How does technology fit into a broader philosophy of value creation?
Riccardo Basile: Using technology to enable, improve and grow businesses is a key lever in any value creation effort. In our case, for instance, the aim is always to help drive multiple value creation initiatives in our funds’ portfolio companies, focusing on what enables long-term growth and hence generates the largest alpha in each specific company. This applies outside the technology sector itself, too, with tech and AI-enabled value creation often proving most powerful in the services, healthcare and consumer sectors.
Tech-enabled value creation is something we deploy across almost all of our funds’ portfolio companies, drawing on 40 years of software investing experience and pattern recognition. What’s more, as a sector focused, growth-orientated investor, we believe in embedding value creation professionals directly within our sector teams (consumer, healthcare, services and technology), as opposed to working on a horizontal, cross-sector basis. This allows them to bring deeper, more relevant expertise to each situation.
Why is a sector-by-sector approach to value creation important when it comes to tech enablement
RB: Each sector has its own unique dynamics, and so its needs around AI and technology enablement will naturally differ. Sector-specific knowledge is essential when it comes to deploying the right tools, at the right time, in the right way.
Technological maturity levels also vary significantly across sectors. A healthcare services business, for instance, may have strong operational discipline but less software fluency, so bringing in a generic tech expert risks misalignment. Instead, a sector-integrated model allows us to deploy professionals who understand both themindustry context and the technologymlevers – and who can manage change more effectively.
Ben Rogers: By working on similar challenges repeatedly, sector-specific value creation professionals build deep knowledge and pattern recognition skills. We have found that this resonates with management teams and allows us to build trust quickly, and hence help them accelerate their pace of transformation.
The second benefit of such specialism is that we develop repeatable solutions and reusable playbooks that we can scale to support across the portfolio.
Finally, a sector specialism enables us to bring together our funds’ portfolio companies in regular forums for AI and tech leaders. In these sessions, leaders share AI wins, challenges and ideas. After all, with generative AI evolving so quickly, that connectivity is particularly critical to accelerate learning and increase AI execution speed across companies.
We deliberately make some of these sessions cross-sector too – it’s quite powerful to cross-fertilise ideas and success stories from technology businesses to tech-enabled services and healthcare businesses, for example.
Looking ahead, where do PE firms need to focus their efforts for the next stage of technology-enabled value creation?
BR: We’ve moved past pilots. We’re in the era of scaled adoption. Agentic AI, for instance – consisting of systems that can reason, act and adapt within workflows – holds even more transformational potential. Already, around 30 percent of our funds’ portfolio companies are deploying agents in use cases
such as customer support, data extraction and processing, and service delivery.
This requires boldness around operating models. The companies that will win are those willing to reorganise the architecture of their workflows around AI. That means rethinking governance, talent, performance metrics – the whole stack.
One philosophy we hold is that every portfolio company needs a chief AI officer, or equivalent, with a seat at the management team table. It’s critical they are at the centre of a company’s value creation strategy – not tucked away in IT. Those individuals must be empowered to shape company-wide thinking, prioritise and deliver use cases, and make AI an integrated part of the business’s operating model.
We’re excited about the future and it’s clear that the next stage of AI value creation will depend on a combination of strategy, execution and deep sector expertise.
Looking outside of the tech sector, then, where do you currently see AI and advanced technologies presenting the most value creation opportunities?
RB: It’s worth remembering that generative AI is just the latest tool and accelerator of transformation in private equity’s value creation toolkit – not dissimilar to the internet, cloud and mobile transitions before it. That said, right now we’re seeing outsized opportunities to leverage tech enablement in sectors like legal and information services, education and healthcare services – domains with a high volume of manual processes but which are built upon deep expertise and proprietary data that can power AI solutions.
To provide an example, generative AI can unlock new service delivery models. Traditionally, services businesses have scaled by adding headcount. AI enables margin expansion by decoupling growth from people costs – not by replacing people, but by augmenting them. The goal isn’t to replace expert knowledge, but to elevate it. AI frees up capacity so that specialists can spend more time on what matters most to their clients. We’re now seeing businesses profitably serve markets that would have been uneconomic under a purely human delivery model.
There’s also the emergence of ‘services-as-software’ models, where companies wrap proprietary platforms around their expertise. That mindset shift in go-to-market and product design is something we’re actively driving across our services and healthcare portfolio.
Today, every company in our funds’ portfolios has at least one live generative AI use case. In services and tech alone, more than 80 percent have integrated AI into their operations, and we’re seeing this momentum extend into consumer and healthcare as well.
BR: For context, we’ve been exploring automation, data analytics and AI for nearly a decade. Digital, data and AI capabilities help with two key things within portfolio companies: first, they allow us to manage cost bases while still driving growth; and second, they are embedded into the very products and services that these companies deliver. That makes these technological solutions part of the value proposition, not just the back office.
Can you give some examples of successful AI and technology-enabled value creation strategies?
BR: We’ve seen real traction where AI is applied within sector-specific workflows, especially in healthcare and services. It’s not about flashy pilots, but instead about targeted deployment that changes business outcomes.
Take Quotient Sciences, a pharma services business. By embedding AI into its drug formulation process, Quotient has reduced cycle times from around 10 weeks to just four to five days. The approach now requires only 10-20 unique formulations, compared with more than 30 using traditional methods. This means fewer experiments, faster regulatory timelines and better drugs, improving both speed and quality of care.
Then, at Ergomed, which provides global pharmacovigilance and clinical trial services, AI has been embedded into case-processing workflows. This has reduced adverse event case intake time by 25 percent, while increasing auditability and process transparency. It’s a clear example of AI driving scale
without adding headcount.
In the legal services space, Axiom uses AI to automate the lawyer-toproject matchmaking process, dramatically improving speed and win rates. But it goes further: the company is also equipping lawyers with best-in-class, legal-specific generative AI tools, enabling them to analyse contracts faster than ever.
What’s exciting is that this is facilitating expansion into new markets and segments of work, for example bulk contract analytics, that were previously uneconomical to serve. This ‘expert talent plus tech’ model is one we think can power a lot of growth for services organisations that operate in highly expert domains.
Even in tech, where maturity is higher, generative AI-driven innovation is happening at pace. Zendesk – a leading customer service platform – was among the first companies globally to release an agentic AI product that autonomously resolves customer issues. Already, up to 30 percent of support tickets are resolved with no human intervention, freeing up agents for higher-value and more complex customer enquiries.
Where are LPs focusing their attention in relation to AI and technology?
RB: Investors are very interested and engaged in AI right now. Broadly, they are focused on outcomes and increasingly want to see how managers are using AI to deliver measurable results. It’s not enough to experiment – they want to see traction and tangible value creation across the portfolio.
In 2024, for example, we achieved generative AI-related incremental revenues of €290 million across our portfolio, and that number will continue to grow as we embed more AI and generative AI into our companies’ processes. That’s exactly the sort of thing LPs need to hear.
We are also seeing significant productivity gains in customer support, where we have delivered real efficiencies across the portfolio. Costs associated with white-collar work amount to €300 billion across the portfolio. Even a small, 5-10 percent productivity gain through effective AI adoption will generate huge value in the years to come.
Ultimately, LPs are looking for clear strategies on AI, repeatable deployment methodologies and performance delivery at scale. A hands-on approach to AI value creation across the portfolio is the key to success in this respect.
