Supply Chain Decision Makers - Accurate Forecasts Unused

I was in a meeting last month with a global supply chain team. "Our demand forecasting model is 94% accurate," the data scientist said proudly. "So why are we still using last year's spreadsheet?" the supply chain director asked. Awkward silence. I've seen this movie before. Multiple times. Accurate models gathering dust. Change management initiatives stalling. Org transformation projects burning through budgets with minimal adoption . And I used to think the problem was either: 1. The model isn't good enough, OR 2. The organization is too resistant to change But that's not it. The problem is simpler and more complex: the decision system is broken. Here's what I mean: When you introduce an AI model into an organization, it doesn't just get evaluated on accuracy. It gets evaluated against: 1. How transparent is the logic? (Can people understand why it recommends X?) 2. Do incentives reward using it? (Or do they reward sticking with the old way?) 3. Is the decision authority clear? (Who actually decides, and based on what?) 4. Is there feedback? (Do people learn when the model fails?) 5. Can people override it? (Does forcing reliance breed trust or resentment?) Get any of these wrong, and even a 94% accurate model sits on a shelf. Starting today, I'm diving into Decision Intelligence. The integrated discipline that asks: How do you design organizational decision systems, structure + process + technology + culture, so that AI actually gets used AND improves decision quality? Day 1 insight: AI doesn't fix bias. It amplifies what you build into it, at scale. Over the next 4 weeks, I'm researching: How cognitive biases hide in strategic decisions How to structure decisions to counteract bias How technology amplifies or reduces bias How to actually change organizations The goal: A framework that explains why some orgs nail AI transformation and others get stuck. If you've lived the "accurate model, low adoption" paradox, I want to hear your story. What got in the way? #AI #Transformation #DecisionMaking #OperationalExcellence #DataScience #Leadership #Change #OrganizationalBehavior