Builder's Perspective
Why I Build Practical AI Products
My personal approach to finding narrow business problems, building useful AI workflows and judging products by operational value rather than novelty.
By Damir Grubisa, CEO of Group 4 Networks ยท Executive perspective
I start with repeated friction
The most useful product ideas usually begin with a problem that appears again and again. A person copies information between systems, reconstructs context from several tools or spends time on a decision that follows a recognizable pattern. That friction is a better starting point than a broad instruction to add AI.
A narrow job creates a stronger product
I prefer a product that does one valuable job clearly over a platform that promises to transform everything. A narrow scope makes it easier to define the user, connect the right data, test the workflow and measure whether the product actually helps.
Integration is part of the product
An AI feature that sits outside the daily workflow often becomes another tab that people stop using. Useful products connect with the systems where the work already happens and return an answer or action at the right moment.
Guardrails are a design feature
Logging, permissions, approval steps and safe failure modes are not obstacles added after the product is built. They are part of the product. The amount of autonomy should match the consequence of the action.
I judge the result in operations
A successful demonstration can create excitement, but I care about what happens after launch. Are people using it? Is the output reliable enough for the task? Does it save time, improve service, reduce risk or create useful capacity? Practical AI earns its place through those outcomes.
About the author
CEO of Group 4 Networks. I write about what I am learning while building and operating technology businesses across managed IT, cybersecurity, automation and practical AI.