By John Wheeler | Wheeler Intelligent Systems
It is 8:15 on Monday morning. A customer wants an update on an order. Purchasing is waiting for a supplier to confirm a delivery. Accounting has found an invoice that does not match the purchase order. The owner wants to know which of these problems could affect this week’s cash or sales.
Nobody needs a brilliant essay about the situation. They need to know what changed, what matters, who owns the next step, and what needs a decision.
That is a useful place to begin with AI.
When business leaders consider AI, it is easy to focus on the most visible work: writing content, making images, or answering questions. But there is another kind of work worth testing. It is the daily effort of finding problems, gathering the facts, and preparing someone to act.
My view is that this overlooked work can be a strong starting point for an established business. It has a clear purpose, it can be tested on a small scale, and the people doing it already know what a useful result should look like.
The work hiding between your systems
A company can have good software and still spend a great deal of time connecting the pieces.
An order is in the ERP system. A supplier’s revised date is in an email. A customer’s request is in a sales note. The latest explanation is in someone’s head. A spreadsheet pulls some of it together, but someone still has to check whether the story makes sense.
Consider a fictional distributor. Each morning, an operations manager reviews open orders, checks shortages, reads supplier updates, and asks the warehouse about yesterday’s shipments. The manager then decides which customers need a call.
The report is only part of the job. The more useful work is explaining the exceptions: why an order is at risk, whether the promised date is still realistic, and what someone should do next.
That gives us a concrete AI experiment. Could an AI assistant prepare a reliable first draft of that review, using records the company has approved for the task?
The answer should come from a test. A polished demonstration alone would not settle it.
Pick a problem that already has an owner
“We should use more AI” is a hard instruction to carry out. “Help our operations manager review tomorrow’s at-risk orders” gives people something they can evaluate.
A suitable first task has a clear owner, a repeated need, and a result that someone can check. The business should also understand the cost of getting it wrong.
For our fictional distributor, the owner is the operations manager. The repeated need is the morning order review. The result is a short list of orders that may miss their promised dates, with supporting records and suggested follow-up.
The first version would prepare information for the manager. Sending customer messages, changing order dates, or making delivery promises would remain with the people who already hold that authority.
This keeps the test manageable. It also makes the question sharper: does the assistant help the manager reach a sound decision with less effort?
Separate the calculation from the explanation
Some parts of this task call for ordinary software rules. If the promised ship date is tomorrow and the item has no available stock, a report can flag the order. If an invoice total exceeds a set limit, a rule can identify it.
Use those rules where they work. There is no need to ask a language model to recreate a calculation that your business system can perform reliably.
AI may be useful in the next layer: reading an approved supplier note, connecting it to a flagged order, summarizing what changed, and drafting a question for the buyer.
For example, a report might show that an order is short of stock. The assistant could add that the supplier’s latest email gives a revised arrival date, while the order record still shows the old date. It could then prepare a request to confirm which date is valid.
That division of work matters. The report supplies the flag. The source records supply the facts. The assistant prepares an explanation. The manager checks the result and decides what happens next.
Require evidence with every useful claim
A morning brief should help its reader check the facts quickly.
Compare “The customer’s order will probably be late” with a more useful statement: “Order 1042 has a promised ship date of October 8. The inventory record shows no available units. The supplier update dated October 5 lists an expected arrival of October 10. Please confirm whether another source is available.”
The second statement gives the manager a path to verification. It also separates the records from the open question. These dates and order numbers are illustrative, but the format is something a business can test.
The assistant should identify missing or conflicting information just as clearly. If there is no current supplier confirmation, it should say so. If two records disagree, it should show the disagreement.
A confident guess about a missing delivery date can create more work than an honest statement that the date is unknown.
Test it on yesterday before using it tomorrow
One practical way to begin is with a small set of past cases that the business is permitted to use.
Choose ordinary cases and a few difficult ones. Include a late delivery, an order that looked risky but shipped on time, a stale note, and a case with missing information. Have the person who owns the work identify what a useful review should have found.
Then give the assistant only the information that was available at the time. Keep the later outcome out of its input. Otherwise, the test becomes much easier than the real job.
Compare its brief with the owner’s review. Did it find the important problems? Did it flag harmless situations? Did it connect the right records? Did it state an unsupported cause? Could the manager check its work without reopening every file?
This is also where you learn whether AI is needed. A better report or a clearer process may solve much of the problem. That is still a useful result.
Measure the whole job
Fast output is only one part of the economics.
Suppose, as an illustration, the current review takes 45 minutes each morning. An assistant prepares a brief in two minutes, but the manager spends 30 minutes correcting it. The business has reduced human effort by 15 minutes, assuming the quality is at least as good. It has not saved 43 minutes.
If the manager can check the brief in 12 minutes, the potential time gain is larger. But the business still needs to account for setup, upkeep, software costs, and the effort of dealing with errors.
Track the original preparation time, the new review time, and the work created by mistakes. Also track whether the review catches important issues soon enough to help.
Time saved is not always cash saved. If the manager uses that time to resolve more customer problems, the gain is added capacity. If fewer overtime hours are needed, the gain may appear in payroll. Describe the benefit that actually occurs.
Keep the brief short enough to use
The goal is to help someone manage the day. A ten-page summary can become another item on the manager’s to-do list.
For the pilot, try a simple format: the few issues that need attention, the evidence behind each one, the person responsible, and the next question or action. Put background detail where the reader can open it when needed.
Set priorities with the people who own the work. An order serving a major customer may deserve attention before a small internal mismatch. A possible production stoppage may outrank both. Those choices should reflect the business’s judgment.
The assistant’s role is to apply those agreed priorities and make the facts easier to review. When a case falls outside the rules, it should bring that case to the owner.
Give the experiment a clear stopping point
A two-week pilot could be enough to learn whether this task deserves further work. That is a proposed test window, not a promise that every company can be ready in two weeks.
Keep the scope to one workflow and one owner. Use the existing review alongside the AI draft until the team has evidence that it can rely on the new approach. Record missed issues, false alarms, review time, and corrections.
Decide in advance what would cause you to stop. Repeatedly mixing up orders, hiding missing facts, or taking longer to check than the existing report are good reasons to pause and repair the process.
At the end, ask the manager a plain question: would you choose to keep using this because it helps you do your job?
That answer should be backed by the record of the pilot. Enthusiasm matters, but it cannot replace evidence.
Start with the work your team keeps chasing
This approach applies beyond order reviews. A finance team could test preparation of invoice exceptions. A service company could test a review of jobs waiting for parts. A sales team could test gathering the facts needed for overdue quote follow-up.
Each case starts with a business problem. Each needs approved data, a responsible owner, clear limits, and a way to check the result.
At Wheeler Intelligent Systems, this is the kind of practical question I want to explore: where can AI help a business get useful work done, and what evidence would show that the help is worth keeping?
Your first project does not have to transform the whole company. It can begin with the daily work that someone is already tired of chasing.
What does your team spend time checking or following up on every week, even though most of the facts already exist somewhere in the business?