We’ve been running AI pilots across procurement, finance, and operations for nearly eighteen months now. The models work, the insights are solid, and we’re showing real opportunity for cycle time reduction and cost savings. Yet we can’t get past pilot phase. Process owners keep nodding in steering meetings but then delay rollout or find reasons to keep manual checks in place. The feedback we hear most often is that recommendations feel like a black box, and people worry automation will either break edge cases they handle daily or make their expertise irrelevant.
We’re now debating whether to invest heavily in explainability tooling, run more process mining to show owners the data foundation behind AI, or double down on change management and communication. Some folks argue we need governance frameworks and human-in-the-loop designs baked in from the start, not bolted on after. Others say it’s simpler: we just haven’t proven the value clearly enough in their language.
Curious how others have moved process owners from skepticism to adoption. What actually shifted the conversation in your organization? Was it transparency and explainability, tighter governance, proof through small wins, or something else entirely?
Governance and human-in-the-loop were non-negotiable for us. We implemented a design where AI flags exceptions and proposes actions, but process owners approve anything that crosses a threshold or involves compliance risk. That alone reduced fear of losing control. People stopped viewing AI as a replacement and started seeing it as a decision support tool they could override when needed.
One thing that helped us was admitting upfront that AI isn’t perfect and showing how we handle failures. We set up audit trails so every AI decision could be traced back to the data and logic. When process owners saw we had controls in place and weren’t just blindly automating, they were more willing to try it in production. Transparency around limitations built more trust than overselling capabilities.
We’re still stuck in the same place you are. Leadership loves the demos, but our procurement team won’t fully commit because they don’t understand how the models make recommendations. We tried explainability dashboards, but they were too technical. What actually resonated was when we showed them side-by-side comparisons of AI recommendations versus manual decisions over three months, with clear accuracy metrics and examples of where AI caught things humans missed.
We had success by targeting low-risk, high-volume tasks first—things like invoice matching and shipment tracking. Process owners could see automation working without major consequences if something went wrong. Once they had confidence in those areas, they were far more open to applying AI to higher-stakes processes. Starting small and proving value incrementally mattered more than any single explainability feature.
The issue we kept hitting was that AI recommendations didn’t match the actual workflows people followed day to day. Turned out our process documentation was years out of date. We used task mining to capture what people actually did, then rebuilt the AI use cases around real workflows instead of idealized ones. That alignment was critical. Process owners stopped saying the system didn’t understand their work because we showed them it did.