By Synova Apps Team
Most companies that decide to "do something with automation" start with the wrong process. Not because they pick something impossible, but because they pick the process that is most annoying to the person in charge. Annoying is a real signal, but it is not the same as valuable, and it is definitely not the same as easy to automate well.
This guide walks through how we help teams decide which business processes to automate first. It is a simple scoring method you can run in an afternoon with a spreadsheet, plus a few worked examples and a list of processes that usually make good (and bad) first projects.
Before you score anything, write down the repetitive work your team actually does. Ask each team lead to list the tasks that happen at least weekly and involve copying, checking, chasing or reformatting information. You are looking for things like:
For each item, capture four facts: roughly how often it happens, how long it takes each time, who does it, and which systems are involved. Estimates are fine. You are trying to see the shape of the work, not build a time-and-motion study.
Score each process from 1 to 5 on six criteria. Higher is better for automation.
| Criterion | Score 1 when... | Score 5 when... |
|---|---|---|
| Volume | It happens a few times a month | It happens dozens of times a day |
| Time per instance | Under a minute | Ten minutes or more of focused work |
| Rule clarity | Every case needs judgment | Most cases follow written or obvious rules |
| Input quality | Inputs are messy, missing or verbal | Inputs arrive in a consistent digital form |
| System access | Key systems have no API or export | All systems have usable APIs |
| Error tolerance | A mistake is expensive or hard to undo | Mistakes are cheap and easy to catch |
Add the scores up. Anything above about 22 is a strong candidate. Between 16 and 22, look closer: there is usually a narrower slice of the process that scores higher than the whole. Below 16, park it for now.
Two of these criteria deserve more explanation, because they are where most first projects go wrong.
Traditional automation tools need explicit rules. Modern AI models are good at the fuzzy middle: reading an email and deciding whether it is an order, a complaint or a question, or pulling a delivery date out of a messy PDF. That widens what you can automate. But it does not remove the need for rules. Someone still has to define what "urgent" means, which customers get priority, and what happens when the model is unsure.
A useful test: could you write a one-page instruction sheet that a new hire could follow for 80% of cases? If yes, the process is a good automation candidate, with or without AI. If the answer is "it depends, you just learn it", you need to do that documentation work first.
A misrouted support ticket costs a few minutes. A wrong price on a supplier invoice that gets paid costs real money. Both can be automated, but the second one needs review steps, matching against purchase orders and clear audit trails. If you are new to this, pick a first project where a mistake is visible and cheap, then build up to higher-stakes work once you trust your monitoring. Design those review steps up front rather than adding them after the first incident.
Once you have a shortlist, do a rough payback estimate for each. The formula is simple:
Be conservative on the "share of cases" number. In our experience, a first version often handles the clear majority of cases automatically and routes the rest to a person. Assuming 100% coverage is the most common way these estimates go wrong.
Also count the value that does not show up as hours. Faster response times, fewer missed follow-ups and cleaner data in your CRM are often worth more than the time saved, even if they are harder to put a number on. Just do not invent a number for them. Write them down as qualitative benefits.
Take a hypothetical wholesale distributor with a small inside sales team. Their inventory produced these three candidates:
Scoring them:
| Criterion | A. PO entry | B. Weekly report | C. Quote approval |
|---|---|---|---|
| Volume | 5 | 1 | 2 |
| Time per instance | 4 | 5 | 2 |
| Rule clarity | 4 | 5 | 3 |
| Input quality | 3 | 5 | 4 |
| System access | 4 | 5 | 4 |
| Error tolerance | 2 | 4 | 4 |
| Total | 22 | 25 | 19 |
The weekly report scores highest, and it is a great first project: low risk, clear rules, and everyone can see the result on Monday morning. But PO entry has by far the biggest payback. Roughly 60 × 6 minutes is six hours of keying every working day, plus the cost of shipping errors.
A sensible plan here is to do both in sequence. Ship the automated report in a couple of weeks to build confidence and shake out access to NetSuite and HubSpot. Then start on AI document processing for purchase orders, with a review queue for anything the model is unsure about. The quote approval flow is a quick win you can bolt on later with a Slack approval button.
Across industries, a few patterns come up again and again as strong starting points:
Some work looks automatable but tends to disappoint as a first project:
A quick warning that applies to every candidate on your list. Automating a messy process gives you a fast, messy process. Before you build anything, spend an hour mapping the current steps with the people who actually do the work. You will almost always find a step that exists only because of a workaround from years ago, or two people doing the same check.
Simplify first, then automate what is left. The automation will be smaller, cheaper to build and easier to maintain.
Before you commit to a first automation, check that you can answer yes to most of these:
That last point matters more than it looks. A small automation running in production teaches you more about your data, your edge cases and your team's appetite for change than any amount of planning. Many good automation roadmaps are just a sequence of small projects, each one picked using the same scoring method once the previous one is live.
It is tempting to start from "we should use AI" and look for somewhere to put it. Flip that around. Start from the process, then ask which steps need reading, classifying, summarizing or drafting. Those are the steps where a model like Claude or GPT earns its keep. The rest (moving data, calling APIs, sending notifications, applying fixed rules) is ordinary workflow automation, and should stay that way because it is cheaper, faster and more predictable.
A good first automation is often 80% plain workflow and 20% AI. That ratio is a feature, not a lack of ambition.
Run the inventory, score your top ten candidates and pick one that is both valuable and safe. If you would like a second opinion on your shortlist, or want to see how we scope and ship automation projects, take a look at how we work. When you are ready to talk specifics, you can tell us about your project and we will come back with honest thoughts on where to start.
These are the services behind what this article describes.
We design and build automations that connect your CRM, inbox, accounting system, spreadsheets and storefront. Plain rules handle the predictable steps, AI handles the messy inputs, and people stay in the loop where judgment matters.
ExploreWe turn multi-step business processes into automated workflows across your apps, with approvals in Slack or Teams, clear ownership and a log of every step.
ExploreWe automate the data collection, cleanup, reconciliation and delivery behind your recurring reports and dashboards, plus the data feeds that keep your systems in sync.
ExploreHuman-in-the-loop design lets you put AI to work on real processes while people stay in charge of the decisions that matter. Here are the patterns, thresholds and review screens that make it work.
A step-by-step method for estimating the ROI of an automation project before you build it and measuring it honestly afterward, with a clearly hypothetical worked example.
How AI document processing turns invoice and purchase order PDFs into validated, structured data in your ERP or accounting system, and the checks that keep it accurate.
Tell us how it works today. We'll tell you honestly what's worth automating, what it would take, and what it would cost.