BOTTOM LINE UPFRONT
In this episode of AI & PE: The Future of Value Creation, Kyle Roemer and Giacomo Sonnino (Advisory Director at Charlesbank Capital Partners and former North American COO of QuantumBlack) break down what it actually means to be “AI exit ready” today, why the bar has risen dramatically in the past 12 months, and what separates companies that are genuinely transforming with AI from those stuck in pilot purgatory.
Here are seven takeaways that shape how PE-backed companies should think about AI as a present-day driver of value creation and exit readiness:
1. The definition of AI exit readiness has changed (and changed fast)
Twelve months ago, having a roadmap and a few pilots was enough. That bar no longer holds. Today, buyers expect:
- Proven use cases embedded in live workflows
- Demonstrable bottom-line impact
- An offering that has already evolved with AI
And the bar is still moving; the standard 12 months from now will be higher still.
2. Value creation from AI operates at three levels
Three distinct levels of impact are emerging:
- General productivity: Tools like Claude, meeting assistants, and email AI that give time back but rarely move the bottom line directly
- Proven use cases: De-risked applications across software development, sales, customer experience, and back-office functions like finance and legal
- AI in the core of the business: Redesigning products, workflows, and tech stacks around AI; harder to execute, but where differentiation is actually built
Differentiated companies build across all three and are increasingly leaning into the third.
3. AI is now on the agenda at every IC meeting
AI is now part of every IC conversation. Buyers are consistently evaluating what a business looks like in an AI-shaped world, and what that means for long-term and terminal value. It’s a lens applied to every deal, not just a diligence checkbox.
4. Most companies get stuck in pilot purgatory for four reasons
The most common failure mode: accumulating pilots without ever scaling. The root causes:
- AI programs led by the CTO or CIO rather than business leadership
- No meaningful CEO involvement or organizational buy-in
- No AI strategy tied to the value creation plan
- No clear line from pilot activity to bottom-line impact
The fix requires CEO-level conviction, a business-led AI leader, and the infrastructure to drive workflow redesign and change at scale.
5. AI theater is easy to spot in diligence
Buyers see right through attempts to dress up AI readiness for an exit process. The signals are clear:
- Has the offering changed?
- Have workflows been redesigned?
- Is there quantifiable bottom-line impact, or just capacity that hasn’t translated to cost reduction or revenue uplift?
The gray area exists only for companies genuinely mid-transformation. Everything else is binary.
6. Finance is a proven, high-priority opportunity
The use cases are well-established and actionable today:
- Automating 13-week cash flows that currently consume significant FTE time
- Compressing month-end close cycles
- Transforming FP&A into a real business partner function
And looking ahead: the finance function will be built around a small number of anchor systems, AI agents that connect and operate those systems, and a lightweight orchestration layer – something that would have required significant investment just a few years ago, now increasingly accessible.
7. Change management is the binding constraint
Most mid-market companies can realistically drive one to three AI transformations per year. No organization has the capacity to absorb ten. That makes prioritization non-negotiable: the AI roadmap must link to the value creation plan, ROI must be quantified, and resources must be concentrated on the highest-impact initiatives.
At the end of the day: if something in your business can be automated or redesigned with AI, it will be. Better you do it first than someone else does it for you.
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