Why Private Equity Firms Need an AI Partner That Creates Value Across the Portfolio

Private equity firms are under growing pressure to create value through operations, not only through financial engineering. In the current market, returns depend more on clarity of operating model, leadership quality, digital capability, and resilience across the holding period. At the same time, AI is moving from experimentation into the core of dealmaking, portfolio optimisation, and exit preparation. That is why the question is no longer whether private equity should work with AI advisers. The real question is which kind of AI partner can turn portfolio-company complexity into measurable results.

The old private equity playbook is no longer enough

The PE firms pulling ahead are not treating AI as another software pilot. They are using it as part of a broader value-creation system. Bain describes a race to unlock real portfolio value with generative AI. BCG says digital transformation is now the essential foundation for AI deployment in PE-backed companies. Deloitte's private equity work groups the opportunity into talent development, revenue growth, margin expansion, product differentiation, and asset protection. In other words, AI matters because it can improve how a business runs, how it grows, and how defensible it becomes.

Why so many AI initiatives still stall

If AI is so promising, why do so many programmes underperform? Because most organisations are not failing on enthusiasm. They are failing on readiness. McKinsey finds that almost all companies invest in AI, but only 1% see themselves as mature, and the biggest barrier to scaling is leadership rather than employees. FTI makes a similar point in private equity specifically: too many firms still lack clear milestones, KPIs, governance, and portfolio-wide orchestration. For an operating partner, that means one thing. A credible AI partner must be able to do more than deliver tools. They must help management teams prioritise use cases, build a roadmap, train people, and drive adoption.

Where the real value often sits inside portfolio companies

Many businesses already hold valuable data, but they are not yet turning that data into performance. MIT Sloan's research shows that most data monetisation returns come from improving operations and customer experience rather than simply selling information to third parties. McKinsey argues that the frontier has shifted from data resale to intelligence-driven business building, where scalable, cloud-native data products create decision-grade value. For PE-backed companies, that can mean better forecasting, faster sales cycles, higher service quality, stronger products, more efficient operations, and entirely new revenue lines built on existing assets.

What private equity should expect from an AI partner

A serious AI partner for private equity should be able to work across strategy, delivery, and governance. They should identify where AI can affect EBITDA, revenue growth, and competitive position. They should help the portfolio company build the right data foundation. They should make teams AI-ready rather than leaving capability concentrated in a few specialists. And they should build governance into the work from the start, because trust, security, privacy, and explainability are now part of value creation rather than an afterthought. NIST's AI RMF, the ICO's AI guidance, and the EU AI Act all point in the same direction: responsible AI requires accountability, transparency, fairness, and risk management across the lifecycle.

Why Liplyn is a strong fit for this moment

This is where Liplyn has a compelling story to tell. Liplyn's broader service profile spans Data & AI Strategies, Cloud-based Data Platforms, Data & AI Products, Agentic AI, AI Search Optimisation, and Cyber Security. That is important because private equity does not need another adviser who can only run workshops or only build tools. It needs a partner that can help portfolio companies become AI-ready, redesign business models around data, create modern data products, improve operational efficiency, and strengthen how companies are discovered and trusted in an AI-driven market.

Liplyn's public footprint already reinforces that positioning. Publicly, the group offers AI search visibility services and tooling, runs practical AI learning through Liplyn Academy, and operates an ecosystem of media and data assets that includes Datafloq, Drimble, and sector-focused platforms. Liplyn also publicly positions itself with 20+ years of expertise, 2,000+ campaigns across 50+ countries, and an existing diagnostic offer in the form of a free AI Search Audit. That combination gives Liplyn a differentiated angle: not only helping companies adopt AI internally, but also helping them win externally in the age of AI search, AI answers, and zero-click discovery.

AI readiness is now also a governance issue

For European portfolio companies in particular, AI readiness is no longer only about productivity. It is also about compliance and trust. Article 4 of the EU AI Act already requires a sufficient level of AI literacy among staff and relevant users. Transparency obligations for certain AI interactions and AI-generated content become applicable from 2 August 2026. The ICO's guidance also makes it clear that organisations deploying AI must think carefully about transparency, fairness, accountability, and data protection impacts. A private equity firm that wants faster adoption without governance risk should want a partner that treats enablement and oversight as part of the same delivery model.

What this looks like in practice

The most effective starting point is not a giant transformation plan. It is a disciplined first phase. Begin with an AI value scan for one portfolio company or one functional domain. Map the most promising use cases against the investment thesis. Review data assets, workflow bottlenecks, team readiness, risk exposure, and go-to-market opportunities. Then build a prioritised roadmap with clear owners, milestones, adoption plans, and measurable business outcomes. FTI notes that weak milestones and unclear KPIs remain a recurring issue in PE AI programmes. The firms that solve that early are better placed to scale value across the portfolio.

The firms that win will combine value, adoption, and trust

Private equity does not need more AI theatre. It needs partners who can connect strategy to execution, training to adoption, data to value, and governance to speed. That is the strongest case for Liplyn: a partner that can help portfolio companies modernise how they operate, turn existing data into new advantage, and strengthen their commercial position in an environment where both buyers and AI systems increasingly shape who gets found, trusted, and chosen.

If you are an operating partner, PE deal team, or portfolio-company leadership team looking to maximise results from a participation, Liplyn's opportunity is to help you answer three questions fast: where AI can create value now, what your teams need to become AI-ready, and how to build a more modern, more visible, and more defensible business for the next stage of growth.

Request a confidential portfolio AI value scan — or start with a free AI Search Audit for one portfolio company.

Frequently asked questions

What should private equity firms look for in an AI consultant?

They should look for a partner that can link AI to value-creation levers such as revenue growth, margin expansion, product differentiation, talent readiness, and asset protection, while also bringing a repeatable operating model, milestones, and governance rather than isolated pilots.

Can AI create value in portfolio companies without selling data?

Yes. Research from MIT Sloan shows most data-monetisation returns come from improving operations and customer experience rather than selling data itself, while McKinsey argues that the next phase is intelligence-led productisation built on strong data products and infrastructure.

Why does AI training matter so much for portfolio companies now?

Because scaling AI is largely a leadership, adoption, and governance challenge. McKinsey says the biggest barrier is not employee readiness but leadership steering, and the EU AI Act already requires a sufficient level of AI literacy for relevant staff and users.

Why should private equity care about AI governance and transparency?

Because AI governance now directly affects trust, compliance, and enterprise risk. NIST's AI RMF, the ICO's AI guidance, and the EU AI Act all emphasise lifecycle risk management, transparency, fairness, and accountability.

Where does AI search visibility fit into private equity value creation?

It supports commercial excellence and exit readiness. FTI's PE analysis highlights the importance of improving how a company sells and of building AI-driven transformation into the sell-side narrative. Liplyn's GEO and AI visibility tooling can be positioned as one practical lever within that broader growth and differentiation story.