Sector

AI leadership for private equity portfolios

Last updated

Justas Butkus is an AI expert and fractional AI officer based in Vilnius, Lithuania, working with mid-market companies and scale-ups across the UK, EU and US at two to four days a month. He builds and operates the production AI systems he advises on, rather than only advising on them.

Short answer

Portfolio companies get AI mandates from investors more often than from customers, and the mandate usually arrives without a method. The value concentrates in a small number of operational processes per company, and almost never in the same place twice.

Where does AI actually pay for itself in private equity?

The processes worth automating in any sector share three properties: they repeat constantly, they consume qualified people, and nobody enjoys them. In this sector that usually means:

  • Shared-service functions – finance, HR and customer support, where the same process runs across several holdings.
  • Sales and lead handling – speed of response is measurable and usually poor.
  • Reporting and data consolidation – manual assembly every month, every company.
  • Customer support volume – the first place headcount scales with revenue.
  • Diligence readiness – being able to answer AI governance questions at exit.

None of these are exciting. That is the point – the value comes from volume, not sophistication, and the exciting use case is usually the one that fails.

What makes this sector different

  • A portfolio-wide AI mandate usually underperforms a per-company diagnosis, because the expensive repeating process differs by business model even within one sector.
  • Diligence now asks AI questions. Buyers and insurers increasingly ask what AI is in use, who approved it and what data passed through it. A holding that cannot answer takes a discount.
  • Shadow AI is a portfolio-level risk. Unapproved tool use is near-universal and concentrates the exposure at fund level.

How an engagement here typically runs

  1. Inventory what is already runningIncluding the unapproved tools. In regulated environments this is usually the most uncomfortable and most valuable step.
  2. Map the repeating processes against costFrequency multiplied by fully loaded cost per occurrence. The ranking usually surprises people.
  3. Establish governance proportionate to the riskWho approves what, what happens when a system is wrong, and how a decision is reconstructed months later.
  4. Ship the unglamorous one firstProve the pipeline end to end before attempting the ambitious thing.

Frequently asked questions

Should AI strategy be set at fund level or company level?

Governance and diligence readiness benefit from a fund-level standard. Use-case selection does not: the expensive repeating process differs by business model, so a portfolio-wide mandate tends to produce activity rather than results.

What do buyers ask about AI in diligence?

What is in use, who approved it, what data passed through it, and who is accountable when a system produces a wrong answer. Holdings that cannot answer tend to take a discount rather than lose the deal.

What is the fastest value in a portfolio company?

Usually the highest-frequency operational process rather than anything customer-facing. Frequency multiplied by fully loaded cost per occurrence ranks candidates faster than any workshop.

If this describes your operation

The useful first conversation is about one specific process rather than about AI in general.

Get in touch