The Evolution of Error: What Future Errors Will Look Like

For decades, automation has made digital products more reliable by removing routine work, reducing mechanical errors, and making processes predictable. AI accelerates this shift by taking on not only execution, but also task interpretation.

As people and AI increasingly create outcomes together, errors no longer have a single clear source. A system may execute a command correctly yet misunderstand intent, lose context, or suggest a convincing but shallow solution. This raises a new question: not only who made the mistake, but whether it was a mistake at all.

This talk explores the evolution of error through design and customer experience—from familiar defects to failures of interpretation, context, trust, and shared decision-making. We will consider which errors automation removes, which it creates, and how digital-product quality should be defined today.