Artificial Intelligence in Financial Management: What's Really Changing
An overview of the issues that are currently shaping the CFOs’ agenda and the practical tips discussed during the session.
Artificial intelligence in financial management is moving beyond the experimental stage and is gradually being incorporated into processes such as account reconciliation and financial close. However, many of these initiatives fail to achieve the expected impact. The reason usually lies not in the technology chosen, but in how its implementation is approached: what data is used, who leads the process, and, above all, how the reactions of the people who have to work with it are managed.
These are some of the topics that Sage explores in depth in its webinar “Practical AI for CFOs, ” which we’ll review here as an introduction.
Artificial Intelligence in Financial Management: The New Role of the CFO
The CFO’s role is expanding. Artificial intelligence is also changing that role, which is no longer limited to managing the budget: the CFO is taking on increasing responsibility for what data the company uses, how it is structured, and where it makes sense to apply AI first. This involves decisions ranging from data infrastructure to building a team with more technical and analytical profiles, as well as coordinating with the executive committee and the technology department.
In short, it is a role that combines financial judgment with a much more strategic view of the company’s processes and systems. At PKF Attest, as Sage X3 partners, we often see finance departments eager to move forward with AI but unsure of where to start within their own ERP system.
The real obstacle isn't technological
Although technology is advancing rapidly, the weakest link in AI adoption is often human. In any organization, there are those who embrace it unreservedly, others who view it with suspicion, and a majority who fall somewhere in between. Managing this diversity of reactions—and the often-exaggerated expectations generated by any disruptive technology—is just as critical as the technical implementation itself.
"The biggest obstacle to AI adoption isn't technological—it's human."
Added to this is an ongoing debate: when AI begins to make decisions autonomously, who bears responsibility? Which decisions must always remain under human oversight?
Where Does It Make Sense to Apply AI Right Now?
Not all financial processes are equally suitable for automation. The difference usually lies in two types of tasks:
Without high-quality data, AI won't work
AI learns from a company’s historical data. If that data is duplicated, incomplete, or out of date, the decisions based on it will inherit those same errors. Having clear data governance—and an ERP system capable of integrating with other systems to provide reliable information—is often the true starting point for any AI project in the finance department.
As Sage X3 implementers, we at PKF Attest often emphasize this point to our clients: technology rarely fails; the data that feeds it, however, does—more often than we would like.
The Most Common Mistakes When Implementing AI
One of the most common mistakes is to start with technology rather than strategy: getting dazzled by a flashy demo and building a business case around it, instead of starting with a real problem the company faces. Adopting AI due to competitive pressure or because it’s trendy—without the decision stemming from strategy—usually leads to poorer results.
And if the first implementation fails, it takes twice as much effort to regain the team's trust for the next attempt.
How to Tell if AI Is Actually Working
Measuring the return on investment for each use case is essential to justify the investment to the executive committee. It is important to focus on three areas:
Time: hours saved on reconciliations, financial closings, and invoice processing.
Costs: reduction in errors and repetitive manual tasks.
Strategic impact: incremental improvements versus a complete overhaul of a process, with different potential and varying degrees of difficulty in predicting the outcome.
Artificial intelligence can deliver a real boost in productivity within the finance function, but only when it is based on a clear strategy, supported by high-quality data, and takes into account the human dimension of change.
Frequently Asked Questions
Watch the full recording
During the session, Elena Feo, CFO and controller, and Javier Recuenco, founder and CEO of Singular Solving, delve into each of the topics covered in this article, offering examples and practical recommendations. Fill out the form to watch it whenever you like.
Practical AI for CFOs
