How Private Equity and Venture Capital use AI and Automation
16 September 2025
Table of Contents
AI is Reshaping Investment Workflows
Walk into any private equity or venture capital firm today, and you'll notice something different. Junior analysts aren't drowning in spreadsheets at 11pm. Due diligence timelines that once stretched for weeks now wrap up in days. Portfolio monitoring that required quarterly deep-dives now happens in real-time.
What changed? AI stopped being a buzzword and started delivering results.
The numbers tell the story. Recent research from Bain & Company reveals that nearly 20% of portfolio companies have already operationalized generative AI use cases and are seeing concrete returns. More striking still: when Bain surveyed private investors representing $3.2 trillion in assets under management, they found that a majority of their portfolio companies were actively testing and developing generative AI applications.
This isn't incremental improvement—it's transformation at speed. But here's what's fascinating: while senior partners debate strategy, a shadow revolution has been quietly unfolding. Junior professionals are already embedding AI into their daily workflows, discovering organic applications that save hours on routine tasks. The real question isn't whether AI will reshape private equity and venture capital. It's whether your firm will lead or lag in this transition.
The firms pulling ahead share one trait: they're not waiting for perfect solutions. They're learning by doing, failing fast, and scaling what works.
Why Investment Firms Can't Afford to Wait
The investment world has always rewarded speed and insight. Spot an opportunity first, complete diligence faster, and execute decisively—these advantages compound. AI amplifies all three.
Consider the scale of inefficiency AI addresses. A typical investment analyst spends the majority of their day processing documents and extracting data. Confidential Information Memorandums, financial statements, market reports, due diligence materials—hundreds of pages that demand attention but don't require strategic judgment. This creates a bottleneck that limits how many deals firms can evaluate and how deeply they can analyse each opportunity.
The competitive dynamics are shifting rapidly. Data from Bain suggests that more than 60% of private equity firms are already using at least one AI tool to improve sourcing, screening, or diligence. If you're not among them, you're starting to fall behind. And given that most venture capital firms review over 1,000 proposals annually with the majority rejected within minutes, the firms using AI to process this volume have a clear edge.
But speed alone isn't the prize. AI's pattern recognition capabilities can surface insights that human analysts miss—subtle correlations in market data, hidden red flags in financial statements, or emerging opportunities in overlooked sectors. When Blackstone acquired AirTrunk for $16 billion in Q3 2024 (the largest deal of that quarter), you can bet their analysis was AI-augmented.
The reality is stark: AI adoption in financial services has reached an inflection point. The technology has moved from experimental to essential. Firms that master it will spot better deals, complete diligence more thoroughly, and create more value across their portfolios. Those that don't will simply lose deals to competitors who can move faster and see further.
Building Your AI Advantage: Four Critical Components
Most firms understand they need an AI strategy. Fewer know what that actually means beyond "we should probably look into this." Let's cut through the ambiguity.
An effective AI adoption strategy starts with brutal honesty about where you are today. Not every firm needs the same approach—Vista Equity Partners has gone all-in with an internal army of AI specialists, while Apollo Global Management built a centre of excellence staffed with external experts. Your model depends on your firm's culture, specialisation, and resources.
But every successful strategy includes these four elements:
1. Strategic Use Case Identification
Start by auditing where your team spends time on high-volume, repetitive tasks. The sweet spot for AI sits at the intersection of "this takes forever" and "this doesn't require creative judgment." For most firms, that means:
- Processing incoming deal flow (screening pitches, scoring opportunities)
- Extracting data from due diligence materials
- Monitoring portfolio company performance metrics
- Generating reports for investment committees
- Conducting competitive intelligence and market research
Don't fall into the trap of trying to automate everything. AI works brilliantly for pattern recognition and data extraction. It struggles with nuanced judgment calls and relationship management. Focus on freeing up your team to do what humans do best.
2. Tool Selection and Integration
The AI tools landscape has exploded. You've got specialised platforms for document processing (V7 Go, Hebbia), CRM systems with AI intelligence (Affinity, Salesforce Einstein), knowledge management tools (Glean), and general-purpose models (ChatGPT, Claude, Google Gemini).
Here's what matters: security first, especially for PE/VC firms handling sensitive financial data. Any AI solution must offer enterprise-grade security, preferably with private deployment options. You need to ensure that confidential deal terms, proprietary strategies, and portfolio company data never leave your control.
Integration is the second critical factor. Tools that don't fit into existing workflows create friction and get abandoned. The best solutions either integrate seamlessly with your current systems or replace them entirely with something demonstrably better.
3. Capability Building
Technology alone won't deliver results. Your team needs to know how to use it effectively. Vista Equity Partners requires each portfolio company to submit quantified goals and benefits from their generative AI initiatives. Apollo runs workshops demonstrating what's working across their portfolio. Hg Capital encourages portfolio companies to share learnings with each other, leveraging their tight focus on similar business models.
The approach varies, but the principle holds: invest in AI literacy. That might mean hiring AI specialists, partnering with implementation experts, or simply creating space for experimentation and learning. The firms winning with AI treat capability building as seriously as the technology itself.
4. Governance and Risk Management
AI introduces new risks—data security, output accuracy, regulatory compliance. Large language models occasionally produce "hallucinations"—plausible-sounding but incorrect information. For investment professionals whose reputations depend on reliable analysis, these mistakes carry serious consequences.
Effective governance means establishing clear protocols: when to use AI, how to validate outputs, who bears responsibility for AI-assisted decisions. It means ensuring human oversight remains in place for critical decisions. And it means staying current with evolving regulations around AI use in financial services.
Real-World Applications: Three Automations That Deliver ROI
Theory matters less than practice. Here's how leading investment firms are putting AI to work:
Accelerated Deal Sourcing Automation in Private Equity and Venture Capital
The Problem: VC and Private Equity firms drown in deal flow. Thousands of pitch decks flood in annually, each requiring evaluation. Manual screening creates a bottleneck and risks missing promising opportunities buried in the volume.
The AI Solution: Modern AI platforms can analyse pitch decks at scale, extracting key metrics—market size, revenue trajectory, team credentials, competitive positioning. The system scores each opportunity against the firm's investment thesis and flags high-potential deals for human review.
The Results: Leading firms report processing 10x more opportunities in the same timeframe, with improved accuracy in identifying companies aligned with their strategy. More importantly, they're catching opportunities that previously slipped through purely due to volume.
Example: One mid-market VC firm implemented AI screening and discovered they were consistently missing promising early-stage companies in specific sectors—not because the opportunities weren't there, but because volume overwhelmed their analysts' capacity to review everything thoroughly.
Accelerated Deal Sourcing Automation in Private Equity and Venture Capital
This AI Automation helps investment teams accelerate their deal sourcing by automatically analyzing company, industry, and performance metrics from pitch decks, enabling the review of 10× more opportunities through significantly faster and more efficient opportunity processing.
Deal Sourcing
PitchBook, Grata
Pitch Decks
Automatically analyse company, industry and metrics
Faster processing of investment opportunities
Review 10× more opportunities
Intelligent Due Diligence Automation in Private Equity and Venture Capital
The Problem: Due diligence requires extracting and analysing vast amounts of unstructured data from diverse documents—CIMs, financial statements, contracts, compliance records. It's time-intensive, error-prone, and delays deal execution.
The AI Solution: AI document processing platforms parse complex financial documents, extract relevant data points, identify regulatory red flags, and cross-reference claims across multiple sources. The technology creates structured, searchable knowledge bases where every contract clause, financial statement, and compliance requirement is instantly accessible.
The Results: Firms report cutting due diligence timelines by 40-60% while improving the depth and accuracy of analysis. Deloitte research shows meaningful reductions in processing times for compliance and contract review tasks through AI automation.
Example: Apollo's portfolio company Cengage has driven costs down 40% in select content production processes and 15-20% via automated lead generation, while simultaneously improving output quality.
Due Diligence Automation in Private Equity and Venture Capital
This AI Automation helps investment teams streamline due diligence by parsing prospective company documents to identify critical red flags, delivering a significant reduction in review time and increasing overall deal efficiency by 40% to 60%.
Due Diligence
Drooms, Orbital
Documents from potential investment
Parse documents and identify red flags
Reduction in time due diligence takes
40-60% reduction in time
Portfolio Performance Monitoring in Private Equity and Venture Capital
The Problem: Traditional portfolio monitoring relies on quarterly reports submitted in inconsistent formats. By the time issues surface, valuable time has been lost. Management teams spend excessive hours preparing reports rather than running their businesses.
The AI Solution: AI-powered analytics dashboards aggregate data continuously across portfolio companies, identify performance trends automatically, and alert teams to emerging risks or opportunities in real-time. The system normalises data across different formats and generates customised reports for different stakeholders.
The Results: Portfolio managers gain visibility into their companies' performance without waiting for quarterly cycles. Management teams spend less time on reporting, more on execution. Issues get flagged early enough to address proactively.
Example: Vista Equity Partners tracks AI adoption and results across its 85+ portfolio companies systematically, using regular councils where executives share learnings. This portfolio-wide approach accelerates adoption and amplifies impact.
Portfolio Performance Monitoring in Private Equity and Venture Capital
This AI Automation helps Private Equity and Venture Capital companies streamline their data analysis for current and prospective portfolio companies by extracting and then aggregating data metrics from disparate data sources, saving considerable time and effort.
Portfolio Performance Monitoring
Planr, Haptiq
Quarterly Reports
Extract and aggregate data metrics
Metrics in a format that can be easily analysed
Saves 1 hour per month per company
These aren't theoretical use cases. They're live implementations generating measurable returns today. The firms deploying them have moved beyond pilots to scaled production.
Navigating the Risks: What Could Go Wrong
AI implementation isn't risk-free. The firms succeeding are those that acknowledge the challenges head-on rather than dismissing them.
Data Security and Confidentiality
PE and VC firms handle extraordinarily sensitive information. A single data breach could destroy relationships with portfolio companies and limited partners alike. Traditional cloud-based AI solutions raise legitimate concerns about where data flows and who has access.
The solution lies in private deployment options—AI platforms that operate within your security perimeter rather than external cloud services. This ensures sensitive financial data never leaves your control while maintaining SOC2 and GDPR compliance. It's non-negotiable, not optional.
Output Accuracy and Validation
AI tools occasionally produce plausible but incorrect information. For investment decisions involving millions or billions of dollars, "occasionally" is too often. Every AI-generated insight requires human validation, especially for critical decisions presented to investment committees.
Leading firms implement verification protocols: AI outputs must trace back to source documents, conclusions must show their reasoning, and qualified professionals must review and approve before any output influences investment decisions.
Change Management and Adoption
Technology alone doesn't transform organisations—people do. And people resist change, particularly when new tools feel threatening rather than empowering. Apollo encountered this reality and responded by building an ecosystem of AI specialists to support portfolio companies through implementation. Vista gamifies adoption through hackathons where companies compete to develop the best AI use cases.
The pattern is clear: successful firms invest as much in change management as they do in technology. They address employee concerns directly, demonstrate value quickly through quick wins, and create environments where experimentation is encouraged rather than punished.
Integration Complexity
Every PE and VC firm operates with established systems and workflows refined over years. New AI tools that don't integrate cleanly create friction and get abandoned. The firms succeeding focus on solutions that either integrate seamlessly with existing platforms or offer such compelling improvements that wholesale replacement makes sense.
Hg Capital's David Toms put it bluntly on Bain's Dry Powder podcast: portfolio companies are more receptive to solutions shared by peers than those imposed from above. The best implementations work with the grain of existing culture rather than against it.
The Firms Leading the Charge: Vista, Apollo, and Hg
Three firms exemplify different but equally valid approaches to AI mobilization and their experiences offer roadmaps for others.
Vista Equity Partners: All-In Commitment
Vista has declared generative AI a paradigm shift in innovation representing a multitrillion-dollar opportunity. Firm leaders believe AI's impact will be so profound that it will rewrite the Rule of 40—the traditional yardstick for evaluating SaaS companies. As AI helps companies enhance products and cut costs, Vista expects the new standard for revenue growth plus margin will reach 50% or even 60%.
That conviction drives action. Vista has assembled an internal army of professionals dedicated to helping its 85+ portfolio companies apply AI across product innovation, R&D, go-to-market strategy, and operations. As part of annual planning, Vista requires each portfolio company to submit goals and quantified benefits from generative AI initiatives.
The results speak volumes. 80% of Vista's majority-owned portfolio companies are deploying generative AI tools internally or developing new AI products. Portfolio company LogicMonitor's Edwin AI solution generates an average $2 million annual savings per customer, driving meaningful recurring revenue growth. Avalara uses generative AI from Drift to increase sales rep response time by 65%.
Vista has even gamified AI adoption through annual hackathons in the US and India where companies compete to develop the best use cases. Projects launched less than two years ago have already become revenue-generating products at scale today.
Where AI-enabled solutions can deliver ROI for the end customer (not just the portfolio company), the odds of outpacing the market soar - Vista Equity Partners
Apollo Global Management: Ecosystem Orchestration
Apollo took a different path, building a centre of excellence staffed with external AI experts rather than relying solely on internal resources. This CoE serves as a central resource keeping portfolio companies current on technology trends and proven solutions.
The centre appraises vendors, evaluates use case ROI, and creates an environment of continuous learning. It runs regular workshops demonstrating what's working across the portfolio and what delivers highest ROI. Each workshop begins with tangible success stories generating meaningful returns and ends with homework assignments for portfolio company leaders.
Apollo's approach recognizes that its diverse portfolio requires flexibility. The CoE connects management teams with appropriate implementation partners based on specific needs rather than imposing one-size-fits-all solutions.
Portfolio company Cengage is executing eight AI projects improving productivity in sales enablement, customer care, content production, and software development. Early results show costs down 40% in select content production processes, 15-20% via automated lead generation, and 15% in customer care. Cengage has also launched two new AI products generating additional revenue streams.
Apollo's Shutterfly launched an AI auto-fill feature for photo book creation that generated $5 million in new revenue in its first year, while AI-enabled code assist produced 22% productivity gains in a critical replatforming project.
Hg Capital: Specialization as Leverage
Hg takes advantage of its tight focus on midsize business software companies with similar operating models. Because portfolio companies face the same problems, solutions that work for one often work for others. This specialization creates natural knowledge sharing amplified by management teams' desire to both help and compete with each other.
Hg is using generative AI to "refactor" legacy code from outdated software languages to modern ones, extending the life of popular products across the portfolio. The firm also uses AI to mine massive databases for sales prospects and M&A targets with specific characteristics.
One accounting software company in Hg's portfolio deployed an AI layer that completely transformed the user experience. Instead of presenting raw numbers requiring interpretation, the AI analyses data overnight, identifies what needs attention based on historical patterns, and prioritizes clear actions when accountants open the application each morning. This gives highly paid employees a jump-start on their day, freeing time for higher-value activities.
Hg's David Toms encourages portfolio companies to explore AI use cases without excessive constraints: "Don't put an adult in the room because the adult will start saying, 'Why don't you do this with the blocks?'" Instead, Hg identifies common challenges and encourages teams to solve them collaboratively, leveraging their natural instinct to help each other.
They want to talk to other people in the arena fighting the battle - David Toms, Managing Director at Hg
The Path Forward: Making AI Work for Your Firm
The firms profiled above demonstrate that there's no single correct approach to AI adoption. What works depends on your firm's culture, specialization, and resources. But certain principles hold regardless of approach.
Start with Strategy, Not Technology
AI is a tool in service of strategy, not a strategy itself. The firms succeeding are those challenging portfolio companies to identify their top business priorities first, then determining how AI might accelerate progress against those objectives. Unfocused experimentation with AI yields unfocused results.
Move Fast and Learn
Perfect information won't arrive. The technology is evolving too quickly. The firms pulling ahead are comfortable making decisions with incomplete information, running experiments, and iterating based on results. Vista's hackathons, Apollo's workshops, and Hg's peer-to-peer learning all embody this principle.
Invest in Capabilities
Technology without people who know how to use it delivers no value. Every successful firm invests heavily in building AI literacy across their organizations—through dedicated teams, external partnerships, or systematic knowledge sharing. This isn't optional overhead; it's core infrastructure for modern investment firms.
Focus on Value Creation, Not Cost Cutting
The highest ROI comes from using AI to enhance products, boost revenue, and create competitive advantages—not merely cutting costs through automation. Vista's emphasis on delivering customer ROI, Apollo's focus on new product development, and Hg's attention to transforming user experiences all reflect this value-creation mindset.
Solve for Speed and Focus
New technologies like agentic AI and advanced reasoning models are moving with blinding speed. The firms that will benefit most are those that can rapidly assess opportunities, marshal resources, and apply new capabilities to strategic imperatives. This requires both decisiveness and discipline—moving quickly on high-potential applications while avoiding distraction from tangential opportunities.
Three Questions Your Firm Should Be Asking
Bain & Company suggests every PE and VC firm should ask itself:
- Have we assessed the risks and opportunities generative AI creates for each of our portfolio companies? Not in general terms, but specifically: which companies face disruption from AI-enabled competitors? Which could use AI to accelerate growth or defend market position?
- Are our companies using generative AI to tackle their most important strategic priorities, or are they still dabbling? Pilot projects are fine for learning, but transformation requires focusing AI on initiatives that matter.
- Have we thought through which approach to AI mobilization best suits our firm's culture and resources? Building internal teams, creating partner ecosystems, directly supporting portfolio companies, or facilitating peer learning—each has merits. The right choice depends on who you are.
Add a fourth: Are you moving fast enough? The window for competitive advantage through early AI adoption is closing. Within 18 months, AI literacy will be table stakes rather than differentiator. The firms benefiting disproportionately are those acting decisively today.
The Bottom Line
Generative AI isn't a panacea. It's a powerful tool that must be applied purposefully, practically, and strategically. Like any technology, learning by doing is key to harnessing its potential.
The data is clear: firms that have operationalised AI use cases are seeing concrete results. Portfolio companies are launching new products, improving margins, and gaining competitive advantages. Investment teams are evaluating more opportunities more thoroughly in less time.
But perhaps most importantly, the professionals using AI effectively report spending more time on work that matters—building relationships, thinking strategically, and making the complex decisions that create genuine value. They're freed from the drudgery of data extraction and document processing to focus on what machines can't do: exercise judgment, spot opportunities, and build the relationships that make deals happen.
The race is on. The question isn't whether AI will transform private equity and venture capital—that's already happening. The question is whether your firm will be among those leading the transformation or scrambling to catch up.
The time to get started is now.
Exactimo helps ambitious companies make the most out of AI and Business Automation. Find out more here.