AI governance for PE-backed businesses: what operating partners need to know
There is a version of AI adoption happening across PE portfolios right now that looks like progress but is not. A portco spins up a pilot. The CEO reports green shoots. A few teams start using AI tools. Nobody has defined what data those tools can access, who approved the use case or what happens when the model produces something wrong. The board deck shows momentum. The risk register shows nothing.
This is the gap that will create problems - not at exit, when advisers find it, but now, when it is still cheap to fix.
AI governance is not a compliance exercise. For operating partners, it is a value protection and value creation discipline. Get it right and you accelerate safe deployment across the portfolio. Get it wrong and you inherit a liability that complicates your investment thesis, your due diligence story and eventually your multiple.
This article sets out what good AI governance looks like inside PE-backed businesses, why the standard approaches fall short and what operating partners should actually be doing about it.
Why most portcos are flying blind on AI risk
The typical portco AI posture looks like this: usage has outrun policy. Employees are running customer data through consumer-facing LLM tools. Finance teams are automating reports using models they do not understand. Someone in commercial has built a workflow on a third-party AI platform that is now embedded in the sales process. None of this was approved. Most of it is not visible to the CISO - if there even is one.
This is not a criticism of management. It is a structural problem. AI tools have become cheap and accessible faster than governance frameworks have matured. The velocity of adoption at team level has consistently outpaced the institutional response.
For operating partners, the specific risks are:
- Data exposure - proprietary customer, commercial or financial data passed to third-party model providers whose retention and training policies are poorly understood
- Model reliability - outputs used in decisions without adequate validation, audit trails or human oversight
- Regulatory exposure - sector-specific obligations (particularly in financial services, healthcare and any business processing personal data at scale) that AI usage may be breaching without anyone realising
- Exit risk - a buyer's technical due diligence team that finds ungoverned AI usage will discount, requestion or renegotiate
The last point matters most to the people reading this. A business that cannot demonstrate controlled, documented AI usage is harder to sell and harder to value. Acquirers - particularly strategics and PE sponsors - are increasingly running AI-specific diligence tracks. If your portco cannot answer basic questions about model governance, data lineage or usage policy, that becomes a negotiating lever for the buyer.
What AI governance actually requires at portco level
Governance is not a policy document. A policy document that nobody reads and nothing enforces is not governance - it is paper.
Real AI governance at portco level has four components.
Inventory and classification. Know what AI tools are in use, who is using them, what data they access and what decisions they influence. This sounds obvious. It almost never exists. Start here. A structured discovery exercise across the business - pulling from IT, finance, operations, commercial and HR - typically reveals three to five times the number of AI touchpoints management is aware of.
Risk tiering. Not all AI usage carries the same risk. A content drafting tool used by marketing is different from a model that scores credit applications or flags customer churn. Build a simple risk matrix: high-risk use cases (those touching regulated decisions, sensitive data or customer-facing outputs) need formal approval, validation and oversight. Lower-risk use cases need lighter-touch controls. The goal is proportionality, not paralysis.
Ownership and accountability. Somebody has to own this. In a portco with a strong technology leadership function, that might be the CTO or CDO. In leaner businesses, it falls to the CFO or COO. The operating partner needs to ensure accountability is explicit and that it sits with someone who has both the authority and the technical fluency to act. Naming AI governance as a standing agenda item on the board or ExCo is a minimum.
Model documentation and audit trails. For any AI system influencing material business decisions, you need documentation: what the model does, what data it was trained or fine-tuned on, how performance is monitored and what the escalation path is when it behaves unexpectedly. This is not bureaucracy for its own sake - it is the evidence base that supports a clean due diligence process.
The operating partner's role: portfolio-level leverage
Operating partners are well-placed to do something individual portco management teams cannot: drive standards across the portfolio and share what works.
A manufacturing business that has successfully governed its AI-assisted demand forecasting has solved a problem that three other portcos in the fund are facing. A retail business that has built a compliant AI usage policy has a template that saves every other business six weeks of management time. The operating partner is the transmission mechanism for that learning.
In practice, this means building a portfolio-level AI governance playbook. Not a one-size-fits-all mandate - portcos differ too much in maturity, sector and scale for that - but a common framework with defined minimum standards and documented pathways for escalation.
The minimum standards worth embedding across a portfolio:
- No AI tool accesses production data without documented approval from IT and legal
- All high-risk AI use cases are reviewed quarterly by a named accountable owner
- Every portco maintains an AI tool inventory, reviewed at least annually
- Any AI system influencing financial reporting, customer decisions or regulatory obligations requires formal model documentation
These are not onerous. A well-run portco should be able to meet all four within 90 days. The discipline is in requiring evidence, not just assertion.
Where AI governance connects to value creation
Governance is not the opposite of speed. Businesses that govern AI well deploy it faster - because they have cleared the path.
Consider a distribution business preparing for a commercial AI deployment: natural language querying of sales and margin data, automated territory reporting, AI-assisted pricing recommendations. Without governance foundations, that deployment stalls at procurement, gets blocked by legal and loses six months in back-and-forth. With foundations in place - a documented risk framework, clear data access controls, a named owner - the same deployment goes live in eight weeks and starts generating commercial insight that supports the exit story.
That is not a hypothetical. It is the pattern we see repeatedly. The businesses that invest in governance infrastructure early are the ones that can move fast later without creating liability.
For funds with digital transformation as part of the value creation thesis, AI governance is a precondition. You cannot credibly claim AI-driven operational improvement if you cannot demonstrate that the AI is operating within a controlled, auditable framework. Buyers will ask. Sellers who cannot answer will give ground.
The cost of waiting
The window in which AI governance is easy to retrofit is narrowing. As usage deepens - as models get embedded in workflows, as outputs get used in decisions, as third-party dependencies accumulate - the complexity of establishing control grows non-linearly. What takes six weeks to implement now will take six months in two years.
Operating partners who treat this as someone else's problem are accumulating risk they do not yet see on a risk register. The businesses in your portfolio that are furthest through AI adoption without governance frameworks are your highest-exposure assets.
The right starting point is a structured diagnostic: map current AI usage, assess it against a risk framework and identify the ten to fifteen specific actions that close the most material gaps. That is a short, contained piece of work. Rodan runs these diagnostics across portcos and fund portfolios - typically completed in two to three weeks, with a board-ready output that gives operating partners a clear picture and a prioritised action plan.
If AI governance is not yet on your portfolio review agenda, make it the next agenda item you add.
