When coffee machines are marketed as "AI-powered," the term has clearly lost its descriptive precision. The same inflation has hit "metaverse" and "blockchain" before it, but with AI the gap between label and substance is unusually wide because the underlying technology is genuinely transformative. The challenge is separating the real advances from the noise.
A significant driver of hype is the investor market. When OpenAI announces products like Sora months before general availability, the primary audience is not consumers but investors. These announcements are timed to influence valuations and funding rounds. The dynamic explains why AI startups routinely reach billion-dollar valuations despite offering products that are functionally similar to competitors. For the companies involved, the incentive is to promise aggressively and deliver incrementally, because capital allocation follows narrative momentum, not deployment metrics.
In practice, the push for AI in organizations frequently comes bottom-up: employees who use ChatGPT at home bring their expectations to work. Companies themselves tend to approach adoption cautiously, running pilots and looking for measurable returns before scaling. This hesitancy is often criticized as falling behind, but it is a reasonable response to a technology whose capabilities and limitations are still being mapped. The productive question is not whether to adopt AI, but which specific workflows benefit most from it.
Most AI discussions center on efficiency: doing the same work faster. The more interesting effect, and the one I see most often in practice, is quality improvement. When routine tasks are automated, people spend more time on the parts of their work that require judgment, creativity, and strategic thinking. The output is both faster and genuinely better. This distinction matters for how organizations measure AI's return on investment. Tracking only time saved misses the larger benefit.
There is a persistent asymmetry in how we evaluate AI versus human performance. Self-driving cars must demonstrate near-perfection before the public accepts them, while human drivers cause roughly 1.35 million deaths per year globally, according to WHO data. AI systems do not need to be flawless to be valuable; they need to be demonstrably better than the alternative. Failing to apply this standard consistently slows adoption of technologies that would, on balance, reduce harm.
The most consequential development in the next few years will be the emergence of "AI-first" companies that redesign entire industries around AI capabilities, rather than another foundation model. Consider an insurance provider that builds its actuarial models, claims processing, customer service, and fraud detection on AI from day one. It could operate at a fraction of incumbent costs while offering better service. When that company enters the market, the competitive pressure on traditional players will be immediate and severe. The parallel to early digitalization holds: not every company adopted email immediately, but eventually it became non-negotiable. The difference with AI is that the transition will compress into years rather than decades.
Marketing frequently applies the 'AI-powered' label to products with minimal or no genuine AI integration, inflating expectations beyond what the technology can deliver. Even consumer appliances like coffee machines are marketed as AI-driven. This overuse of the term creates a gap between public expectations and the actual capabilities of AI systems.
Many companies are approaching AI cautiously and experimentally, which is a healthy strategy. Adoption pressure often comes from employees who already use tools like ChatGPT privately and want similar productivity gains at work. The focus should be on identifying meaningful use cases rather than rushing to implement AI everywhere.
The AI hype is heavily driven by investor dynamics. Companies like OpenAI announce new technologies long before they are available, partly to attract investment and influence valuations. This results in billion-dollar valuations for AI startups whose products are often barely distinguishable from competitors.
Beyond efficiency gains, a significant benefit lies in quality improvement. AI enables employees to spend more time on creative and strategic work rather than routine tasks. This can lead to better outcomes and more innovative solutions beyond mere speed improvements in existing processes.
The emergence of 'AI-first' companies is expected to disrupt traditional markets through systematic AI integration. For example, an insurance company could offer services at a fraction of the usual cost by building its operations around AI from the ground up. Such disruption will force incumbents to adapt.
Society applies double standards to AI: human errors are accepted as normal, but AI systems are expected to be nearly perfect. This is especially visible with technologies like self-driving cars, which must demonstrate near-flawless performance to gain public acceptance, while human driving errors cause tens of thousands of deaths annually.
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Copyright 2026 - Joel P. Barmettler