My journey from automating a Business Central report to thinking differently about ERP intelligence
A few weeks ago, I was looking at a Business Central reporting process that, on the surface, seemed completely normal. The data was already there.
Customer ledger entries. Sales transactions. Invoices. Outstanding balances. Credit information.
Nothing was missing and yet, turning all of that information into something management could actually use still required a surprisingly manual process. Someone had to extract the data, move it into a spreadsheet, analyze the numbers, identify the customers that needed attention, interpret the trends, prepare a summary, and finally turn everything into a report that could be presented to management. The process worked. That was precisely the problem.
We had built an efficient way to report what had already happened, but we were still spending human time figuring out what it actually meant.
That thought stayed with me. Business Central already knew what was happening inside the business. The information was sitting there, transaction by transaction, waiting to be interpreted. So I asked myself a different question:
What if we stopped asking Business Central for another report and started asking AI to help us understand the story behind the data?
That question led me to build a small AI workflow. Initially, I thought I was simply automating a reporting process. But as I started connecting Business Central data with AI analysis, I realized I was working on something much more interesting. I wasn’t trying to make a report faster.
I was trying to turn Business Central data into decision-ready intelligence.
And that small distinction changed the way I started thinking about reporting, automation, and ultimately the role AI can play inside Business Central because the real opportunity isn’t reducing a 30-minute reporting task to 30 seconds.
The real opportunity is reducing the time between a business signal appearing in Business Central and someone knowing that it matters.
The 30 Minutes Were Not the Real Problem
The original reporting process took around 30 minutes. The workflow I built reduced it to roughly 30 seconds by connecting Business Central data with AI analysis and turning customer ledger information, sales, invoices, balances, and exposure into an executive-ready report with risks and recommendations. The automation removed the spreadsheet work, copy-paste, and repetitive analysis but the time saving wasn’t the real breakthrough.
Saving 29 and a half minutes is useful. Recognizing a business risk 29 minutes earlier can be far more valuable.
That realization changed how I looked at Business Central reporting. Business Central already records what happened across the business—payments, invoices, sales orders, inventory movements, customer balances, purchase orders, and countless other transactions but management doesn’t need another report telling them what happened.
They need to understand what those events mean.
A finance manager wants to know where credit exposure is becoming a risk. A sales manager wants to know where customer behaviour is changing. An operations manager wants to know which inventory shortage could disrupt the business.
That is the difference between reporting and intelligence.
A report tells you what happened.
Intelligence helps you understand what matters—and what may require action next.
That is where I believe AI can fundamentally change Business Central reporting.
The future isn’t about generating reports faster. It’s about shortening the distance between a business signal appearing in Business Central and the decision that needs to follow.
From Data to Insight
When I built the workflow, I deliberately avoided making AI just another summarization tool. Saying that a customer has outstanding invoices or that sales increased last month is easy. The real value comes from understanding what those numbers mean and where management should focus next.
I wanted the AI to look at the information more like an experienced Business Central consultant—identifying customers that may require attention, recognizing emerging risks, connecting patterns across sales and receivables, and highlighting the information that could influence a business decision.
AI doesn’t replace the decision-maker. It helps the decision-maker see what deserves attention.
And that led me to another realization: we don’t necessarily need more data. Business Central already contains an enormous amount of business intelligence—customer and vendor transactions, sales and purchase activity, inventory movements, general ledger entries, dimensions, payments, credit limits, orders, invoices, approvals, and much more. The challenge is rarely the absence of information.
The challenge is connecting the right information, understanding its context, and turning it into something meaningful.
AI can provide that additional layer by bringing relevant Business Central information together, analyzing relationships and patterns, and presenting the outcome in a way that people can understand and act upon.
The future of intelligent ERP may not be about creating more data. It may be about unlocking more intelligence from the data Business Central already has.
I Didn’t Want Another Dashboard
This was another important realization for me. Organizations already have Power BI, Excel, Business Central reports, dashboards, and KPIs. The problem isn’t necessarily a lack of information. It is that there is often too much information and not enough prioritization. A dashboard can show dozens of metrics. A report can contain hundreds of customers. A spreadsheet can contain thousands of transactions. The data may be completely accurate, yet someone still has to determine what actually matters.
That is where AI becomes interesting.
I wasn’t trying to build another dashboard. I wanted to build a layer that could help people understand where they should look first.
This changed my thinking about reporting automation.
The opportunity isn’t simply to generate the same report every morning without human effort. It is to have AI identify increasing credit exposure, emerging inventory risks, unusual transaction patterns, approval bottlenecks, or other signals that deserve attention before they become business problems.
The system isn’t simply reporting yesterday. It is helping people understand what deserves attention today.
But there is an important boundary. AI should not become the decision-maker simply because it can analyze the data. Business Central contains financial and operational information where recommendations require context, validation, and accountability.
The AI can identify a potential risk, explain why it matters, provide supporting information, and suggest possible actions.
The person responsible for the business still makes the decision.
That is how I see the most valuable role for AI inside Business Central—not replacing human judgment, but making that judgment faster, better informed, and more focused.
AI should accelerate judgment—not remove it.
What I Learned From Building It
The biggest lesson from this experiment wasn’t about the AI model, the workflow, or the technology. It was about asking the right question before building anything.
If I had started by asking, “How can I automate this report?”, I probably would have stopped when the process went from 30 minutes to 30 seconds. Instead, I started asking what information management actually needs, which decisions they are trying to make, and which signals already exist inside Business Central that could help them act earlier.
That changed the entire objective.
The report became the output. The intelligence behind it became the real product.
And I believe this is only the beginning. The same approach can extend far beyond customer receivables. Finance could receive proactive risk insights. Sales could identify changing customer behaviour. Purchasing could detect supplier and overdue-order risks. Inventory teams could receive early warnings before shortages affect operations.
Eventually, these shouldn’t be isolated reports.
They could become a continuous layer of operational intelligence working alongside Business Central.
Business Central records the business.
AI can help the business understand itself.
That is the direction I find most exciting. When I started this experiment, my objective was simple: take Business Central data, run it through an AI workflow, and produce a useful report but the more I built, the more I realized that the report wasn’t the destination. It was the beginning of a different way of thinking about ERP.
For years, we’ve asked Business Central to tell us what happened. We’ve built reports to analyze it, dashboards to visualize it, and processes for people to interpret it. AI gives us the opportunity to add another layer—one that continuously analyzes information, connects patterns, highlights risks, explains why something matters, and helps people focus their attention where it can create the greatest value.
Business Central doesn’t need more reports. It needs to help people make better decisions from the information it already contains.
That is what I am exploring through AI and this workflow is only one small example of what becomes possible when we stop thinking of AI as a faster way to produce reports and start thinking of it as a way to turn ERP data into decision intelligence.
The future of Business Central may not be about having more information. It may be about knowing what matters before it becomes a problem.
The Workflow Behind the Idea
I’ll include a short video below showing the workflow I built and how Business Central data moves through the AI process to produce an executive-ready report.
For me, the interesting part isn’t watching the report being generated in 30 seconds.
It is seeing what becomes possible when Business Central data stops being treated as something to report and starts being treated as something from which intelligence can be created.
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