BOTTOM LINE UPFRONT
Data challenges are the biggest barrier to AI adoption across private equity portfolios, but they shouldn’t be the reason finance stands still. CFOs that start with focused use cases and “good enough” data can build momentum today while positioning the business for higher valuations tomorrow.
Every sponsor has given the same order: prioritize AI, now. Not everyone’s moving.
Here’s why: our recent 2026 PE AI Adoption Benchmark found that when Operating Partners ranked the barriers to scaling AI, data infrastructure came out on top by a wide margin, ahead of talent gaps and legacy tech stacks combined. That’s the real reason deployment feels daunting, especially for CFOs who aren’t necessarily technologists: most are inheriting a data problem before anyone even gets to strategy.
But inherited or not, the problem won’t wait. Eighty-six percent of Operating Partners expect buyers to pay a premium for AI-enabled finance capability within two years. Which means a CFO who’s still waiting on a perfect data environment when that conversation happens is leaving that premium on the table.
We’re here to tell you: if you’re not moving on AI right now, you’re letting perfect be the enemy of good-enough. You don’t need to wait for a perfect data environment to kick-start AI, nor should you, if you want to meet sponsor and market expectations. Instead, ask these questions:
Which use cases should I tackle first?
There’s no easy AI button; we can’t just snap our fingers and AI-ify all the less-than-efficient processes. What CFOs can do is identify select areas that would most benefit from automation, then get them AI-ready in digestible phases. Focus on specific, high-impact use cases: a) where manual processes dominate b) where business units are already data-aware or tech-forward or c) where a quick win could build internal momentum.
Skip this step and you end up in “pilot purgatory,” where 41% of PE firms are already deploying AI across multiple portfolio companies with no operational playbook. They’re past the pilot stage and spending real budget, but without focus, they’re compounding technical debt instead of value.
The highest-value targets tend to sit in FP&A, forecasting, and close acceleration, but those are also the hardest to get right first. A smaller, faster win elsewhere can build the credibility to get there. Invoice processing is a good place to start: manual data entry, verification, and approval routing that’s time-consuming, error-prone, and delays the month-end close. Deploying Visual-AI capability with automated processing can be a major win. It’s not a one-size solution that fixes every manual bottleneck, but it doesn’t have to be.
How clean does my data really need to be?
Yes, AI is built on solid data foundations, but you don’t need to solidify your entire universe of company data right now. Look at the data that powers the use cases you’ve identified (invoice processing, for example) and ask: Do I have access to the correct data? How complete, consistent, and trustworthy is it? Where are the gaps?
The goal isn’t perfection, just a right-sized data set: more than starter data, less than a multi-year investment. That often means mining foundational domains like customer, project/SKU, pricing, and transaction-level financials, data that’s high-value yet manageable to clean.
How do I get my people on board?
AI readiness initiatives can’t succeed in isolation. They touch cross-functional workflows, impact decision-making, and reshape day-to-day roles, so your people need to be part of the process.
And it’s worth the effort. Operating Partners rate the shortage of AI-literate finance talent a 4.1 out of 5 as a constraint on portfolio AI ambitions, and that capability can’t simply be hired. It’s built through doing, which is exactly why aligning your existing team early beats waiting for a perfect roster.
Get your people on board early. Align priorities with business leaders, make sure they understand what’s changing and why, and frame AI as a job enabler rather than a threat. Securing buy-in is what makes AI adoption stick.
How do I operationalize AI once it’s ready?
Your data and people are aligned. Now integrate AI into how your business operates. Too many AI projects stall here; CFOs get paralyzed by the sheer number of processes that need efficiency gains. The good news: you’ve already targeted use cases that passed the test of business relevance, feasibility, and data readiness.
Now operationalize. The goal isn’t full autonomy out of the gate; it’s supervised autonomy, where AI handles the transactional lifting and a human reviews the exceptions. For invoice processing, that means:
- Designing workflows that automate invoice intake, extraction, and routing in a repeatable way;
- Building feedback loops that capture how users accept, correct, or override AI recommendations, to refine the model’s logic over time; and
- Maintaining a full audit trail of every action the system takes, so the model holds up when buyers run diligence on it.
Where do tech and governance fit in?
Technology and governance matter, but they support the business rather than steer it. The tech and governance health check should come after you’ve solidified your AI goals.
For the tech stack, make sure core systems (ERP, CRM) support the AI services aligned to your use cases, and enable seamless data flow across systems with flexible, cloud-based technology that can scale as you evolve. Don’t invest in a huge, custom tech platform before you know where your AI journey is headed.
When it comes to governance, many boards or sponsors won’t let anything AI-related move forward without it, but you don’t need a full-fledged model to get the show on the road. Right now, that’s a real opening: 63% of portfolio companies are operating with no formal AI structure beyond informal guidance, and only 9% of sponsors have a fully operational AI Center of Excellence – which means even a light governance model puts you ahead of most of the market. Start light: secure, enterprise-grade tools, clear usage guidelines, and a process to prioritize use cases against business goals.
The AI race is in full swing. Here’s what we know: winning doesn’t mean deploying all the AI, all at once, but losing means not getting started at all. Buyers are already asking about AI-enabled finance capability in diligence. The CFOs who start now, imperfect data and all, are the ones who’ll have an answer.