How agentic AI is redefining the accounting profession

A single email can sometimes trigger a week’s worth of work—searching for invoices, double-checking receipts, or cross-referencing regulatory changes. In just a few years, the daily routine for accounting professionals has expanded to include a dizzying array of digital documents, client queries, and shifting tax codes. It’s no wonder there’s growing interest in leveraging technological tools to lighten this load and keep errors at bay. The field has now arrived at a pivotal moment, shaped by rapid advances in artificial intelligence.

As described by Thomson Reuters, the next generation of AI goes far beyond automation and scripted responses. Agentic AI, a term steadily gaining traction, has emerged as a catalyst for a new era—not just automating tasks, but managing entire workflows and making informed decisions. This article explores how these systems are poised to reimagine accounting roles, and what professionals need to know to stay ahead.

Understanding agentic AI

Agentic AI refers to systems built to achieve specific goals independently—executing multi-step processes, adapting to real-time data, and making decisions when conditions change. Unlike traditional AI, which might follow a static set of rules or only generate content, agentic AI acts more like a proactive collaborator.

Capabilities of agentic AI include:

• Orchestrating complex tasks autonomously
• Sourcing and integrating information from multiple databases, tax codes, or even live financial data
• Adjusting approach on the fly when legal frameworks or client circumstances change
• Making decisions within set boundaries, while still pursuing overall objectives
• Communicating with other digital systems and tools to achieve its aims

The rise of agentic AI comes hand-in-hand with breakthroughs in large language models (LLMs), which supply the flexibility and nuance previously missing from automation. These modern AI agents often contain several generative systems working together—enabling more complex and scalable solutions.

Agentic AI versus traditional AI technologies

To understand the significance of agentic AI, it helps to compare it to previous AI applications within the accounting field.

Predictive AI has already been widely adopted by firms to analyze historical trends and forecast future outcomes, such as assessing audit risk or estimating tax obligations using past data. These systems excel at recognizing patterns and helping professionals plan ahead. Learn more on the Thomson Reuters website.

Generative AI represents another leap—creating initial drafts of client memos, summarizing complex regulations, or providing explanations based on user prompts. Chatbots like ChatGPT exemplify this level, producing content in response to queries but not executing real-world actions or coordinating projects.

Agentic AI advances even further by:

• Independently managing and executing task sequences—such as identifying a cost-saving tax adjustment, preparing the appropriate documents, and initiating filings
• Seamlessly drawing from multiple sources, including real-time law updates and client data
• Adapting to any new information without intervention
• Making choices about next steps, guided by—but not confined to—predefined rules

This shift transforms AI from a predictive advisor or content producer into a proactive actor that can own significant segments of the professional workflow.

Comparing agentic AI and robotic process automation

Automation is hardly new to accounting offices—robotic process automation (RPA) has streamlined repetitive, rules-based tasks for years. But agentic AI is not just a faster robot; its design and capabilities diverge markedly from RPA.

• Agentic AI brings autonomy and adaptability. Unlike RPA, which mimics human keystrokes or mouse clicks according to set scripts, agentic AI employs sophisticated reasoning. It analyzes new data, reshuffles priorities, and responds to evolving scenarios, making it suitable for dynamic environments.
• RPA offers speed and consistency for static processes—such as transcribing invoices or extracting figures from statements—where requirements rarely vary.

The essence of the distinction: agentic AI introduces a level of cognitive flexibility, learning and evolving with new information. RPA, by contrast, excels in highly predictable settings, achieving efficiency through rigid process adherence.

Impacts of agentic AI on tax and accounting work

The integration of agentic AI into accounting workflows has brought sweeping changes, allowing for a more efficient division of labor between machines and professionals.

• AI agents can now take on major responsibility for researching evolving tax regulations, sifting through financial data, and compiling comprehensive reports.
• Client engagement is changing, too—routine inquiries, appointment scheduling, and even basic consultations can now be handled by smart agents, freeing time for higher-value activities.
• With built-in monitoring capabilities, agentic AI can track compliance requirements and flag potential issues—sometimes initiating filings automatically.
• Audit processes benefit as well; AI systems can autonomously examine records for anomalies and prepare preliminary audit findings.
• For strategic planning, AI agents now help spot market patterns and provide insight into long-term financial scenarios.

As digital automation expands, tools that help professionals manage a flood of distributed information are growing in importance. Services like Zenceipt allow users to connect their email accounts and automatically monitor for receipts or invoices relevant to bookkeeping, helping professionals maintain oversight as their workflows become increasingly complex.

What the future holds for AI in accounting

Agentic AI has the potential to redefine what it means to be an accounting or audit professional. Far from replacing accountants, intelligent systems will shoulder routine and labor-intensive tasks—leaving more time for nuanced advisory work, relationship building, and oversight of ethical and regulatory requirements. The continued evolution of AI will demand upskilling and adaptability from practitioners, particularly as collaborative work with AI agents becomes the norm.

For the profession, this means preparing for continual change: refining the ability to interpret technology’s outputs, working alongside digital colleagues, and developing unique human expertise that no machine can replicate. As AI’s presence expands, the challenge will be to wield these tools thoughtfully—ensuring they serve both efficiency and integrity in financial practice.