What ten investment management firms actually have in place for AI governance, measured against a five-point maturity scale. Reported in aggregate; no firm is identified.
Round One closed at ten firms. Each completed the same self-assessment, which scores twelve practices on a five-point scale, averages them, and returns a maturity level from Initial to Optimized along with the three highest-return actions for that firm.
The cohort was made up of hedge funds, private equity firms, private markets managers, an advisory practice and a family office. Responses were self-initiated, so the cohort skews toward firms already working on the problem. Read the findings as a first cut rather than an industry standard.
Number of firms at each maturity level, out of ten.
Scores ran from 2.00 to 3.33. The entire sample sits inside the middle two levels: firms that have policy but cannot enforce it, and firms whose policy is written but not automated. Nobody was below a written standard, and nobody was above a manual one.
Share of the ten firms receiving each action in their top three.
The recommendations cluster far more tightly than the scores. A firm at 2.00 and a firm at 3.33 received the same two instructions at the top of their list: find out what AI is actually in use, then watch it.
Every assessment attaches an effort estimate to each recommended action. Adding up the top three per firm, totals ranged from seven to sixteen weeks, and eight of ten fell between eight and thirteen. For most firms in this sample, the distance between Aware and Managed is one focused quarter.
The three highest scorers were not the firms with the most policy. Their weakest link had already moved past discovery to vendor questions — AI-SBOMs, model provenance, training data. Every firm below the median was flagged for the same absence: no structured way to request a tool and get an answer.
Round Two is open. The assessment returns your own benchmarked score alongside the full cohort results before publication. Questions: hello@clarier.ai