How PE firms are using AI for portfolio value creation
Most private equity firms talk about AI. Few have worked out what to do with it.
The pressure is real. Hold periods are longer. Exit multiples have compressed. LP expectations have not. In that environment, every operating partner is looking for ways to accelerate value creation between acquisition and exit - and AI has become the obvious place to look.
The mistake most firms make at this stage is treating AI as a technology decision. They push portfolio companies to "adopt AI", run a workshop or two, maybe fund a pilot. Then eighteen months later, the EBITDA bridge looks much the same as before. The problem was never the technology. It was the absence of a clear thesis about where AI creates commercial value in a specific business, and who is accountable for delivering it.
This article sets out how the most commercially serious PE firms are approaching AI for portfolio value creation - not as a technology rollout but as a structured operational intervention. It covers where the value actually concentrates, how to sequence investment and what good execution looks like at portfolio company level.
The value creation thesis has to come before the technology
AI is not a strategy. It is a capability that amplifies an existing strategy - or exposes the absence of one.
Before any PE firm deploys AI into a portfolio company, the operating partner needs a clear answer to one question: what is the primary value creation lever in this business, and how does AI accelerate it?
In a B2B services company, the lever might be pricing discipline - the ability to quote accurately, avoid margin leakage and identify which customers are underpriced relative to their true cost to serve. In a consumer ecommerce business, it might be retention - understanding which customers are about to churn and intervening before they do. In a distribution business, it might be procurement efficiency or demand forecasting accuracy.
Each of these has a different AI application, a different data requirement and a different implementation path. Treating them as interchangeable is what produces the vague, unfocused AI initiatives that consume management attention without moving EBITDA.
The firms getting this right start with a structured diagnostic that maps the value creation plan to specific commercial processes, identifies where data already exists to support AI-driven decisions and surfaces the highest-value interventions. That diagnostic should take weeks, not months. It should produce a ranked list of initiatives with effort estimates and expected commercial impact - not a technology roadmap.
Where AI value actually concentrates in portfolio companies
Across mid-market portfolios, AI-driven value concentrates in a small number of areas. Understanding which ones apply to a given business is the operating partner's job.
Commercial intelligence and pricing. Most mid-market businesses underperform on pricing because pricing decisions are made on intuition and precedent, not data. AI-enabled commercial intelligence systems can ingest transaction history, customer segmentation, competitor pricing signals and margin data to surface pricing anomalies in near real time. A distribution business turning £600m in revenue with a two-point pricing improvement opportunity is looking at £12m of recoverable EBITDA. That is not a marginal gain.
Demand forecasting and inventory. For product businesses, poor forecasting destroys working capital and service levels simultaneously. Machine learning-based forecasting models consistently outperform statistical baselines - particularly where demand is seasonal, promotional or affected by external signals. A consumer goods business that reduced forecast error by 20% released meaningful working capital ahead of exit, improving both EBITDA and net debt at the point of sale.
Customer retention and revenue operations. Churn is expensive. In subscription and repeat-purchase models, AI-based propensity models can identify at-risk customers weeks before they leave, enabling targeted intervention. The commercial case is straightforward: if a SaaS business with £50m ARR reduces annual churn from 12% to 9%, the uplift in enterprise value at a 6x ARR multiple is £9m. A single well-scoped retention initiative can justify the entire AI investment across a portfolio.
Back-office efficiency. Agentic AI systems can automate significant portions of finance, procurement and compliance operations - invoice processing, contract review, supplier onboarding. These are not transformational bets. They are high-certainty, fast-payback initiatives that free management capacity and reduce headcount dependency. They are also easy to demonstrate to a buyer at exit.
The sequencing question most firms get wrong
Operating partners often want to know which AI initiative to start with. The answer is rarely the most exciting one.
The right sequencing principle is: start with the initiative that has the shortest path from data to decision to commercial outcome, and that requires the least organisational change.
In practice, this usually means starting with a commercial intelligence or reporting layer - giving the management team better visibility of what is happening in the business right now - before attempting anything that requires changes to operational processes or customer-facing systems.
A portfolio company cannot implement an AI-driven demand forecasting system if its ERP data is unreliable. It cannot run a churn propensity model if it does not have a clean customer data set. The diagnostic phase should surface these blockers. If data quality is poor, the first investment is in data infrastructure, not AI models.
A useful sequencing framework:
- Diagnose: Map value creation priorities to data assets. Identify gaps.
- Stabilise: Fix data infrastructure where required. This is unglamorous but non-negotiable.
- Instrument: Deploy a commercial intelligence layer - rapid insight generation, automated reporting, KPI visibility.
- Intervene: Layer in predictive and prescriptive models where data quality and commercial case are both established.
- Automate: Deploy agentic systems to remove human bottlenecks in high-volume, rule-based processes.
Each stage builds on the last. Firms that skip to stage four without completing stages one and two consistently fail to generate returns.
What the operating partner's role actually looks like
AI value creation in portfolio companies does not happen without sustained operating partner involvement. Management teams at mid-market businesses rarely have the capability to drive this internally, and they should not be expected to.
The operating partner's role is not to be a technology expert. It is to hold the commercial frame, ensure initiatives stay connected to the value creation plan and make resourcing decisions at the right pace.
In practice, this means three things.
First, ensure the right external capability is in place from the outset. A portfolio company that hires a single data scientist and calls it an AI strategy will be disappointed. The work requires a combination of data engineering, modelling capability, commercial acumen and change management. Most mid-market businesses cannot hire that full stack. They should not try to - they should bring it in.
Second, set clear commercial targets for each initiative before implementation begins. Not "improve our use of data" but "reduce forecast error by 15% within six months" or "identify £2m of pricing upside in the SME segment by Q3". Targets create accountability. They also make it easier to kill initiatives that are not working.
Third, build AI capability into the exit narrative early. Buyers are increasingly sophisticated about data and AI maturity. A business that can demonstrate systematic AI-driven improvement in commercial performance - with the data to prove it - commands a better multiple. That story takes time to construct. Operating partners who start thinking about it eighteen months before exit are too late.
The cost of the workshop approach
The firms that will generate real AI-driven returns from their portfolios over the next three to five years are not the ones running the most pilots. They are the ones with the clearest thesis, the tightest sequencing and the commercial discipline to connect every initiative back to enterprise value.
AI is now a standard part of the value creation conversation in PE. That means the baseline is rising. Doing nothing, or doing it performatively, is no longer a neutral position - it is a relative disadvantage against competitors who are doing it seriously.
If you are an operating partner building out an AI approach for your portfolio - or a mid-market business preparing for a PE-backed growth phase - the right starting point is a structured diagnostic. Not a strategy presentation, not a vendor demo. A diagnostic that maps your value creation priorities to your data assets, identifies the highest-impact interventions and gives you a sequenced plan with commercial targets.
That is exactly the kind of engagement Rodan was built to deliver. Book a diagnostic to start the conversation.
