The accounting and auditing professions are entering a transformative new era. While the last few years were defined by the marvel of generative AI and Large Language Models (LLMs) acting as "copilots," the next phase is shifting toward AI-enabled agents that function as "autopilots". These autonomous systems represent a significant leap forward, moving beyond simple text generation to executing complex, multi-step workflows with minimal human oversight,.
What is Agentic AI?
Unlike traditional AI-enabled tools that follow rigid, pre-set rules, agentic AI has agency and the capacity to perceive its environment, reason through problems, and take independent actions to achieve predefined goals.
While a standard LLM generates content upon request, an AI agent can independently devise actionable plans and interact with external tools like spreadsheets, proprietary applications, and search engines,.
The "brain" of an AI agent is typically is thought of as having four core capabilities:
- Perceive: The ability to observe and interpret data across various formats, such as text, numbers, or sensor signals.
- Reason: Processing information to form judgments, prioritize actions, and make logic-based decisions, even with incomplete information.
- Act: Interacting with the environment by triggering workflows or manipulating digital tools to achieve objectives.
- Learn: Improving performance over time by incorporating feedback and updating internal models to better align with goals.
The Auditor’s New "Digital Teammate"
In internal auditing, agentic AI is redefining the entire lifecycle, aligned with the Institute of Internal Auditors (IIA) Global Internal Audit Standards.
Engagement Planning: AI agents can function as intelligent planners, gathering internal risk assessments and external regulatory data to detect risk indicators like spikes in high-value transactions,. They can then autonomously draft planning memoranda that propose specific audit scopes and procedures.
Engagement Work (Fieldwork): Acting as an intelligent investigator, an agent can automate evidence collection from KYC files and system logs, using anomaly detection to highlight suspicious activities.
Engagement Communication: As an intelligent author, the agent synthesizes findings into structured reports, selects tailored visualizations, and ensures compliance with IIA requirements before a final human review.
Agentic AI vs. RPA: What’s the Difference?
It is important to distinguish agentic AI from Robotic Process Automation (RPA).
RPA is "muscle" and is ideal for repetitive, rule-based tasks but is rigid and cannot "think" or adapt if a process changes. Agentic AI is the "brain" as it uses LLMs to support decision-making, adapts to new scenarios, and handles tasks that lack a single correct solution.
While RPA requires high maintenance when software interfaces change, AI agents can navigate software interfaces autonomously.
Transforming Finance and FP&A
The impact extends beyond audit into broader finance functions:
- Financial Forecasting: Agents can continuously gather real-time data from ERP and banking systems to produce forecasts that update daily, helping CFOs manage cash flow more effectively.
- Accelerated Financial Close: By orchestrating reconciliation and consolidation end-to-end, early adopters have reported slashing close times by 30-50%.
- Accounts Receivable (AR): AI agents can prioritize collections by analyzing customer payment behavior and adapting follow-up strategies in real-time.
The Importance of "Human-in-the-Loop"
Despite this autonomy, human oversight remains critical. The accounting profession's reliance on professional judgment means that humans must validate AI-generated insights and ensure compliance with ethical principles.
AI "hallucinations" are still possible, and the "black box" nature of some AI reasoning can breed mistrust. Therefore, firms are encouraged to maintain structured oversight and use "human-in-the-loop" verification, where the AI flags transactions based on confidence thresholds and the human acts as the final reviewer.
Ethical considerations, such as mitigating algorithmic bias, ensuring data privacy (GDPR/SOC 2 compliance), and maintaining transparency, must be the cornerstone of any AI strategy.
Roadmap to Readiness
For firms looking to embrace this "Agentic Revolution," I suggest a four-phase roadmap:
- Discover: Conduct an AI readiness assessment and identify high-friction, repetitive processes.
- Pilot: Select 1–3 high-value use cases (like tax research or bank reconciliations) and track specific KPIs.
- Operationalize: Embed AI into workflows, integrate it with core systems, and upskill the workforce.
- Scale: Expand successful use cases across the firm and reallocate human capacity toward high-value advisory services.
It's possible that 15% of day-to-day work decisions will be made autonomously by 2028. Therefore, the goal is not to replace the auditor or accountant but to amplify their potentia.
By automating the "how," agentic AI allows professionals to focus on the "why" and provide the strategic insights and advisory that clients value most.
Charlie Barmore, CPA, PC