According to Fast Company, the term “artificial intelligence” has become one of the most abused words in modern business, moving from a technical concept to a near-meaningless marketing buzzword. The article notes that every startup now markets itself as “AI-powered,” every legacy tool claims “AI-driven insights,” and every clunky customer-service bot is rebranded as an “AI assistant.” This rampant mislabeling has led to predictable outcomes: widespread public confusion, deep skepticism, and a growing conviction that the technology is fundamentally overhyped. When the author consults with governments and businesses about legitimate AI applications—like streamlining compliance or reducing workload—the most common pushback they hear is that “AI doesn’t work” or “AI can’t do that yet.” Initially, this was mistaken for a simple resistance to innovation, but the core issue is that people are rejecting the disappointing tools mislabeled as AI, not the technology itself.
Stakeholder Impact
So, what does this mean for everyone involved? For users and customers, it’s a mess. They’re constantly bumping into these half-baked “AI” features that don’t work, which trains them to ignore or dismiss any future claims about intelligence. It creates a massive trust deficit. Why would you believe the next company promising an AI solution when the last ten were just glorified rule-based chatbots or simple analytics dashboards?
For developers and engineers building actual machine learning models, it’s incredibly frustrating. Their serious work gets lumped in with the marketing fluff, making it harder to communicate real value and breakthroughs. And for enterprises making purchasing decisions? The noise is deafening. It becomes nearly impossible to separate signal from noise, to find tools that provide tangible ROI versus those just riding the hype wave. This slows down adoption of useful technology that could actually help, because the decision-makers have been burned before.
Here’s the thing: this cycle isn’t new. We saw it with “cloud,” “big data,” and “blockchain.” A powerful concept gets diluted by opportunism until the original meaning is almost lost. But with AI, the stakes feel higher because the potential applications are so broad. The danger is that the backlash against bad AI could stifle investment and interest in good AI. Companies might pull back, thinking the whole field is a bust, when the reality is they just haven’t encountered the real thing yet. It’s a classic case of a few bad apples spoiling the bunch—except in this case, it’s more like a few good apples buried in a mountain of plastic fruit.
