BOTTOM LINE UPFRONT
Accordion’s Founder & CEO Nick Leopard spoke with Jack Arrowsmith of Private Equity Wire’s Alternative Views video series, where they break down where AI is actually creating value inside portfolio companies, what it takes to get a business exit-ready, and why sponsors and CFOs need to close the gap on who owns AI transformation.
Where should CFOs be applying AI day to day, and what’s the payoff?
AI gives CFOs back the hours they used to spend assembling the past. There are five wins we’ve seen across portfolio companies, where AI is really moving the needle:
- Close acceleration: AI-powered journal entries, reconciliations, and transaction matching cutting close time 20 to 30%. One industrial client cut reporting time in half and improved forecast accuracy by 35%, both of those in a single quarter.
- AP automation: OCR-plus-AI invoice capture and matching, cutting cost per invoice roughly in half and processing time 60 to 70%.
- Cash and collections: improving working capital through DSO reduction, lowering bad debt by 10%, and predicting churn.
- FP&A and reporting: one SaaS client processed 200,000 contracts, rebuilt eight years of AR history, and surfaced a $2.5 billion potential valuation uplift.
- Contract and data cleanup: a $100 million firm cut a $5 million annual manual-interpretation cost while hitting 95% accuracy on contract queries.
“The payoff we’re seeing is two to four times the annualized EBITDA uplift for every dollar deployed in AI transformation.”
With the IPO market sluggish, what role can AI play in getting portfolio companies exit-ready?
Every business needs to prep for an exit, whether the buyer is public or private, and the IPO window is narrower than the headlines suggest. In Q2 2026, IPOs made up 31% of total exit deal value but only 5% of deal count. That means large, mature companies that spent 20 to 24 months preparing are the ones capturing that value, and most private equity-backed companies are still moving toward private sale and staying inside that ecosystem.
The companies positioned to seize an IPO window are the ones treating it as a 12-to-24-month runway, with AI-driven operational readiness as a core part of building that runway.
“Companies waiting for the IPO market to open before they get ready are already behind.”
Should AI be deployed as a single portfolio-wide solution, or does each portfolio company need something bespoke?
Both, at different layers. Sponsors are moving fast: 41% say they’re deploying AI across their portfolios, largely without an operational playbook, which is one of the most precarious spots in the market right now. Scaling AI adoption isn’t the same thing as systematizing it and driving real ROI, and adoption is landing unevenly by sector and segment.
Part of the issue is sequencing. Service and deploy firms partnering with the big technology players have gotten the order backwards, building and deploying agents before nailing down the business case they’re actually solving for.
Broad, foundational infrastructure makes sense to standardize across a portfolio: a shared data layer, a common AI governance framework, an approved tool catalog. The value-creating use cases, though, need functional expertise and change management specific to each business. The same tool looks completely different deployed into healthcare revenue cycle management versus a manufacturing shop, with different data plumbing, different outcome definitions, and different adoption dynamics.
“Everyone’s talking about deploying engineers, but that misses the real functional expertise. That’s where the opportunity is: partnering with the service providers who bring that expertise to the table.”
Who should be driving AI implementation: the sponsor or the CFO?
There’s a real mismatch right now. Sponsors expect CFO-led AI engagement 65% of the time, but only 15% of CFOs see it as their own responsibility, with the rest assuming it sits with the CEO, the CTO, or the board.
That gap shows up again on ownership: just 17% of CFOs have claimed AI transformation as a strategic mandate themselves, even as most sponsors already treat it as one. Sponsor mandate without CFO ownership produces scattered pilots with no coordination. CFO ownership without sponsor backing produces no budget and no bandwidth.
The fix is both, sequenced correctly, with an operationally focused CFO connecting AI directly to EBITDA, cash flow, and exit readiness.
“That gap between what sponsors expect and what CFOs actually own is exactly what slows AI adoption down.”
Watch the full episode: