Where I would start with AI in a private-equity portfolio

I would begin with the people carrying out the process. Ask for the last completed example and one that needed extra attention. A leadership description might call the task reporting, while the operator spends most of their time chasing missing inputs and reconciling conflicting versions. That difference changes what is worth building.

An initial candidate could be preparing a recurring operational update from approved company records. The desired result would be a draft with source references and clearly marked gaps, ready for the company's owner to review. This is an illustrative starting point, not a claim that I have deployed a portfolio-wide reporting system.

A candidate also needs a visible finish. If nobody can say what makes the update acceptable, adding AI will leave that disagreement intact. I would settle the review responsibility and the expected output before spending time on tool selection.

A multi-company environment creates a temptation to treat similar labels as identical processes. Two teams may both report an open order, for example, while using different definitions and different source systems. A shared summary can hide those differences unless someone has agreed how to interpret them.

I would keep the source definitions close to the workflow and involve the people who use them. Where a common definition is possible, document the agreement. Where it is not, preserve the distinction. Consistency should help a reader understand the work rather than force every business into an inaccurate template.

Access should follow the same care. A shared implementation does not mean that every operator or assistant needs every company's information. I would establish which material belongs to the local team, what may be included in a group view, and who can authorize that movement. Those decisions belong in the brief before the first example is assembled.

I would choose one team willing to work through a bounded trial. Its operator should be involved in the design, have time to inspect the output, and be able to say when the proposed process creates extra work. Enthusiasm from a central team cannot substitute for that local ownership.

The trial needs examples that reveal the real workload. A missing input, an unfamiliar exception, or a change in the reporting period can be more informative than another successful run on the same clean sample. I would compare the new process with the team's current method, including the time spent correcting and checking the result.

I would keep the initial output in draft form while those checks are being worked out. The trial should tell us whether the team can produce an acceptable result repeatedly and understand the failures. It should also tell us whether ordinary software, a better template, or a clearer handoff resolves part of the problem more simply.

After a useful trial, I would separate the reusable method from the company-specific setup. A review checklist or a pattern for citing sources may transfer well. An integration, a status definition, or a named approval responsibility may need to be rebuilt with the next team. Reuse should start from what has been checked.

There is ongoing work here. Someone must maintain the workflow when source systems change, answer questions, and decide whether a failure requires a correction or a pause. I would include that responsibility in the expansion decision. A pilot that depends indefinitely on its original builder is an unfinished handoff.

My first aim would be a process that one operating team can use and explain. From there, the group can make a more informed decision about what to share, what to adapt, and what to leave local. That gives the broader AI program something concrete to grow from.