Private equity deal value surged in Q3 2025, crossing $537 million globally. That growth, however, masks some ongoing structural challenges in the market. Deal volumes remain well below last year’s levels, exit activity continues to lag far behind pre-2022 norm, and fundraising is at a decade-long low despite increasing dry powder. Firms are crowding into a narrower band of high-quality assets, driving up competition and compressing timelines. With unpredictable exit windows and holding periods lengthening, execution is no longer just a differentiator — it’s a survival strategy.
That’s why leading firms aren’t just experimenting with AI, they’re operationalizing agentic AI across the deal lifecycle. These systems don’t just automate back-office tasks; they mimic how investment teams work, accelerating private equity research, standardizing insights, and freeing up analysts to focus on strategic thinking. In a market where timing and insight drive returns, agentic AI is becoming the backbone of competitive execution in private equity analytics.
How Generative AI is Breaking Bottlenecks in Private Equity
Private equity firms compete on speed, yet many still grapple with persistent operational frictions that slow the deal cycle. Across our client base, the same constraints and recurring issues that not only delay decisions but pull analysts away from high-value work:
- High-volume deal screening that strains limited analytical bandwidth
- Inconsistent, manual company profiling across teams and geographies
- Industry research pulled from disparate sources, requiring time-consuming synthesis
- Preliminary diligence cycles that stretch for weeks, with teams reviewing CIMs and management presentations manually
Generative AI is beginning to reset this equation. Early applications—company profiles, thematic briefs, and first-pass diligence summaries—are compressing turnaround times in ways that materially affect pace. At one European multi-strategy private equity firm, early adoption improved private equity research throughput by roughly 15%.
This firm has long experienced early-stage private equity research as a bottleneck. We partnered with them for several years, embedding more than two dozen specialists across origination, portfolio work, and private credit. Our 24/5 analytical coverage already allows deal teams to delegate more time to client engagement and revenue-linked activities. Automated reporting within Power BI further reduced repetitive manual effort.
As generative and agentic AI matured, it became clear that even more of the private equity research burden could be lifted. In one instance, leveraging the client’s enterprise ChatGPT environment, our team rapidly extracted and standardized veterinarian clinic addresses as part of a thematic thesis—ultimately mapping ~800 locations into an interactive Power BI tool. A task that once took hours of manual compilation became a near-real-time input into evaluation.
Three factors proved essential to scaling these AI applications effectively:
- Security and governance. Because our teams operated inside the client’s virtual desktop environment, all AI experimentation remained within approved firewalls and compliant processes.
- Integration into existing workflows. Deep familiarity with how deal teams used data ensured AI outputs fit naturally into established processes rather than disrupt them.
- Human oversight. Analysts validated each AI-generated output, ensuring accuracy, reinforcing trust, and keeping judgment at the center of the process.
As the Head of Innovation & Insights put it: “A fantastic example of the importance of creative thinking and resourcefulness. Far exceeded expectations.”
Applied with discipline, generative AI doesn’t simply streamline tasks—it expands organizational capacity and helps deal teams move at the speed today’s market demands.
What We’ve Learned about Implementing Agentic AI
More than chatbots, the real inflection point for private equity lies in agentic AI—systems that don’t just answer prompts but orchestrate multi-step workflows, connect to diverse data sources, and execute tasks autonomously with human oversight. AI integration isn’t about encouraging deal teams to “use ChatGPT more.” It requires systems thinking: clear guardrails, defined handoffs, and thoughtful integration across data environments.
Across our PE clients, agentic AI is already reshaping the early-stage investment value chain:
- Deal Sourcing: Agentic AI can scan thousands of companies using inputs from broker and consulting reports, industry associations, and leading databases—aligning results to predefined criteria. Instead of spending days assembling long lists, analysts start with a curated, thesis-aligned pipeline and can immediately assess strategic fit. This demands more than surface-level web search; effective sourcing depends on integrating structured and unstructured intelligence from tools like Capital IQ, FactSet, AlphaSense, Third Bridge, and other providers. Firms adopting this model have generated 25–30% more qualified opportunities per week and meaningfully sharper pipelines.
- Company Profiling: Drawing on both open-web intelligence and subscription databases, agentic AI rapidly extracts information from filings, databases, and market sources to produce structured profiles in minutes. It excels at summarizing business descriptions, management overviews, recent developments, and company history, while also supporting basic financial and valuation context. Analysts then refine these drafts to match the investment thesis and analytical lens. Across clients, this has driven 15–20% efficiency gains, with greater standardization and less manual rework.
- Industry Research: For sector and thematic work, agentic AI aggregates insights from broker research, consulting reports, industry associations, databases, and macro sources—highlighting growth drivers, competitive dynamics, regulatory shifts, and emerging risks. Analysts still rely on hard data for market sizing, projections, and share estimates, but AI dramatically compresses the time required to synthesize the broader narrative. This human–AI pairing has improved private equity research efficiency by 10–15%.
- Preliminary Due Diligence: Early diligence relies heavily on first-party materials, and agentic AI can ingest CIMs, management presentations, and financial statements to generate zero-draft summaries of business models, competitive positioning, and key financial metrics. AI reliably produces foundational sections—overviews, organizational details, business segments, competitive landscapes, and extracted financials—with minimal intervention. Analysts then apply judgment to the elements that matter most: transaction details, modeling, growth opportunities, SWOT, risks, and return analysis. Firms using this framework have achieved roughly 15% efficiency gains in early diligence.
The impact is tangible: faster turnaround times, improved consistency, and more capacity for high-value analysis. While firms are seeing up to 30% gains across execution, the larger benefit is strategic. By shifting analysts away from low-leverage tasks and toward deeper evaluation and judgment, agentic AI strengthens decision quality—ultimately improving how firms deploy capital.
Where AI Can Easily Fail
Adopting too much AI too quickly can introduce real risk. When underlying data sources aren’t transparent—or when it’s unclear which steps require human judgment—assumptions compound quietly. Decisions begin to rest on shaky foundations. That’s why AI should always function as a tool, not an autonomous decision-maker. Teams need to understand where AI-generated analysis must be stage-gated, reviewed, and fact-checked before it informs an investment thesis.
Evalueserve’s approach is designed to mitigate these risks. We embed agentic AI within a human-led quality framework: our analysts validate key outputs, apply deal-relevant logic, draw on the right proprietary and public data sets, and ensure every deliverable is structured, investment-grade, and ready for use.
Our framework is built on:
- Specialized agents, tailored for PE-specific workflows, working in coordination
- Analyst oversight to ensure accuracy, coherence, and deal relevance
- Secure integration within client environments and data governance protocols
The mandate is not to deploy AI for its own sake, but to strengthen the mechanics of good investing: evaluating targets quickly and rigorously, identifying themes with precision, and preparing IC materials that drive clarity. When those foundations improve, teams move faster—and with far more confidence.
Final Word: Transformation Starts with Strategy
Deal flow may be cyclical, and the next surge may be uncertain, but operational excellence is always a forward-looking investment. Firms that get agentic AI right will be positioned to scale decisively when the market turns.
If your team is still stitching together fragmented data sources, building materials from scratch, or relying on manual processes, the risk isn’t inefficiency—it’s falling behind. In a competitive environment, the firms that lead will be the ones that address operational bottlenecks now and bring their operating model into the AI era.
