AI, Fixed. Vol 4 tells the story of how a banking AI chat system was used by vulnerable customers as a discreet lifeline during situations of financial and domestic abuse. It highlights how standard automation workflows can unintentionally create risk, and how rebuilding AI with safety signals, human intervention, and protective design transforms it into a secure and supportive channel.

AI, Fixed. Vol 3 examines how AI transcription tools fail in global B2B environments when they misinterpret accents and industry-specific language, producing confidently incorrect outputs. Through a real-world case, it reveals the operational and reputational risks of unchecked automation and how confidence thresholds, human-in-the-loop design, and domain calibration are essential to fixing AI systems.

AI, Fixed. Vol 2 explores a real-world case where AI tools introduced into a luxury environment technically worked but failed in practice. It tells the story of how staff spent more time correcting AI outputs than using them, revealing the hidden “correction tax” that appears when automation doesn’t align with high-context, high-trust workflows and how fixing it required precision, not replacement.

AI, Fixed. Vol 1 looks at an AI implementation that worked technically but failed in practice inside a real company due to workflow, user adoption, and operational constraints.