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There is a conversation happening in boardrooms and portfolio company leadership teams across Europe right now. It goes something like this: the CEO asks what the business is doing about AI. The CFO defers to the IT director. The IT director outlines a technology roadmap. The board nods. A working group is formed. Eighteen months later, the finance function is still producing the same reports, running the same variance analysis, and spending the same proportion of its time on work that adds limited strategic value.
This is not a technology failure. It is a missed leadership opportunity. And it is one the CFO is uniquely positioned to correct.
Artificial intelligence is not, at its core, a technology question for the finance function. It is a strategic question about where the CFO's time and the finance team's capacity should be directed — and who in the organisation is best placed to make that call. The CFO, working alongside IT and with the full support of the leadership team, is that person. The finance function sits at the intersection of every commercial data stream in the business. Nobody is better placed to understand what better insight would look like, or to lead the charge in building it.
The CFOs who will define the next decade of PE-backed finance leadership are not waiting for a technology roadmap to land on their desk. They are running experiments, testing tools, and systematically redirecting their teams toward the work that actually creates value. They are treating AI not as an IT project to be managed, but as a strategic lever to be pulled — hard, and early.
This is a rally call. Get your own house in order. Shape the agenda. Move with intent.
The Opportunity That Most Finance Functions Are Sitting On
The finance function in a PE-backed business carries a significant and largely invisible tax on its time. It is the tax of low-value, high-volume work: variance commentary that follows a template, management reporting that takes three days to produce and is read in thirty minutes, reconciliations that exist because the data infrastructure was never properly integrated, FP&A cycles that consume disproportionate resource relative to the insight they generate.
This is not a criticism of finance teams. It is a description of the environment most of them have inherited. The systems were built for a different era. The processes were designed before the tools existed to automate them.
AI — applied thoughtfully, with the CFO actively leading adoption — is automation and digitisation on steroids. It does not just speed up existing processes. It fundamentally changes what is possible.
Variance analysis that currently takes a senior analyst two days can be produced overnight, with natural language commentary flagging material movements and their likely causes. Month-end close processes that currently require significant manual intervention can be compressed materially. Cash flow forecasting models that rely on static assumptions can be rebuilt as dynamic, scenario-aware tools that update as trading data changes. FP&A functions that currently spend the majority of their time gathering and reconciling data can flip that ratio entirely — redirecting capacity toward the analysis and insight that actually informs decisions.
From month-end scorekeeper to real-time commercial co-pilot. And it is available now, to any CFO prepared to build for it.
The Bigger Prize: Getting Commercial
The process efficiency gains are real and meaningful. But they are not the most exciting part of this story.
The most significant opportunity AI presents to the PE-backed CFO is not doing the same work faster. It is doing work that was previously impossible within the constraints of a normal finance team's bandwidth — and specifically, unlocking the commercial data that has always existed in the business but has never been fully interrogated.
Every business generates an enormous volume of commercial data exhaust. Sales activity data. Pipeline velocity. Pricing decisions and their outcomes. Customer behaviour at the cohort level. Retention patterns. Product usage. Renewal trends. Discount governance. For most finance functions, this data sits in CRMs, in sales platforms, in customer success tools — theoretically accessible, practically unanalysed, because there was never enough time or resource to get to it at month end, let alone quarterly.
AI changes that calculus entirely.
Take a SaaS business as an illustration. The CFO now has the tools to ingest cohort-level retention data, model pricing evolution across customer segments, identify the revenue signals that predict churn before it happens, and build a genuinely granular view of unit economics that goes far beyond what a traditional FP&A process would produce. The commercial insight that previously required a dedicated data science team — or simply went unseen — becomes available to the finance function as a matter of course.
The same principle applies across sectors. A services business can interrogate utilisation patterns and pricing realisation at a level of granularity that exposes margin leakage invisible to a traditional reporting process. A consumer business can model cohort behaviour across channels in real time rather than in arrears. A manufacturing business can connect operational data to financial outcomes in ways that surface efficiency opportunities before they become variance explanations.
This is what genuine commercial partnership with the CEO and leadership team looks like in practice. Not the CFO as reporter of what happened last month. The CFO as the person in the room with the deepest, most current, most granular view of what is actually driving the business — and the analytical firepower to turn that view into decisions.
The Chief Revenue Officer wants to understand which customer segments are generating the best lifetime value. The Chief Commercial Officer wants to know whether the pricing architecture is holding under competitive pressure. The CEO wants a forward view of cash generation under three different growth scenarios. With the right AI-enabled infrastructure, these are questions the finance function can answer in hours, not weeks.
That is the shift. From month-end scorekeeper to real-time commercial co-pilot. And it is available now, to any CFO prepared to build for it.
The variance commentary can largely take care of itself. The CFO's job is to be in the room when the strategy is being shaped — armed with better data than anyone else at the table.
How to Actually Move: Run Your Own Experiments
The CFOs making the most progress on AI adoption share one characteristic: they are running their own proof of concepts, on their own initiative, with their own team. They are not waiting for a business case to be approved by a technology committee. They are identifying a specific, high-volume, low-value process in their function, selecting a tool, running a structured trial, measuring the outcome, and making a decision based on evidence.
This is exactly the kind of commercial rigour the role demands in every other domain. Start with the highest-volume, lowest-value work in your function. Build a short list. Identify the tools available — there are more of them, at lower cost, than most finance leaders realise. Run a structured pilot. Measure time saved, accuracy improvement, and — critically — what your team did with the capacity that was freed up.
That last measure is the one that matters most. The goal is not to automate for automation's sake. The goal is to buy back your team's time and redirect it toward the commercial insight work that a PE sponsor is actually paying for.
The Areas Worth Moving on First
Not all AI applications in finance are equal. In Esker's view, the highest-return areas for a PE-backed CFO to focus on first are:
Reporting and commentary automation. The production of management accounts, board packs, and investor reporting is a high-volume, template-driven process well-suited to AI-assisted generation. The insight comes from the CFO's interpretation, not the assembly of the pack.
FP&A and forecasting. Dynamic forecasting models that ingest real-time trading data, update scenario assumptions automatically, and present sensitivity analysis without manual rebuilding represent a step change in the quality of financial insight available to the board and sponsor.
Month-end close compression. Automated reconciliation, exception-based review, and intelligent transaction matching can materially compress the close cycle — freeing the finance team from the month-end treadmill and creating space for forward-looking work.
Commercial data ingestion and cohort analysis. This is the area of greatest untapped potential. AI tools can now ingest data from CRMs, sales platforms, customer success systems, and pricing engines and synthesise it into the kind of granular commercial analysis that finance teams have always wanted to produce but rarely had the bandwidth to attempt. For a SaaS business, that means cohort-level retention modelling, pricing evolution analysis, and churn prediction built into the regular finance cadence rather than treated as a quarterly project. For any business, it means the finance team finally getting a handle on the commercial data exhaust that has always existed but never been fully harvested.
Cash and working capital management. AI-driven cash flow forecasting, with pattern recognition across receivables, payables, and seasonal trading dynamics, gives the CFO a materially better real-time view of liquidity than most traditional treasury processes allow.
Cross-functional commercial partnership. Perhaps the most important application of all — using the analytical capacity freed up by automation to genuinely support the CEO, CRO, and GTM leadership with insight that helps them make better commercial decisions faster. Finance's value in a PE-backed business is ultimately measured by its contribution to the exit outcome. AI makes it possible for the finance function to earn that contribution in a way that was previously out of reach for most teams.
The commercial data your business generates every day contains insights that your CEO, your CRO, and your board would act on immediately if they could see them.
The Broader Point
The finance function has been on a long journey from back-office cost centre to strategic value driver. AI is the next — and most significant — leg of that journey. But it will only be taken by CFOs who choose to lead it actively rather than observe it passively.
The investors who have seen what an AI-enabled finance function looks like — faster close, better commercial insight, more time on strategy, genuine cross-functional partnership — will increasingly expect it as standard. The CFO who is building toward that model is having a very different conversation with their board than the one who is not.
The tools are there. The opportunity is clear. The commercial data your business generates every day contains insights that your CEO, your CRO, and your board would act on immediately if they could see them.
The question is whether your finance function is the team that surfaces them — or whether that opportunity goes to someone else.
Esker works exclusively with PE-backed businesses on CFO and finance leadership hiring. The CFOs we place are selected not just for technical capability but for the strategic ambition and commercial drive to lead their functions through exactly this kind of change. If that is the standard you are hiring to, we should speak.
