Finance : The driving force behind AI transformation
The function that was ready for it
The most visible AI transformations of recent years have focused on market-facing functions—marketing, sales and product. Finance has largely watched from the sidelines, more often called upon to assess the ROI of AI than to benefit from it directly.
Yet few functions offer such favourable conditions. Finance touches every part of the organisation without being at the heart of any single one: it consolidates, forecasts and controls, but business performance does not depend directly on it. This creates a tolerance for experimentation that is rarely found elsewhere in the organisation. Finance also benefits from a deeply embedded culture of tools and data, along with large volumes of documents and processes that provide natural raw material for AI.
The consequences of this relative neglect can be seen in teams’ day-to-day work: most Finance departments still devote the majority of their time and energy to producing information rather than analysing it. Reports are compiled manually, consolidation processes mobilise entire teams, and unexpected questions often cannot be answered quickly. Tools that have barely evolved over the past twenty years are largely responsible. Three trends are now beginning to change this.
1. Managing the business with greater depth
For many years, strategic performance management relied on static dashboards: a few dozen KPIs defined once and updated at fixed intervals. This approach has a well-known limitation: whenever an analytical question arises outside the predefined framework—during an executive committee meeting or a performance review, for example—a team must produce an ad hoc report, sometimes several days later.
Generative AI is removing this constraint. It is now possible to query data using natural language—to ask questions of a system that understands not only the structure of the data, but also its meaning. Executives and controllers can explore figures independently, combine unexpected dimensions and investigate anomalies in depth without relying on a data team.
This capability opens the door to a second opportunity: scenario simulation. Supported by a semantically enriched data infrastructure, AI can rapidly model assumptions—such as a price shock, a change in product mix or a market slowdown—and assess their financial impact in near real time. What was once a quarterly exercise can become part of continuous business steering.
The conditions for success are demanding. They require serious work on data semantics—in other words, ensuring that systems understand what your data means, not simply where it is stored. This represents an investment, but it is also what separates a compelling demonstration prototype from a tool that creates sustainable value.
2. Forecasting with greater accuracy
Financial forecasting is not a new topic. Yet in the vast majority of companies, it remains significantly underused—and the current wave of AI provides an opportunity to put it back on the agenda with genuine ambition.
In revenue forecasting, today’s models can go far beyond traditional regression techniques. They can integrate external signals—search trends, macroeconomic data and market behaviour—and produce both short-term forecasts, such as month-end or quarter-end projections, and medium-term forecasts over 18 or 24 months to support strategic demand sensing. For companies operating in volatile or seasonal environments, this fundamentally changes their relationship with risk.
Margin analysis is another frequently overlooked area. Breaking down margins by product, channel or entity remains a cumbersome, semi-manual exercise in many companies, often completed too late to be genuinely actionable. Well-designed models can automate this level of granularity, make it continuously accessible and turn margin analysis into a genuine decision-making lever rather than an accounting reconciliation exercise.
In both cases, the value does not come from forecasting for its own sake, but from the ability to make better and faster decisions as a result.
3. Operating more efficiently
This is the most recent trend—one that has truly emerged over the past six to nine months—and probably the most visible in teams’ day-to-day work.
AI agents with modest, clearly defined scopes can automate a multitude of micro-tasks that currently consume time, attention and mental energy. In accounting, invoice verification and reconciliation can be handled automatically. In tax, managing intra-EU VAT—with its country-specific rules and high transaction volumes—is particularly well suited to this type of automation. In management control, consolidating P&Ls across entities becomes faster and more reliable. In procurement, supplier onboarding and compliance monitoring can be largely digitised and automated.
None of these tasks is strategic in isolation. Taken together, however, they absorb a considerable share of teams’ capacity. Automating them frees up time for higher-value activities. Teams spend less time entering and reconciling data, and more time analysing information and advising the business.
The right way to approach this opportunity is not through a large-scale, multi-year transformation programme. It starts with pragmatically identifying the most frequent pain points, followed by the gradual deployment of targeted agents designed and built alongside the teams that will use them. Over time, a new way of working emerges—an evolution similar in nature to the digital tools revolution of the 2010s, but likely to be adopted much faster.
Different challenges require different responses
These three dimensions do not have the same organisational implications, and it would be misleading to treat them as a single, uniform transformation.
Strategic performance management and forecasting affect the quality of decision-making. Their value is measured through the choices they enable—or help organisations avoid. Their impact may be diffuse, but it can be highly significant. These topics primarily concern CFOs and their leadership teams.
Operational automation, meanwhile, directly transforms teams’ day-to-day work. It requires a serious change-management effort: understanding resistance, providing training on new tools and redefining roles. What unites these three trends is their convergence towards a Finance function that is more autonomous, more analytical and more closely connected to business decisions.
A transformation that deserves the right foundations
Getting started requires clear choices regarding priorities, data foundations and sequencing. The main challenge is generally not technical. It lies in identifying use cases that deliver genuine value, building the foundations required to make them sustainable and engaging teams over the long term.
At eleven strategy, we support Finance departments at every stage of this journey—from diagnosing priorities and designing data architectures to operational deployment and capability building. Our position at the intersection of strategy consulting and AI expertise allows us to address these challenges holistically.
If these topics resonate with your current priorities, we would be delighted to discuss them with you.
