Private equity and private credit firms face a fundamental challenge: how to evaluate more deals, faster, without sacrificing the judgment that sets them apart.
The traditional approach armies of analysts building Excel models until 2 AM, doesn't scale. Deal flow keeps accelerating, and the best opportunities disappear in days, not weeks.
AI changes this completely. Not as a replacement for human expertise, but as a force multiplier that lets deal teams focus on what actually matters: strategy, relationships, and judgment calls that drive returns.
Key takeaways
- AI reduces deal evaluation time from weeks to days by automating financial modeling and preliminary screening
- The highest ROI comes from automating due diligence—not just origination or reporting
- Successful firms treat AI as a way to do current processes 100x faster, not reinvent their entire approach
- Show, don't tell: The best AI implementations start with 2-day proof of concepts, not 6-month planning cycles
- Speed compounds: Firms processing 50% more deals with the same team size see exponential returns on market opportunities
The Three Pillars of PE Operations (And Where AI Fits)
Most private equity firms organize their operations around three core functions. Understanding where AI adds value in each area helps explain why some implementations succeed while others become expensive experiments.
Deal Origination: Finding and Qualifying Opportunities
This is where deals begin. A fund gets word that a company is for sale. Or they proactively hunt for targets in their thesis areas.
Traditional origination looks like this:
- Manually screening inbound opportunities from known funds
- Building target lists from industry databases
- Qualifying deals through preliminary analysis
AI transforms this process by doing these things automatically:
- Automatically enriching and scoring inbound opportunities
- Identifying patterns in successful past deals to find similar targets
- Flagging market signals that indicate selling intent
Due Diligence and Underwriting: The Make-or-Break Analysis
Here's where the real work happens. Teams ingest confidential information memorandums (CIMs), financial statements, and market data to answer one question: should we invest?
According to insights from Osman Ghandour, co-founder of Soal Lab, "The bread and butter of what analysts and associates do is really grunt work, Excel monkey work that should be done at the snap of a finger." His firm has seen teams cut financial modeling time by 90% through intelligent automation.
The transformation is dramatic:
- Before AI: Analysts spend 90% of time crunching numbers, 10% on judgment
- After AI: Flip the ratio—10% on data processing, 90% on strategic analysis
Portfolio and Fund Management: Post-Investment Operations
After the deal closes, the reporting begins. Monthly, quarterly, and annual reports flow between portfolio companies and the fund, each with slightly different metrics and formats.
AI helps in several ways:
- Standardizing reporting across portfolio companies
- Automatically flagging performance anomalies
- Creating real-time dashboards instead of static Excel files
Why Due Diligence Is the Highest-ROI Use Case for AI
Many firms start with origination tools, but the real money is in due diligence automation. Here's why.
Speed Changes Everything
When you can run financial models in hours instead of weeks, you can:
- Evaluate 50% more deals with the same team
- Run multiple scenarios to stress-test assumptions
- Spend more time on strategic questions that actually impact returns
Quality Improves, Not Just Quantity
Faster analysis doesn't mean sloppier work. AI-powered due diligence actually reduces errors by:
- Eliminating manual data entry mistakes
- Ensuring consistent methodology across all deals
- Flagging outliers and anomalies humans often miss
The Compounding Effect
As Uttam Kumaran, CEO of Brainforge, notes: "If you can run scenarios quicker, you can run more of them. You're not limited by time anymore."
This creates a virtuous cycle: more deals analyzed → better pattern recognition → improved deal selection → higher returns → more capital to deploy.
The Build vs. Buy Trap (And Why Services Win)
Many PE firms face a familiar dilemma: build capabilities in-house or buy off-the-shelf software?
The reality, according to Soal Lab's experience, is that neither approach works well in isolation.
Why Pure Software Fails
"These companies are pretty opinionated on how they get things done," Ghandour explains. "Our pitch is: we're not asking you to do things differently. We're asking you to do what you currently do, but at 100x speed."
Pre-built software forces firms to adapt their processes to the tool. But in PE, the process IS the competitive advantage.
Why In-House Builds Struggle
Building internally seems attractive but faces major hurdles:
- Top engineering talent won't join PE firms ("Nobody is going to work at your company," as one consultant bluntly told a client)
- Even if you recruit well, it takes 5 years instead of 1 to build what specialists can deliver
- Maintaining and updating systems becomes a permanent distraction from core business
The Services Solution
The most successful implementations come from specialized services firms that do these things well:
- Understand both PE workflows and modern AI/data capabilities
- Build custom solutions that match existing processes
- Deliver in weeks, not years
- Bring lessons learned from multiple implementations
The Power of Proof: Why 2-Day POCs Beat 6-Month Plans
One of the most striking insights from the Soal Lab experience is the power of rapid prototyping.
Traditional Approach: Analysis Paralysis
Big consulting firms quote 6-month discovery phases. Requirements gathering. Stakeholder interviews. Strategic roadmaps.
By the time they deliver, the market has moved on and the solution is already outdated.
The Show-Don't-Tell Method
Modern AI implementation looks different:
- Initial conversation to understand the use case
- 2-3 days to build a working prototype with synthetic data
- Live demo showing actual functionality
- Iterate based on feedback
"No one has ever come to sales meetings with something we just wanted to show you," Kumaran observes. "We can screenshot or send a Loom, and that puts us over the edge."
Why This Works
- Tangible beats theoretical: Seeing their actual workflow automated creates instant buy-in
- Fast feedback loops: Problems surface immediately, not after months of development
- Competitive advantage: While competitors are still planning, you're already implementing
What Metrics Actually Drive Results
When PE firms implement AI, they often focus on the wrong success metrics. Here's what actually drives results.
Deal Volume Increase
The most tangible metric: how many more opportunities can your team evaluate?
Top firms report 50% increases in deal flow capacity without adding headcount. This isn't about doing more busy work—it's about having bandwidth to pursue opportunities you'd previously pass on due to resource constraints.
Time to Initial Screening
Cutting pre-screening time from 2 weeks to 2 days transforms your competitive position. You can engage with sellers faster, building relationships before competitors even know the opportunity exists.
Model Accuracy and Consistency
When every deal uses the same proven methodology, implemented consistently by AI, you can actually trust cross-deal comparisons. No more wondering if Deal A looks better than Deal B because different analysts used different assumptions.
Why AI Amplifies Rather Than Replaces
Despite the automation potential, successful PE firms understand that AI is a tool, not a replacement for human judgment.
What AI Does Well
- Process standardized data at scale
- Run complex calculations instantly
- Flag anomalies and patterns
- Maintain perfect consistency
What Humans Do Better
- Build relationships with sellers and management teams
- Make judgment calls on market dynamics
- Spot opportunities that don't fit standard patterns
- Navigate complex negotiations
The firms winning with AI use it to eliminate the grunt work so their people can focus on high-value activities that actually drive returns.
Making AI Work With Legacy Systems
One of the biggest challenges PE firms face is integrating new AI capabilities with existing technology stacks.
The Typical PE Tech Stack
Most firms run on these systems:
- Excel for financial modeling
- Specialized platforms like iLevel or Chronograph for portfolio reporting
- CRM systems for relationship management
- Email and document management systems
The Integration Challenge
"There's no versioning. There's no one source of truth because there's three different Excel files and they all show slightly different numbers," Ghandour notes about the typical firm's data chaos.
The Solution Architecture
Successful implementations typically involve:
Building a central data warehouse (usually Snowflake or similar).
Creating clean data pipelines from all source systems.
Implementing transformation logic that's transparent and auditable.
Delivering insights through purpose-built applications or dashboards.
The key is starting small—integrate one critical workflow first, prove the value, then expand.
Move Fast or Get Left Behind
The PE industry is at an inflection point. Firms that embrace AI now will have compound advantages that become impossible to overcome.
The Early Mover Advantage
Consider two firms evaluating the same deal:
- Firm A (traditional): Takes 3 weeks for initial modeling, can evaluate 20 deals per quarter
- Firm B (AI-enabled): Takes 3 days for modeling, evaluates 100 deals per quarter
Firm B doesn't just move faster—they see 5x more opportunities, recognize patterns earlier, and build stronger seller relationships through rapid response.
The Talent Magnet Effect
"Can you attract better people? Are you able to keep your best people?" Ghandour asks. "If they're working with cutting-edge systems that allow them to do so much more with their time, you're going to keep your team more happy."
As millennials and Gen Z enter decision-making roles, firms without AI capabilities will struggle to recruit top talent who expect modern tools.
The Compounding Returns
Speed creates a flywheel effect:
Faster analysis → More deals evaluated.
More deals → Better pattern recognition.
Better patterns → Improved deal selection.
Better deals → Higher returns.
Higher returns → More capital to deploy.
Each cycle strengthens your competitive position.
What happens when you get this right
When PE firms successfully implement AI for due diligence and deal flow, the transformation is dramatic.
Deal teams stop drowning in Excel and start spending time with portfolio companies.
Partners get cleaner data faster, enabling better investment decisions.
LPs see improved returns from better deal selection and faster execution.
The firm builds a sustainable competitive advantage that compounds over time.
The best part? This isn't some far-off future vision. Firms are implementing these systems today and seeing results within months.
"We're not rushed. There's always a sense of urgency, but we're not rushing to see results today or tomorrow." - Osman Ghandour, Soal Lab
FAQ
How long does it take to implement AI for due diligence?
Initial prototypes can be built in 2-3 days. Full implementation typically takes 2-3 months, significantly faster than traditional 6-12 month enterprise rollouts.
What's the typical ROI on AI implementation for PE firms?
Firms report 50% increases in deal evaluation capacity and 90% reductions in financial modeling time. The real ROI comes from better deal selection and faster execution.
Do we need to hire a team of data scientists?
No. The most successful implementations come from partnering with specialized services firms that understand both PE workflows and AI capabilities.
Will AI replace our analysts and associates?
AI handles the repetitive work so your team can focus on high-value activities like relationship building and strategic analysis. It amplifies human capability rather than replacing it.
How do we get started without disrupting our current processes?
Start with a single use case like financial modeling automation. Build a proof of concept in days, not months. Scale based on what works.
Summary
Private equity firms face a clear choice: embrace AI to transform their operations or watch competitors pull ahead with every passing quarter.
The highest ROI comes from automating due diligence—turning weeks of Excel modeling into days of strategic analysis. But success requires the right approach: working with specialists who understand PE workflows, starting with rapid prototypes instead of lengthy planning cycles, and treating AI as a force multiplier for human judgment rather than a replacement.
The firms moving fastest aren't trying to reinvent their entire process. They're taking what works and making it 100x faster. They're showing, not telling, with 2-day proof of concepts that demonstrate real value. And they're building competitive advantages that compound with every deal evaluated.
The technology is ready. The question is… are you?
Schedule a consultation to see how AI can transform your deal flow
