AI spend is rising in every portfolio company, measured differently in each. How operating partners can get one comparable measure and a value creation lever.
Across a private equity portfolio, AI spend has gone from negligible to material in two years. Engineering teams use coding agents, finance uses AI for analysis, support uses it for customer replies. Each company reports the spend differently, if it reports it at all, and almost none can say what the money bought.
For an operating partner, that is both a risk and an opportunity. A growing cost with no unit economics is a margin risk. A cost that can be cut without cutting output is a value creation lever.
Comparing AI efficiency across companies is hard when one tracks tokens, another tracks seats and a third tracks nothing. Without a common measure, the portfolio cannot tell which companies are using AI well, which are overspending, and which practices to spread.
Start with a two-week baseline in two or three companies with the highest AI spend. Compare cost per completed task. Roll out model routing where the baseline shows the biggest gap, then extend to the rest of the portfolio with the same measurement in every company.
CompletionPrism™ produces the same measurement in every company it runs in, so cost per completed task can be compared across the portfolio. It lowers the number by choosing the right model for every step, reduces dependence on any one provider, and keeps work running when a provider goes down. For portfolio companies that sell software with AI features, the Embedded License lowers cost to serve and lifts gross margin directly.