Module 1: Why Everyone Is Talking About AI (And Why Most of It Is Noise)
The past few years have seen an explosion of chatter about artificial intelligence (AI). From conference keynotes to social media feeds, it seems everyone is talking about AI. High-profile successes like ChatGPT going viral in 2023 catapulted AI into the mainstream, fueling grand predictions about an “AI revolution.” Companies suddenly felt pressure to declare AI initiatives, fearing they’d be left behind. But behind the buzz lies a sobering reality: much of what’s being said is exaggerated or unrealistic. In other words, a lot of it is noise. To lead effectively, business leaders must cut through this noise and distinguish genuine opportunities from hype.
Why the sudden frenzy? Breakthroughs in generative AI (AI that can produce text, images, videos etc.) captured public imagination. Media headlines touted AI as a magic solution to nearly every problem. Influencers and tech evangelists proclaimed that AI (especially generative models like large language models) would dissolve corporate hierarchies, reinvent business models, and unlock limitless growth overnight. As this developed, so did the fear of missing out as people asked: if AI could truly do all that, who wouldn’t want to jump on board?
However, history teaches caution. New technologies often follow a hype cycle: early excitement leads to inflated expectations, then a reality check sets in. AI is no exception. In 2023, generative AI shot straight to the “Peak of Inflated Expectations” on Gartner’s hype cycle. The hype was huge, fueled by cherry-picked demo successes, but it wasn’t grounded in the day-to-day reality of most businesses. Companies and commentators showcased the most impressive AI outputs while glossing over limitations, giving many businesspeople an unrealistic impression of AI’s readiness.
The pitfall of this hype is unrealistic expectations. When people believe AI is a near-magical solution, they may underestimate the effort and change required to implement it. Glossy demos do not equal easy deployment. Enthusiastic narratives often oversimplify the complexities of AI adoption. They might highlight a chatbot answering questions perfectly in a controlled setting, but omit how much tuning, data cleaning, and human oversight were behind the scenes. As a result, some leaders dive in without fully understanding costs, data needs, talent requirements, or risks. The hype can create a false sense that “everyone else is doing AI successfully, so we should too”, when in fact many of those efforts are still struggling or at pilot stages.