AI products are often presented as universal tools, but many are designed around a narrow imaginary user: technically confident, verbally precise, cognitively unburdened, and able to recognize when the system is wrong.

Real users may be aging, disabled, overwhelmed, grieving, frightened, distracted, unfamiliar with technology, or making decisions under pressure. These are often the people who could benefit most from AI, yet they are also the people most likely to be excluded by unclear interfaces, uncertain outputs, inaccessible workflows, and products that quietly transfer verification back to the user.

Drawing from her experience building consumer safety technology after her mother was targeted by a sophisticated scam, Cat Karow examines how AI systems fail when they are designed for capability rather than real human conditions.

Attendees will learn how to identify hidden assumptions in AI experiences, design for users with different levels of confidence and capacity, and build products that remain useful when life is messy, stressful, and deeply human.

Small Print

Session Type: AI / Product Design / Accessibility / Human-Centered Technology

Audience: Developers, product managers, designers, founders, AI practitioners, accessibility professionals, technical leaders, and anyone responsible for building technology for broad audiences.

Technical Level: Beginner to Intermediate

Preferred Format: 45-minute presentation plus 15-minute Q&A

Attendee Takeaways:

Identify the “ideal user” assumptions embedded in AI products
Evaluate how cognitive load, stress, disability, age, and technical confidence affect AI usability
Design clearer confidence signals, recovery paths, escalation options, and safer defaults
Recognize when an AI product is shifting complexity and risk back onto the user

This is a vendor-neutral educational session. It does not promote a product or service.

First Public Delivery: New for 2026–2027