For all the time and money finance teams have poured into automation, familiar challenges remain. Month-end closes still take longer than they should. Forecasts miss the mark more often than CFOs would like. And too many hours are still spent tracking discrepancies and fixing manual errors.
While many businesses have adopted AI in some form, studies say only 58% of companies have taken the next step: putting autonomous AI agents to work inside their finance operations. These agents don’t just follow instructions. They assess, adapt, and act.
Interestingly, the early results are hard to ignore. Companies using agentic AI report up to 41% faster close cycles, 95% fewer reconciliation errors, and an 86% drop in manual journal entries.
In this guide, we’ll explain agentic AI in finance, how it compares to generative AI, where it fits into accounting and treasury workflows, and why it might just be the upgrade finance leaders have been waiting for.
Agentic AI in finance uses autonomous AI agents that can independently execute critical financial tasks, respond to changing conditions, and improve financial workflows without instructions. They can adapt to new data and independently manage operations with minimal human input.
Agentic AI doesn’t rely on fixed rules or scripts. These systems read what’s happening in real time and act on it. If a reconciliation doesn’t line up, they try to fix it. If there’s a variance, they flag it early.
In practice, they stay on top of what’s happening, respond to change quickly, and reduce the back-and-forth that finance teams are used to. Moreover, agentic AI, when used for functions like financial close and treasury, helps teams move faster, with fewer errors, and more time to focus on decisions that matter.
In 24 months, finance without agentic AI won’t just be slower—it’ll be obsolete.
Are you doing enough to keep up?
Download Agentic AI GuideMost AI tools in finance need someone to give them input before they do anything. That includes Gen AI, which is creating content but waits for prompts. Agentic AI in finance finishes mission-critical tasks like forecasting or reconciliation independently, with little to no human input.
Most finance teams have already deployed RPA, dashboards, or forecasting tools. Yet month-end still drags, forecasts are often off, and compliance feels reactive. These tools digitize tasks, but they don’t think, adjust, or adapt to the dire needs of an evolving business.
That’s where agentic AI comes in. It acts independently within set rules, fixes problems as they arise, and keeps learning. For CFOs, it means fewer errors, faster cycles, and a team that can focus on strategy, not spreadsheets.
Agentic AI is changing how accounting teams manage the close process. Instead of automating just a few steps, Agentic AI makes a measurable impact across core stages of the accounting process, particularly in close, reconciliation, and journal entry management, enabling finance teams to close books faster and with fewer errors, even in complex environments.
One clear example is account reconciliation. Traditionally, teams spend up to 30% of their time matching records line by line. An agent can do this in minutes—learning from historical matches, resolving known gaps, and routing only the valid exceptions to the team , reducing matching time by up to 90% and achieving ~99% accuracy using pattern-based logic.
Similarly, for financial close, financial teams are always under the stress of box-ticking all the close checklist elements and finishing it on time for a seamless audit. However, there are always chances of a file not being uploaded or a task sitting untouched. Agentic AI enables the system to notice all of these slowdowns and notify the team, preventing errors and crises at the last moment, thereby paving the way for a 30% faster close.
These agents also support overall financial processes with agentic AI enabled accounting automation. They streamline journal postings across the system, check entries as they come in, catch mistakes early on, and log changes for review. Therefore, finance teams have more productive time that they can use for what matters more than fixing issues after the fact.
In many organizations, treasury and accounting don’t always stay fully connected. That disconnect can lead to gaps, especially when cash data isn’t visible across systems. Agentic AI helps close that loop by pulling in information from banks, ERPs, and payment platforms while simultaneously updating forecasts, checking balances, and flagging risks as they happen.
In liquidity risk management, delaying action could mean missed obligations or unnecessary borrowing. With agents watching balances in real time, treasury can make quicker, more informed moves.
In cash forecasting, the agent doesn’t just pull data from ERP and banks. It updates the forecast based on what’s happening. This helps the treasury avoid shortfalls and place idle funds more effectively.
Agents also flag potential risks—like when a balance drops below a set level—then alert the right people or take pre-approved steps. This kind of action is already helping firms improve cash accuracy and reduce manual adjustments, ensuring frictionless real-time treasury operations.
CFOs know automation alone won’t get them there. The real goal is to build a finance function that runs smarter, not just faster. Agentic AI gives finance teams more than speed—it opens the door to scale and smarter decisions.
Reconciliations and journal checks often take up hours. With AI agents handling this in the background, that time gets freed up. Companies can reduce manual journal entries by 86%, easing pressure at month-end by leveraging automated journal entry software. When something slows down, the agent can shift the task or move it along, without needing a person to jump in.
Mistakes in matching transactions are common, and they slow down the process. AI agents can use past records to handle the match more accurately. Automated transaction matching software helped teams reach nearly 99% accuracy. That means fewer corrections and a much cleaner close. Plus, audit logs are kept automatically.
Traditional forecasting often misses real-time shifts. With agentic AI, numbers get updated as soon as something changes, without needing a complete rebuild. Treasury teams get an earlier heads-up when balances dip or liquidity shifts. That means fewer surprises and faster decisions.
The less time teams spend on busywork, the more they can focus on what needs thinking, not clicking. Controllers can spend more time checking patterns instead of chasing fixes. Treasury teams can plan ahead instead of just reacting. CFOs get updates while they’re still relevant, not after the fact.
Agentic AI comes with serious potential—but like any high-impact technology, it also carries real risks if deployed without the proper structure.
An agent may act on the wrong signals if the inputs are outdated or inconsistent. That’s why clean ERP and bank data are essential for cash forecasting or reconciliation to work as expected.
Generative models sometimes fabricate outputs. While agentic AI is more task-driven, it still learns from patterns. If those patterns are flawed, so are the decisions. That’s why agentic AI has both the state-of-the art autonomous AI and also scope of improvement to be done by users manually, and configurable guardrails, especially in early rollouts.
Strategic ROI doesn’t have to take years. With agentic AI, finance teams can start seeing value in weeks—not months—through targeted pilots focused on high-impact areas like reconciliation or forecasting. HighRadius helps teams move quickly by delivering modular rollouts, measurable outcomes, and faster time to results.
Automation brings uncertainty. Training teams to interpret AI outputs and evolve their roles is critical. Tools are only useful if teams know how—and when—to use them.
Before rolling out agentic AI across your finance team, it’s smart to start small. Go for a few quick wins first—things that are clearly manual, slow, or error-prone. That’s where businesses will see impact right away.
Look at places where delays or missed approvals are slowing the business down. If forecasts are getting tweaked after the fact or exceptions are piling up, agents can help.
Reconciliation is a good place to test. It’s repetitive, takes time, and is low-risk. Let the agent take a first pass, then review what it finds.
Businesses must keep their test cycles short. There is no need to wait for months to see if it’s working. Try it on one process, check the results after two weeks, and adjust on the go.
Instead of worrying about letting the agent act, keep a human in the loop. Let the agent recommend—but not execute—until the team feels confident.
AI shouldn’t be a black box. Walk through how the agent “thinks,” what data it uses, and what actions it can take.
Once a business proves value, it must scale the agent into areas like treasury forecasting, intercompany settlements, or cash concentration.
Most AI solutions for finance don’t operate inside the workflows—they work around them. They analyze and suggest measures. But when it’s time to act, they wait. That’s the gap. Finance doesn’t require another dashboard or assistant. It requires systems that do the work autonomously using data-driven insights.
HighRadius solves this by embedding intelligent, decision-capable agents directly inside the platforms where treasury and accounting teams operate daily. Instead of making the solutions a plug-in or a patch, our software, such as treasury management and autonomous accounting, brings agentic AI as a part of the operating system. This is ideal for CFOs looking to run leaner, faster, and with more control, along with real-time visibility and end-to-end, streamlined accounting processes.
AI agents match transactions 10x faster, flag true exceptions, and reduce human intervention. Further agents clear intercompany mismatches using learned resolution logic. Our reconciliation software uses AI-driven agents to match transactions, flag exceptions, and apply learning-based rules for accurate, automated reconciliation. AI-driven reconciliation agent enables finance teams to achieve 99% accurate matching.
HighRadius Cash Flow Forecasting Software uses agentic AI to continuously refresh forecasts with live ERP and bank data. Agents detect shifts in real-time cash positions, update projections automatically, and help treasury teams manage liquidity proactively—without relying on manual inputs.
HighRadius Treasury Manager Software uses agentic AI to monitor cash positions in real time, optimize allocations across accounts, and flag liquidity risks instantly. Treasury teams gain continuous visibility and can act faster—without waiting on manual updates or fragmented reports.
HighRadius Financial Consolidation Software, powered by agentic AI workflows, automates real-time data collection, currency translation, and intercompany elimination. It supports top-side adjustments and streamlines the close, delivering 99% accurate eliminations and boosting consolidation efficiency by 60%.
HighRadius Financial Reporting Software, powered by AI agents, enables faster, more accurate reporting through automated drill-down analysis, dynamic variance reporting, and personalized templates. Teams achieve 80% faster reporting cycles with 95% customization to meet specific financial disclosure needs.
Agentic AI handles routine work and helps finance teams act on real-time data. That means faster turnaround, fewer errors, and better access to insights across the board. The real value? Everyone works from the same up-to-date picture, and time gets freed up for higher-value work.
For instance, a finance team spends hours matching entries for reconciliation. With an agent in place, it learns the logic, clears the easy stuff, and flags anything unusual. No waiting on rules. It just runs—similar to a teammate who knows the process and doesn’t need hand-holding.
Generative AI is good at creating reports or summaries when you give it a prompt. Agentic AI is different—it does the work. In finance, that could mean updating a forecast or clearing a reconciliation without anyone asking.
AI helps finance teams stay ahead by eliminating guesswork. It picks up patterns in financial data, so strategies adjust when the numbers change. Therefore, businesses get a clearer view of where things stand, making it easier to spot what’s coming and act before it becomes a problem.
Agentic AI handles the day-to-day work that takes time but follows a pattern. It steps in to match transactions, track cash movement, or call out issues. It reacts to what’s happening in real time—so things don’t stall. It’s not taking control—it’s doing the grunt work so your team can focus on bigger goals.
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Colleen Zdrojewski
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