From where I sit, advising software companies and investors, few areas show the impact of AI more clearly than finance. The change goes deeper than improved reporting or forecasting. The operating model and architecture of the finance function are beginning to evolve.
Most finance teams today rely on people to connect the dots between ERP, CRM, billing, payroll, banking and tax systems. They reconcile information, investigate exceptions, move data between tools and rebuild management views in spreadsheets. This often leads to multiple versions of the truth, slow month-end processes and significant key-person dependencies.
With proper foundations, AI should absorb an increasing share of this work: data continuously reconciled, exceptions identified as they arise, forecasts refreshed as operating conditions change, routine workflows prepared and increasingly executed within defined limits. Finance teams are increasingly moving from processing transactions and compiling information to overseeing workflows and exercising judgment.
That requires a different foundation. Finance operates in an environment where accuracy, repeatability, and auditability matter. AI therefore needs trusted data, clear definitions, permissions, controls and a traceable record of what happened and why. Where those foundations are weak, greater automation can solve an existing problem faster without making the answer more reliable or auditable.
This is why data architecture and orchestration are moving up the CFO agenda. Finance leaders increasingly need to think about how information comes together, which systems and people own it, which rules govern it, and how quickly the organization can turn it into action.
The CFO’s role moves with it. The strongest finance leaders will spend less time overseeing the production of information and more time designing how the business uses it.
From assistance to governed autonomy
Much of AI in finance today remains assistive: searching information, extracting data, drafting commentary, explaining variances, and helping people perform existing tasks faster.
The direction of travel is towards greater execution. Software will increasingly perform the normal flow of a financial process and route exceptions to people. Humans remain responsible for policy, judgment, and oversight, while a growing share of routine activity occurs beneath them.
That could progressively reshape workflows across the function. Reconciliations can happen continuously rather than only during close. Cash positions can refresh as underlying information changes. Expenses can be checked against policy as they occur. Forecasts can absorb new operating signals without waiting for the next planning cycle.
Finance sets a particularly high bar for this transition because many actions carry accounting, regulatory, or cash-flow consequences. Permissions need to be explicit. Actions need to be traceable. Segregation of duties still applies. Exceptions need owners. Material decisions need to remain explainable and, where appropriate, reversible.
The opportunity is significant: a finance function operating closer to real time, with people spending more of their capacity on judgment, scenario analysis, business partnering and capital allocation.
What this means for CFOs
For CFOs, the immediate priority is the foundation. There is a window today to step back and ask a small number of fundamental questions:
- Do we have a single, trusted version of our critical financial and operating metrics across the organization?
- Where does the authoritative definition of each metric live?
- Which workflows remain dependent on spreadsheets, manual reconciliation, or individual/key contributor knowledge?
- Which finance workflows consume the most human effort?
- Which controls, permissions, and ownership structures would we need before allowing AI/software to perform more of the workflow?
- Which outcomes would tell us that an investment has actually improved the function?
The practical goals are straightforward: one version of the truth, less key-person dependency, and faster time-to-clarity.
From there, the discussion can move towards deeper workflow transformation, with AI assuming more of the routine workload.
The talent model follows. Finance teams will increasingly need people who can manage exceptions, challenge assumptions, design policy, understand data, and partner with the business. A team spending less time producing the numbers has more capacity to understand what drives them and help management act earlier.
What this means for Office of the CFO software
The same transformation is increasingly visible in how I see investors and strategic buyers assess Office of the CFO software businesses.
Growth, retention, profitability, and capital efficiency remain important. AI has added another dimension to the underwriting: what does this software own that remains valuable as intelligence becomes broadly available?
Workflow ownership is becoming central to that answer. Software deeply embedded in a mission-critical process should have a stronger position than a product sitting at its edge.
Data rights and integration depth reinforce that position.
A platform that understands the customer’s financial context, connects deeply into core systems, and carries a history of how the workflow has been executed has accumulated something difficult to reproduce.
Governance adds another layer. Permissions, policy, auditability, and control determine how far software can move from recommending an action towards safely performing it.
The final test has an increasingly measurable impact. Almost every software company now has an AI story. What matters more is what changed after deployment.
Did days-to-close fall? Did auto-match rates improve? Did DSO move? Did forecast accuracy increase? Did manual intervention decline?
From a dealmaker perspective, these questions increasingly sit alongside the traditional financial metrics. Investors want to understand where a product sits in the workflow, what data and permissions it controls, how deeply it integrates, and whether its economic relevance increases as software performs more of the work.
That should increasingly separate strategically scarce platforms from products whose differentiation sits primarily at the interface.
Build the engine, place the bets, track the results
One path to make this practical for CEOs and CFOs is this:
Build the engine. Establish trusted data, common definitions, clear ownership, and the controls required for systems to act safely.
Place the bets. Focus investment on a limited number of workflows where automation can genuinely change how the function operates.
Track the results. Every serious investment should ultimately move an operating or financial KPI: close time, forecast accuracy, working capital, cost per transaction, manual intervention, audit effort, or finance capacity.
This last step matters. AI initiatives can otherwise accumulate without changing the business’s economics.
The strongest founders I have worked with consistently combine ambition with operating discipline. That discipline gives them the confidence to take bigger bets because they understand the perimeter, the downside, and what success should look like.
The same principle applies here. Every improvement in operating efficiency creates capacity that can be reinvested. Every reduction in time-to-clarity increases the speed at which capital can be allocated. Over time, those advantages compound.
The companies that pull away will be the ones that build the financial engine early enough to benefit from that compounding.
Build the engine, place the bets, and measure what they deliver.
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