How Is AI Transforming Finance Teams?

If you deal with the finance or accounting teams of your own company or an organization, you have probably wondered many times how AI is affecting the performance of finance teams. AI technology is no longer a side tool for answering simple prompts or getting simple reports; it can be embedded in account closing, risk analysis, liquidity forecasting and even strategic decision-making. In this article, we look at exactly what changes AI has brought to the structure and role of finance teams and which tools are currently the most widely used. We also look at what outlook intelligent financial agents (Agentic AI) are shaping, and finally whether AI is really replacing finance professionals or coming to their aid.

 

How has AI changed the structure of finance teams?

The arrival of AI in every field has brought many changes. In finance, too, the traditional structure of finance teams, built around repetitive tasks, data entry and report preparation, has been transformed. Part of the operational duties of finance teams has been handed over to AI, and as a result, expectations of the human workforce in finance have shifted toward analysis and participation in the organization's strategic decisions.

This shift comes with notable figures, both in AI adoption rates and in forecasts about the future workforce in this field:

 

• The AI adoption rate among finance professionals reached 56 percent in 2026, double the 2023 rate, although the finance function has still embraced AI later than other parts of the organization.

 

• According to a Deloitte survey of about 1800 finance leaders and employees, 63 percent of finance functions say they have fully deployed AI solutions in their processes, but 84 percent have not yet redesigned jobs and roles to match this change.

 

• According to data from Cambridge Judge Business School, views on AI's impact on finance employment through 2030 remain polarized; a large share of respondents expect «widespread reskilling» without a net loss of jobs, while in some sub-sectors such as payments, a significant share of participants still predict workforce reductions.

 

• Standard Chartered has announced that by 2030 it will cut about 15 percent of its back-office staff by relying on AI.

 

• In the US financial activities sector, an average of about 28 thousand jobs per month were lost in 2026, and according to labor market analysts, this sector could be the next industry to experience the biggest impact of AI on employment, since nearly a quarter of jobs in this field are administrative and support roles, which are the most vulnerable to automation.

 

• The use of intelligent financial agents (Agentic AI) is also growing at a remarkable pace; estimates show that the share of finance teams using this technology will reach about 44 percent by the end of 2026, a growth of more than 600 percent compared to previous years.

 

The common thread in all these figures is this: AI does not necessarily mean the complete elimination of the human workforce, but it has fundamentally redefined the structure of finance teams, the ratio of junior to senior staff and the skills required for hiring.

 

From manual data entry to strategic analysis: the changing role of finance professionals

About 5 years ago, most of the time of accountants and finance professionals was spent on tasks such as entering financial data, reconciling accounts, preparing reports and processing invoices. Now, however, AI can gradually take over these tasks and carry them out automatically, giving finance professionals more time for analysis and strategic advisory.

 

Shorter financial close times

One of the most tangible changes after AI was embedded in financial processes is the faster account closing process. According to a joint study by MIT and Stanford, accountants who use AI cut an average of 7.5 days from their monthly close, shift 8.5 percent of their time from processing work to higher-value activities, and improve the level of detail in financial reports by up to 12 percent. 

 

New skills required: critical thinking and ethical judgment

With repetitive operational work handed over to AI, what creates value for finance professionals is the skills AI is still weak at or cannot achieve, such as critical thinking, ethical judgment, creativity, empathy, etc.

Today, business owners are looking for people who, in addition to accounting knowledge and work skills, can add value to the decision-making process and give it direction. On the other hand, when AI becomes responsible for analysis, data collection and proposing decisions, the responsibility for judging and evaluating AI's decisions and recommendations still remains with specialists who have soft skills such as ethical judgment and critical thinking; something that still cannot be handed over to an AI algorithm.

 

AI tools and applications in finance teams

AI is used in almost every layer of financial operations, from processing raw data to analyzing financial transactions, as well as forecasting liquidity and suggesting financial strategies. The table below shows some of the most important areas of application along with example tools, their main function and their measurable impact:

 

 

The rise of intelligent financial agents (Agentic AI)

The new generation of AI in finance is no longer limited to giving a prompt and receiving an answer; intelligent agents (Agentic AI) can automatically plan, execute and monitor multi-step processes, returning only exceptions and restricted cases to humans for the final decision. Using intelligent financial agents helps finance teams in many ways, and below we cover 2 of the most important.

 

Automated management of liquidity and working capital

In treasury, intelligent agents are replacing manual cash flow analysis with intelligent processes. For example, banks that have turned to intelligent agents report cutting operating costs by more than 20 percent. 

However, finance leaders stress that trust in these systems depends on the explainability of their recommendations, not just their accuracy; that is, the finance team must know why an intelligent agent has suggested moving liquidity or changing the short-term investment portfolio.

 

The shift from monthly close to real-time finance models (Real-Time Finance)

Another trend that has accelerated with the rise of intelligent agents is the move from the «monthly close» model toward «real-time finance». In the traditional model, financial figures remain «soft» and provisional until the final days of each period, but in the AI-based model, data reconciliation and validation happen continuously throughout the month. According to estimates, only about 8 to 12 percent of finance organizations, mainly large tech-driven companies, have currently achieved a fully real-time close, but a larger share of organizations are transitioning to a «proactive close» model, in which reconciliation work is spread across the month and the close itself is limited to two to three days of analysis and final approval.

 

Explainable AI and its importance in risk management

The bigger the role AI plays in financial decisions, the more important transparency becomes in how these systems reach their conclusions; this is known as Explainable AI. According to a Cambridge report titled 2026 Global AI in Financial Services Report – Adoption, Impact and Risks, 78 percent of financial regulators consider explainability critical for their supervisory purposes. Meanwhile, only 50 percent of industry players have applied Explainable AI methods in their operations. Model opacity and lack of explainability have also been cited by 56 percent of regulators as one of the most significant AI risks in finance. It is worth noting that this concern is not merely theoretical. Regulations such as the EU AI Act classify applications like creditworthiness assessment and credit scoring as «high-risk» AI systems. This law requires providers of such systems to comply with specific requirements, including technical documentation, data governance, risk management and the provision of human oversight mechanisms.

Will AI replace financial professionals?

No, but people's roles are changing. AI can significantly enhance and carry out finance and accounting work without necessarily sidelining the people who do it. That said, vulnerability is not the same across all roles. Finance jobs that are repetitive, rule-based and require little judgment, such as data entry and some administrative tasks, are the most likely to be automated, while roles that require professional judgment, an understanding of the business context, negotiation and human interaction will continue to depend on human expertise. That is why we suggest keeping yourself up to date with AI developments in line with the needs of your job.