From 90 Days to 35: The AI Due Diligence Framework That's Transforming PE Deal Velocity

Standing in our war room at 5 AM, surrounded by data from five portfolio companies, we faced a familiar challenge: our traditional due diligence process was killing deal velocity. With a $750M fund and an aggressive thesis around industrial tech consolidation, we couldn't afford to spend 90 days on each target's diligence.

That night changed how we approach institutional deal flow forever.

The Hidden Drag on PE Returns

If you're running deals at a mid-market PE firm, you know the real costs:

• Deal teams spend 12-16 weeks on target validation
• Premium targets lost to faster-moving competitors
• Value creation delayed by extended pre-close periods
• Portfolio company integration timelines stretching 6+ months

The math is brutal: every month spent in diligence is a month of delayed EBITDA improvement and multiple expansion opportunities lost.

Institutional-Grade AI Deployment: Beyond the Hype

When we first proposed AI-driven diligence to our investment committee, the response was immediate: "This isn't some Silicon Valley startup—we need institutional-grade analysis."

But the metrics changed minds quickly:

• IRR improvement: +240bps on average
• Deal velocity: 2.5x increase in validated targets per quarter
• Value creation timeline: Advanced by 55 days on average
• Risk detection: 40% more material issues identified pre-LOI

The Institutional Framework That Scaled

1. Pre-LOI Intelligence Layer

Kira and Luminance were deployed across the entire deal pipeline. The critical difference: we built custom models trained on our investment thesis criteria and previous deals.

Key Focus: Pattern matching against our successful exits.

2. Real-Time Thesis Validation

Integration of PowerBI and Tableau with our deal team's financial models. The breakthrough: automated scenario analysis tied directly to exit multiples and IRR targets.

3. Systematic Value Creation Acceleration

Datasite and Intralinks implementation with proprietary ML models that map directly to our 180-day value creation playbook.

Case Study: $250M Industrial Tech Roll-up

Recent deployment for a precision manufacturing roll-up:

• 5 target companies
• 15,000+ contracts
• 3 international jurisdictions

Traditional approach: 4-5 months minimum AI-driven approach: 35 days to binding offer

Results:

• Identified $14M in synergy potential missed by traditional analysis
• Accelerated value creation timeline by 60 days
• Improved exit multiple projection by 1.2x

Implementation at Institutional Scale

1. Investment Thesis Alignment: Map AI capabilities to your specific investment strategy
2. Deal Team Integration: Build models that augment (not replace) deal professional judgment
3. Value Creation Acceleration: Direct link between diligence findings and first-year EBITDA improvement
4. Risk Mitigation: Institutional-grade compliance and reporting frameworks

Your Next Steps

For PE firms targeting the industrial tech middle market: I've developed a comprehensive AI Due Diligence Integration Framework specifically for institutional investors. It includes our proprietary value creation acceleration models and risk mitigation frameworks.

Request the institutional framework and consultation →

About the Author

Marco Giunta serves as Operating Partner focusing on accelerating value creation across portfolio companies. After identifying systemic inefficiencies in traditional PE due diligence, he developed an institutional-grade framework for modernizing deal processes using artificial intelligence. His methods have helped PE firms accelerate deal velocity while improving IRR by 200-300bps.

What's your take on AI in institutional investing? I'm particularly interested in hearing about your firm's approach to accelerating value creation. Share your thoughts or reach out directly at https://marcogiunta.com.