Much of the effort involved in using an ERP system happens before someone completes a transaction. An employee needs to understand why something is outstanding, determine whether the information is complete, or find the person who can approve the next step.
The transaction itself may be straightforward, but getting to the point where it can be completed often takes considerably more work.
For businesses considering AI, a useful place to start is the time employees spend researching issues and figuring out what to do next. AI could help bring the relevant information together, explain what needs attention, and prepare the next step for someone to review.
Consider a project with completed work that hasn’t been billed yet. Accounting can see the unbilled amount, but the number alone doesn’t explain what should happen next. Someone may need to review the project, check time entries, confirm approvals, examine billing terms, and contact the project manager. Only then can they determine whether an invoice should be prepared.
Similar investigations occur throughout a business. A purchasing employee researches an overdue order. A production manager checks why a job has stopped progressing. An accountant traces an unexpected balance through several transactions. Each situation requires someone to gather information, interpret it, and coordinate a response.
This effort can be difficult to see because it is spread across screens, emails, conversations, and spreadsheets. A transaction has a recorded status, but the work required to understand that status may leave little trace. Employees develop their own methods for keeping things moving: follow-up lists, personal reminders, shared trackers, and recurring meetings.
Those workarounds are useful clues. They reveal where people need more context, clearer ownership, or a better way to manage outstanding work. Understanding what employees are accomplishing through them is an important first step toward improving the process.
AI could help reduce this effort by gathering relevant information and preparing it for review. In the unbilled-project example, an AI-assisted process might bring together the project’s billing status, outstanding approvals, missing entries, and relevant notes. Instead of starting an investigation from scratch, an employee could begin with a summary linked to the supporting records.
Established workflow rules can handle predictable routing and required checks. AI may add value when employees need to interpret notes, combine context from several sources, or prepare an explanation for review.
That context also makes dashboards more useful. A list of unbilled projects tells someone where to look. An explanation of why each project is on the list helps them decide where to spend their time. One project may need a missing time entry, another may need approval, and a third may be waiting for a future billing milestone.
The next step is connecting that explanation to responsibility. Who needs to supply the missing information? Who can approve the work? Who should review whether billing can proceed? AI could help prepare a follow-up or recommend a next action, while established workflow rules route the item to the appropriate person. A defined workflow gives those recommendations somewhere useful to go.
To evaluate the improvement, identify the information employees consult, the decisions they make, and the handoffs involved. Then measure whether AI assistance reduces investigation time, repeated follow-ups, unresolved items, and rework.
A project that hasn’t been billed isn’t necessarily a problem. Its contract may require billing at a milestone that hasn’t been reached. The customer may need to accept the work first. An outstanding approval may represent a necessary review. Recognizing these distinctions requires an understanding of the business process.
The meaning of ERP information depends on that context. A status can control what happens next. A date can represent a commitment, an estimate, or an accounting period. Two amounts that look comparable may follow different definitions. An AI-generated explanation can sound reasonable while overlooking a rule that changes the conclusion.
This is where process owners, experienced users, and ERP specialists have an important role. They help define which information matters, which rules must be followed, and which situations require judgment. Their knowledge is also needed to evaluate whether a proposed action makes sense across departments. A decision that appears helpful to one team may create additional work or an incorrect result elsewhere.
The workflow should make it easy for a reviewer to check the supporting information, recognize uncertainty, and correct a recommendation. Established business rules can validate required conditions, while people review the decisions that depend on interpretation or approval. The level of review should reflect the consequences of the action.
For an organization getting started, the opportunity is concrete: choose a familiar process where employees spend significant time figuring out what to do, and make that work easier. Combining AI assistance with clear rules, ownership, and human judgment can help turn ERP information into timely, well-supported action.