What must be in place before AI can automate the work?
AI cannot reliably make decisions if the business has not agreed what the inputs mean, when an exception matters or who is authorised to approve it. Define the real work and test the edge cases before treating automation as the solution.
AI automation depends on defined inputs, decisions and exceptions
AI can summarise, classify and automate information that already exists. It may infer what the business means by a valuable customer, scope change or delivery risk, but an inference is not an agreed operational definition or decision rule.
If the underlying business logic is unclear, more technology does not remove the ambiguity. It processes it faster.
Define the underlying work before automating it
Define what changed in the customer request
An AI system can find the sentence in an email or meeting note. The business still has to define what it means.
Does the request change scope? Does it affect price? Does Delivery need to approve it? Is it a recurring demand that should become a separate service? Should it change how similar future deals are qualified?
The useful foundation is not simply more data. It is explicit decision logic around the data.
The same definitions support reporting and automation
Tracing customers from demand through sale, handover, delivery and margin forces the business to define the events and information that matter. That improves reporting first. It also makes later automation and AI less dependent on guesswork.
I start with the decision or operating problem AI is supposed to improve, then examine whether the available information can support it.
Questions to resolve before choosing an AI tool
Automation magnifies unclear definitions just as readily as good processes.