I spent nearly fifteen years at McKinsey and Deloitte watching technology programs succeed or fail, and the pattern that predicts failure most reliably is who’s in the room when the buying decision gets made. The technology has nothing to do with it.
In most companies I work with, the AI conversation is owned by a combination of IT, operations, and whoever on the senior team is most personally interested in the topic. The CFO is invited to review the budget. That’s the wrong sequencing, and it’s costing companies real money.
The CFO arrives too late
Here is how the typical AI buying process works in a mid-market company. A business unit leader or IT director identifies an opportunity, evaluates tools, runs a pilot, builds a business case, and presents it to the leadership team. The CFO reviews the financial projections in the business case, asks some questions about payback period, and either approves or defers.
By the time the CFO sees the business case, every important decision has already been made: the workflow, the vendor, the success metrics, the design of the pilot that already ran. The financial projections were built by the people who want the project funded, using assumptions the finance team did not validate.
Deloitte’s Q4 2025 CFO Signals Survey found that technology transformation emerged as a top strategic priority for CFOs in 2026. But Bain & Company’s survey of senior finance executives found that only 15 to 25 percent of CFOs have fully scaled AI in their own departments. CFOs care about AI. They’re just not driving it. That gap is where the value disappears.
The takeaway: by the time an AI business case reaches the CFO, the decisions that determine its return are already locked. Bring finance in before the pilot exists, when the workflow and the metrics get chosen.
Finance speaks a different language than technology
The case for putting the CFO in charge comes down to language, and CFOs speak the one that boards understand and investors require. It has nothing to do with CFOs being smarter or more strategic than CIOs.
When a technology leader builds an AI business case, the success metrics tend to be operational: processing speed, accuracy improvement, user adoption rate, tickets deflected. These are legitimate metrics. They’re also invisible to the people who allocate capital.
When a CFO builds a business case, the success metrics are financial: cost reduction, revenue impact, margin improvement, cash flow effect. These are the metrics that show up in board materials and valuation conversations, and the ones that justify the next investment. That difference in vocabulary decides whether an AI program reads to the board as activity or as return, and only one of those gets funded twice.
The CFO is the most undertapped AI leader in your company, and the reason is structural.
Oliver Wyman’s 2026 CFO research, covering finance leaders at public companies worth roughly 12 percent of global market capitalization, found that 80 percent of them rank data, automation, and AI among their top three priorities for transforming finance. The most sophisticated finance leaders have already claimed AI as their own capability to build.
The takeaway: an AI program measured in operational metrics reads to the board as activity, and activity does not get funded twice. Have finance restate every success metric in dollars before the board sees it.
The measurement problem starts at design
In my diagnostic framework, the dimension that scores lowest across most assessments is whether AI results show up in the financials, meaning the ability to convert AI outputs into dollars the business can act on. These companies are fine at finance. They score low because the finance team wasn’t in the room when the AI initiative was designed.
This matters at the pilot stage, before any scaling conversation happens. If the finance team isn’t in the room when the pilot is designed, three things don’t get built.
First, the dollar baseline does not get captured properly. The operational team knows the workflow takes too long. The finance team knows what “too long” costs. Without them in the room, the pilot measures improvement in minutes saved instead of dollars recovered.
Second, the cost structure gets modeled wrong. AI tools have a cost profile that looks different from traditional software: usage-based pricing, token consumption, compute scaling. A technology leader evaluates these costs against the tool budget, while a CFO weighs them against the fully loaded cost of the workflow being replaced, including the opportunity cost of the headcount that could be redeployed.
Third, nobody pressure-tests the payback timeline. Most AI business cases use straight-line projections: “If we save X hours per week at Y cost per hour, the annual savings are Z.” Finance teams know that straight-line projections ignore adoption curves, integration costs, change management drag, and the discount rate on uncertain future savings. Without that pressure test, the business case is optimistic to the point of fiction.
The takeaway: a pilot designed without finance measures minutes instead of dollars and carries a payback number nobody challenged. Put finance in the design meeting, where the measurement gets built.
What the CFO-led model looks like
The companies I’ve seen get the most value from AI share a specific pattern: the CFO or a senior finance leader is involved before the pilot is designed, not after it concludes.
In practice, the CFO co-authors the success criteria: what “measurable” means, the financial baseline the pilot has to beat, and the evidence that would justify a production budget. That threshold gets named before the pilot starts, so the team knows what they are proving.
The Consero Global 2026 CFO Survey found that fully embedded AI in finance nearly doubled in a year, from 22 percent of firms to 42 percent. And Deloitte’s Q4 2025 survey found that 87 percent of CFOs now expect AI to be extremely or very important to their finance department. The finance leaders moving fastest have learned where the value actually sits, and it sits in the measurement discipline around the tool, which is what a CFO brings.
The takeaway: in the CFO-led model, the financial bar a pilot has to clear gets named before the pilot starts. Have the CFO write that bar into the pilot plan before anything runs.
The mid-market advantage nobody uses
In a Fortune 100 company, getting the CFO involved in an AI pilot is a political act. It runs through matrix reporting and P&L boundaries, on calendars booked eight weeks out. The structural overhead is real.
In a mid-market company, the CFO is often sitting in the next office. The senior team eats lunch together. The quarterly planning conversation happens in a room with six people, and the CFO is one of them. The structural advantage is proximity and simplicity.
And yet, in most of the mid-market companies I assess, the CFO’s involvement in AI decisions follows the same late-stage pattern as the Fortune 100: review the budget, ask about payback, approve or defer. The mid-market is copying enterprise process when it could be using its own structural advantage to do something better.
The better model: the next time someone proposes an AI pilot, ask the CFO to sit in on the design meeting rather than the approval meeting. Ask the CFO to name the number the pilot has to move, the financial baseline it has to beat, and the threshold that would justify a production budget. Do this before anyone selects a vendor, sets a timeline, or builds a slide deck. The CFO’s involvement at the design stage changes the entire shape of the initiative, and it takes one meeting.
One Meeting
Get the CFO in the room when the pilot is designed, not when the budget is reviewed. Before the vendor is selected. Before the timeline is set. Before anyone builds a slide deck.
One conversation this quarter
If you are a CEO or COO reading this, here is the move. Before your next board meeting, sit down with your CFO and ask one question: “Of the AI spending we have approved in the past twelve months, how much of it has a measurable financial return that you would put in front of the board?”
If the answer makes you uncomfortable, look at who was in the room when that spending was designed. Your most financially literate leader was almost certainly brought in too late to build the measurement. That’s fixable, and it starts with the next pilot.
How RLK Can Help
My AI Diagnostic measures whether your AI results are showing up in the P&L, and the results frequently reveal that the finance function is disconnected from AI decision-making. The AI Business Case engagement is specifically designed to build the financial framework, with CFO involvement from day one, that converts AI activity into board-ready numbers. Start the conversation.