Beyond the model: where AI workflow defensibility may emerge

An impressive AI demonstration can make a difficult task look effortless. The more demanding question is whether a company can turn that capability into a durable business.

The distinction begins with the workflow. What happens before the model produces an answer, and what must happen afterwards for that answer to become useful? A product may need to gather information from several systems, recognise exceptions, obtain approval and record the result. Its value may depend as much on fitting that sequence as on the quality of a single output.

This suggests a more precise way to examine defensibility. Rather than asking whether a company “has AI”, ask what a customer would need to replace.

That might include integrations, carefully structured data, domain-specific evaluation, an established distribution channel or a product that people trust with consequential work. None is automatically a competitive advantage. An integration can be copied; access to data may be non-exclusive; an apparent switching cost may reflect inconvenience rather than genuine value. Each proposition needs evidence.

A bounded example is more useful than a vague efficiency claim. An eight-week pilot could test whether handling time falls by 15% while quality remains constant. Those are illustrative design assumptions, not results: the assessment must include exceptions, supervision and whether released capacity produces a real economic benefit.

The economics deserve the same attention. A useful product must do more than generate activity. It should solve a problem for which someone has a budget, with a delivery cost that leaves room for a sustainable business. Human review, implementation and support belong in that assessment, alongside model usage.