How finance teams are making AI work for everyone

When they were first introduced by Motorola in 1983, cell phones were dismissed as “toys for the rich.” They were too big, could only be used for 30 minutes at a time, and were priced at today’s equivalent of $10,000. Now, it’s hard to find a person who doesn’t own one.

Resisting new technology before embracing it is normal, and it’s just what is happening with AI today. In just a few years, it has moved from being seen as an unclear and futuristic concept to becoming a standard part of our lives, including our work. As Nicolas Boucher stated in an interview, “Now it’s almost an anomaly that somebody doesn’t use the AI.”

Finance is no exception. And while there is still some hesitation, fear and, as Gartner’s Director Analyst Alex Levine put it in the latest Gartner CFO & Finance Executive Conference in London, a general feeling of overwhelm, more and more finance teams are incorporating AI tools into their daily operations. From automating reports to detecting fraud, AI is set to become finance’s copilot — not its replacement.

In this article, we’ll explore how finance professionals are already using AI for their everyday tasks and how finance leaders are overcoming resistance so their teams can take full advantage of AI.

In just one year, AI adoption in finance has shifted from niche experiments run by a few to a mainstream occurrence.

Data from a Gartner Inc. survey shows that, in 2024, 58% of finance leaders were using AI for process automation, anomaly and error detection, analytics, and operational assistance. In 2025, that percentage has increased, with Deloitte’s CFO Insights report from Q1 2025 revealing that 79% of finance leaders are now willing to use AI in tasks such as data entry and analysis.

AI usage in finance as we’re seeing it today is aimed at automating and facilitating day-to-day tasks and freeing teams for higher-value work. Rydoo’s Smart Audit, for example, uses AI to analyse expense claims and automatically spot when they’re out-of-policy, notifying both the end-user and the approver. This kind of automated system reduces the time needed to handle expenses and eliminates the back-and-forth that’s usually required to process, categorise and approve them.

AI has become a standard in finance, and a must-have for teams that want to be manage their time and workload efficiently.

AI has been revolutionising finance processes, especially when it comes to automated reporting, flagging expense mismatches in seconds, or chatbots that answer routine queries instantly.

In a recent interview, Bastienne Föeller shared that her team at TIS (Treasury Intelligence Solutions) “primarily use Copilot to drive reports [and] analyses, and also automate some of the [more] tedious tasks”, which allows her to free up people’s time for other high-value assignments that AI can’t handle.”

Christian Moldenhauer, Managing Director & Founder at expertpowerhouse, describes how his team is “experimenting with [AI] to have faster customer service and a more accurate matching of invoices and payments.”

Other AI use cases in finance include:

• Fraud detection: Using AI-based solutions such as Rydoo’s Smart Audit module can help identify any non-compliant details, resulting in fewer fraudulent claims getting approved.
• Budget forecasting: AI’s advanced data analysis capabilities allow finance teams to uncover patterns in historical data to forecast and plan budgets more effectively.

These examples showcase how AI-powered automation tools can drastically reduce manual work in finance teams. Reducing, not eliminating, is the focus. The final judgement, whether that’s getting an expense approved or a report finalised, is still in professional human hands.

Embedding AI into everyday workflows can simplify certain tasks and boost productivity, becoming a team’s greatest ally.

In fact, a Citibank report from 2024 shows that 54% of banking jobs have a high potential to be fully automated, predicting that, in under five years, all finance teams are likely to have AI tools as “colleagues.”

However, as much as it can automate jobs, AI isn’t likely to fully replace finance professionals in the near future. Human judgment, oversight, and ethics will still be as important, if not more, than they were before.

As the Citibank report notes, early AI adopters have found the quality of its outputs unreliable or inconsistent. Deloitte’s CFO Insights report from Q3 2023 backs this, showing that 57% of CFOs are worried about AI’s impact on risk and internal controls, mentioning concerns over the accuracy and quality of AI outputs.

This shows that, even though technology is consistently evolving, finance professionals still need to oversee the usage of AI tools and ensure the output aligns with business guidelines and goals, as well as with local and global regulatory standards.

Despite the value it can bring, AI is still met with resistance by a number of finance teams. They worry AI makes too many mistakes to be trusted, can make them look unskilled if they don’t dominate it, or that it will replace them altogether. Although unlikely, reports like one by Bloomberg from early 2025, which showed that over 200,000 jobs could be replaced in less than five years, instil fears of job loss among finance professionals.

It’s no wonder that 48% of CFOs surveyed by Deloitte mentioned staff resistance as the biggest hurdle to AI adoption in their businesses. These are natural and valid concerns that finance leaders should address to ensure AI usage is as beneficial as possible.

This starts by offering training and learning opportunities as a team. Bastienne Föeller shares that her team recently did a workshop with an expert on the topic of AI for finance, and is now “using AI tools daily in [their] operations, multiple times per day.” She understands why people might fear “looking like a newbie” when they first start out using something as new as AI. This is why she believes finance leaders and teams should “give [themselves] allowance to learn,” even when that means failing at something and trying again. For the learning and sharing process, she advises “committing to 15-30 minutes every day” as the first step to seeing incremental successes in AI adoption.

Nicolas Boucher, Founder of the AI Finance Club, further stresses the importance of learning together and showcasing successful AI use cases. “Once somebody shows just one use case for their work, they get hungrier”, he explains “They will try the second use case, the third use case, and they will show it to their colleagues.”

This is why Boucher encourages people to learn together, especially due to how fast AI changes. Keeping up with it can be challenging, but it’s made easier if everyone shares their experiences and findings with each other.

Openly sharing both the successes and failures around AI in finance helps transform teams’ resistance to using it into confidence. Mistakes can be turned into learning opportunities, and wins should be celebrated to keep your team motivated to continue experimenting with and embracing new technologies.

Much like cell phones, AI has found its place among us, and it’s here to stay. But embracing it doesn’t mean having to master it overnight.

The most successful finance teams treat AI adoption as a shared learning journey, investing in training, showcasing wins and successes, and supporting professionals through their concerns. This approach is vital to addressing employees’ fears and building the confidence they need to use AI tools more effectively.

It’s likely that we’ll witness many more AI advancements in the years to come, and that they will further impact how finance teams work. Fostering a collaborative learning environment early on is key for professionals to become faster and more comfortable at adapting to these changes as they come.