Why are we so obsessed with building for the person who doesn’t exist? In Silicon Valley, we worship at the altar of the “ideal user”—that frictionless, high-speed, perfectly focused individual who interacts with our software in a vacuum. We treat the messy, unpredictable reality of human life as an edge case, something to be patched later if the metrics dip.
The real story here isn’t the speed of AI model deployment—it’s the profound failure of leadership to design for the human on the other side of the screen. Figen Ozmen, an insurtech leader with 28 years of experience, recently shared a revealing moment from her career. During a design review where a model was technically sound and the timeline was perfect, she hit the brakes. She realized the team had ignored users navigating the platform during a crisis, perhaps on a device with a cracked screen or under significant stress.
The Cost of the Ideal User
When we optimize for the ideal, we aren't just ignoring accessibility; we are building fragile systems. By prioritizing speed over inclusivity, companies often find themselves retrofitting accessibility into their architecture long after the foundation is poured. In Ozmen’s case, that realization forced a pivot that delayed the launch by almost 10 weeks.
While a ten-week delay might cause panic in a boardroom fixated on quarterly deployment quotas, it is arguably the most strategic move a leader can make. The result wasn't just a more accessible product; it was a better-performing platform for every single user. When you build for the person struggling with the interface, you inevitably simplify the path for everyone else.
Why AI Models Fail to Launch
The tech industry is currently caught in a gold rush, with boardrooms hyper-focused on automation rates and technical capability. However, the graveyard of enterprise software is littered with systems that worked perfectly in the sandbox but crashed in the real world. In Ozmen’s experience, the AI programs that stall six to 12 months after launch almost always stall for the same reason: the technology worked, but the confidence didn’t.
This is the "trust gap." If an employee doesn’t trust an automated workflow, they will find a way to route around it. If a customer finishes an AI-assisted journey only to call a human agent to verify the output, the automation has failed. These are not technical glitches or model hallucinations; they are fundamental failures of leadership design. When leaders focus exclusively on the "how" of the model, they neglect the "who" of the user, creating a system that is technically brilliant but functionally invisible to those who need it most.
The Strategic Value of the "Hard Question"
There is a prevailing myth in tech that technical depth and human-centered design are separate, perhaps even competing, skill sets. This is a mistake. Navigating technical environments often requires the ability to hold two conflicting realities at once: the technical execution goal and the human impact.
Those who consistently slow down meetings to ask about accessibility or trust are not being "soft" or "difficult." They are performing a critical strategic function. They are identifying the friction points that will inevitably cause a system to fail once it hits the market. As we lean further into an era of rapid AI integration, the ability to ask the uncomfortable human question is becoming the most valuable asset in the room.
The next reading of long-term user retention metrics for AI-integrated enterprise workflows will show whether leadership is finally prioritizing human-centered design over the illusion of the ideal user.
