Private equity is entering a new era of accelerated transformation driven by AI. In this conversation for The Bridge, Elina Ashkinazi-Ildis, Partner & Lead Private Equity, and Jérôme Petit, Managing Partner, explore how AI is redefining portfolio value creation, fund operations, and investment strategies.
Elina Ashkinazi-Ildis began her career in strategy consulting specialising in transactional support before joining Artefact’s Private Equity practice, where she focuses on due diligence, data, and AI transformation across funds and their portfolios.
Jérôme Petit has spent over 25 years serving global private equity firms and leads Artefact’s work with top-tier funds worldwide.
Scaling AI across the investment lifecycle
Private equity firms are not approaching AI as an experimental technology but as a structural lever for competitiveness. Industry data confirms the pace of change: three-quarters of private equity firms have already invested significantly in digital transformation, with AI representing the majority of those investments. The momentum is clear, yet many funds still struggle to translate ambition into execution.
The opportunity spans the entire investment lifecycle. Before deals are signed, AI facilitates sourcing and screening by identifying relevant acquisition targets and benchmarking them against comparable assets. AI also reveals market signals faster than traditional research methods.
During due diligence, large language models accelerate the analysis of complex documentation, automate data extraction, and enhance financial and operational benchmarking. This shortens transaction timelines while improving analytical depth.
Once assets enter the portfolio, AI continues to create value. ESG teams use it to track performance indicators, investment teams rely on it for market monitoring and add-on acquisition scouting, and investor relations functions leverage it to respond to LP requests with greater speed and precision.
What emerges is not a single use case, but an integrated intelligence layer supporting decision-making across the fund.
Securing AI at the core of the fund
Deploying AI in private equity requires navigating a uniquely sensitive environment. Funds manage highly confidential data, from portfolio performance, deal pipelines, and proprietary operating metrics to investor reporting. Security, traceability, and governance, therefore, become foundational requirements rather than technical add-ons.
Elina highlights this balance through the example of Ardian, where secure generative AI capabilities were deployed within the fund’s own cloud infrastructure.
“Security is absolutely primordial in this industry,” she explains. “We deploy solutions inside the client’s environment to ensure full control over data, access, and traceability.”
Custom architectures also enable performance gains. Advanced retrieval frameworks ensure that models deliver precise, auditable answers rather than generic outputs. Cost optimization is another driver: bespoke deployments can reduce AI operating costs by four to ten times compared to standard licensing models, depending on usage intensity.
The result is a controlled yet scalable AI environment, designed for institutional rigor rather than experimentation.
Beyond tools: Reengineering how funds operate
While much attention is given to technology deployment, the deeper transformation lies elsewhere. AI adoption inevitably exposes structural inefficiencies in existing workflows: manual reporting chains, fragmented data access, and duplicative analyses become bottlenecks once automation is introduced.
As Jérôme explains, “Technology is no longer the hardest part. The real work is rethinking processes end-to-end to fully leverage AI.”
This is why many transformation programs focus as much on operating model redesign as on technical enablement. Initiatives span deal teams, ESG, investor relations, fund administration, and marketing functions, aligning them around AI-augmented workflows.
The impact can be rapid. In some cases, comprehensive operating model redesign can be delivered within a three-month timeframe, fundamentally reshaping how general partners access insights, collaborate, and make decisions. In this way, AI becomes a catalyst for organizational reinvention, not just productivity gains.
Activating value within portfolio companies
If AI enhances fund operations, its greatest potential for creating value lies within the portfolio companies themselves. Private equity firms increasingly view AI maturity as a driver of valuation. Historically, however, advanced analytics capabilities were concentrated in large enterprises with significant technical resources. This paradigm is shifting.
“AI was once seen as a luxury reserved for large firms. Today, even mid-market companies can deploy targeted, high-impact use cases,” notes Elina.
The first step is diagnostic: assessing each company’s data and AI maturity, exposure to disruption, and value creation potential. This evaluation typically considers:
- Operational and commercial data availability
- Competitive exposure to AI disruption
- Internal digital capabilities
- Strategic growth priorities
Then, prioritized use cases are identified based on feasibility and business impact. Common cross-portfolio opportunities include:
- Internal GenAI copilots to automate reporting, emails, and CRM updates
- Lead generation and B2B marketing intelligence
- RFP and tender response acceleration
- Sales knowledge management and proposal drafting
These are often complemented by sector-specific initiatives, tailored to manufacturing, healthcare, services, or industrial contexts. Speed is critical. Initial maturity assessments can be conducted in under a month, with pilot deployments launched shortly thereafter. This rapid activation model allows funds to test value creation hypotheses before committing to large-scale investments.
Building the data foundations
Underlying both fund-level and portfolio-level AI is a shared prerequisite: unified, accessible data. As Jérôme puts it, “No data, no AI.”
Many funds are therefore investing in centralized data platforms that consolidate information from portfolio companies, third-party providers, and internal reporting systems. This was implemented at Ardian through the creation of a cloud-based analytical data platform designed to:
- Aggregate financial and operational data
- Automate reporting pipelines
- Improve calculation speed and latency
- Enhance data accuracy and governance
Such platforms function as living ecosystems. New datasets, tools, and applications are progressively integrated, enabling continuous expansion of analytical capabilities. Data marketplaces further accelerate adoption by enabling direct access to external intelligence sources without complex API integrations. For funds, this infrastructure becomes the backbone of AI scalability, ensuring that insights are not siloed but shared across investment and operating teams.
From experimentation to everyday adoption
One of the most striking evolutions in private equity AI adoption is cultural rather than technical: what began as consultant-driven experimentation is becoming team-led innovation.
Once secure AI environments are deployed, deal teams, ESG specialists, and investor relations professionals rapidly identify new applications aligned with their daily challenges.
As Elina observes, “AI is no longer a gadget. It’s becoming part of daily work. Teams themselves are finding new ways to use it that we didn’t anticipate.”
This bottom-up adoption reflects a broader shift: AI is moving from specialized capability to institutional reflex, embedded in how funds operate, analyze, and communicate.
Conclusion: A new strategic imperative for private equity
AI’s impact on private equity is no longer theoretical. It is operational, measurable, and accelerating. Funds that embrace AI are not only improving efficiency but reshaping how value is created across the investment lifecycle. From sourcing and diligence to portfolio growth and exit readiness, AI is becoming a multiplier of both speed and insight.
The firms that will lead this transformation are those that view AI as infrastructure, not a toolset. By investing in secure data foundations, redesigning operating models, and scaling capabilities across their portfolios, they are redefining the very mechanics of value creation.
In an industry where timing defines returns, the ability to operationalize intelligence at scale may well become the next frontier of competitive advantage.
