A short course on rolling out one operating model across companies at different maturity levels.
Key takeaways
A playbook that mandates a specific tool for every portfolio company breaks the moment it meets a company whose systems don't support it. A playbook that mandates principles, review checkpoints, ownership structure, reporting metrics, adapts across very different starting points.
This distinction is what makes a playbook usable across a portfolio spanning companies from basic spreadsheets to sophisticated ERPs.
Rolling out the same pace of adoption to every company regardless of their starting point sets up the least-ready ones to fail visibly and publicly to their own boards. A short maturity assessment at the start of the relationship avoids that.
This assessment doesn't need to be elaborate: data quality, existing systems, and management appetite for change cover most of what matters.
If every portfolio company measures AI impact differently, the fund can't produce a credible portfolio-level answer when LPs ask about it. Standardising on a small set of metrics, hours saved, cost avoided, error reduction, from the outset avoids a scramble later.
This is easier to establish at the start of a platform relationship than to retrofit after each company has built its own reporting habits.
Early in a fund's AI rollout, some flexibility in how each company implements the playbook is reasonable. As more companies successfully adopt it, the fund can afford to tighten expectations for laggards rather than treat every deviation as a special case.
This progression mirrors how most operating disciplines mature across a portfolio, starting loose and consolidating around what's proven to work.
A 30-minute call is enough to tell you whether AI pays for itself here.
