A business owner who had self-implemented Business Central recently pointed an AI tool at their system and asked it for a full audit. Twenty minutes later they had a 12-page report. Professional headings. Severity ratings. Specific configuration references. It looked like something a consulting firm would charge thousands for. That is resourceful. I genuinely respect it.
But when an experienced consultant reviewed that report, most of the findings were not real problems. And some of the actual problems were not mentioned at all.
If you have done something similar, or you are thinking about it, this post is for you. Not to tell you it was a waste of time, because it was not. But to help you understand what those AI reports actually mean, where they mislead, and how to get real value from them.
What AI Gets Right
Let me start with the positives, because there are genuine ones.
AI is good at scanning a system and spotting things that look unusual. If you have vendors with no payment terms set up, it will find them. If you have posting groups that are incomplete, it will flag them. If you have users with overly broad permission sets, it will call that out.
It is also good at producing a structured, readable document. If you have never had a formal system review, an AI-generated audit gives you a baseline. It tells you what is in your system and how it is configured. That alone has value if you have been running your business without ever stepping back to look at the big picture.
The fact that you cared enough to do it says something. A lot of businesses run their ERP on autopilot until something breaks. If you are proactively checking the health of your system, you are already ahead of most.
Where AI Gets It Wrong
Here is where it gets tricky. AI understands Business Central in general terms. It knows what fields exist, what configurations are possible, and what a textbook setup looks like. But it does not know why your system is set up the way it is.
That matters more than you might think.
The report flagged that vendors had open balances. The AI treated this as a problem. But vendors having open balances is completely normal. It means you have ordered goods and have not paid yet, or you have partially paid. That is just business. A human who knows your operation would never flag that. The AI did because it does not understand context.
Similarly, it flagged outstanding customer receivables. Also normal. You invoiced customers who have not paid yet. Unless those balances are ancient and overdue, this is not a configuration issue. It is just your accounts receivable doing what accounts receivable does.
Meanwhile, the AI missed things that actually matter. It did not consider whether your number series were set up in a way that would cause conflicts as you grow. It did not check whether your approval workflows matched your actual signing authorities. It did not evaluate whether your integration points had proper error handling. These are the things that cause real problems in the real world, and AI does not know to look for them because they require understanding how your specific business operates.
The False Confidence Problem
When you receive a professional-looking document with severity ratings and specific technical references, it feels authoritative. It feels like an expert reviewed your system. And that feeling can push you in two risky directions.
The first is panic. You see 15 items flagged as high severity and you think your system is falling apart. You start making changes to fix things that were never broken. Or you lose confidence in your implementation and start questioning every decision you made during setup.
The second is false comfort. You see that the AI only found minor issues, and you assume everything is fine. But the things that actually need attention, the integration gaps, the process inefficiencies, the scalability risks, those were never checked because the AI does not know to look for them.
Neither reaction serves you well. The report is a conversation starter, not a conclusion.
How to Actually Use an AI Audit
If you have already generated one of these reports, here is what I would suggest.
Do not throw it away. It has value. But treat it as a list of questions, not a list of answers. For each finding, ask yourself: do I understand why this was flagged? If you do not, that is a signal to get expert input on that specific item.
Separate the cosmetic from the critical. Missing payment terms on a vendor you used once two years ago is not urgent. A posting group that sends transactions to the wrong GL account is. The AI will not distinguish between these for you, but a knowledgeable person can do it in minutes.
Look for what is missing, not just what is flagged. The most valuable part of a system review is identifying risks that are not visible yet. What happens when your order volume doubles? What breaks if that external API changes? Can your month-end close be automated further? AI reports rarely cover these questions, and they are often the ones that matter most.
If you want to take it further, bring the report to someone who knows BC well. Not to validate it line by line, but to use it as a starting point for a real conversation about where your system is strong, where it is fragile, and what you should prioritise next.
A Word About Vibe-Coded Integrations
Something that concerns me more than AI audits is business owners using AI to build small applications that connect to their ERP. Pull data out, push data in, automate a workflow.
The results can be impressive. One person built an app over a weekend that downloads invoices from a system. They were thrilled that they accomplished in two days what IT would have quoted a hundred hours for.
But here is what they did not think about. What happens when the API changes? When the connection fails at month-end? When bad data gets written into your financial system because there was no validation layer?
Using AI to assess your system is low risk. The worst case is you fix something that was not broken. Using AI to build integrations that write data into your production ERP is a different story entirely. If you are doing that, please get someone experienced to review it before it touches your live data.
When Expert Input Is Worth It
You do not need a consultant for everything. If your business is straightforward and your BC setup is simple, an AI audit combined with your own business knowledge can take you a long way.
But there are situations where expert input pays for itself quickly. If your business is growing and your systems were built for a smaller operation, growth will expose weaknesses that were invisible at lower volumes. If you operate in a compliance-heavy industry like food and beverage, where traceability is non-negotiable, a general AI audit will not cover what matters most. You need someone who understands both the technology and your industry. And if you are connecting BC to external systems like warehouse management, e-commerce, or logistics platforms, the connection points are where things break. An AI audit will not test those.
At Equerra, this is the kind of conversation we have with businesses every week. Every engagement is led by a principal consultant who has seen what good looks like across dozens of industries and complex environments. We are not going to sell you things you do not need. But we will tell you where your system is strong and where it needs attention.
The Bottom Line
Using AI to audit your ERP was a smart move. You have more visibility into your system than you did before, and that is genuinely valuable.
Just remember that visibility is not the same as understanding. The AI gave you a map, but it does not know which roads lead where for your specific business. Use it as a starting point, ask the right questions, and do not let a professional-looking document trick you into either panic or complacency.
Your business is too important for either.
Regards,
Tharanga Chandrasekara
