Why PE Technical Due Diligence Must Include AI Assessment in 2026
Private equity is sitting on a problem.
Private equity is sitting on a problem.
31,000 portfolio companies. $3.7 trillion in value. Record backlog waiting for exits.
That’s according to Bain’s latest data. It’s the highest backlog ever recorded. And it’s about to collide with the biggest technology shift since cloud computing.
I’ve led technical due diligence on 100+ M&A transactions for private equity and hedge funds. What I’m seeing now is different. AI isn’t just another technology trend to evaluate. It’s becoming the primary driver of value creation and destruction in portfolio companies.
Here’s why your technical due diligence process needs to change.
The AI Integration Gap
Half of Morgan Stanley’s portfolio companies in their private equity middle market funds now have active AI initiatives. Half.
This isn’t a future consideration. It’s a present reality.
But here’s the problem: most technical due diligence processes weren’t built for this. They evaluate code quality, infrastructure scalability, security posture, and technical debt. Important factors. But they miss the AI question entirely.
When I assess a company now, I’m asking:
Does this company have an AI strategy, or are they hoping the problem goes away?
Are they using AI to create competitive advantage, or are competitors using AI against them?
What’s their data infrastructure? Can it support AI workloads?
Do they have the talent to execute on AI, or will they need to acquire it?
What’s the AI-related technical debt they’re accumulating?
These questions determine value more than traditional metrics in 2026.
The $880 Billion Deployment Problem
US private equity dry powder dropped to $880 billion in September 2025, down from $1.3 trillion at its peak. Capital is getting deployed. But is it getting deployed wisely?
The pressure to exit is real. PE confidence hit 86% in Q4 2025, up from 48% in Q1. Firms are looking to move. But valuations depend on demonstrating growth potential.
AI is increasingly that growth story.
Menlo Ventures reports that coding AI alone captured $4 billion in enterprise spend in 2025. That’s a single category. The total enterprise AI market hit $37 billion.
Companies that can demonstrate AI-driven efficiency gains, AI-enhanced products, or AI-enabled business models command premium multiples. Companies that can’t explain their AI strategy face skeptical buyers.
Your technical due diligence needs to identify which bucket a target falls into.
What AI Assessment Actually Looks Like
Traditional technical due diligence asks: “Is the technology sound?”
AI-informed technical due diligence asks: “Can this technology compete in an AI-transformed market?”
Here’s my framework:
1. AI Strategy Assessment
Does leadership have a coherent view of how AI impacts their business? I’ve seen companies with no AI strategy at all, companies with vague “we’re exploring AI” positioning, and companies with specific, funded initiatives tied to business outcomes.
The difference in value is substantial.
2. Data Infrastructure Readiness
AI runs on data. Period. Companies with clean, organized, accessible data have options. Companies with fragmented data across legacy systems face a two-year remediation project before they can do anything meaningful.
This is the hidden cost that kills AI initiatives.
3. AI Technical Debt
Some companies have rushed AI implementations. They’ve deployed models without proper governance. They’ve created shadow AI systems. They’ve accumulated AI-specific security vulnerabilities.
In 2025, 13% of companies reported AI-related security incidents. Of those affected, 97% acknowledged lacking proper AI access controls.
This is a liability that needs quantification.
4. Talent and Capability Assessment
Can the existing team execute on AI, or is there a capability gap? The cybersecurity world has a 4.8 million worker shortage. AI talent is similarly constrained.
Understanding the talent gap is understanding the investment required post-acquisition.
5. Competitive AI Positioning
Where does the target sit relative to competitors on AI adoption? First movers in AI are building moats. Fast followers can still catch up. Laggards face existential risk.
This competitive positioning affects exit multiples more than most traditional factors.
The Value Creation Angle
PE firms don’t just buy companies. They create value and exit.
AI is becoming the primary value creation lever.
Goldman Sachs’ CFO recently said PE dealmaking is improving, with many “jumbo” deals “percolating.” The deals getting done share a pattern: they have clear AI-driven value creation theses.
A manufacturing company with no AI strategy might sell at 6x EBITDA. The same company with a proven AI-driven efficiency improvement story might command 8-10x.
That’s the difference technical due diligence should quantify.
Here’s what I evaluate for value creation potential:
Process automation opportunities. Where can AI eliminate manual work?
Product enhancement possibilities. Can AI improve the core product?
Customer experience improvements. Can AI reduce support costs or improve retention?
New revenue streams. Does AI enable products or services that don’t exist today?
Each of these translates to specific financial projections that support valuation.
The Risk Side
AI isn’t just opportunity. It’s risk.
Shadow AI is the new shadow IT. Employees are using unapproved AI tools with sensitive data. The IP exposure risk is real and growing.
Companies deploying AI agents without proper governance are creating insider threat vectors they don’t understand. Machine identities now outnumber human identities 82 to 1 in typical enterprises.
Technical due diligence needs to surface these risks:
What AI tools are employees using without IT approval?
What data is flowing through third-party AI services?
What AI governance frameworks exist (or don’t)?
What’s the liability exposure from AI-generated outputs?
These aren’t hypothetical concerns. They’re measurable risks that affect deal terms.
Practical Recommendations
If you’re a PE firm with a portfolio company backlog, here’s what I’d recommend:
Update your due diligence checklist. Add AI-specific questions to every technical assessment. The standard 2023 checklist is obsolete.
Assess your portfolio’s AI readiness. Before you market a company for exit, understand its AI positioning. Weaknesses can be addressed. Unknown weaknesses can’t.
Factor AI into value creation plans. Every operating partner should have an AI playbook. The firms that do will outperform.
Build AI assessment capability. Either internally or through partners who understand both M&A and AI. The intersection is where value lives.
The $3.7 trillion backlog will work through the system. The question is: will your exits capture AI-era multiples, or pre-AI valuations?
The answer depends on whether you’re asking the right questions in diligence.
Angel Ramirez has led 100+ technical assessments for private equity and hedge funds and is building TechScore, an AI-powered platform for M&A technical assessment.
