AI Agents in Finance: The Next Generation of Finance Automation
AI agents automate core finance workflows by connecting to your ERP, automating manual work and transforming how teams close the books, forecast, and report.
June 6, 2025
Generative AI has already changed the world in just three short years since OpenAI’s launch of ChatGPT. Adoption has been staggering: ChatGPT became the fastest-growing consumer app in history, reaching 100 million users in just two months, and generative AI tools are now used by over 60% of enterprises globally. McKinsey estimates that generative AI could deliver up to $4.4 trillion in annual economic value, with finance and operations among the top beneficiaries.
This isn’t just a new tool—it’s the foundation of a new kind of workforce: one where AI agents act as always-on analysts, accelerating workflows, improving accuracy, and fundamentally transforming how global teams operate.
AI agents connect directly to your systems, learn your workflows, and act on real-time business data. Unlike static dashboards or templated scripts, agents operate with context—tailored to your processes, data models, and goals.
You stay in control while they handle the heavy lifting: pulling data, generating analysis, and executing routine tasks with speed and precision. If generative AI was the spark, agents are the engine—poised to drive 10x the impact by embedding intelligence into the systems and decisions that power every business.
Interest in AI agents is accelerating fast. In the U.S. alone, searches for “AI agents” have surged 638% over the past year. But this shift isn’t just about hype—it’s about rising expectations. Teams are outgrowing generic copilots and basic automations. They want systems that understand how work actually gets done—and can deliver real outcomes, not just recommendations. Whether it’s variance analysis, budget updates, or vendor reporting, agents are moving from experiment to execution, reshaping finance from a reactive function to a real-time engine for decision-making.
AI agents aren’t just gaining traction—they’re already transforming how work gets done across the enterprise. Much of the enterprise still runs on outdated, fragmented systems that struggle to integrate effectively with modern workflows. For years, teams have attempted to compensate for these limitations through manual workarounds and custom scripts, acknowledging that enterprise software is often clunky and frustrating. AI agents represent a fundamental shift toward dynamic, intuitive, and cross-functional systems. The market momentum is undeniable: AI agent startups raised $3.8 billion in 2024, and every major tech player is already developing AI agents or offering the tooling for them.
The Shift Toward AI Agent Workflows Has Already Begun
Adoption is accelerating. According to KPMG, 88% of organizations are either exploring or piloting AI agents. That interest is translating into concrete implementation, with teams across industries embedding agents into daily workflows.
Engineering teams were the first to go all-in. Developers have quickly adopted tools that enable them to code more efficiently, identify bugs earlier, and automate repetitive tasks. Tools like Cursor, Windsurf, and Cognition are driving this shift. Cursor alone writes 1 billion lines of accepted code daily. To put that into perspective, the entire world produces just a few billion lines a day. According to Y Combinator's Gary Tan, for about a quarter of the current YC startups, 95% of the code was written by AI — and similar patterns are emerging at the enterprise level. Google CEO Sundar Pichai recently stated that well over 30% of new code at Google is now written with the help of AI.
Marketing teams are following suit. They're automating more of their workflows than ever before–using AI agents to:
- Write copy (Jasper, Copy.ai)
- Design visuals and ad creative (Adcreative, firsthand)
- Generate video content (HeyGen, Synthesia)
What started as point solutions are now becoming full-stack marketing agents, helping teams scale content, design, and outreach without growing headcount.
Sales teams are seeing the same transformation. AI agents such as Artisan AI's Ava, Apollo AI, 11x, and UnifyGTM manage full outbound workflows, from prospect research to personalized outreach and follow-ups. Others, like Docket AI, act as real-time sales engineers during calls, answering questions and handling RFPs. These tools don’t just boost speed—they elevate precision and consistency across the sales funnel.
As adoption grows, teams are undergoing a complete transformation in how they operate. Repetitive, rules-based tasks are increasingly handled by AI agents, allowing people to focus on strategic and high-value work.
Why Finance Teams Are Ready for AI Agent Adoption
Finance is the next frontier for AI agents — and the opportunity is even greater than in the functions where adoption first took off. They’re at the center of budgets, forecasts, headcount, and planning—deeply involved in almost every business decision. But as expectations rise, the tools they rely on are showing their age.
The Workload Has Changed–The Systems Haven’t
Most finance teams still rely on tools like Excel, QuickBooks, and NetSuite—platforms that have been around for more than 25 years. They were built for a different era and haven’t kept pace with the speed or complexity of modern finance. Key workflows such as closing, forecasting, and reporting, still require manual inputs and extensive spreadsheet work.
At the same time, the pressure is rising. In 2024, 83% of finance leaders reported talent shortages. More than 300,000 accountants and auditors have left the profession in the last three years. Leaders are asking teams to do more with fewer resources, but legacy systems often struggle to keep up.
The problem isn’t just the software. It’s the rigid workflows they create. Excel requires deep formula knowledge. Legacy ERPs force users to learn specific languages to operate them. Even basic tasks involve toggling between tabs, exporting data, and manually piecing together answers.
AI agents reverse that dynamic. Instead of learning the system, users describe what they need in plain language. The agent handles the steps behind the scenes, pulling the correct data and returning the result. Prompting replaces procedures. Teams stop working around the system and start working with it.
Implementation Speed: Minutes vs. Months
This ease of use extends to deployment as well. The implementation timeline difference between traditional enterprise software and AI agents represents a fundamental shift in how organizations deploy new capabilities. While traditional software implementations require extensive technical integration and change management (which often take quarters), finance teams can deploy AI agents immediately with minimal infrastructure changes.
Traditional Software Implementation
Analysis & System Selection (3-6 weeks): Financial process mapping, vendor evaluations, RFP responses, contract negotiations, and compliance assessments
System Configuration & Data Migration (6-12 weeks): Chart of accounts setup, legacy data extraction and cleansing, financial module configuration, and third-party integrations
User Training & Testing (8-12 weeks): Role-based training programs, month-end close simulations, financial statement testing, and process documentation
Go-Live Support & Stabilization (2-4 weeks): Production monitoring, issue resolution, performance optimization, and post-implementation adjustments
Total Implementation time for Traditional Finance Software: 4-8 months
AI Agent Implementation
Setup & Configuration (<10 minutes): Connect data sources (ERP, banking, expense platforms), configure access permissions, invite team members
First Prompt (30 seconds):Ask your question in natural language
Total Implementation time for finance AI agents: <15 minutes
While traditional software requires months of planning and training, AI agents are ready to use immediately and improve with continued use. As workflows evolve, agents evolve with them—no retraining, reimplementation, or downtime required.
To read more about how to think through AI Agent implementation, read our full guide here.
5 High-Impact AI Agent Use Cases in Finance
Here’s how AI agents free up time and reduce friction in five high-impact finance workflows:
1. Variance Analysis and Reporting
Before: Analysts spend hours gathering data from NetSuite and other systems, calculating variances manually, and drafting explanations to justify budget-to-actual gaps.
After: The agent automatically pulls the right data, runs the calculations, and drafts narrative insights. You review and make adjustments as needed. It also handles follow-up questions, so teams don’t need to start from scratch every time.
2. Real-Time Forecasting and Scenario Planning
Before: Forecasts live in siloed Excel files, making scenario modeling slow and disconnected. Answering “What if we hire 10 more engineers?” might take hours of coordination and recalculation.
After: The agent pulls real-time data from systems like your ERP and HR platforms, updates models instantly, and generates multiple scenario views. Teams can quickly explore options and adjust the course based on live inputs.
3. Faster Month-End Close
Before: Finance teams spend up to two weeks chasing inputs, updating schedules, and manually preparing reconciliations. Missing data often causes delays.
After: The agent continuously monitors close status, gathers required data, flags anomalies, and compiles key summaries. Review cycles shrink from weeks to days, freeing the team to focus on exceptions, not collection.
4. Expense Classification and Anomaly Detection
Before: Teams manually tag hundreds of transactions each week and scan line items for errors or duplicates. As volume grows, this becomes increasingly error-prone.
After: The agent classifies expenses based on historical behavior, flags outliers like duplicate charges or unusual vendors, and routes questionable entries to the right reviewers. What once took hours now happens in minutes, with fewer errors.
5. Board and Investor Reporting
Before: Preparing updates for leadership takes weeks of data collection, slide building, and last-minute formatting. Insights often arrive late or lack context.
After: The agent pulls metrics from source systems, builds visualizations, and drafts context-aware talking points. Custom views and filters allow stakeholders to drill down instantly, enabling better conversations and faster decisions.
AI Agent Security and Compliance for Finance
Data Security Standards
When evaluating AI agents for finance implementations, we recommend ensuring they operate under strict data isolation principles with zero training guarantees, meaning your financial data never improves the underlying model or becomes visible to other users. Look for systems that maintain SOC 2 Type II Compliance and meet the same security standards as your ERP systems. Not all AI agents adhere to these regulatory frameworks, so establishing these requirements upfront is crucial for finance implementations.
Human-in-the-Loop Controls
Finance leaders need to stay in control of AI-generated outputs, which means being able to see exactly how the AI got to its answer. Use agents that show you the data sources used, the steps taken, and the reasoning behind recommendations–all in language that makes sense to finance teams, not technical jargon. You should be able to quickly validate the AI's work and make informed decisions about whether to approve or modify the output before it is shared with stakeholders or included in official reports. Finance teams should be able to follow the agent’s reasoning step by step, in clear language, so they can review, validate, and approve outputs with confidence before anything goes out.
The Future of Finance Is Autonomous
Concourse is building AI agents for corporate finance teams. These agents work closely with finance teams to support day-to-day operations by taking on the most manual and time-consuming tasks. They enable team members to retrieve and analyze data more effectively through natural language, resulting in a 10x increase in daily productivity.
Want to see what AI agents can do for your finance team? Join our waitlist or email us at hello@concourse.co for early access.
