Everyone in private equity can now see the AI opportunity across the portfolio. Now the real work is choosing the right workflow, getting access to the right systems, redesigning the process, measuring the output, and getting the team to adopt a new way of operating.

This is where a lot of AI initiatives get stuck. A portfolio company might have 20 plausible use cases: engineering velocity, customer support, finance operations, professional services automation, compliance, reporting, product modernization. But not all of them are good first projects. Some are commercially irrelevant. Some need too much integration before they can show value. Some are too broad to land. Some are too small to matter.

The best first use case is usually not the most futuristic one. It is the workflow where the pain is already obvious, the task repeats often enough, the data is accessible enough, and a business owner is frustrated enough to change how the work gets done. That combination is much rarer than people think.

My bet is that AI value creation in PE will increasingly become a deployment discipline, not an ideation exercise. The firms that win will not be the ones asking every company to "do more AI". They will be the ones that can repeatedly identify where AI can move cost, speed or quality quickly, and then help the company absorb that change before the momentum disappears.