Pilots have stalled. Operating model rebuilds are moving multiples. Here’s what the early movers are doing differently.
Only last year, private equity firms were experimenting with AI.
A few short quarters later, the world is shifting from experimentation to enterprise value creation.
The shift is fast and uneven, and that’s an opportunity. Operating partners are looking at portfolio dashboards and asking a sharper question: Proof of impact is mounting, so why hasn’t AI moved EBITDA, velocity, or exit outcomes yet?
The answer is reshaping how value gets created and how quickly capital gets returned.
Five forces are colliding inside operating companies right now:
- Compressed hold periods are forcing earlier value realization
- Buyers are paying premium multiples for AI-native operating models
- Pilot-heavy AI programs are failing to translate into measurable EBITDA
- Tooling costs are accumulating without operational redesign
- Internal engineering teams remain constrained by legacy SDLC and governance
Any one of these would force a strategic rethink. Together, they are rewriting the value-creation playbook and reshaping who gets paid at exit.
Four Lessons from Private Equity Firms Creating AI-Driven Value
A clear pattern is emerging across nearly 70 portfolio companies Ascendion serves today. A small group has moved from pilots to measurable enterprise value creation. Most are still in experimentation.
Many firms still frame AI as a technology question, and that framing is what keeps them stuck. The early movers are operating from a different premise: AI will not create meaningful enterprise value inside private equity-backed companies until workflows, governance, and engineering operations are redesigned together.
From there, a structural advantage emerges: higher MOIC across the hold, faster post-acquisition execution, a re-priced quality-of-earnings narrative at exit, and a genuinely redesigned operating model the next buyer will pay for.
Four moves separate the early movers. None are exotic. The dollars are showing up for the firms that are nailing the execution.
1. Connect AI to Three Numbers
The fastest way to kill an AI initiative inside a portfolio company is to disconnect it from the three financial outcomes boards actually lock into:
- Revenue expansion
- EBITDA expansion
- Cash flow efficiency
AI initiatives that don’t directly move one or more of these metrics rarely survive beyond experimentation.
In customer operations, AI delivers faster resolution and higher retention. The financial signal is revenue expansion. In workflow automation, AI reduces cost per transaction. The signal is EBITDA expansion. In engineering, AI compresses release cycles and accelerates monetization. The signal is execution velocity, growth, and a stronger cash conversion profile.
This lands at the unit level. For a construction software company, we reduced cost overruns, improved budget accuracy, and optimized capital spend across the construction lifecycle. For a healthcare technology company, we enabled doctors to see three to four more patients per day, reduced wait times and costs, and improved outcomes, patient and care team satisfaction, and productivity.
2. Hard-Code AI Into the Deal Models to Speed Up the Exit Clock
Every private equity company rises and falls around a financial model and a 100-day plan.
Traditional modeling assumptions about throughput, cost per unit, and time to value still matter. The early movers are pulling AI assumptions into the model before close, ahead of the work itself. They treat AI as a core value-creation lever alongside pricing, go-to-market, and cost transformation. Early injection of AI into modeling gets agentic value mechanisms inside the 100-day window.
The exit clock pulls forward when this happens. When software delivery velocity doubles, operational bottlenecks shrink, quality improves, and the quality-of-earnings narrative changes materially. Buyers pay premiums for businesses demonstrating scalable AI-native operations rather than isolated AI experimentation.
For a manufacturing company, we improved uptime, streamlined workflows, and provided predictive alerts across a global user base of 150,000. For a healthcare technology firm, we delivered a 50–60% gain in code conversion accuracy with cleaner Java output, accelerating the modernization timeline and the quality of the conversion process.
Agentic AI is shifting the cost curve and throughput ceiling at industrial scale. Modeling it into the financial and execution plans help ensure that impact shows up in the field and at the exit.
3. Focus on the Operating Model, Not Just the Tech
This is where most portfolio companies lose the thread. Dollars have flowed into point AI deployments, platforms, and data products. Velocity hasn’t meaningfully changed. Margins look the same. Revenue is stuck. Activity is high. Outcomes are scarce.
“The math is simple. Inside a legacy operating model, AI lifts margin a point or two. Built into an operating–model redesign, the same investment compounds into meaningful multiple expansion.”
Most operating models still organize AI as a function. Reshaping how work moves across the company puts every value-creation lever in the thesis in play. That’s the gap most operators have yet to close. The leaders organize the work as a stack and price it as one. Each layer compounds the next:
- Engineering velocity. AI-native SDLC, automated quality engineering, release acceleration.
- Workflow transformation. Finance, customer operations, underwriting, and service delivery rebuilt around humans and AI as one operating system.
- Operating-model redesign. Autonomous execution within governed boundaries. AI-native governance.
- Enterprise value creation. EBITDA expansion. Faster integration. Improved quality of earnings. Multiple lift.
Engineering velocity creates the surface area for workflow redesign. Workflow redesign forces operating-model change. Operating-model change drives enterprise value creation. Building all four layers is what earns the multiple at exit.
The cases that matter are the ones where the four layers stack. For a financial services company, we reduced false positives, accelerated investigations, ensured compliance, and successfully delivered end-to-end workflows. For a supply chain company, we delivered 80%+ accuracy and full event workflow automation. For a hospitality company, we used AI to streamline infrastructure automation, minimize costs, improve operational efficiency, reduce API management overhead, and enhance system visibility and troubleshooting.
Software is at the core of most private equity-backed value propositions. Finance, customer operations, and support sit around it. The leaders start with the core because that’s where revenue gets realized. When the core is redesigned, the rest follows quickly.
It’s easy to find excuses to stick with yesterday’s operating model. Waiting for the data to be perfect. Convening another governance committee. Waiting for the stars to align. These are excuses masquerading as business reasons. Pick one high-impact workflow. Redesign the whole thing as an agent-led use case tied to EBITDA. Once one workflow flips, the firm has a template, a proof point, and an operating system it can scale across the portfolio.
4. Govern AI as a New Power Source (Not an ERP Upgrade)
Governance is where many agentic programs collapse, and where serious operators separate themselves. Buyers, lenders, and regulators all underwrite governance now.
Deploying a successful agentic operating model across portfolio companies is NOT like upgrading a creaky CRM or using the cloud better for enterprise storage. Think of it more like a new power source that needs to be managed as such because it can have a profound positive impact on the metrics that matter to private equity decision makers.
This does not require a whole new (expensive, slow) re-boot of company operations. It does require governance systems to evolve to include the impact of AI:
- Human-at-the-wheel controls on every consequential decision
- Auditability across agent actions
- Explainability calibrated to the regulatory context
- Hardened guardrails against hallucination, drift, and unauthorized action
- Security and compliance designed into the architecture from the start
- Clear ownership for model risk, data lineage, and incident response
Done right, governance becomes a multiple expander. It’s what turns AI-enabled into investable, durable, repeatable enterprise value.
Plug AI Into Private Equity Operating Models to Capture Value
Fast-moving private equity firms have stopped asking whether AI matters. They’re redesigning operating models around it.
The valuation gap between firms operating with AI-native execution models and those still experimenting with disconnected AI initiatives widens with every operating cycle. The fastest movers are bringing in an AI-native engineering partner during diligence, before the 100-day plan is finalized. They are buying speed, conviction, and a repeatable operating system that compounds across the portfolio.
This is also a commercial model shift. AI arbitrage, the use of intelligent agents to augment every knowledge worker, is the new value lever, succeeding the wage arbitrage that defined the last thirty years of services and offshoring. Engineering to the Power of AI is the method that makes it operational. Portfolio companies that build it into the operating model from day one deliver more value and earn the multiple.
By the next exit cycle, the gap will be priced in. The window to redefine the operating model is open now.
Watch the full interview behind this article here
About the Author
Rohini Williams is SVP & Global Head of Strategic Growth for Private Equity and Restructuring at Ascendion. She partners with private equity firms, restructuring advisors, and portfolio company leadership to accelerate product innovation, modernize legacy platforms, and unlock new revenue through AI engineering models. She brings deep experience aligning value creation initiatives with 100-day plans and long-term growth strategies. Rohini has led go-to-market for large-scale enterprise transformation programs and built partnerships across private equity firms, global advisors, and high-growth technology companies.
