By Kelley Jamison, Founder & Managing Partner of Kestrel Bay & Heidi Lanford, Founder & CEO of NavAlytix AI
Artificial intelligence is everywhere in private equity – but results are not.
AI is now a centerpiece of private equity strategy. Investment committees discuss it, operating partners promote it, and it is embedded in value creation plans. Boards expect portfolio company CEOs to have a robust AI strategy. Yet across the mid-market, a clear pattern is emerging: most AI initiatives never move beyond experimentation and fail to deliver measurable results.
Despite near-universal belief in AI’s potential, very few portfolio companies have translated experimentation into sustained EBITDA impact by improving operations, margins, or growth.
For private equity firms facing extended holding periods, slower exits, and greater pressure to generate operational value, this gap between promise and performance matters more than ever. AI is not a future capability. It is an immediate value creation lever – and firms that fail to operationalize it risk falling behind.
The AI Paradox in the Mid-Market
Recent surveys of mid-market portfolio company CEOs reveal a striking disconnect:
- 98.5% believe AI will create value for their company
- Only ~7% have implemented a coordinated AI strategy
- More than half remain stuck in isolated pilot projects
This phenomenon – known as “pilot purgatory” – involves running isolated use cases that never scale into core operations. Companies experiment with chatbots, analytics tools, or automation scripts. They run proof-of-concept pilots. Teams attend AI workshops. Portfolio companies bring in consultants. But these initiatives are not driving real, scalable results. For private equity sponsors, this gap represents lost time, missed value creation, and ultimately, lower exit outcomes.
Why This Matters Now for Private Equity
The private equity model is evolving. Holding periods are lengthening well beyond historical norms, and a significant backlog of companies remain in portfolios awaiting exit. As financial engineering has become less reliable as a sole driver of returns, operational performance has taken center stage.
This shift changes the role of technology. AI is no longer simply a “digital initiative.” It is becoming a core lever of value creation – on par with pricing strategy, cost optimization, and go-to-market execution.
The question is no longer whether to invest in AI—but how to implement it in ways that directly improve financial performance.
Why AI Initiatives Stall
Across portfolio companies, three obstacles appear repeatedly.
- AI Projects Are Not Linked to Value Creation
Many companies pursue AI because it feels strategically important, not because it solves a clearly defined business problem. Without a direct link to revenue growth, EBITDA improvement, margin expansion or working capital improvement, pilots rarely scale. - Data Foundations Are Weak
AI models depend on high-quality, accessible data. Yet in many mid-market companies, data is fragmented across ERP, CRM, and legacy systems. It is also inconsistent or poorly governed, and difficult to integrate into analytics tools. Without a strong data foundation, AI initiatives struggle, and fail, to produce reliable results. - Organizations Lack an AI Operating Model
Even with the right tools and data, many portfolio companies lack the structure needed for successful AI implementation. Ownership of AI initiatives is unclear. Internal AI expertise is limited. Humans resist workflow changes. Incentives are misaligned and don’t reward adoption.
AI is not just a technology project—it is an operating model transformation.
The Shift: From Experimentation to Value Creation
Private equity firms that are succeeding with AI take a different approach.
They start with business outcomes, not technology.
Instead of asking “Where can we apply AI?”, they ask: “Where can AI improve financial performance?”
Once a focused set of high-impact opportunities is identified, initiatives are prioritized based on impact versus the organization’s ability to successfully implement them. Crucially, this is not the simple “Value versus Effort” matrix; companies must avoid the common “quick wins” trap, which leads to fragmented pilots and stalled momentum. Instead, successful companies take a more disciplined, multi-dimensional approach to prioritization – ensuring that early initiatives can scale into meaningful operational improvements.
The Importance of AI Readiness
Before launching major AI initiatives, companies must first evaluate a critical question: Is the organization ready?
AI readiness spans multiple dimensions, including our “Top 9”: data maturity and quality, technology infrastructure, governance and compliance, operational workflows, leadership alignment and incentives, strategic integration, AI investment commitment, measurement frameworks, and overall organizational AI literacy.
Without an honest foundational understanding, even well-designed initiatives struggle to scale.
AI as an Exit Multiplier
When implemented effectively, AI drives value in two ways.
First, it improves operational performance by enabling margin expansion, efficiency gains, and revenue acceleration. Second, it strengthens the technology narrative at exit. Buyers of all kinds increasingly reward companies that demonstrate:
- Scalable digital infrastructure
- Advanced analytics capabilities
- Data-driven decision making
Companies that embed AI into core workflows present a more compelling growth story, positioning them to command stronger valuations and differentiate themselves in competitive exit processes.
The Bottom Line
The AI conversation in private equity is evolving quickly. The industry is moving beyond hype and experimentation toward disciplined implementation. For deal partners, operating partners, and portfolio company CEOs, the takeaway is clear: AI cannot remain a collection of pilots. It must become a structured component of the value creation plan.
Private equity firms that operationalize AI effectively will not only improve portfolio performance; they will bring companies to market with stronger results and a more compelling story.
And in an increasingly competitive exit environment, that difference matters.