AI is transforming finance, just not in the way most people expect. LLMs (Large Language Models) have changed how we interact with software by summarizing documents, extracting data, analyzing policies, and answering complex questions. Naturally, finance teams see this and think “Finally, no more manual work.”
But companies quickly learn a hard truth, LLMs by themselves cannot automate finance.
Finance requires precision, controls, and predictable outcomes. LLMs, by design, cannot guarantee that. AI models are built on statistical inference. In other words, they use patterns or historical data to predict the likelihood of outcomes. To truly automate finance, organizations need something more structured, such as agentic workflows + finance logic.
This is the foundation behind AI native platforms like BillAgent and why they look nothing like AI “features” bolted onto legacy tools.
Let’s break down the difference.
LLMs Are Great at Language, Finance Requires Precision and Execution
LLMs excel at unstructured tasks such as summarizing contracts, extracting data from PDFs, drafting memos or narratives, explaining rules or policies.
Finance automation isn’t just about reading, extracting, and interpreting documents. It’s about governance and execution. Finance needs systems that generate billing schedules, calculate and allocate revenue with precision, enforce ASC 606 / IFRS 15, create audit ready logs and sync results to the General Ledger.
The core problem is that LLMs predict the “most likely” answer based on patterns or historical data. LLMs generate answers through probability models, not rule execution. This prediction leaves room for errors. Finance requires 100% accuracy and cannot run on probabilities. The same input needs to yield the same answer every time.
As a result, raw LLM automation exposes risks to an organization if LLMs by themselves are used for core financial processes.
Agentic Workflows for Finance = AI That Takes Action, Not Just Answers Questions
Agentic workflows are AI that follows a predefined set of finance steps like a digital accounting assistant, so work gets done accurately, consistently and with controls. A well-designed agent can:
LLMs alone cannot orchestrate these processes. Agents can, especially when paired with finance logic. This list is only a starting point. Agents can handle far more.
What is Finance Logic?
Finance logic is the set of rules, calculations, policies, and controls that govern how financial outcomes must be determined. It is the foundation of every billing, revenue, and accounting process, ensuring results are accurate, consistent, compliant, and audit ready.
It includes billing logic, such as tiered and usage-based pricing, ramps and renewals, proration, and discount calculations.
It also includes revenue recognition logic (ASC 606/IFRS 15), including identifying performance obligations, applying standalone selling price, and handling variable consideration, and accounting for contract modifications.
Finally, it includes controls and compliance logic, such as approval workflows, segregation of duties, exception handling, and audit trails.
Finance logic removes ambiguity. It ensures that numbers are computed exactly, not “predicted”.
The Magic Happens When All Three Work Together
When LLMs, agentic workflows and finance logic work together, you get end-to-end billing and revenue workflows that are accurate, explainable, and fully audit-ready every time.
This is the architecture behind AI native finance platforms like BillAgent, where AI doesn’t just read finance but operationalizes it.
LLMs interpret. Agents execute. Finance logic ensures accuracy.
Together, they automate the routine work, enabling finance teams to focus on the high‑impact decisions that truly require human expertise.
Note: This post is for insights and discussion only, not professional advice. Every business is different, so check with your CPA or financial advisor before making decisions.
