Asim Rais Siddiqui’s newest article has been published on HackerNoon titled “Why Vertical AI Beats Generic AI, But Only If You Understand the Industry’s Mess.”
The piece explores one of the most important conversations happening in AI right now: why generic AI tools are useful, but not always enough for businesses operating inside complex, regulated, and workflow-heavy industries.
Generic AI can summarize, write, answer, and assist across a wide range of use cases. But as Asim explains in the article, real businesses do not operate on clean prompts and perfect data. They operate through legacy systems, missing records, compliance requirements, scattered communication, manual judgment, and workflows that are often understood by operators long before they are ever documented.
That is where vertical AI becomes different
Asim argues that vertical AI does not win simply because it uses industry data. It wins when it understands the actual work. That means knowing the edge cases, approval paths, compliance risks, escalation points, source-of-truth systems, and the messy operational details that decide whether AI can safely move from demo to production.
In the article, he breaks down why generic AI often struggles in real client environments. A model may understand the words in a request, but it may not understand what those words mean inside a specific workflow. For example, summarizing a patient intake note is very different from knowing what data is missing, who owns the next step, which cases require escalation, and what compliance risk exists if the wrong action is taken.
That distinction is central to the future of enterprise AIAsim Rais Siddiqui on Why Vertical AI Beats Generic AI
For teams building AI products, Asim’s message is clear: choosing a vertical is easy, but earning that vertical is hard. Builders cannot rely only on prompts, documents, or surface-level industry language. They have to sit with operators, understand how the workflow behaves when things break, and build systems that support real decisions instead of just producing polished answers.
This perspective also reflects how TekRevol approaches AI development for industries where accuracy, workflow depth, integrations, and human review matter. In sectors like healthcare, logistics, home-based care, fintech, and other operationally complex markets, the real value of AI comes from building systems that work alongside existing processes and reduce repetitive load without removing the judgment that still belongs to people.
The article’s core takeaway is simple and timely: the future of AI will not be defined only by larger models or better prompts. It will be defined by systems that understand industries deeply enough to be trusted with real operational decisions.
Or as Asim puts it, the future belongs to AI that is not afraid of the mess.
Read Asim Rais Siddiqui’s full article on HackerNoon right here
