Start where the maths is easy
The first AI build in a portfolio company has one job beyond its direct return: it has to prove, quickly and visibly, that AI in this business means production systems with numbers attached, not pilots and presentations.
That gives the first project a clear brief. It should be live within weeks, address a process the team already resents and produce a before-and-after number a CFO would sign off. Across the portcos we have worked in, three builds repeatedly meet that standard.
Build one: automated lead response
The problem. Inbound enquiries sit in a shared inbox or a web form queue. Response times are measured in hours or days. Conversion research is unambiguous: contact a lead within minutes and you convert at multiples of the rate you get after a day. Slow responses lose revenue to the competitor who answers first.
The build. A system that reads each incoming enquiry, qualifies it against your customer profile, drafts or sends a tailored response, routes it to the right salesperson, and logs everything in the CRM. The salesperson starts the day with warm, sorted, pre-researched leads rather than a raw inbox.
Effort and payback. Production takes two to four weeks. Track inbound conversion rate and speed to first response. Our speed-to-lead piece covers the full case for this build and why the economics are unusually clear in the mid-market.
Build two: document intake
The problem. Somewhere in every portco, people retype invoices into the finance system, purchase orders into the ERP, and applications, claims, delivery notes or onboarding forms into other records. It is high-volume, error-prone work with a measurable cost per document, done by people hired to do something better.
The build. An AI pipeline that receives documents from whatever channel they arrive in, extracts the fields, validates them against existing records, pushes clean data into the system of record, and flags only the exceptions for a human. The team stops processing documents and starts reviewing edge cases.
Effort and payback. Three to six weeks depending on document variety. The number is cost per document processed, and error rate. For a business handling thousands of documents a month, this build often clears its cost inside a quarter. We built exactly this class of system for a Series A insurtech, where intake speed was core to the product itself.
Build three: reporting automation
The problem. Finance teams commonly spend 40 to 60 percent of their time pulling data from systems, reconciling it in spreadsheets, and formatting board packs and management accounts. A monthly close that takes ten days can produce a pack the board reads for twenty minutes, leaving little time in the process for analysis.
The build. An automated pipeline from the source systems (ERP, CRM, bank feeds, billing) into reconciled, board-ready reporting, refreshed on schedule, with anomalies and variances flagged automatically. The finance team's job shifts from producing the pack to interrogating it.
Effort and payback. Four to eight weeks. The numbers are days to close and finance hours per month. This one has a second-order benefit funds particularly value: the fund gets faster, cleaner portfolio reporting without asking the portco for anything extra.
Why these three
The three builds share useful characteristics. Each targets a high-volume, rules-based process that today's AI can handle reliably and has a natural baseline metric, so the impact can be measured. Each also frees capacity in a way the team can feel: sales receives better leads, operations loses its worst task and finance spends less time producing spreadsheets.
The same pattern holds well beyond these three. At a Big-6 accountancy backed by a major PE fund, a payroll process tied up a three to four person team for days in every cycle, roughly 20 to 30 days of team time a year. A deterministic script now covers about 80% of the process, with the remaining 20% escalated to a partner. It took a few days to build.
Avoid customer-facing AI, a platform decision or the most ambitious idea in the workshop as the first project. Choose the build with the clearest economics, then let management select the second and third after seeing it work. The fastest project we have run paid back in twelve weeks, which is a sensible standard for the first build.