AI and the future of private market investing

This podcast episode is based on the Deloitte Luxembourg article below and includes content generated, assisted, or edited using artificial intelligence technology. It has been reviewed by a human prior to publication. The voices featured are synthetic. This podcast is provided for general information purposes only and does not constitute any kind of professional advice rendered by Deloitte Luxembourg. Deloitte Luxembourg accepts no liability for any loss or damage whatsoever sustained by any person who uses or relies on the content of this podcast. 

Introduction

The first article in this four‑part series established why agentic artificial intelligence (AI) matters for private markets operations. Where generative AI (GenAI) boosts individual productivity, agentic systems are transformational: they chain tasks, use tools, and operate autonomously. This article focuses on where that distinction matters most: the investment lifecycle itself, from initial deal screening to long-term portfolio oversight.

Nearly two-thirds of private equity (PE) general partners (GPs) are running AI pilots¹, yet 95% of these enterprise initiatives fail to deliver a measurable return on investment (ROI).² The gap is not technological maturity, but a lack of genuine AI engineering capability combined with deep private markets domain expertise. AI without investment logic encoded directly into its reasoning layers remains a productivity tool, not a strategic capability.

Industry challenges: Where the investment lifecycle breaks down

Complexity sits at the core of the private markets’ investment lifecycle - the space where opportunities are screened, risk is priced, and portfolios are monitored across PE, infrastructure, real estate, and private credit.

Take deal origination. A mid-market PE manager typically logs upwards of 600 teasers per year but only has the analytical bandwidth to deep dive into fewer than half of them.³ Analysts waste eight to 12 hours every week just parsing confidential information memorandums (CIM) and typing data. Without systematic filtering against fund mandates, firms burn diligence capacity on irrelevant opportunities and risk missing the highest-value deals.

Due diligence amplifies this strain. Financial advisers produce quality of earnings (QoE) adjustments, legal teams review covenants, specialists in environmental, social and governance (ESG) evaluate compliance, and commercial teams stress-test market assumptions. These parallel workstreams rarely converge, leaving insights to be manually cobbled into investment committee (IC) materials late in the process.

Portfolio monitoring brings its own friction. Reconciling data for quarterly valuation packs across 25 to 30 assets consumes one to two days of analyst time per asset. Portfolio companies report in non-standard formats weeks after month-end.

Managing 10 or more assets across healthcare, software, and industrials means facing incompatible data schemas, clashing revenue policies, and unique covenant definitions. Data management systems range widely from enterprise resource planning (ERP) platforms to manual Excel trackers, meaning early warning signals often surface too late.

Finally, limited partner (LP) reporting expectations are formalizing through frameworks like the Institutional Limited Partners Association (ILPA), yet many managers still crunch data manually each quarter. These are structural gaps in how investment judgment and data converge, compounding with every fund cycle.

Where AI is accelerating value creation

Every friction point has a corresponding agentic solution. Four use cases stand out because firms are already deploying them to achieve measurable results.

1. Deal screening breaks the origination bottleneck. A purpose-built agent ingests a CIM in seconds, extracts key metrics, and scores the opportunity against mandate criteria. It then writes directly into the deal pipeline and customer relationship management (CRM) system, routes shortlisted deals to the right team, and automatically flags exclusions. What used to take half a day now takes minutes.
2. Due diligence solves the convergence problem through parallel agent architecture. Dedicated agents run simultaneously: one parses the QoE report, another checks contracts for covenant risk, and a third scans ESG disclosures against fund policy. A synthesis agent then cross-checks all streams to assemble a structured risk summary. Deloitte client deployments demonstrate up to a 90% reduction in risk memo production time, often compressing an eight-day process to a single day.
3. Valuation benchmarking tames the quarterly crunch. The agent retrieves sector comps from market data sources, filters outliers, runs discounted cash flow (DCF) sensitivities, and proposes a valuation range within minutes. It surfaces key assumptions for the investment professional to challenge rather than hiding behind a black box. International Private Equity and Venture Capital (IPEV) guidelines allow material discretion on comparable selection and discount rates; that discretion must be encoded into guardrails, never delegated blindly to a model.⁴
4. Portfolio monitoring closes the loop. The system continuously monitors covenant compliance, triggers alerts as leverage ratios approach breach thresholds, and flags budget variances long before the quarterly pack is assembled. In Article 8-classified PE funds—which grew from 25% of new onboardings in 2022 to 40% by 2024⁵—this same layer handles ESG key performance indicator (KPI) normalization alongside financial metrics.

The gains are real, especially for unstructured data and high document volumes where vendor tools fall short. But capturing value at scale requires more than pointing an off-the-shelf model at a database and hoping for insights.

Tech × industry knowledge as a requirement

International Data Corporation (IDC) research shows that for every 33 AI pilots launched, only four reach production, a meager 12% conversion rate.⁶ In private markets, where data foundations are fragile, and technology transformation experience are limited, the odds are even steeper.

There is no off-the-shelf agentic AI that works reliably in high-stakes investment environments. Foundation models do not natively understand domain-specific judgment calls. Without encoding that expertise, models hallucinate, misclassify risk, and deliver unactionable outputs - eroding trust and stalling adoption.

Successful firms embed deep industry knowledge into the agent from day one. This demands strong executive sponsorship to align investment, technology, compliance, and operations teams. Effective deployment teams combine data engineers, machine learning (ML) engineers, and investment professionals who validate outputs and encode edge cases immediately. Firms fine-tuning models on proprietary data build systems that improve with use rather than plateauing after the pilot.

Trust compounds through accuracy in hard cases, not easy ones. A valuation agent that handles straightforward comps but fails on distressed assets or minority stakes immediately loses analyst confidence. Human-in-the-loop design must define precisely where agents escalate, what triggers IC review, and how output sharpen professional judgment rather than bypassing it.

What managers must do now

The question is not whether to act, but whether firms can afford to wait. Even an imperfect deployment builds a clean data foundation - a massive strategic asset for whatever comes next. More importantly, it forces teams to adapt. The firms that start now will outpace the ones still debating the timing.

Embedding AI into the investment lifecycle is an operating model decision. Leaders should follow a disciplined sequence:

• Phase 1: Foundations. Audit data across the lifecycle, including CIMs, QoE, portfolio KPIs, ESG disclosures, and fund financials. Standardize and define KPI taxonomies by asset class. Every downstream agentic system is only as reliable as the data layer beneath it.
• Phase 2: Targeted use cases. Choose one or two bounded pilots, such as a deal screening agent for a single sector fund, or automated covenant monitoring for a credit strategy. Define success metrics before deployment to keep pilots from drifting.
• Phase 3: Governance and scaling. Clarify which outputs flow directly into workflows and which require human sign-off. Define escalation triggers and audit trails and formally integrate them into IC and valuation committee processes.
• Phase 4: Strategic integration. At maturity, AI becomes a core competitive advantage. Embed it into value creation playbooks and LP reporting. Forward-looking GPs are already positioning proprietary AI infrastructure as a fundraising differentiator.

Firms that follow this sequence will emerge with AI deeply embedded into how capital is allocated and monitored. Treating AI as disconnected tools risks spending two fund cycles in pilot purgatory as the gap widens.

^{All infographics were generated using AI technology (Gemini) based on prompts and conceptual direction provided by Deloitte.}