It's not about the model being wrong. It's about 𝗮𝗰𝘁𝘂𝗮𝗹 𝘁𝗿𝘂𝘀𝘁.
When users don't understand why the AI suggested something, or they can't override a dodgy result, they just... stop using it. Quietly. 📉
We've found three things that actually work:
𝗦𝘁𝗮𝗿𝘁 𝘀𝗺𝗮𝗹𝗹, 𝘀𝘁𝗮𝗿𝘁 𝗯𝗼𝗿𝗶𝗻𝗴 📋 Pick the manual work nobody wants to do anyway. Invoice processing. Data entry. The stuff people actively avoid. Win there first.
𝗠𝗮𝗸𝗲 𝗶𝘁 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 🔍 Show confidence scores. Explain the reasoning. Let humans override anything. Build in a proper review process for uncertain outputs.
𝗧𝗿𝗲𝗮𝘁 𝗶𝘁 𝗮𝘀 𝗮 𝗽𝗲𝗼𝗽𝗹𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 👥 You're not deploying software, you're 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗵𝗼𝘄 𝘁𝗲𝗮𝗺𝘀 𝘄𝗼𝗿𝗸. Train champions properly. Set up governance before things go sideways, not after.
The progression that works: 𝘀𝗼𝗹𝘃𝗲 𝗮𝗻 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 → 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘀𝗲 𝗶𝘁 → 𝘀𝗰𝗮𝗹𝗲 𝘄𝗵𝗲𝗻 (𝗮𝗻𝗱 𝗼𝗻𝗹𝘆 𝘄𝗵𝗲𝗻) 𝗽𝗲𝗼𝗽𝗹𝗲 𝗳𝗲𝗲𝗹 𝗶𝗻 𝗰𝗼𝗻𝘁𝗿𝗼𝗹. ✅
One client went from 4,500 manual reviews taking weeks to automated processing with human oversight in days. Not because the AI was clever, but because 𝘂𝘀𝗲𝗿𝘀 𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗰𝗼𝘂𝗹𝗱 𝘀𝗲𝗲 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗿𝗼𝗹. 🎯
Planning an AI project? Ask yourself:
Where's the human override? 🤔 What happens when confidence is low? Who actually owns this day-to-day?
📥 𝗪𝗲'𝘃𝗲 𝗽𝘂𝘁 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿 𝗮 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗔𝗜 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗖𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀.