When a Business Central change is ready for approval, finance leaders do not need to know how the code was produced. They do need to know what it was checked against.
A useful way to think about this is to separate the business promise from the technical implementation. The promise might be that invoices over a defined amount require a particular approval, or that a posting routine must prevent a certain combination of dimensions. Finance can understand and approve those statements without reading AL. They are the business definition of what the change is meant to achieve.
Once that definition exists, testing has a clear job. It should provide evidence that the finished change behaves in line with the agreed rule. If the rule is only reconstructed after the code has been written, there is a greater chance that the explanation of what was intended quietly bends toward what was actually built.
This is especially useful when people change. The person who originally requested a customization may not be the person reviewing it a year later. A clear requirement gives the organization a durable reference point. It means the conversation does not depend entirely on somebody remembering what was agreed in a workshop or buried in a project thread.
For finance leaders, that is the practical value of independent evidence. It is not a software-development doctrine they need to police. It is a way of making sure the business rule remains in charge of the technology, rather than allowing the technology to become the definition of the rule.
That sounds obvious, but it becomes important when AI can help create both the implementation and the tests around it. If both start from the same mistaken interpretation, they can agree with each other without delivering what the business actually requested.
The safeguard is not more technical complexity. It is a clear requirement that exists independently of the finished code. That requirement gives the delivery team something external to test against.
For finance, the practical question is simple: was the intended behavior written down before the change was built, and does the evidence show that the finished change meets it? That is much more useful than being told that the code compiled or that a test suite passed.
AI may change how quickly a Business Central customization is produced. It should not change what "correct" means. The business requirement still has to be the reference point.
A written requirement does not have to become a huge specification document. In many cases, a handful of precise acceptance statements are more useful than pages of prose. The important thing is that the business can recognize them as the intended outcome before the implementation is presented as a fait accompli.
For example, finance may care that a particular posting is blocked when a dimension is missing, that a defined role can approve up to a limit, or that an exception follows a different route. Those are statements the business can validate. They also give the delivery team concrete behaviors to prove.
That's especially valuable when a change is revisited later. Instead of asking what the code appears to do and assuming that must have been the intention, the organization can compare current behavior with the original business expectation. That makes support, upgrades and future modifications easier to reason about.
The principle is modest but powerful: decide what success means before asking technology to demonstrate success. AI can accelerate the work in between. It should not be allowed to rewrite the destination after the journey has started.
