AI: turning advanced technology into sustainable performance at Ardian

08 October 2026
Ardian

Reading time: 4 minutes

Ardian began its data science journey in 2018 across energy, transport and digital assets. By 2025 the approach had evolved into a firm-wide industrialization of AI, embedding proprietary tools to drive productivity and accelerate value creation in portfolio companies.

Industrializing AI in Private Equity: Ardian’s strategic vision

In 2025, artificial intelligence reached an unprecedented level of adoption across businesses. According to McKinsey’s “State of AI” report, 88% of companies now use AI in at least one function. Yet scaling remains limited: only 11% have successfully embedded the technology across their organization and generated meaningful financial impact. The real challenge has shifted from access to technology to the practical tasks of transforming organizations, structuring large data flows and adapting operating models.

7

Buyout portfolio companies supported by Data Science in 2025
500,000+

queries processed on GAIA in 2025
800+

monthly active GAIA users

How Ardian is embedding AI into the Private Equity operating model

Against this backdrop, 2025 marked a decisive shift for Ardian toward the industrialization of AI. The firm pursued a dual objective: strengthening internal decision-making capabilities to create a sustainable competitive edge, and supporting operational value creation across portfolio companies. This transformation was led jointly by the Data Science and Digital Transformation teams through the development of analytical tools, infrastructure modernization and process evolution. The Data Science team formed in 2018 as part of the Infrastructure fund. Its original mandate was to bring a more data driven approach to operational value creation in portfolio companies spanning energy, transport and digital infrastructure. Data scientists were embedded with the investment teams rather than kept as a separate support function, and this early integration established the industrial mindset that has guided Ardian’s AI journey ever since.

“We support portfolio companies on concrete operational topics"

Pauline Thomson, Head of Data Science

Five strategic levers to scale AI in Private Equity

In 2025, the firm’s approach was structured around five clear priorities. First, capturing productivity gains through gen erative AI, with a particular focus on embedding the proprietary platform GAIA into day-to-day workflows. Second, enhancing portfolio construction through more systematic use of data driven insights. Third, deploying AI across portfolio companies to support their value creation plans. Fourth, strengthening market screening and execution capabilities. Fifth, accelerating acculturation and training to ensure broad and effective adoption across all teams. GAIA, Ardian’s own generative AI plat form, became the most visible internal success. In 2025, it processed more than 500,000 queries and is now in use by more than 800 employees each month. Specialized applications – such as Draft AI 58 for investor relations activities and GenComp for the generation of valuation comparables – help automate repetitive tasks, allowing teams to reallocate time toward higher-value analysis and improving the quality and consistency of investment decisions.

GAIA has been a success because it was built not just by IT and Data Science teams, but with all teams. It marks a shift toward a more collaborative, transversal ‘One Ardian’ strategy”

Vincent Joly, Head of Information Technology

AI at Scale: Data, technology and long‑term value creation across Ardian’s portfolio

At the portfolio level, AI has moved from experimentation to structured deployment. The Data Science team supported seven Buyout companies during 2025 on concrete operational topics including churn reduction, margin improvement, network optimization, product prioritization and advanced simulations. A data and AI maturity assessment was implemented across all funds to identify transformation priorities and build clear roadmaps. Infrastructure investments also benefited directly. Ardian observed strong demand for new data-center capacity in Europe, driven by hyper-scalers and GPU as-a-service providers, and reinforced by European regulation and sovereignty considerations. On the portfolio-construction side, advanced simulation tools enable historical performance analysis across different macro environments, comparison against public and private benchmarks, and modeling of Value-at-Risk, macro scenarios and partial allocation optimization. This represents a clear shift from a largely descriptive approach to a more quantitative and forward-looking framework for risk management. Recognizing that technology alone is not enough, Ardian also invests heavily in acculturation. Training programs tailored by seniority level, combined with data and AI maturity assessments, help ensure teams across the firm can work effectively with these tools and use them to deliver real value. This approach has transformed AI usage from isolated pilots to an embedded part of the firm’s industrial operating model. While many organizations remain at the experimentation stage, Ardian has demonstrated its ability to deploy AI at scale – positioning it as a durable lever of performance and long-term value creation.