Benefits and Challenges of Using AI Agents in Finance

In brief

• AI agents in finance can extend automation end to end, combining reasoning, decision-making, and execution across complex workflows.
• Turning pilots into enterprise value remains a major challenge, as poor data and processes, fragmented systems, governance gaps, and unclear priorities stall progress.
• Greater autonomy makes strong AI governance critical, with CFOs pointing to governance and compliance as the leading cause of AI failure.
• Workforce readiness is a critical barrier to scaling Agentic AI, as only 12% of leaders say their workforce is AI-ready.

Agentic AI is quickly moving from emerging technology to an investment priority for finance leaders. Unlike traditional automation that follows predefined rules or Generative AI that primarily responds to prompts, AI agents in finance can reason, interpret context, make informed decisions, and take action across complex workflows with greater autonomy — opening the door to automating finance work that previously required significant human judgment and intervention.

The potential is significant: From accounts payable and receivable to financial planning and reporting, Agentic AI can help finance organizations automate more complex, end-to-end processes, respond faster to changing business conditions, and create capacity for higher-value work.

Adoption is gaining momentum. While nearly half (45%) of organizations remain in the early stages of adopting Agentic AI in finance, 28% are actively rolling out initiatives and 22% have fully implemented the technology, according to APQC’s 2026 Financial Management Priorities and Challenges report.

And 84% of CFOs expect their AI spending to increase in the next 12 months, Grant Thornton’s 2026 Q3 CFO Survey found.

But growing investment is not translating consistently into results. More than 90% of organizations in Grant Thornton’s 2026 AI Impact Survey acknowledge their AI is underperforming, underscoring the gap between AI ambition and measurable business value.

For CFOs, closing that gap requires more than deploying AI agents. Finance organizations need the right use cases, processes, data, technology ecosystem, governance, and workforce capabilities to scale Agentic AI effectively, while maintaining the accuracy, control, and accountability expected of the finance function.

This article examines common Agentic AI challenges that can stand in the way of success for finance teams, and what they need to consider as they move toward enterprise-scale adoption.

5 key benefits of using AI agents in finance processes

AI agents in finance and accounting extends automation beyond individual rules-based tasks to more complex finance workflows, creating opportunities to improve efficiency, decision-making, scalability, and employee productivity.

1. Greater efficiency through end-to-end automation

Even when repetitive tasks are automated, employees often still need to move between systems, interpret information, investigate exceptions, make decisions, and trigger next steps. AI agents in finance and accounting can help bridge those gaps by coordinating work throughout a workflow.

Agents can analyze information and determine next steps, instruct Robotic Process Automation (RPA) and Intelligent Document Processing (IDP) to execute rules-based tasks, and loop in humans for approvals or higher-risk exceptions. This end-to-end process results in fewer manual handoffs, faster processing, and greater capacity without proportional headcount growth.

2. Faster, more adaptive decision-making

Agentic AI can work toward an objective while adapting its actions as conditions change, enabling agents to continuously evaluate information, identify anomalies or exceptions, and recommend — or, within appropriate guardrails, execute the next action. For finance, that can mean faster responses to issues affecting cash flow, forecasting, working capital, financial reporting, and risk, particularly when decisions depend on information spread across multiple intelligent systems.

3. Better scalability and operational agility

Agentic AI also gives finance organizations another way to expand capacity as workloads or business requirements change. Instead of adding employees every time transaction volumes increase, organizations can use digital workers to absorb additional activity and flex capacity as needed, especially during rapid growth, acquisitions, seasonal peaks, or other periods of increased finance workload.

4. More strategic finance teams

As agents take on more transactional execution, information gathering, and routine tasks and decision-making, finance professionals can spend more time on work requiring analysis, business judgment, exception management, and collaboration with the business. This fundamental shift can create capacity for higher-value work while employees increasingly review, direct, and supervise digital work rather than perform every process step themselves.

5. Higher process visibility and continuous improvement

Agentic automation can provide end-to-end visibility across AI agents, robots, enterprise applications, and people, helping finance leaders identify bottlenecks, exceptions, and opportunities for improvement. It can also provide better insight into whether automation is improving finance performance through outcomes such as cycle time, accuracy, cost, working capital optimization, and employee capacity — not the number of agents deployed.

So, what does this look like in practice? Here are some high-impact use cases showing how Agentic AI can transform the finance function.

6 Agentic AI challenges — and strategies for success

Realizing these benefits at scale depends on more than the capabilities of the agents themselves. The biggest AI agent challenges often stem from whether finance organizations have the processes, data, technology, governance, and workforce readiness to support greater autonomy.

1. Choosing the right use cases and proving value

With new Agentic AI capabilities emerging rapidly, finance leaders can easily get caught up in what the technology can do rather than where it can deliver real value. Grant Thornton’s 2026 CFO survey research shows that while 97% of organizations are piloting, scaling, or fully integrating AI, many lack a disciplined approach to prioritizing and scaling initiatives — spreading resources across multiple pilots without a clear path to sustained enterprise value.

The 2026 AI Impact Survey reinforces the strategy gap: 51% of executives identify strategy as the biggest driver of AI ROI, but only 22% of operations leaders have a fully developed and implemented AI strategy. Finance leaders should therefore prioritize use cases based on business value, feasibility, potential risks, and scalability — and establish upfront how success will be measured.

2. Poor data and process readiness

Agentic AI raises the stakes for data and process readiness. Because agents can use enterprise data to reason, make decisions, and take action, fragmented, inaccurate, or inaccessible information can affect not only the quality of their insights, but also the actions they take.

The problem is particularly relevant to finance. Grant Thornton’s 2026 CFO survey research identifies poor data quality, internal system fragmentation, and technology constraints as the top barriers to digital transformation in the finance function.

Deloitte’s 2026 research tells a similar story: While 54% of CFOs rank integrating AI agents in finance and accounting as their top transformation priority, 52% also prioritize improving data quality, access, and usability.

Here’s the challenge: clean data alone isn’t enough. AI cannot fix a broken process.

AI agents operating within poorly standardized workflows, unclear business rules, excessive exceptions, or weak controls can amplify existing problems rather than solve them. Before expanding agent autonomy, finance organizations need reliable data, optimized processes, mature controls, and clear accountability structures.

3. Integration complexity, orchestration, and agent sprawl

Agentic AI adds another layer of complexity to already fragmented technology environments. Agents may need to interact with enterprise resource planning systems (ERPs), application programming interfaces (APIs), automation tools, enterprise applications, other agents, and employees to complete tasks across a single finance process.

As adoption expands, the challenge shifts from deploying individual agents to coordinating how work moves among them — managing handoffs, exceptions, and approvals while maintaining visibility and control across end-to-end processes.

Without that orchestration, organizations risk agent sprawl, as different teams deploy agents independently across platforms and processes, creating duplicated capabilities, inconsistent management, and another layer of technology fragmentation.

Scaling Agentic AI in finance depends on coordinating agents with existing automation, applications, data, and people as part of a connected operating environment.

4. Managing risk and control as AI autonomy increases

Agentic AI introduces new risks for finance because agents can do more than generate outputs. They can make decisions and take action. That means an error can lead to an incorrect transaction, change to financial data, compliance issues, or other downstream consequences.

As agents gain greater access to sensitive data, applications, and systems, security, privacy, and compliance requirements also increase — making clear permissions, access controls, and oversight critical.

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Gartner’s 2026 CFO research warns that errors or misjudgments can propagate quickly across interconnected processes, shifting the risk from the accuracy of an individual output to the broader finance process an AI agent executes.

Yet governance is struggling to keep pace. Only 52% of business leaders say their boards have established AI policies or governance expectations, Grant Thornton’s AI Impact Survey reports.

Even fewer appear prepared to demonstrate or respond to those controls in practice. Only 22% are very confident their organizations could pass an independent audit of AI governance and controls within 90 days, while just 20% have an AI-specific incident response plan with defined owners that has been tested through tabletop exercises, according to Grant Thornton AI research.

Not surprisingly, organizations point to governance and compliance barriers as the leading cause of AI failure, Grant Thornton found.

The challenge for CFOs is balancing autonomy with security and control amid pressure to deploy Agentic AI quickly. Organizations need clear boundaries around what agents can access and execute independently, when human approval is required, and who remains accountable.

Continuous monitoring, audit trails, and escalation procedures are essential to maintaining oversight as autonomy expands.

5. Workforce skills and change readiness gaps

Agentic AI is changing not only what work gets done in finance, but how finance professionals work. As agents take on more transactional activities and routine decisions, employees will increasingly need to manage AI-driven workflows, review exceptions, exercise judgment, and supervise digital work.

However, many finance organizations lack the skills to support that transition. In a 2026 CFO survey, Gartner found that building AI talent in the finance function ranked among their two most challenging priorities.

Unfortunately, investment in workforce readiness is lagging, with only 12% of leaders saying their workforce is AI-ready, Grant Thornton’s 2026 AI Impact Survey found. Some 34% of finance leaders say training is underfunded — the most underfunded AI investment area in the survey.

That gap can leave employees without the role-specific skills and guidance needed to integrate AI effectively into day-to-day work.

Employees may also be uncertain about how AI will affect their roles, while finance leaders may hesitate to delegate decisions to agents they do not yet trust. Clear expectations around how agents will support employees, where human judgment remains essential, and who retains decision-making authority can help build confidence as ways of working change.

Just as important, finance organizations need to redesign roles and ways of working around a human-agent operating model — determining how work is divided and preparing employees to direct, review, and collaborate with digital teams.

6. Choosing the right technology and transformation partner

The rapid growth of Agentic AI has created a crowded and fast-changing technology landscape. Automation providers, enterprise software companies, and emerging Agentic AI vendors are all introducing agent capabilities, often within different technology ecosystems.

For finance leaders, choosing the right technology means looking beyond specific agent capabilities to whether the broader platform can operate securely and reliably at enterprise scale. A successful proof of concept may demonstrate value for a specific task, but scaling across finance requires much more.

Autonomous AI agents must work with existing ERP and finance applications, automation investments, data sources, and APIs, while supporting the human approvals, governance, and visibility finance requires.

Avoiding vendor lock-in is also critical. As organizations adopt agents built on different AI models and platforms, interoperability will determine how easily they can integrate technologies and adapt their strategies as the market evolves.

But successfully deploying finance AI agents requires more than technology selection. Many finance organizations lack the AI expertise and resources to integrate Agentic AI into operations while managing the complexity and risks of implementation.

This is changing how CFOs approach finance outsourcing. Rather than building every capability internally, modern finance teams are increasingly looking to providers that combine finance expertise, advanced technology, and operational execution to accelerate Agentic AI adoption and deliver measurable results.

Everest Group’s 2026 Finance and Accounting Services PEAK Matrix report highlights growing demand for outsourcing partners that can support end-to-end finance transformation in an AI-first world. By embedding Agentic AI into established, well-governed finance operations, these providers are helping organizations move beyond isolated pilots to enterprise-scale adoption.

Why Auxis Grant Thornton: Turning Agentic AI potential into finance value

Capturing the value of Agentic AI does not mean making finance fully autonomous overnight. A more realistic approach is progressive autonomy — expanding what finance AI agents can do as they demonstrate reliability and organizations build the processes, data, integration, governance, and workforce capabilities needed to support them.

For CFOs, the priorities are clear:

• Start with high-value, well-defined finance workflows
• Establish measurable success criteria upfront
• Strengthen the underlying data, processes, and controls
• Increase autonomy based on performance and business value
• Maintain human oversight and accountability for higher-risk financial decisions

Auxis Grant Thornton brings the expertise and capabilities to put this approach into practice. Recognized as a leading finance outsourcing provider by Everest Group for four consecutive years — placing top five for market impact in its new 2026 mid-market assessment — Auxis Grant Thornton combines deep finance transformation and outsourcing expertise with advanced cross-platform automation capabilities. Its credentials include elite status as a Diamond Partner and Agentic Automation Fast Track Partner for UiPath, a leading agentic automation platform.

Through its differentiated operate-to-transform model, Auxis Grant Thornton lowers the cost and risk of AI adoption — leveraging guaranteed outsourcing savings to self-fund modernization while embedding Agentic AI into finance operations tied to measurable business outcomes.

The result is a practical path to the Intelligent Finance Enterprise — where AI agents, automation, processes, data, and human expertise work together to continuously improve performance, drive smarter decisions, and create lasting business value.

Ready to transform your finance function with Agentic AI? Schedule a consultation with our finance transformation experts today, or visit our resource center for more finance transformation tips, best practices, and success stories.

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