Building Trust with AI

How do you build trust when AI isn't 100% certain?

Your board expects certainty, BUT your AI delivers probability.

AI adoption is different to SaaS platforms and their 'deterministic systems'. But people are used to systems that are 100% predictable (think ERP, CRM).

AI just doesn't work that way.

AI operates in probabilities, not guarantees. And that probabilistic nature isn't a bug to be fixed in the next release, it's the core of how these systems function.

I've been researching / reading up on the best ways to build that trust with AI solutions.

𝐓𝐡𝐫𝐞𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐭𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤:

🎯 𝐅𝐫𝐚𝐦𝐞 𝐭𝐡𝐞 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐑𝐢𝐬𝐤, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐭𝐡𝐞 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲

Instead of promising 95% accuracy, define the 5% risk. For a demand forecasting AI, that means saying: "The model is 95% confident in its prediction. For the remaining 5%, we have a human review process to manage exception cases." This shifts the conversation from technical specs to operational risk management—a language every leader understands.

🎯 𝐁𝐮𝐢𝐥𝐝 "𝐏𝐫𝐨𝐨𝐟 𝐨𝐟 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭" 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬

Trust is earned through results. Create simple, visual dashboards showing the AI's performance over time. When your CFO can see that user feedback and new data improved forecast accuracy from 87% to 94% in six months, the AI stops being a cost centre and becomes a visibly appreciating asset.

🎯 𝐌𝐚𝐤𝐞 𝐭𝐡𝐞 𝐛𝐥𝐚𝐜𝐤 𝐛𝐨𝐱 𝐭𝐫𝐚𝐧𝐬𝐥𝐮𝐜𝐞𝐧𝐭

You don't need to explain the code, but you must explain the logic. For any given AI recommendation, be able to show why. For example: "The model recommended this supplier because of their 99.8% on-time delivery record over the last 50 orders, which is the most heavily weighted factor." This provides the business context and accountability needed for confident decision-making.

Trust isn't built by pretending AI is flawless. It's built by proving it's accountable, improving, and bounded by appropriate human oversight.