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The Companies That Execute AI Will Win

Updated 1 April 2026

A clear separation is emerging between companies testing AI and those building it into their operating model. Winning requires the ability to execute, measure, and prove outcomes. CEO and CFO ownership is now mandatory.
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Scott EnglerAveran · 2026-04-01

A clear separation is emerging across the market between companies that are testing AI and companies that are building it into their operating model. The gap is widening faster than most leadership teams realize, and it is already showing up in how buyers think about valuations.

Six Core Findings

Adoption Is Not Scale

Most AI efforts remain isolated experiments, disconnected from how decisions are actually made in the business. Real value shows up only when AI is integrated into the operating cadence, not when it's deployed in one department as a productivity tool.

Execution Is the Constraint

The challenge is not access to AI technology. It's translating AI capability into consistent execution across teams and processes. These are operating problems, not technical ones, and they require operating leaders who own them.

Ownership Has Moved to CEO and CFO

AI decisions are now directly tied to capital allocation, cost structure, and growth strategy. Without explicit C-suite ownership, initiatives remain fragmented and unmeasurable. The CEO and CFO must own the AI agenda, not delegate it.

Infrastructure Determines Success

Data quality, system integration, and platform design are the foundations of effective AI deployment. Companies that prioritize tools over infrastructure consistently struggle with reliability and scalability at the point where it matters most.

Governance Is Falling Behind

Deployment is moving faster than oversight in nearly every organization. Establishing AI governance that mirrors financial discipline (with clear ownership, measurement, and accountability) is necessary for AI to be trusted and scaled.

Value Will Concentrate Quickly

A small group of companies will align leadership, redesign core workflows, and execute effectively enough to create durable, compounding advantage. The rest will continue to experiment without compounding results, and buyers will sort them accordingly.

Scott's TakeThe CFO is the right person to anchor AI governance in a PE-backed company. They already own the measurement infrastructure, the capital allocation decisions, and the operating rhythm. AI governance is a natural extension, and the CFO who builds it will be able to tell the AI story with proof at every management presentation.
AIExecutionValue CreationOperating Model

If you want this done for you

What happens next

Reading about it and having it done are different things. Here is exactly what happens if you want the second one.

  1. The call, twenty minutesYou describe the business and where the numbers are letting you down. We tell you honestly whether we can help, and what we would start with. No pitch deck.
  2. We look at the fileYou give us read access to the books as they are. We come back with what is wrong, what it will take to fix, and whether the answer is bookkeeping, controller work, or something else.
  3. A written scope and a priceWhat we do each month, what you get, the date it lands, and the fee. Agreed before anything starts, and it does not move without you agreeing it.
  4. Transition, then the first closeAccess, systems, and the opening balances. The first close lands on the date in the scope, and every one after it does too.
Start with the call →How an engagement runs

Averan is not a CPA firm and does not file taxes. Your CPA keeps that, and we keep the books they file from.

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Questions we get asked

How is AI changing finance leadership in PE-backed companies?

AI is shifting the CFO role from data production to data interpretation. Finance functions that previously spent 70 percent of capacity on close and reporting can redirect that capacity to analysis and decision support. PE-backed companies that adopt AI-native finance infrastructure gain a structural advantage in reporting speed, scenario modeling, and board communication, all of which directly affect multiple at exit.

What financial data capabilities should a PE-backed company build before a transaction?

Before a transaction, a PE-backed company should have a single source of truth for financial data, contract-level P&L visibility, a documented EBITDA bridge. Reporting infrastructure that can respond to buyer data requests within 72 hours. The data room is where preparation either protects or loses purchase price, and the cost of building the infrastructure is always less than the value it protects.