The 3 building blocks for AI ROI in private equity

We recently joined hundreds of private equity leaders at the PEI Operating Partners Forum in New York. As with just about every conference these days, AI was the first, second, and third thing anyone wanted to talk about.

But behind the hype, there was a clear through line: data infrastructure is what separates firms merely dabbling in AI from those actually realizing value from it.

Here are three themes we heard again and again throughout the conference.

1. Data first: AI only works on a clean foundation

AI is the shiny new object in PE, and for good reason. It’s poised to reshape how firms invest and operate.

But, at the risk of being a wet blanket, it’s worth reminding everyone that production-quality AI depends on the less glamorous stuff: clean, connected data and a single source of truth.

Most of the GPs we spoke with aren’t short on data. What they’re lacking is the infrastructure to make it usable across portcos and at the fund level.

So, as you look ahead to 2026, keep thinking about how AI can add value to your firm. Just don’t forget to ask if your data is ready for it.

What AI-ready data looks like

Clean data doesn’t mean perfect data. It means structured, accessible, trusted numbers that flow seamlessly across portcos and back up to the fund.

The top-performing firms we spoke with are investing in several foundational capabilities:

One source of truth

Every stakeholder—portfolio operators, GPs, board members—should be working from the same numbers. That requires a single reconciled dataset, sourced from ERPs, CRMs, billing tools, HRIS, and banks, and connected into fund-level roll-ups.

No manual pulls

Your analysts weren’t hired to download CSVs from 10 systems and stitch them together in Excel. Well, maybe they were…but they can add much more value than that.

This work should be automated. When it is, teams make fewer mistakes and free up bandwidth for higher-value tasks.

Trust you can defend

AI capabilities are moot if your data isn’t trustworthy. Strong auditability is table stakes. Every number should be traceable—where it came from, who owns it, and how it was calculated.

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Pitfalls to watch out for

• Launching a massive data lake before proving value: It’s tempting to go big from the start, but starting small is usually the better option. Solve a specific use case, build trust in the data, and expand from there.
• ‍Deploying AI on shoddy data: Every automation, every alert, every AI application depends on quality inputs. Garbage in, garbage out.

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2. Standardize reporting across portcos and the fund

Amid all the AI chatter, one of the most persistent problems in PE is getting lost in the shuffle: fragmented reporting. Each portco has its own templates and workflows. Every board pack is a reinvention of the wheel. GPs end up spending more time validating numbers than acting on them.

Fragmented reporting makes it almost impossible to build a high-trust, high-speed portfolio. Standardizing it is the fastest way to get everyone on the same page—literally and figuratively.

What good portfolio reporting looks like

Standard reporting framework

Install one common reporting format for all portcos. This makes roll-ups infinitely easier to compile and keep track of.

Apples-to-apples comparisons

If “Opex” means one thing at Portco A and another at Portco B, your roll-ups will never tell a clean story. Shared metric definitions eliminate the translation layer and make performance reviews smoother and more defensible.

Live portfolio view

Data-mature firms use connected dashboards to track performance in real time, with drill-down views into individual portcos and line-item drivers. It’s a faster and more transparent way to support decision-making.

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Pitfalls to watch out for

• Email + Excel workflows: Just…don’t. It’s where splintered versions propagate and reporting goes to die.

• KPI drift across portcos: Good luck telling a clean story at the fund level if every portco calculates CAC payback a little differently. Performance comparisons hinge on KPI consistency.

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3. Turn FP&A into an AI-enabled command center

As AI and automation take on more and more of the FP&A grunt work—report generation, data cleanup, board deck creation, and even variance analysis—finance is able to move up the value chain. They’re shifting into a more strategic role steering the business.

GPs at PEI told us that their expectations for FP&A teams are ratcheting up. They don’t just want a recap of last month’s results. They want real-time answers about what’s shifting, why it’s happening, and what to do next.

What strategic, AI-enabled FP&A looks like

Signal-driven forecasting

High-performing finance teams no longer wait until month-end to learn what changed. Modern tools can refresh models automatically as inputs shift and actuals come in, allowing funds to flag anomalies and immediately follow up with portcos.

Value-creation tracking

Every major initiative—whether it’s a pricing shift, GTM push, or cost program—gets mapped to an owner, a leading indicator, and a target outcome. When something drifts off course, finance can pounce on it well before the next board meeting.

Cash and exit-ready by default

Getting exit-ready used to be a drawn-out process. Now, GPs want it to be the default.

Every portfolio should have a live 13-week cash view with built-in runway and covenant alerts. Investor materials should be ready at all times. And the underlying data—forecasts, hygiene, and decision signals—needs to be just as polished.

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Pitfalls to watch out for

• Black box outputs: If no one can explain how the number was calculated, LPs and ICs won’t buy it. Visibility builds trust and speeds up decisions.

• Annual plan mindset: When forecasts freeze in Q1, the rest of the year becomes a reaction game. The best teams update plans regularly, tie them to real-time signals, and let strategy steer the model—not the other way around.

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Build the data scaffolding AI needs

For all the AI noise in PE, the firms actually seeing returns from it have one thing in common: they’ve nailed the fundamentals:

• Clean, connected data that’s available on demand
• Standardized reporting across the portfolio
• FP&A teams that have automated the basics and stepped into a more strategic role

When these building blocks are in place, everything else gets easier, and the path gets cleared for widespread AI adoption.

Aleph is the go-to choice for PE funds looking to clean up their data and gain visibility across their portfolios. See how we’re doing it.