The AI credibility gap is real
Lately, I’ve seen a specific pattern emerge as organizations make AI claims. “We’re AI-first.” “We’re AI-native.” “We’re agentic.” The language is confident, forward-looking, and nearly universal. The results are generally not. Last year, MIT found that billions of dollars in enterprise Gen AI pilots yielded nothing measurable. Gartner predicts more than 40% of agentic AI projects will be canceled by the end of 2027, and a recent Gallup survey showed that just 13% of U.S. employees use AI daily. Even “frequent use,” defined as a few times a week or more, sits at only 28%. So there’s a gap. Leaders describe AI as the most important shift since electricity. Their teams are still deciding whether to open the tools. The headlines are calling this an adoption problem, but I think the real issue is credibility. SWAP ABSTRACT ANSWERS FOR HELPFUL FIXES I spend a lot of time with customers across the U.S., Europe, and Asia. In those conversations, nobody talks about LLM architecture or multimodal reasoning. They ask: How can I see which projects are at risk before they become a crisis? How do I save my team from spending hours every week manually building status reports? How do we prioritize hundreds of incoming requests without adding headcount? Our AI communications should be based on the answers to these questions: pragmatic, rooted in reality, and genuinely helpful. Our own research confirms this. In a recent study, 52% of respondents said accuracy was the most important quality in an AI tool. Speed came next at 47%, followed by ease of use at 46%. People aren’t looking for a flashy digital assistant that impresses in a demo and disappears when the work gets complicated. They want something that understands and improves their workflow, whatever that might look like. DELIVER PROOF ALONGSIDE PROMISES Saying “you need to use AI” in 2026 is like saying “you need to use computers