AI has moved from finance’s someday list to its operating reality. To benchmark how far, Consero Global surveyed 102 finance leaders at investor-backed companies, a mix of CFOs and VPs of finance spanning software, healthcare, e-commerce, and investment management. The headline numbers are striking: 97% already use AI inside their finance function, and 76% report a return on that investment within 12 months.
Consero’s Tim Neville, who heads the company’s Fusion Lab, unpacked the findings with two people who watch this shift from very different seats: Kim Blascoe, Senior Director of CAS Professional Services at CPA.com, and Stephen Hedlund, Head of Finance at Rillet, the AI-native ERP.
Their conversation covered where AI is delivering returns, where it stalls, and what finance teams should prioritize heading into 2026. Here’s what stood out.
Adoption Hit 97%, and It Exposed an Efficiency Gap
Nobody in the survey had sat out AI entirely. That settles the adoption question and surfaces a harder one. Even with 76% seeing ROI inside a year, a 45% efficiency gap remains, because most teams bolt AI onto their existing processes and never rethink the work underneath.
Blascoe reframed the real measure of maturity. The question has moved past whether a team uses AI to whether AI is changing how that team practices, delivers, and creates value.
“We see many organizations using AI, but it’s stacking it on top of the processes they have today, rather than really reimagining the work itself.” — Kim Blascoe, CPA.com
From Chatbots to Connected Systems
AI agent usage jumped 70%, and the panel traced why. A year ago, AI in finance meant a chatbot you pasted financials into. Model context protocol (MCP) changed the shape of the tooling by wiring systems together directly, so an ERP, an inbox, and a Slack workspace can all feed the same agent.
Hedlund described entire prepaid modules that populate with no human touch, plus accrual estimates and forecasts his team’s AI handles inside the ERP. That capability is what pushes AI from novelty toward high-volume, repeatable workflows.
“Now with MCP, our systems are connected directly. You can connect your ERP directly, your email directly, your Slack directly. The opportunity set is much broader for what’s possible.” — Stephen Hedlund, Rillet
Doing More With the Same Team
Both partners were emphatic on one point: they aren’t watching clients fire people after adopting AI. One Rillet customer running a little over $100 million in revenue automated AR and revenue recognition, then committed to zero additional headcount for two years and redeployed that controller-level talent into higher-value work.
The pattern held across CPA.com’s firm network too. Teams are growing into the same headcount and using AI to supplement where they once would have added a body.
“It’s not about replacing jobs. It’s about making those jobs more strategic and more impactful.” — Tim Neville, Consero
Making the ROI Case
AI ROI is famously hard to pin down, so the panel leaned on proxies. Across Rillet’s own customer base and RAMP’s published spend data, the companies adopting AI fastest are the same ones growing fastest. On the ground, Neville pointed to an FP&A model build that used to eat 16 to 20 hours now finishing in about 45 minutes.
Hedlund’s team measures the return by output. They ask what people built this week, because a shipped agent or a new dashboard is concrete, and it makes the ROI conversation far easier than a self-reported tally of hours saved.
“Everyone’s going to overestimate how many hours they’re saving. But if I can prove this person built this agent this week, it becomes a lot easier to say the investment is worth it.” — Stephen Hedlund, Rillet
Bad Data Breaks Everything Downstream
The survey named four structural roadblocks, and data readiness topped the list: quality, accessibility, and completeness. Siloed systems compound it. AI-native tools have an edge here because they’re built for clean, accessible APIs from day one, though the panel was candid that older stacks can be retrofitted with real effort.
The discipline underneath matters just as much as the tooling. An operating system that closes on a predictable monthly cadence gives AI something trustworthy to work with, and a shaky one does the opposite.
“If your data’s bad, then stacking AI on top of it doesn’t help anything.” — Kim Blascoe, CPA.com
Deal-Making Is Now Always-On
99% of the finance leaders surveyed expect a material transaction, whether an add-on, an equity raise, a debt financing, or a full exit. At that frequency, transaction readiness becomes a permanent operating condition, which raises the stakes on data quality even further.
That constant pressure is why CFOs increasingly want a finance partner embedded in the function, often a third-party F&A partner who can carry the load between deals and during them.
“That transactional concept is no longer something you can think about once in a while. It now becomes part of your operation. You have to be transaction-ready at all times.” — Kim Blascoe, CPA.com
Exit Readiness: “Growth Covers All Sins”
Nearly half of CFOs expect an exit while juggling every other priority. Blascoe laid out the non-negotiables for that moment: clean financials, predictable cash flows, strong reporting, and scalable processes. The worst outcome is a surprise surfacing in due diligence, where hidden weakness drags down valuation.
Hedlund offered the pragmatic version from his own operating history, including a startup that rode wildfires and COVID from $40 million to $100 million in nine months before reality set in. Strong growth can paper over an imperfect data room. When growth softens, audit and diligence readiness carries the whole story.
“The two paths I’ve seen: growth covers all sins. If growth is not covering the sins, then audit readiness and having your data rooms put together becomes a very different story.” — Stephen Hedlund, Rillet
The Path Forward: Lead From the Front
The panel closed on the shape of the future finance leader: part technologist, part data architect, and still a financial steward. Hedlund recounted a CFO who told him he didn’t need to learn AI because he’d already made his career. The counter is simple. A leader who won’t touch the tools stalls adoption across the whole team.
Neville tied the hour together with a line that doubled as marching orders for everyone on the call.
“Lead from the front. Be visible, and be as much of a change agent as possible for your company.” — Tim Neville, Consero
The throughline across all eight findings is a simple trade: AI raises the ceiling on what a finance team can deliver, and it raises the bar on the data, systems, and leadership needed to get there. Consero pairs a curated technology stack, automation, and an expert finance team so investor-backed companies can move past pilots to a finance operation that stays audit-ready and transaction-ready by default.
