10 Business Processes Worth Automating First

The hardest part of automation is rarely the building. It’s the choosing. Teams stall not because the technology is out of reach but because they’re staring at a hundred candidate processes with no obvious place to begin, so they either pick something glamorous that turns out to be fiendishly complex, or they don’t start at all.

This is a shortlist of business processes to automate first: ten that, in our experience running cohorts on teams’ real workflows, reliably pay back quickly. Just as useful is the why: the criteria that make these good first candidates, so you can spot the equivalents in your own organisation. They’re the same starting points we work through in our intelligent process automation programme.

How to pick the first process

Before the list, the filter. A process is a strong first candidate when it scores well on four traits:

• Repetitive and frequent. Automation earns back its build cost through volume. The more often a process runs, the faster it pays for itself.
• Rule-based. The clearer and more consistent the steps, the more reliable the automation, and the less governance overhead you take on.
• Reasonably consistent inputs. Predictable inputs are the easy win. Wildly variable inputs are automatable too, but they need more intelligent handling and more care.
• Costly or error-prone today. If the manual version burns real hours or regularly produces mistakes, the payback is obvious and easy to demonstrate.

The sweet spot is a process that scores well on all four and that someone visibly cares about, because your first win has a second job: proving the value so you can fund the next one. With that filter in mind, here are ten that usually qualify.

The ten processes

1. Invoice processing and accounts payable

Reading invoices, matching them to purchase orders, routing for approval, and entering them into the finance system. High volume, highly repetitive, error-prone by hand, and a textbook case for AI-assisted extraction to read documents that arrive in a hundred different layouts.

2. Data entry between systems

Anywhere a person copies the same fields from one application into another: CRM to billing, form to database, spreadsheet to ERP. This “swivel-chair” work is pure overhead: automation removes both the minutes and the typos, and it’s often the single quickest win available.

3. Report generation

The weekly or monthly report someone assembles from the same sources every cycle. Pulling the figures, formatting them, and distributing the result is predictable, scheduled work, exactly what automation is for. It also frees your most capable people from production to analysis.

4. Document data extraction

Forms, contracts, applications, statements: anything where information is locked inside a document and someone has to read it out by hand. AI-powered extraction turns unstructured documents into structured data, unlocking a class of work that older automation couldn’t touch.

5. Employee and customer onboarding

Provisioning accounts, sending the right documents, creating records, and kicking off the follow-up steps when someone new arrives. Lots of small, predictable tasks across several systems, tedious to do by hand, and easy to forget a step. Automation makes it consistent.

6. Approvals and routing

Getting the right request to the right person at the right time, with reminders, instead of chasing it through email threads. Automating the flow (not the decision) removes the delays and the “it’s been sitting in someone’s inbox for a week” failures.

7. Reconciliation

Matching two sets of records that should agree (payments against invoices, bank statements against the ledger, system A against system B) and flagging the ones that don’t. Rule-based, high-volume, and unforgiving of human fatigue, which makes it ideal for automation.

8. Status-chasing and follow-up emails

The “just checking in”, “your document is ready”, “this is overdue” messages that someone sends on a schedule. Templated, triggered, and repetitive. Automation sends them reliably and at the right moment, so nothing slips and no one spends their morning copy-pasting.

9. Data validation and cleanup

Checking that records are complete, correctly formatted, and free of obvious errors before they flow downstream. Catching a bad record at entry is far cheaper than chasing the consequences later, and the rules are usually clear enough to automate with confidence.

10. Helpdesk and request triage

Sorting incoming tickets, emails, or requests: categorising them, routing them to the right queue, and drafting a first response for the common cases. AI classification handles the “what is this and where does it go?” step that used to need a person reading every message.

What “automate first” really means

Notice what these have in common: they’re mostly back-office, high-volume, repetitive work, not the strategic, customer-facing, judgement-heavy processes that tend to grab attention. That’s deliberate. The back office is where automation is lowest-risk and highest-certainty: the rules are clearer, the inputs are more consistent, and a mistake is recoverable rather than reputational.

“First” also means one. Pick a single process from this list, automate it end to end, and measure the hours it returns against a baseline you captured beforehand. A concrete result (“this gave us back nine hours a week”) is what funds the second project and the third. Trying to automate five processes at once is how programmes stall.

Common traps to avoid

A few ways teams turn a good first candidate into a bad first project:

• Automating a broken process. If the workflow is wasteful, automation just makes the waste run faster. Redesign first, automate second. (More on this in how to map a workflow before you automate it.)
• Picking the most complex process to prove a point. Save the hard, prestigious workflow for later. Your first build should be winnable.
• Skipping the baseline. If you don’t measure the “before”, you can’t prove the “after”, and unproven automation loses its budget.
• Forgetting the exceptions. Every process has edge cases. Plan a human review step for them rather than pretending they don’t exist.

The list above is a starting menu, not a prescription. The real skill is learning to look at how work flows through your team and recognise the shape of a good candidate: repetitive, rule-ish, costly, frequent. That’s exactly the capability we build, hands-on, using your own processes.

Not sure which of your processes to start with? Talk to us about a cohort that helps your team pick, and ship, the first one.