AI in Private Equity: The Sleeping Giant of AI Consulting

TL;DR

• Private equity firms control thousands of portfolio companies but lack systematic AI strategies that compound across holdings
• AI due diligence during acquisition is almost nonexistent — most firms assess AI maturity by headcount and vendor spend, missing actual capability gaps
• Portfolio-level AI playbooks create arbitrage: build once at the platform level, deploy at marginal cost to 10, 20, or 50 companies
• Value creation plans that include AI adoption timelines tied to specific operational KPIs outperform generic “digital transformation” mandates

Private equity firms are sitting on what might be the largest untapped AI opportunity in enterprise software. A mid-market PE firm manages 15 to 40 portfolio companies across industries. Each of those companies faces the same AI adoption challenges — talent scarcity, unclear ROI, vendor confusion, and implementation risk. Yet most PE firms treat AI strategy as a portfolio company problem, handled independently by each management team with no shared playbook, no platform leverage, and no systematic approach.

This is unusual. PE firms systematize everything else — financial reporting, procurement, talent management, operational benchmarking. They build platform capabilities and deploy them across the portfolio to create value that individual companies could not achieve alone. AI is the one area where that playbook has not been applied, and the firms that apply it first will have a structural advantage in both value creation and exit multiples.

The Portfolio AI Arbitrage

Consider the math. A PE firm with 25 portfolio companies wants to implement AI-driven customer churn prediction. If each company approaches this independently, they each bear the full cost of vendor evaluation, data infrastructure, model development, validation, and organizational change management. Multiply by 25.

Now consider the alternative. The PE firm’s operating team builds a churn prediction playbook — vetted vendors, reference architecture, data requirements checklist, implementation timeline, and performance benchmarks. Each portfolio company receives the playbook and adapts it to their specific data and business context. The platform investment is made once. The deployment cost is marginal. The learning compounds.

This is not theoretical. It is how PE firms already operate procurement (group purchasing organizations), finance (standardized reporting packages), and talent (shared executive recruiting). AI is just the next operational capability to receive the platform treatment.

AI Due Diligence: The Missing Capability

When PE firms evaluate acquisition targets, they have rigorous processes for financial due diligence, legal due diligence, commercial due diligence, and operational due diligence. AI due diligence is almost nonexistent.

What passes for AI due diligence today is a surface-level review: How many data scientists does the company employ? What AI vendors do they use? What is the annual AI/ML spend? These questions tell you almost nothing about whether the company has meaningful AI capabilities or is spending money on tools nobody uses.

What Actual AI Due Diligence Looks Like

Substantive AI due diligence for acquisition targets should evaluate:

The answers to these questions directly affect the value creation plan. A target with clean, structured data but no AI capabilities is a strong candidate for rapid AI deployment using the platform playbook. A target with sophisticated models but poor data infrastructure needs foundational work before AI can scale. A target with neither needs to be priced accordingly.

AI Risk in Due Diligence

AI also creates risks that should be evaluated during diligence:

• Concentration risk: Is the company dependent on a single AI vendor whose pricing or terms could change?
• Regulatory exposure: Are AI applications compliant with industry-specific regulations (HIPAA, SOC2, GDPR)?
• Technical debt: Are ML models maintained and monitored, or are they “set and forget” deployments degrading silently?
• Talent risk: Is AI capability concentrated in one or two individuals who could leave?

Value Creation Playbooks With AI

PE value creation plans traditionally focus on revenue growth, margin improvement, and operational efficiency. AI intersects all three, but only when tied to specific, measurable operational KPIs — not vague mandates to “adopt AI.”

The key is specificity. “Implement AI for customer service” is not a value creation initiative. “Reduce average handle time by 25% through AI-assisted agent responses within 6 months” is. The initiative has a timeline, a metric, a mechanism, and a clear connection to margin improvement.

Why This Matters for Exit Multiples

Buyers — whether strategic acquirers or the next PE sponsor — increasingly evaluate AI maturity as part of their own due diligence. A portfolio company with documented AI capabilities, proven ROI, and scalable infrastructure commands a premium over a comparable company that has not adopted AI.

More importantly, a PE firm that demonstrates a systematic approach to AI across its portfolio — a repeatable playbook that has been deployed multiple times with measurable results — creates a narrative that attracts LP interest and differentiates the fund’s value creation story.

McKinsey’s 2025 data shows that while 78% of organizations report using AI in at least one business function, only 17% report AI contributing more than 5% to EBIT [1]. The gap between adoption and impact is where PE firms can create differentiated value by focusing not on whether portfolio companies use AI, but on whether AI actually moves the numbers.

The Consulting Opportunity

Most AI consulting firms sell to individual enterprises. They run assessments, recommend vendors, and implement solutions one company at a time. The PE model inverts this: one engagement with the operating team, deployed across the portfolio, with compound returns.

This is why we see private equity as the sleeping giant of AI consulting. The buyer is not a single CTO trying to justify an AI budget. It is an operating partner who controls the technology strategy for 20 or 30 companies and needs a playbook that works at portfolio scale.

If your firm is building an AI strategy across portfolio companies and wants to avoid the pattern where each company reinvents the wheel independently, a sprint zero engagement can establish the shared playbook.

References:

[1] McKinsey & Company. “The State of AI: How Organizations Are Rewiring to Capture Value.” 2025.