The Problem
Technology creates as much friction as it removes. Users spend hours on digital chores: syncing data between apps that should talk to each other, navigating systems designed to obscure rather than clarify, wrestling with tools that demand attention rather than deliver results.
Most AI systems compound this problem. They operate as black boxes—you don't know what they did or why. They create technical friction by requiring multiple tools for simple tasks. They hide their logic to avoid accountability.
Five specific failures:
Black Box uncertainty
Systems produce outputs without showing their work. You don't know what data was accessed, which decisions were made, or why a result differs from the last run. This undermines trust in workflows like claims processing or compliance reviews.
Technical friction
Simple tasks require 12 apps and 47 clicks. Data routes through multiple systems that don't coordinate. Each step adds latency, error risk, and maintenance burden.
Bureaucratic obfuscation
Systems hide their logic to avoid accountability. When something breaks, you can't see why. When compliance requires an audit trail, the trail doesn't exist. This violates HIPAA, GDPR, and SOC 2 requirements.
No observability
Most systems lack built-in monitoring, alerting, and automated recovery. Teams are blind to failures until users report them. There's no Logic Log to review, no decision trail to audit, no way to see what happened.
Integration complexity
Connecting to EMRs, legacy APIs, or compliance workflows requires extensive custom engineering. Each integration becomes a maintenance burden. Existing systems don't become more capable—they become more complex.