How to Build Human-Centered AI Systems

Human-centered AI is not a slogan. It is a design discipline. The system has to help real people do real things in a way that is understandable, useful, safe, and respectful.

The biggest mistake is treating people like obstacles to automation. People are not the problem in the system. They are the reason the system exists.

Start with the human need

Before building an AI system, ask what the person is trying to do. Are they trying to understand information faster? Make a decision? Reduce repetitive work? Find approved content? Learn something? Create a first draft? Compare options?

If the human need is vague, the AI system will be vague too.

“Everyone has access to the same tech and platforms. It’s the data that matters.”
Bryan Gaffin, LinkedIn

Use trusted inputs

AI output is only as useful as the context around it. In enterprise settings, the differentiator is often not the model. It is the data, documents, references, processes, and judgment the organization can bring to the system.

“Can anyone take about 25 years worth of curated NYC inside info to make the perfect night concierge AI? No.”
Bryan Gaffin, LinkedIn

Design for transparency

People should know what the AI is doing, where information came from, what the limits are, and when human review is required. Hidden magic is not a great design principle for important work.

“Here is my special cocktail, my own private stash, my proprietary secrets to what’s happening in NYC.”
Bryan Gaffin, LinkedIn

Make the system feel helpful

A human-centered AI system should feel like support. It should not shame people, confuse them, bury them in process, or pretend to be smarter than it is.

“AI can drive progress, inclusion, and innovation, unlocking potential for everyone.”
Bryan Gaffin, LinkedIn