Agentic AI is reshaping finance, automating everything from reconciliation to risk management. Find out how leading businesses are deploying AI-powered agents across close, treasury, forecasting, and auditing.
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Agentic artificial intelligence has left the realm of isolated experiments and entered the general ledger. From global banks to high-growth startups, AI agents in finance are taking hold as smart support systems driving a range of real benefits—speed, scalability, data accuracy, and real-time insight.
AI agents are autonomous software entities that observe context, pursue goals, and act on behalf of the company without requiring step-by-step instructions or frequent human intervention. In finance, it’s a shift on par with moving from spreadsheets to ERP systems.
Once thought futuristic, agents will soon be an industry standard. In fact, Gartner predicts 90% of finance analytics will be automated in the next 2 to 3 years, a third of enterprise applications will be agentic, and 15% of all work decisions will run autonomously.
As the speed of adoption ramps up, the time is now for finance leaders to learn how pioneering organizations are using AI agents to enhance their function, access better insights, and enhance stakeholder and customer experiences.
In the next 2 to 3 years, 90% of finance analytics will be automated , and a third of enterprise applications will be agentic.
Traditional finance‑automation tools—macros, deterministic RPA, even chatbots—follow scripts. Agentic AI goes further; it blends large language models (LLMs), symbolic reasoning, and embedded policy libraries so software can observe, decide, and act in real time across finance systems.
On the ground, these capabilities reflect in things like continuous ledger hygiene, automated and policy-aware cash stewardship, and always-on cost vigilance. Their behaviors rest on four enabling layers that finance technologists will recognize:
Unified data fabric: Transaction‑level tables from ERP, planning, and treasury platforms feed agents a single source of truth—the “control tower” for autonomous finance.
Reasoning layer: Finance‑tuned LLMs and graph engines interpret accounting guidance, internal policies, and historical resolutions to choose the next best action.
Execution layer: Secure APIs or RPA bots post journals, refresh forecasts, release payments, or open hedge tickets—no swivel‑chair integration.
Guardrails and oversight: Immutable logs, approval matrices, and model‑risk dashboards keep segregation‑of‑duties intact.
Together, these layers create a digital workforce that acts at cloud speed—augmenting human expertise while staying tightly governed. And according to Forbes, agents are already easing the strain of finance talent shortages and limited bandwidth, offloading manual tasks that often bog teams down, and making the work that remains more data-driven, insight-led, collaborative, and well-supported.
Agentic systems are already supporting strategy across the finance lifecycle, stretching from period‑close automation to real‑time cash and spend oversight. Critically, AI agents work best with human oversight, collaborating on tasks and strategies in real time. These examples highlight how leading finance teams are applying AI-powered agents in production today.
Agentic AI injects autonomous execution into processes once bogged down by manual handoffs and stale financial data. Fundamental workflows like procure-to-pay (P2P), record-to-report (R2R), and order-to-cash (O2C) are ideal candidates for agentic AI execution.
Companies are turning to agents to handle tasks and reconcile transactions continuously, matching sub-ledgers to the general ledger, proposing adjustments, and escalating only true anomalies. They can interpret company-specific accounting policy to decide when to auto-clear variances, when to defer to a human, and how to log decisions for audit traceability.
In FP&A, planning agents can refresh forecasting models in real time, sourcing new data from sales, HR, and supply chain systems to maintain living models that never go outdated. Applied successfully, AI agents improve data security, streamline operations, and reduce the need for humans to perform repetitive tasks.
Markets don’t wait, and increasingly, neither do finance teams. Treasury and planning agents are enabling real-time responsiveness by continuously sensing shifts and autonomously proposing or initiating actions.
Treasury agents can monitor intraday liquidity positions across bank accounts and trigger auto-sweeps, short-term investment placements, or internal lending proposals—all while adhering to company risk policies and liquidity thresholds. Planning agents can ingest live sales and macroeconomic data, then re-run scenario models to inform rolling forecasts, explaining key driver changes along the way.
For example, the Wall Street Journal recently reported how luxury brand LVMH deploys AI agents to adjust prices in real time based on currency fluctuations, continuously.protecting profit margins as markets change.
What makes these agents different is that they act and decide within context. They read governance rules, historical responses, and market signals, acting only when moves align with policy and probability thresholds. CFOs no longer wait for end-of-week reports to act on volatility. With agents, the numbers don’t just update—they respond.
Luxury brand LVMH uses agents to adjust prices in real-time based on currency fluctuations to protect profit margins amidst market change.
Continuous control used to mean running batch validations nightly or weekly. With agents, assurance is now a minute-by-minute process, reducing human error and providing clearer audit trails.
Control agents continuously monitor transactions (including unstructured data) as they flow through finance systems, automatically testing them against policy, identifying anomalies, and logging exception rationales. KPMG recently integrated AI agents into its smart audit platform, automating tasks like expense matching and unrecorded liability detection, freeing auditors to focus on higher-risk areas while maintaining a strong human-in-the-loop for oversight.
More advanced agents layer in reasoning, not just rules. When a duplicate invoice is spotted, for instance, the agent might compare it against payment history, supplier behavior, and AP resolution patterns before recommending rejection or manual review. These decisions are documented with full lineage and presented in auditor-friendly formats, dramatically reducing compliance prep and risk exposure.
The finance function has always evolved in decisive spurts—from double‑entry bookkeeping to electronic spreadsheets, from batch ERP to real‑time analytics. Agentic AI is the next leap. With AI agents in finance, organizations are proving agents can support sensitive workflows with accuracy and trust.
The destination is compelling: continuously closing ledgers, predictive cash positions, and compliance that monitors itself. The priority now is to embed AI agents in ways that are durable, auditable, and aligned with business value. The future of finance and financial services is AI-driven—don’t get left behind.
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