AI in Finance
Agentic AI for Finance Teams: What You Actually Need to Know
22 December 2025
If you read analyst reports or vendor materials on AI in finance, you have encountered the phrase “agentic AI” in the last six months. It is being used to describe everything from simple automation to systems that independently execute complex multi-step workflows. The range of what the term covers makes it almost meaningless as currently used.
This post defines it properly, explains what it means for finance teams, and gives a realistic assessment of where the technology is today versus where it is going.
What agentic AI actually means
Agentic AI refers to AI systems that can take sequences of actions autonomously to complete a goal, rather than simply responding to a single input or query.
The distinction matters. A traditional AI tool in finance does one thing: suggest a cost code for an expense, or flag an invoice that looks anomalous. You prompt it, it responds. You decide what to do next.
An agentic system works differently. It is given a goal and it works out the steps to achieve it. If the goal is “resolve this reconciling item”, an agentic system might: identify the item, search the relevant source documents, compare against historical patterns, draft an explanation, prepare the journal entry, flag it for human approval, and then post it once approved. Each of those steps involves decisions. The agent makes them, in sequence, without a human initiating each one.
That is a substantively different capability from what most finance AI tools do today. It is also why the governance questions around agentic AI are significantly more complex than those around narrow, single-task AI.
What this looks like in a finance context
The most frequently cited use cases for agentic AI in finance fall into three areas.
Month-end close. An agentic system could take a checklist of close tasks, execute each one in the correct sequence, flag exceptions for human review, and move to the next task once exceptions are resolved. In theory, this compresses the close timeline and reduces the management overhead of coordinating across a team. In practice, the close process has enough non-standard situations each month that the agent needs either very good exception handling or very frequent human checkpoints.
Reconciliation workflows. Rather than flagging unmatched items for a human to investigate, an agentic system could investigate them: pull supporting documentation, search email for relevant correspondence, compare against prior period patterns, and produce a proposed resolution. The finance team reviews proposals rather than starting investigations from scratch. This is the most credible near-term use case, and I cover the underlying mechanics in more detail in the LLM reconciliation post.
Accounts payable and procurement. An agentic AP process could receive an invoice, match it to a purchase order and goods receipt, apply the payment terms, schedule payment, and update the cashflow forecast. The human reviews exceptions rather than processing everything. The finance automation three-tier model describes the structural pattern here.
All of these use cases are real. All of them are also, in the current state of the technology, more complicated to deploy safely than vendor materials suggest.
The practical questions you need to answer
Before a finance function considers deploying agentic AI in any meaningful way, three questions need clear answers.
What governance do you need before you give an AI agent write access to your accounting system?
This is not a rhetorical question. Most finance functions have approval workflows specifically because no single person or process should be able to post to the ledger without review. An agentic system that can draft and post journal entries is doing something your existing controls are designed to prevent. You do not need to prevent AI from being useful. You do need to design a governance structure that includes AI as a participant in your control environment, not a bypass of it.
At minimum: every agent action that modifies a financial record should require human approval before posting. Every agent action should be logged with a full audit trail. The scope of what the agent can do without approval should be explicitly defined and periodically reviewed. The question of who is accountable when the agent makes a wrong decision needs to be answered before deployment, not after.
What happens when the agent is wrong?
AI systems make errors. Narrow AI tools that do one thing make errors on that one thing, and the impact is bounded. An agentic system that makes an error early in a multi-step workflow can propagate that error through each subsequent step before a human sees it. The error surface is larger.
You need a clear answer to: who catches the error, at what point, and what is the remediation process? This requires understanding the decision logic at each step, not just reviewing the final output. If the agent made a wrong assumption in step two of a ten-step process, the fact that the final output looks reasonable is not reassuring.
Who is accountable?
In finance, accountability for the numbers sits with the CFO and ultimately the board. That accountability does not transfer to a vendor because you used their AI tool. When an agentic system makes a decision that results in a material error, the finance leader is accountable. That is not going to change. The implication: you need the same level of understanding of what your agentic AI is doing that you currently have of what your finance team is doing. Not more permissive. The same standard.
Where the technology actually is today
Agentic AI in finance is real and it is being deployed. It is not science fiction and it is not analyst speculation. Several large financial services organisations have agentic processes running in production. The results are, in most cases, positive in narrow and well-defined workflows.
The honest assessment is that most finance teams are not currently in a position to deploy agentic AI in a meaningful way. Not because the technology is not there, but because the preconditions are not. Agentic AI requires: well-documented processes, clean and consistent data, defined exception logic, and a governance framework that includes AI as a participant in your control environment.
Most finance functions are still working on those foundations for their existing automation, let alone for agentic systems. See AI Won’t Fix a Broken Finance Function for why the foundation question is always the starting point.
The three to five year picture
The technology trajectory is clear enough to plan around.
Within three to five years, agentic AI will be a standard component of how high-performing finance functions operate. The close process, AP processing, reconciliation, and management reporting will all have significant agentic components in mature finance functions. Finance teams will be smaller, more focused on judgment and oversight, and less involved in execution.
That is a significant change. It is not a reason to move faster than your governance and data foundations support.
The finance functions that will benefit most are the ones building those foundations now. The ones that are not will find themselves in 2028 in the same position as the finance functions that skipped ERP implementation properly: unable to benefit from the capabilities that came next.
The right response to agentic AI in 2025 and 2026 is not to deploy it. It is to get ready for it.
What to do now
Three practical steps for a finance leader who wants to be in position when agentic AI is deployable at scale.
First: use narrow, single-task AI tools now. Not as a consolation prize, but because this is the right place to start. Single-task AI tools are lower risk, easier to govern, and produce results that teach you what good AI deployment looks like in your specific context. They also generate the labelled data that more sophisticated systems will need later.
Second: fix the foundations. Process documentation, data quality, system adoption. These are the prerequisites for any meaningful AI deployment, and they are worthwhile investments independent of AI. A finance function with clean data and coherent processes will perform better regardless of what AI tools it uses.
Third: design your governance framework before you need it. Who reviews AI-generated outputs? What requires human approval before posting? What is the audit trail requirement? How do you handle errors? Working through these questions before deployment is significantly easier than working through them after something has gone wrong.
For a deeper treatment of the governance dimension, see the upcoming post on AI governance for finance teams.
The technology will be ready before most finance functions are. The advantage goes to the ones that use the intervening time well.
Explore the full AI in Finance Strategy for how agentic AI fits into the broader transformation agenda.
Maebh Collins is a Fellow Chartered Accountant (FCA, ICAEW) with Big 4 training and twenty years of operational experience as a founder and senior finance leader. She writes about AI in finance transformation from the inside out.