AI · PRIVATE EQUITY · VALUE CREATION
Private equity portfolios have long been experimenting with AI. What matters now is which companies actually turn it into impact on results. The difference lies less and less in access to tools and more in leadership, prioritisation and operating model.
This makes AI a classic value creation question: who translates technology into revenue growth, margin improvement, better decisions or new products – and who is accountable for it?
From productivity to P&L impact
Current analyses from the private equity market show a clear pattern: broad tool usage alone is not enough. McKinsey examined 471 PE-backed companies from 31 industries and distinguishes several levels of AI maturity – from opportunistic use to AI-based business building. In the analysis, higher maturity levels were associated with significantly stronger revenue productivity and higher valuation multiples. BCG similarly describes that many portfolio companies use AI but do not yet derive measurable value creation from it.
The conclusion for investors is less “more AI” than better leadership of the AI agenda.
Four leadership questions for PE portfolios
- Ownership: who is responsible for the economic benefit – the CEO, CIO/CTO, a Chief AI Officer or the individual business owners?
- Prioritisation: which AI initiatives actually contribute to the investment thesis?
- Operating model: how are data, technology, business, risk and external partners brought together?
- Talent: does the management team have the experience to turn experiments into scalable value creation?
AI is also changing management due diligence
When AI becomes part of the investment thesis, the management team should not only be assessed on its previous functional performance. It also becomes relevant whether the CEO and leadership team can translate technological change into operational decisions. This does not mean every CEO must be an AI expert. But it does mean that AI competence, capacity for change and the ability to prioritise become part of the leadership diagnosis.
Where transformation gaps are larger, an additional AI transformation leader, Chief Data & AI Officer or CAIO can make sense. In other situations, the better solution is to redesign the existing CIO/CTO remit and give the business more ownership of results.
AI leadership along the ownership cycle
Diligence
Which AI opportunities and risks are realistic? Which capabilities are missing in the management team? Which data and system prerequisites exist?
100-day plan
Define priorities, responsibilities and measurable business cases. Do not launch ten pilots in parallel, but a few relevant levers with clear ownership.
Scaling
Set up the operating model, data access, platforms and governance so that successful use cases actually scale across functions and sites.
Exit preparation
AI capabilities must be institutionally anchored and visible through robust metrics. A convincing exit story does not come from tool licences but from demonstrable impact.
The right AI leader depends on the situation
For a software company with an AI-based product, a technical product/AI leader can be central. For an industrial company with a complex operating model, a transformation leader may make more sense. In a buy-and-build strategy, a group-wide data & AI function may in turn have the greatest leverage.
That is why the search should not begin with a standard title, but with the investment thesis and the concrete value creation mandate. This is exactly where AI Leadership Search connects with Private Equity Executive Search.