Artificial Intelligence is rapidly reshaping accounting and finance operations.
From accounts payable automation and expense categorization to forecasting, variance analysis, and report drafting, AI tools are increasingly positioned as a way to reduce manual work and accelerate decision-making. Many organizations see AI as the next step in digital transformation, promising faster closes, lower costs, and improved analytical insight.
However, AI systems are fundamentally different from the traditional financial software platforms that accounting teams have relied on for decades. Conventional accounting systems operate on explicit rules, calculations, and deterministic logic.
If the configuration and formulas are correct, the system produces the same result every time. This predictability supports reconciliation, audit trails, and regulatory compliance. Professionals can explain exactly how each number was generated.
AI systems do not operate this way. Instead of following strict rules, they analyze patterns in historical data and generate predictions. Their outputs represent the most statistically likely answer, not a guaranteed correct one.
Even advanced models occasionally misclassify transactions, misinterpret context, omit key details, or produce confident but inaccurate conclusions. These behaviors are not software defects that can be permanently removed; they are inherent characteristics of machine learning technology.
This distinction creates significant implications for accounting and finance. Financial reporting demands precision, consistency, and traceability. Auditors expect clear documentation showing how figures were derived. Regulators require defensible calculations. If an AI tool generates a number or recommendation without an explainable logic path, it becomes difficult to validate or defend during review.
As a result, AI cannot be treated as an autonomous decision-maker within core financial processes. Instead, it must be positioned as an assistive tool that supports human professionals. Activities such as preliminary analysis, anomaly detection, data sorting, and draft reporting may benefit from AI acceleration, provided outputs are reviewed and verified. In contrast, tasks that directly impact financial statements, tax positions, or regulatory filings require stronger oversight and human accountability.
This session provides a practical framework for safely integrating AI into accounting and finance functions. Participants will learn how to assess risk, determine appropriate use cases, implement human-in-the-loop controls, and establish governance practices that maintain accuracy and audit readiness.
The objective is not to discourage AI adoption, but to ensure that innovation strengthens rather than undermines financial integrity.
By understanding the limits of AI and applying proper safeguards, organizations can capture efficiency gains while preserving the reliability and trust that accounting and finance demand.
Why should you Attend:
AI is quickly becoming embedded in accounting and finance operations. Teams are already using it to classify transactions, draft reports, summarize reconciliations, analyze spending patterns, and even assist with close activities. Vendors promise faster processing, fewer staff hours, and smarter insights. The efficiency gains look compelling. The hidden risk is accuracy.
Unlike traditional financial systems that follow explicit rules and calculations, AI systems operate by predicting the most likely answer. That means outputs that appear polished and authoritative may still be wrong. A misclassified expense, an incorrect accrual estimate, a flawed summary of financial data, or a fabricated explanation generated by AI can quietly enter the record. At small scale, this may look harmless. At enterprise scale, these small errors multiply into material misstatements, audit findings, compliance violations, or restatements.
In accounting and finance, "almost correct" is not acceptable. Financial statements, tax filings, and regulatory reports require defensible accuracy. When numbers are challenged by auditors or regulators, professionals must explain exactly how they were calculated. Many AI systems cannot provide that level of traceability or explainability. Yet the responsibility for errors remains with the organization and the individuals who sign off.
This session addresses the uncomfortable reality that AI mistakes are not temporary glitches that will disappear with better tools. They are an inherent feature of the technology. Rather than replacing professional judgment, AI must be managed carefully and used within defined limits.
Attend this session to understand where AI can safely add value, where it introduces unacceptable risk, and how to design controls that protect your organization. Learn how to leverage AI without compromising financial integrity, audit readiness, or professional accountability.
Areas Covered in the Session:
