AI is moving quickly. For private equity firms and their portfolio companies, the challenge is increasingly not understanding what the technology can do, but making sure the organisation can move with it.
That was one of the clearest themes from the recent AI Pathfinder Strategy Day. Across the discussions, three topics repeatedly came to the forefront: technology is moving faster than people; GPs have an opportunity to create stronger AI conversations across their portfolios; and successful adoption needs to remain focused on value creation, people and data.
Technology is moving faster than people
The capabilities available to businesses are evolving at an extraordinary pace. New models, tools and use cases are appearing constantly, making it increasingly difficult for leadership teams to determine where to focus.
But access to technology is rarely the main constraint.
The harder questions are organisational. Do teams understand where AI can genuinely improve the business? Do they have the confidence and skills to use it? Are leaders aligned on priorities? And can the organisation adapt its processes quickly enough to capture the opportunity?
There are also increasingly practical questions for teams to navigate: which AI model is right for a particular task, how to use tokens effectively, and even whether a problem genuinely requires AI or would be better solved through more traditional automation. Understanding these distinctions is important if businesses are to invest their time and resources in the right places.
There is a risk that businesses respond to the pace of AI by accumulating tools and launching disconnected experiments. That can create activity without necessarily creating value.
The more important challenge is bringing people along with the technology.
For portfolio companies, this means treating AI adoption as a business transformation and change-management challenge, rather than simply a technology implementation.
Successful adoption requires clear ownership, practical use cases, appropriate governance and teams that understand how their roles and ways of working may change.
Creating a stronger AI conversation across the portfolio
Another theme from the Strategy Day was how GPs can help portfolio companies learn from one another.
Rather than each portfolio company independently trying to navigate the same rapidly changing landscape, there is an opportunity to create more structured collaboration between CEOs and CTOs across the portfolio.
Regular sessions could bring portfolio leaders together to discuss AI strategy, current use cases, opportunities and lessons from implementation. These sessions are more effective when they are facilitated by AI experts who can bring external perspective, challenge assumptions and help leadership teams translate the discussion into practical opportunities for their businesses.
The value is not simply in discussing new technology. It is about creating a practical forum where leaders can compare what is working, identify common challenges and accelerate learning across the portfolio.
A CEO might bring the commercial and organisational perspective: where could AI change the customer proposition, operating model or cost base?
A CTO can bring the technology and data perspective: what is achievable, what infrastructure is required, and where are the risks or dependencies?
Bringing those perspectives together, and doing so across multiple portfolio companies, can help move the conversation from isolated experimentation towards a more systematic approach to AI-enabled value creation.
For GPs, facilitating these conversations could become an increasingly valuable part of portfolio support, particularly when combined with specialist AI expertise that can help turn shared learning into action
Start with value creation, not AI
Perhaps the most important takeaway was that AI should not become the strategy in itself.
The starting point should remain the investment thesis and the value-creation plan.
Where can the business grow faster? Where can margins improve? Which processes create unnecessary friction? Where could teams make better decisions? Where could the customer experience improve?
Only then should the question become: where can AI materially help?
This changes the conversation from “Where can we use AI?” to “Which of our most important business priorities could AI help us address?”
That distinction matters.
It helps leadership teams prioritise opportunities based on potential business impact rather than technological novelty. It also makes it easier to define what success should look like and measure whether an AI initiative is actually contributing to value creation.
In practice, the highest-return opportunities are rarely the most novel ones. Repetitive, well-understood processes, the kind of work that has often been outsourced or done manually simply because automating it wasn’t worth the effort before, are frequently where AI delivers the fastest and most measurable returns. The problem is already understood; only the economics have changed.
People and change management
Even the strongest AI use case will struggle to deliver value if the people expected to use it are not part of the journey.
AI can automate parts of a workflow, improve access to information and support better decision-making. But capturing those benefits often means changing established ways of working. That requires identifying who will use the technology, involving them early and helping teams understand how AI can support them in their roles.
The change-management question therefore needs to sit alongside the technology question from the beginning.
For leadership teams, the focus should not simply be “Can we implement this?” but also “How will this change the way our people work, and what needs to happen for adoption to succeed?”
Data foundation
Alongside people, data remains fundamental to turning AI ambition into practical results.
The quality, accessibility and structure of a company’s data will determine what many AI applications can realistically achieve. The businesses with the strongest in-house AI capabilities are the ones that treated data as an investment long before AI made it urgent.
Fragmented systems, inconsistent information and unclear data ownership can quickly limit ambitious AI plans. In many cases, exploring AI opportunities therefore exposes broader data and operating-model questions that businesses already needed to address.
For investors and management teams, understanding data maturity is becoming an increasingly important part of assessing where AI can deliver value today, and what foundations need to be put in place for tomorrow.
From experimentation to repeatable value creation
Turning AI initiatives into scalable, measurable business impact
The discussion at AI Pathfinder reinforced that the next phase of AI adoption in private markets is unlikely to be defined simply by who experiments with the most tools.
The bigger opportunity is to develop a repeatable approach: identify the business priority, understand the data and technology requirements, involve the people who will ultimately use it, test the use case, measure the outcome and scale what works.
For GPs, there is an additional opportunity to do this at portfolio level: creating forums where CEOs and CTOs can share experiences, identify common opportunities and build on lessons already learned elsewhere in the portfolio.
The technology will continue to move quickly.
The differentiator will be how effectively businesses, and the people within them, turn that progress into measurable value.
Continue the conversation
If you’re exploring how AI could support value creation across your portfolio, or how to bring CEOs and CTOs together around AI strategy, usage and opportunity, speak with Max and Maria.
They can help you think through the opportunities, challenges and practical next steps for your portfolio companies.
Get in touch with Max and Maria to start the conversation – details below.
Authors
Maria Monton
Director – Data Strategy
Maximilian Evans
MD – Data Strategy
