An AI agent can finish a task and still leave a team with a bigger problem: nobody can explain what it did.
The question for businesses adopting agents is no longer just, “Can it complete the workflow?” It is also, “Can we inspect the decisions that led to the result?”
Consider a simple workflow: an agent reads an incoming request, checks a system of record, drafts a response and queues an action. A polished final answer tells you very little about whether it used the right record, followed the right rule or asked a human when the evidence was thin.
A useful design starts with three layers:
1. Evidence. Record the source material, tool results and relevant timestamps. Make it possible to tell what the agent actually had available at the moment it acted.
2. Boundaries. Define which actions the agent may take independently, which need review and which are off-limits. The boundary should be enforced by the workflow, not left as a polite sentence in a prompt.
3. Recovery. Make consequential actions traceable and, where practical, reversible. A human should be able to spot a bad result, understand the path to it and correct it without reconstructing the entire session.
This is not about slowing agents down. It is about making useful automation dependable. A system that can show its work is easier to review, improve and trust than one that merely sounds confident.
Before adding another agent to a process, try one test: if it makes the wrong call tomorrow, will the team be able to see why and put it right?
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The next useful AI feature may be a clear handoff, not a clever answer. Give agents a defined task, visible evidence and a human decision point. That makes automation easier to trust, test and improve.
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ERP migration is a chance to retire old workarounds, not copy them into a new system. Map who owns master data, approvals and exceptions before configuration starts. AI can then surface gaps against that agreed blueprint for the project team to resolve.
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Cash forecasting is often treated as a spreadsheet problem. In practice, it is a decision-timing problem.
Consider a business with healthy reported profit but an uncertain cash position. Customer receipts land later than expected. Supplier payments cluster on the same week. Payroll and tax dates do not move. A forecast that is refreshed only at month-end can look precise while being too late to help.
The useful AI opportunity in treasury is not to let a model decide which bills to pay. It is to surface exceptions early enough for a finance professional to act.
Start with a 13-week cash forecast tied to the ERP and bank feeds. Keep the mechanics explicit: opening balance, expected receipts, committed payments and forecast closing balance. Then use AI to flag unusual movements, summarise the reasons behind a changed forecast and identify invoices whose expected collection date needs a human review.
The controls matter as much as the model. Record the source and timestamp for every input. Separate confirmed transactions from assumptions. Show forecast-versus-actual variance by driver, not just as one net number. Require a named owner to approve changes to payment priorities or collection actions.
That gives treasury a more useful question to answer each week: which assumptions could materially change our liquidity headroom, and what can we do about them now?
For ERP and finance transformation teams, this is a practical design test. If the forecast cannot be traced back to source transactions and challenged by the people accountable for cash, adding AI will make it faster to produce, not more reliable to use.
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