working system and a decision backed by evidence, not a demo.
Frequently asked questions
Should we automate our biggest pain point first?
Not always. The biggest pain point is often complex and high risk. A smaller, safer workflow can prove the approach and build trust, and then you can tackle the bigger one with evidence.
How many workflows should we run in the first pilot?
One. A single workflow gives clean measurement and a clear decision. Add the next after the first is stable.
What if our data is messy?
Messy inputs are often where AI helps most. What you need is a set of past examples with known correct outcomes. If you lack them, building that set is part of the pilot.
Do we need to hire for this?
Not for the pilot. You need an owner on your side who knows the workflow and can judge output quality.
Want this built for your team? Book a free 30-minute call.
Want to apply this to your business?
Book a free 30-minute call. We will tell you what we would do first.