John Radosta, Principal AI Engineer & Partner
March 5, 2026
8 min read
Private Equity
After more than a hundred enterprise AI transformations across financial services, healthcare, manufacturing, and government — and after working directly with PE-backed portfolio companies under the pressure of defined hold periods and exit timelines — I can tell you that the private equity industry is facing a structural shift that most firms are not yet equipped to navigate.
across their portfolio within defined hold periods?
The economics are straightforward. Bain's 2026 Global Private Equity Report found that typical deals now require approximately 10–12% average annual EBITDA growth to generate the same benchmark 2.5× return over five years — a threshold Bain calls "12 is the new 5." With roughly $3.8 trillion in unrealized value sitting across an estimated 32,000 unsold portfolio companies and average hold periods stretching to seven years, every quarter of operational status quo is a quarter of lost returns. The best PE firms are pulling the AI lever within the first 100 days of ownership — and the data on what it delivers is no longer theoretical.
In This Article
The New Math: Why PE Can't Afford to Wait on AI
The tailwinds that powered private equity returns for a decade — cheap leverage, multiple expansion, and timing — have largely faded. Borrowing costs sit in the 8–9% range, leverage ratios have compressed to 30–40%, and purchase multiples are at record levels. Value creation must now be engineered operationally: through pricing discipline, cost structure redesign, productivity, and increasingly, embedded AI capability that directly impacts margins.
The PE industry is responding. 65% of PE respondents marked AI as a top priority in FTI Consulting's 2025 Private Equity Value Creation Index. EY found that 92% of PE firms are already directing at least 25% of their business unit budgets toward AI, with 38% expecting to spend more than half of their total budget on AI by 2026. Three years ago, the largest share of PE firms invested $15–50 million in AI; a third now invest between $50–100 million.
But investment alone doesn't create value. McKinsey's 2026 Global Private Markets Report found that only 6% of GPs see AI delivering high impact in their own internal operations today — though 70% expect high impact within three to five years. The gap between intention and execution is where most of the unrealized value sits.
PE Firm AI Investment Growth (2023-2026)
Shift in AI budget allocation across PE firms
AI Due Diligence: What the Best Firms Assess Before Closing
The most disciplined PE firms have expanded their diligence frameworks to include AI readiness as a standard workstream — often as part of broader business advisory services during the deal process. BCG found that 73% of firms now run digital due diligence on most deals, and those that use an AI lens to assess digital foundations often find instances where modest digital investments can unlock significant AI opportunities. However, only 22% said that a company's digital readiness actually influences go/no-go decisions — a gap that leading firms are closing fast.
Forbes reported that AI maturity assessment is now being added alongside cloud maturity as a standard diligence dimension, with firms evaluating GPU preparedness, AI technology stack efficiency, team capabilities, and identified or overlooked AI-enabled revenue prospects.
The AI Due Diligence Framework
Effective AI diligence evaluates five dimensions before a deal closes:
| Dimension | Key Assessment Areas | Impact |
|---|---|---|
| Data foundations | Quality, accessibility, governance, and whether existing data infrastructure can support AI workloads without requiring massive remediation | Foundation for all AI initiatives |
| Digital maturity | Cloud infrastructure, API readiness, and application architecture | Digital initiatives alone deliver 15–20% ROI; when AI is built on mature digital foundations, total returns reach 30–35% and time to value accelerates by 40% |
| Organizational readiness | Management team skills, culture, and executive sponsorship to execute an AI-driven value creation plan | Determines execution capability |
| AI use case identification | Highest-impact, fastest-to-value AI opportunities in pricing, operations, customer analytics, and back-office automation | Direct EBITDA levers |
| Governance and risk | AI governance frameworks, data privacy compliance, and responsible AI practices | Non-negotiable in 2026 diligence (Deloitte, FTI) |
$3.75M Revenue Opportunity Required Data Infrastructure First
A mid-market event ticketing platform serving 25-30 clients was sitting on $3.75M in abandoned cart revenue annually with no automated way to recover it. The problem wasn't lack of AI capability — it was that critical sales and customer data lived across three disconnected systems with significant reporting delays, making any automation impossible. Account managers couldn't answer basic client questions without pulling in developers. Before any AI solution could be deployed, the company needed to consolidate and clean its data infrastructure. Once that foundation was built, automated cart recovery campaigns went live, unlocking the revenue opportunity. The lesson: AI doesn't fix fragmented data — it requires accessible, integrated data to function. Companies must assess and fix data readiness first, or AI initiatives stall regardless of budget or ambition.
FTI Consulting shared a compelling example: a PE firm evaluated an MSP target with low AI maturity and identified a potential 10% EBITDA increase if AI tools were applied — making AI upside a cornerstone of the investment thesis.
The Portfolio AI Readiness Assessment
Once you own the asset, the first operational question is: where does this company actually stand on AI readiness, and what's the fastest path to measurable value?
The best frameworks assess six dimensions, drawing on models from Amazon and Google, and validated against what we've seen across 100+ engagements:
AI Readiness Assessment: Six Critical Dimensions
Portfolio company evaluation framework
- Leadership and strategy: Is there executive sponsorship? Is there a clear vision for how AI transforms the business — not just a pilot list?
- Data foundations: Data quality, accessibility, governance, and integration across systems. This is the single most consistent barrier we encounter — 43% of chief data officers cite data quality as their top obstacle, and Gartner projects that through 2026, organizations will abandon 60% of AI projects that lack AI-ready data foundations.
- Technology infrastructure: Cloud readiness, compute availability, and whether the existing stack can support AI workloads without a six-month remediation project
- Organizational capability: Skills, talent, cultural readiness, and change management capacity — BCG's 10/20/70 framework makes clear that 70% of AI transformation value comes from people and processes
- AI governance: Ethics, compliance, responsible AI practices, and risk management protocols
- Use case identification: A prioritized catalog of high-impact, fast-to-value opportunities mapped directly to EBITDA levers
The assessment should produce a clear output: a prioritized roadmap of 3–5 AI initiatives with projected EBITDA impact, required investment, timeline, and dependencies — aligned to the value creation plan the operating partner is already running.
We use our North Star Metric methodology for exactly this step. Rather than building a 50-page AI strategy document, we identify the single metric per workflow that best captures AI transformation value, implement tracking in four weeks, and prove ROI within six months.
Learn More About North Star Metric →