AI and Automation in the Fund Operations—Guide for Private Equity Firms

Walk into any private equity conference and you’ll hear the same mantra: AI will transform investing. Funds appoint AI Operating Partners to deliver AI strategies for their portfolios and launch pilot initiatives. Yet when you look inside most PE firms, a different picture emerges. Many of the most critical processes remain manual and fragmented.

Deal teams still piece together decisions from spreadsheets, PDFs, email threads, and analyst intuition. Operating Partners wrestle with inconsistent portfolio reporting. Investment committees review bloated decks assembled under time pressure. At the same time, sensitive data quietly finds its way into ad-hoc AI tools, often without clear governance.

According to many Operating Partners we speak with, private equity is at least two years behind what the technology already enables. Practices that worked for decades are increasingly falling short in a market shaped by speed, data, and AI-driven leverage.

The weak spot isn’t only in portfolio companies. It’s inside the fund itself. Addressing it with AI doesn’t just shorten the deal lifecycle or improve investment decisions. It also allows funds to test and refine automations they can later scale across the entire portfolio.

This article looks beyond the hype. It outlines how to apply AI deliberately, so it becomes a fund-level capability that accelerates decision-making, increases conviction, and makes value creation more predictable across the entire investment lifecycle.

Why AI matters inside the fund, not just the portfolio

Used correctly, AI acts as a force multiplier across the deal lifecycle:

• It accelerates decision-making, allowing teams to process far more information without slowing down.
• It reduces dead-end deals by testing theses earlier and more rigorously.
• It raises the quality of analysis, from scenario modeling to risk detection and pattern recognition.

The context has changed. Competition for high-quality assets is intense. LPs expect greater discipline, speed, transparency, and evidence-based decisions. Value creation plans are scrutinized earlier and with less patience. In this environment, intuition needs an extra boost. AI will sharpen, not replace it.

Where AI delivers real impact across fund operations

When funds treat AI as a capability rather than a collection of tools, its impact spans the entire investment lifecycle.

1. Target scouting and deal origination

Associates and analysts can only analyze a limited amount of data. AI changes this dynamic. With access to the right data and the right model, an AI agent can act as an assistant, helping make data-driven decisions at scale:

• Continuous target discovery based on strategic filters and look-alike modeling
• Automated add-on mapping for buy-and-build strategies across fragmented markets
• Early trend detection across markets, categories, and adjacencies

Instead of relying primarily on relationships and inbound deal flow, teams can surface opportunities shaped by real market signals.

2. Early due diligence

Due diligence is typically a lengthy and resource-intensive process. In one of our projects for a PE-owned company, sanctions verification that previously took up to seven weeks was reduced to just two hours through automation. In another case, an AI-powered credit risk assessment enriched with an intelligence layer reduced errors by 99%, saved 0.5 FTE, and significantly improved the reliability of risk evaluation.

AI enables:

• Automated sanctions and compliance screening
• Credit risk checks enriched with external data
• Rapid pre-processing of Confidential Information Memorandums (CIMs), vendor materials, and contracts

By automating from due diligence, AI allows teams to move into deeper analysis faster, with clearer red flags and fewer surprises—ultimately shortening the overall deal lifecycle.

3. Deal modeling and thesis validation

An AI agent can act as a consultant, continuously verifying the investment thesis against predefined filters, assumptions, and external data. Much like in deal origination, AI can analyze far more data than a team could manually, and do so in a fraction of the time.

AI enables:

• Multiple downside and upside scenarios generated in hours, not days
• Sensitivity analyses produced and tested continuously
• Automated “challenge reports” that deliberately surface weaknesses in the thesis

Instead of defending a single model, teams explore a broader range of outcomes, increasing both confidence in the investment case and credibility in decision-making.

4. Investment committee decision support

The investment committee must synthesize large volumes of analysis, assumptions, and external inputs under tight deadlines. With access to the right data and clear decision frameworks, an AI agent helps structure, challenge, and complete the investment case.

AI enables:

• Generation of concise investment committee summaries and counterarguments
• Identification of gaps and weak points in the investment thesis
• Synthesis of market, regulatory, and competitive signals

Teams enter discussions with fewer blind spots, stronger preparation, and higher conviction in the final decision.

5. Value creation plan assessment, before the deal closes

Value creation plans are often ambitious by design, but rarely tested before signing. AI can help evaluate whether planned value-creation levers are realistic given the operational, financial, and organizational constraints identified during diligence.

AI enables:

• Evaluation of whether proposed value-creation levers are achievable
• Stress-testing of EBITDA assumptions against operational constraints
• Direct linkage between diligence findings and execution risks

This allows teams to adjust the plan ahead of time—or, when necessary, decide against closing the deal.

6. Reporting and portfolio visibility

Portfolio reporting typically relies on an analyst who receives monthly results from CFOs, usually in Excel, and manually transfers the data into a central dashboard. Investment teams spend hours updating these dashboards, while each portfolio company reports differently, using its own metrics.

Other PE firms use ready-to-use SaaS tools for reporting. However, they still require manual updates and are expensive—especially for larger funds with many portfolio companies, as pricing is typically charged per portco.

With AI and automation, funds can introduce a dedicated reporting interface where portco CFOs input metrics directly and data flows automatically into the fund’s systems. Alternatively, a robot can ingest and structure data from unstructured Excel files without requiring CFOs to change their existing reporting process.

Once data is standardized and flowing consistently, AI can analyze it continuously—flagging issues early, identifying trends, and enabling predictive insights across the portfolio.

7. Operational excellence across the portfolio

These capabilities at the fund level can be scaled across the portfolio. Fund-level implementations serve as proof points to secure buy-in from portfolio company CEOs. Once a solution works in one company, it becomes far easier to build credibility and replicate it elsewhere. Operating Partners can then scale the same automations through repeatable playbooks deployed portco by portco.

Funds can standardize and roll out solutions such as AI-powered order processing, financial operations automation, or working capital optimization across multiple companies. More examples of such automations that directly boost EBITDA are described in a separate blog post.

Why most funds still struggle with AI

The challenge is rarely technical. Most struggle with AI stem from structural issues:

• No governance, leading to risky and inconsistent tool usage
• No training, leaving teams unsure what AI can or cannot do
• Manual, undocumented workflows that AI has nothing stable to attach to
• Fragmented data across spreadsheets, emails, and legacy systems
• Isolated pilots disconnected from value creation and ROI

Many funds start with tools, but the right approach is problem-first. Define the goal, what you want to fix and only then choose the right tools from your toolbox.

The foundations of AI-ready investment teams

Successful funds build foundations before acceleration. At the fund level, this means:

• Clear AI and data governance
• A shared operational data layer
• Standardized processes for deal flow, investment committee materials, and reporting
• Automated portfolio reporting to eliminate Excel-and-email loops
• Practical AI training for analysts, Operating Partners, and partners
• Standardized reporting schemas

The Bottom Line

AI inside the fund is about the value it can multiply—before the deal closes, throughout the holding period, and across the entire portfolio.

• Start with outcomes, not technology.
• Build the foundations first: governance, data, and standardized workflows.
• Use AI where it compounds ROI most: scouting, due diligence, modeling, IC decisions, and value creation plan validation.

Funds that adopt firm-wide AI capabilities today will outperform in the next cycle.