The New AI-First Operating Partner

Private equity has begun a fundamental transformation in how it creates value—and in the profiles of the people who create it. The traditional Operating Partner, typically a former CEO or C-suite generalist with broad management experience, is giving way to a new archetype: the technology-native leader with expertise in AI, data science, and digital transformation.

This isn't incremental change. It's a wholesale reimagining of what makes someone valuable in PE operations. Data scientists and AI experts increasingly command premium compensation packages and are expected to drive measurable value creation that traditional operational backgrounds simply cannot match.

The numbers tell a stark story. Heidrick & Struggles reports that between 2022 and 2024, Operating Partners with prior CEO experience dropped from 26% to just 17% of new hires, while technology specialists surged to become the single largest category at 19%—surpassing sales, marketing, and finance. Meanwhile, an FTI survey reports that 75% of PE firms are leveraging AI for value creation in their portfolio companies or plan to in the next 12 months.

The Value Creation Shift

The economics of private equity have fundamentally changed. Accenture research shows that PE firms now focus 79% of their efforts on operational value creation, with leaders believing financial engineering should account for just 25% of their efforts, down dramatically from 51% in the 1980s.

PE’s ability to execute sophisticated technology transformations has become the difference between outperformance and obsolescence. As leverage costs remain elevated and exit multiples stay compressed, firms must win through operational excellence or fall behind. But having technology-savvy Operating Partners on staff doesn't automatically translate to portfolio results.

The Rise of the Technology-Savvy Operating Partner

Technology-savvy Operating Partners have evolved from peripheral advisors to core value drivers, and in 2024-2025, they achieved full strategic parity with Investment Partners at many leading firms.

This evolution occurred in four distinct phases:

Phase 1: External advisors. Early technology involvement came through consultants—expensive, tactical, and disconnected from ongoing operations. Firms paid for recommendations; execution lagged.

Phase 2: The solo tech partner. PE firms began hiring full-time Technology Operating Partners—typically former CIOs or CTOs who translated technical concepts for deal teams but lacked bandwidth to drive transformation at scale.

Phase 3: Specialist teams emerge. Firms built dedicated teams spanning AI, data science, cybersecurity, and cloud infrastructure, recognizing no single expert could cover the full technology landscape. Apollo and Blackstone now deploy sophisticated playbooks through in-house experts and proprietary vendor ecosystems.

Phase 4: Technology at the GP level. Today, technology-savvy Operating Partners participate in investment committee meetings, shape deal theses pre-LOI, conduct technical diligence, and hold board seats across portfolios.

Within this broader evolution, a new specialization has emerged: the AI-first Operating Partner. Demand for these roles spiked 30% in Q1 2025 alone, driven by recognition that even veteran technology executives often lack the specific depth to architect and scale AI initiatives across diverse portfolio companies. However, very few qualified candidates exist yet to meet demand.

This evolution creates a paradox: firms have never had access to better technology expertise, yet many still struggle to capture AI value. Why?

Quantifiable Value Creation: The 5-25% EBITDA Story

The case for AI-first Operating Partners rests not on theory but on documented, repeatable results. Across private equity portfolios, AI and digital transformation initiatives are delivering 5-25% EBITDA improvements—gains that equal or exceed returns from traditional operational levers.

FTI Consulting’s 2024 analysis documented this 5-25% range across industries, with manufacturing at the lower end and sales- and software-intensive businesses reaching the upper bound. Bain reports that AI-driven portfolio companies are achieving 10-25% EBITDA improvements, with generative AI initiatives alone potentially adding 20%.

Real portfolio examples substantiate these figures:

Apollo's portfolio companies: Cengage achieved 40% content production savings and 10-15% reductions in both lead generation and software development costs. Across the portfolio, AI-driven contract analysis of 15,000+ software agreements yielded 65% cost reductions. Yahoo reported 20%+ engineering productivity gains, while Barnes Group achieved 5x ROI in year one.

Vista Equity Partners: 30% coding productivity gains portfolio-wide, with 80% of portfolio companies deploying generative AI. LogicMonitor saved $2M annually per customer through AI-driven customer service triage, while Avalara increased sales rep response time by 65% using GenAI tools.

AI-driven value creation extends across functional areas:

• Procurement: 15-45% cost savings

• Pricing: 25-50% EBITDA uplift potential—the highest-impact area

• Sales & Marketing: 15-30% gains from AI-augmented prospecting and conversion optimization

• Software Development: 10-30% productivity improvements through AI-assisted coding, testing, and deployment

• Customer Service: 15-40% cost reductions via intelligent automation

These returns can materialize quickly. Companies like Barnes Group achieve measurable ROI within 12 months, with medium-term gains accruing over 12-24 months as pilots scale, and long-term benefits compounding over 3-5 years as firms build proprietary capabilities.

Despite these documented results, many firms with AI-first Operating Partners struggle to capture comparable value. The problem lies in the operating model.

The Integration Gap

Here's what happens when firms hire technology expertise without building integration capability:

Portfolio CEOs can't contextualize recommendations. A PE Operating Partner proposes an AI-driven pricing optimization initiative projected to deliver 15% EBITDA improvement. The portfolio CEO, lacking technology fluency, can't evaluate whether the timeline is realistic, whether the data infrastructure exists to support it, or whether the implementation risk is manageable. The initiative gets deprioritized in favor of strategies the CEO understands better.

Investment committees can't govern effectively. Partners approve technology initiatives they don't understand, then express frustration when results take longer than expected. Without the ability to distinguish between execution challenges and flawed strategy, they either rubber-stamp everything (abandoning governance) or second-guess everything (creating friction).

Deal teams miss opportunities during diligence. When evaluating acquisition targets, deal teams that lack technology fluency don't recognize the questions to ask about data infrastructure, AI readiness, or technical debt. They overpay for companies with hidden technology liabilities or undervalue companies with strong AI foundations.

Technology becomes a silo, not a strategy. Technology Operating Partners present quarterly updates that investment partners can't evaluate meaningfully. Board meetings feature technology discussions that feel disconnected from business strategy. Over time, technology becomes "that thing the tech partner handles" rather than a core value creation lever.

The result: impressive talent, modest results, and frustration on both sides.

What Integration Actually Requires

Firms capturing 5-25% EBITDA improvements are building integration capability across investment teams. This means developing enough fluency to:

• Champion technology initiatives effectively with portfolio CEOs and allocate resources appropriately

• Govern technology investments intelligently, distinguishing execution challenges from flawed strategy

• Integrate technology into deal theses before LOI, recognizing opportunities and risks that inform valuation

• Translate between technology and business strategy, connecting AI initiatives to business objectives

This AI-fluency amongst senior PE leadership enables AI-first Operating Partners to deliver their full value.

The Path Forward: What Building Capability Requires

Private equity firms face a binary choice: build technology capabilities that systematically create value or accept competitive disadvantage. The window for catching up narrows quarterly as leading firms compound advantages through proprietary tools, accumulated knowledge, and network effects.

Success requires executive commitment at the founding partner level. Technology must be positioned as co-equal with traditional value levers and with meaningful resource allocation. The math is compelling: if technology focus adds 2-3% to IRR at 3x MOIC, a $5 billion fund generates $300-450 million in additional value against $150-200 million in operating costs over fund life.

Integration into deal flow is non-negotiable. Technology-savvy Operating Partners must participate in investment committees, conduct diligence pre-LOI, and shape investment theses with explicit digital strategies.

Post-close engagement must be systematic. Technology-savvy Operating Partners should lead 100-day planning, conduct AI readiness assessments, identify talent and infrastructure gaps, develop AI transformation roadmaps, and maintain ongoing involvement to ensure execution doesn't stall.

PE firms must provide supporting infrastructure to portfolio companies: benchmarking systems, playbook repositories, AI operating tools, dashboards tracking technology KPIs, and AI workforce upskilling. Building this institutional capability requires commitment to investment and experienced strategy partners.

A Permanent Transformation

The shift towards technology-savvy, AI-first Operating Partners marks private equity's most consequential capability transformation in decades. This is a structural realignment in how companies compete and value is created.

Limited partners have fundamentally reset their expectations as well. During fundraising, they rigorously evaluate technology credentials, demand quantification of AI-driven EBITDA improvements, and expect technology teams to influence deal selection. Weak answers result in delayed closes, reduced allocations, or exclusion.

Competitive dynamics create self-reinforcing spirals. Firms with strong technology platforms win more deals, outperform operationally, raise capital more efficiently, and reinvest aggressively in capabilities. Those without fall progressively further behind.

The window for building competitive technology operating capabilities remains open—but it will close rapidly with the advent of AI. Firms can move from nascent efforts to integrated operations over 24 months, but only with urgent clarity, substantial resources, and sustained leadership discipline. The gap between leaders and laggards widens quarterly, and firms that delay will find catch-up increasingly difficult and eventually impossible.

For private equity leaders, this moment demands recognition that technology has earned permanent parity with procurement, pricing, and traditional operations, as operating reality. The firms that structure for this reality, fund it appropriately, and lead from the top are writing the blueprint for the next generation of PE value creation.

A Practical Path for Mid-Market Firms

Mid-market PE firms face a unique challenge: the strategic imperative is identical to mega-funds, but the economics differ significantly. Building Apollo-scale technology infrastructure isn't realistic for $300M-$1.5B funds, yet competitive dynamics demand capability development. The solution lies in building fluency before building staff. Rather than immediately hiring full-time Technology Operating Partners, mid-market firms should begin with AI readiness assessments across their portfolios—baselining data infrastructure, workforce capabilities, and cultural readiness at each portfolio company. This identifies the 2-3 companies with highest ROI potential for AI initiatives, allowing concentrated resources where they'll generate the fastest measurable returns. Equally important, these assessments build the firm's pattern recognition for what AI-driven value creation actually looks like across different business models.

The second critical step is developing investment team fluency through embedded learning rather than hiring specialists, who become high-priced tech support. Working with experienced operators who can translate AI strategy into investment committee language, mid-market firms can equip their deal teams to recognize AI opportunities during due diligence, shape technology-informed theses, and govern portfolio technology initiatives intelligently. This capability compounds across every deal and portfolio company without the overhead of dedicated technology staff. 

If you would like to learn more about how to develop practical AI adoption plans, NextAccess can help. Please contact me to schedule a complimentary consultation.

NextAccess Authors: Scott Kosch and Valerie VanDerzee

NextAccess guides organizations through AI transformation by fostering sustainable change that optimizes operational excellence while ensuring individuals are engaged, upskilled, and empowered to flourish. We specialize in helping our clients achieve breakthrough improvements in productivity, efficiency, and quality by unlocking the full potential of their people and capabilities.

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