Key takeaways
- A chatbot retrieves and explains; an agent plans and acts across systems.
- Agents suit multi-step, rules-based tasks with clear success criteria.
- Keep humans in approval steps for anything that changes financial records.
- Start with read-only and draft-only work before granting write access.
Your finance team spends hours each week chasing the same questions: why does this invoice not match its purchase order, where is that shipment, which vendors are past terms. Someone suggests an AI assistant. The useful question is not whether to use AI but which kind: a chatbot that answers, or an agent that does part of the work. They are different tools with different risks.
The practical difference
A chatbot takes a question and returns an answer, usually from documents or data it can search. Its job ends when it responds. An agent is given a goal, decides which steps to take, calls tools such as queries or APIs, checks results and continues until it finishes or asks for help. In an ERP setting that means a chatbot can explain how a posting rule works, while an agent could gather the related documents, compare them, draft a correction and route it for approval.
Where chatbots are enough
Many needs are answered well by a chatbot: explaining procedures, finding a policy, summarizing a document, or helping a user navigate screens and reports. These tasks are read-only, easy to check and low risk. If the value is mostly information retrieval, a chatbot grounded in your documentation is simpler to build, easier to govern and cheaper to run. Choosing an agent for a retrieval problem adds complexity without adding value.
Where agents earn their keep
Agents fit work that spans several steps and several systems, follows fairly clear rules, and has an obvious way to tell whether it succeeded.
- Reconciliation: matching invoices, receipts and orders, and flagging exceptions with the evidence attached.
- Exception triage: reading an error queue, classifying causes and proposing the next action.
- Data preparation: assembling inputs for a report from several sources and validating them.
- Follow-ups: drafting reminders or requests based on status in the system.
In each case the agent removes the tedious gathering and comparing, and a person makes the final decision.
Where agents are the wrong tool
Avoid agents where the rules are unclear, the cost of error is high and hard to reverse, or the process is simple enough for a deterministic script. If a fixed workflow handles every case, automate it conventionally; it will be more predictable. Agents also struggle when data quality is poor, since they may act confidently on bad inputs.
Design controls before capability
- Limit permissions. Give the agent the least access needed, starting with read-only.
- Separate drafting from posting. Let the agent prepare entries; require human approval before anything is written to financial records.
- Log every step, including what data it looked at and what it proposed, so decisions can be audited.
- Define stop conditions and escalation paths for low confidence or unusual cases.
- Evaluate on real historical examples before going live, and keep sampling results afterward.
Also plan for the ERP itself. Well-defined interfaces and clean master data make agents far more reliable. If your integration is fragile, fix that first.
