An always-on AI agent can run any process with three things: steady volume, work arriving as email, forms or documents, and rules that cover most cases. The agent watches for the work, reads it, matches it to your records, acts within the rules, chases what is missing, and hands every decision that matters to a named person. Your team keeps the judgement. The agent takes the reading, matching and chasing.
That shape turns up in every industry. Below are the processes I build most often, grouped by the kind of work, with the strongest published evidence I could find for each. Where a result comes from a vendor's own case study, I say so: most of the public evidence in this field still does, and you should weigh it accordingly.
Claims and customer work
Insurance claims intake
The agent reads the claim notification and attachments, matches the policy, checks for signs of vulnerability, requests and chases missing documents, sets up the claim and routes it to the right handler, and acknowledges the claimant. It never declines, settles or values a claim.
Evidence: Allianz's claims agents in Australia cut processing and settlement time by 80%, from about four days to minutes before human review, and were built in under 100 days, with a person approving every payment (Allianz, company report). Across the industry, 78% of property and casualty insurers use generative AI in claims, but only 4% have scaled it (Bain via Insurance Business). The gap is production, not ambition. I set out the full case, including what the agent never does, in claims intake can run itself.
Customer email and complaints
The agent answers routine email within guardrails and sends complaints, sensitive cases and vulnerable customers straight to a person. Evidence: Octopus Energy's assistant handles around 8,000 emails a week, and its replies scored 76% customer satisfaction against 72% for comparable human replies; vulnerable customers and complex complaints always go to experienced staff (Octopus Energy, 2026).
This is also where my own production proof sits: agentic AI that resolved 67% of customer service cases without a human at one of Europe's largest insurance brokerages.
Repairs and maintenance requests
For property managers and housing providers, the agent triages tenant reports, matches them to the property, books the contractor and escalates emergencies, with damp and mould deadlines tracked automatically. Evidence: one UK letting agent's AI handled 45% of reported maintenance cases on its own (Frost's, 2025).
Finance processes
Credit control
The agent watches the ledger, chases overdue invoices in your tone of voice, logs promises to pay, spots disputes and routes them to a person, and reports the cash position daily. This is the most direct cash lever on this page: money arrives sooner, and the finance director can see exactly why.
Evidence: Finning, the Caterpillar dealer that operates in the UK, Ireland, Canada and South America, cut days sales outstanding by five days and its past-due share from 50% to 24% in four months (HighRadius, vendor case study). Freight forwarder OVRSEA reduced it from 38 to 27 days in its best month (Upflow, vendor case study). The full case is in most late payments can be recovered.
Supplier invoice exceptions
Invoice capture is a crowded, mature market. The value an agent adds is on the exceptions: the invoices that do not match the order or the delivery, which is where the finance team's time actually goes. Evidence: highly automated accounts payable teams process an invoice in 2.9 days against an 8.2-day average (Ardent Partners, State of ePayables 2025).
Reconciliations and month-end close
The agent matches transactions, explains the breaks, and prepares the close items for review. Evidence: half of finance teams take six or more working days to close the books (CFO.com, 2025); in a study of 79 firms by MIT and Stanford researchers, accountants using AI closed 7.5 days faster (CFO Dive, 2025).
Expenses
The agent reads receipts, codes them and chases the missing ones. Evidence: one business cut expense administration from about 160 hours a month to two (Pleo, vendor case study).
Operations
Order entry from emailed purchase orders
For distributors and manufacturers, the agent reads purchase orders that arrive as PDFs and emails, matches customer, item and price, enters them in the ERP and queries the mismatches. Evidence: MKS Atotech cut order processing time by 70%, from up to 15 minutes an order to under a minute and a half (Rossum, vendor case study).
Freight and logistics email
The agent matches carrier invoices and status emails to shipments and chases the gaps. Evidence: DHL Supply Chain's agents handle hundreds of thousands of operational emails a year (DHL, 2025).
Public-sector inboxes
Councils and public bodies receive the same high-volume, rules-heavy email as any back office. Evidence: Manchester City Council's revenues and benefits team triaged 3,218 emails automatically and saved the equivalent of 1.3 full-time staff in the first month (Netcall, vendor case study).
Referral registration
For private clinics and NHS services, the agent registers referrals, matches the patient record, acknowledges and routes. Evidence: South Tyneside and Sunderland NHS Foundation Trust cut referral handling from ten minutes to four and saved more than 8,000 hours (Healthcare Management, 2025). This one involves health data, so it needs more care than anything else on this page.
Contracts and projects
Contract obligations and renewals
The agent extracts obligations, dates and non-standard clauses from your contracts, warns before renewals and deadlines, and flags deviations for review. Evidence: the average business loses almost 9% of contract value every year to poor contract management; the best lose around 3% and the worst 15% or more (World Commerce & Contracting, 2025). In most mid-market companies, nobody owns this. That is the opportunity.
Change requests and milestone billing
On project-based work, the agent logs changes as they are agreed, prices their impact, and matches completed milestones to invoices, so margin does not leak between the project team and finance.
Project status and action chasing
The agent turns meetings and email into actions, chases owners, drafts the weekly status report and the client update, and flags slippage early. Evidence: 72% of project professionals spend half a day or more every month collating reports, and only 36% of organisations mostly deliver projects on time (Wellingtone, State of Project Management 2026). In the NHS Copilot trial across 30,000 staff, AI administrative support saved an average of 43 minutes per person per day (NHS England, 2026). Built well, this tracks actions, never the people doing them.
Tender and proposal responses
The agent watches tender portals, scores the fit, and drafts responses from your content library for the bid team to refine. Evidence: a tender response takes 87.4 hours on average, and teams already using AI report 22% higher win rates and 30% less time spent on bids (AutogenAI research, 2025, vendor survey).
Where an agent should not decide
Some processes look like a good fit but carry decisions that must stay with a person: screening job candidates, credit decisions about individuals, and clinical triage. Several are classed as high-risk under the EU AI Act, and the UK regulator's audit of AI recruitment tools produced nearly 300 recommendations (ICO audit, via Hunton). An agent can still do the scheduling, gathering and drafting around those decisions. It should not make them.
Finding the one that pays first
Every company I work with has more candidates than it can build at once. The right first process is the one with the clearest baseline, the cleanest records and the most painful backlog, not the most impressive demo. A Process Audit measures that in two weeks and ends with a fixed-price spec. A Process Build puts it into production.
If one of these looks like your business, I would be glad to hear about it. Get in touch.