Module 1: Why Everyone Is Talking About AI (And Why Most of It Is Noise)
It’s important to recognise why so much of the talk is noise. Bold claims like “AI will instantly transform your business” make great headlines but rarely reflect what’s actually happening on the ground. Few companies have seen the overnight metamorphosis promised by AI futurists. For example, Cisco’s CEO Study in 2025 surveyed 2,503 CEOs worldwide and found that while 97% planned to adopt AI, only 1.7% felt truly prepared for it. This huge gap between intention and readiness is a classic sign of hype at work. Leaders are saying “we must do AI” due to external pressure, even as they privately admit their organisations aren’t prepared, whether in terms of data, skills, or strategy.
Empirical data on AI initiatives further separates signal from noise. The "Value Gap" identified by the BCG further illustrates the difficulty of scaling AI initiatives. In late 2024, BCG reported that 74% of organisations were making little to no progress on their projects, with only 4% of "elite" companies developing capabilities that consistently drive value. By October 2025, this gap had shifted slightly, with 5% of firms worldwide reaching a state where AI is delivering significant bottom-line value, while 60% still report minimal gains despite substantial investments.
In other words, the vast majority of corporate AI efforts were failing to meet expectations. Even among so-called “AI leaders,” progress was slow and hard-won, not explosive. Claims of AI being implemented effortlessly and delivering great success are likely spinning a difficult reality, hiding the real struggle. The noise would have you believe AI is already ubiquitously delivering game-changing results; the signal is that meaningful success stories are the exception rather than the rule at this stage.
Another reality often lost in the din: AI breakthroughs take time to translate into business impact. There’s usually a lag between tech invention and broad adoption. For instance, while a viral AI demo might emerge in a matter of months, weaving that capability into a large organisation’s workflows, training staff to use it, and dealing with regulatory or ethical questions can take years. Ignoring this leads to frustration, a phenomenon known as the “Trough of Disillusionment” in the Gartner Hype Cycle, which AI is now entering. This is driven by the realisation that slick product demos do not equate to scalable ROI. The financial implications of this phase are stark: organisations spent an average of $1.9 million on GenAI initiatives in 2024, yet fewer than 30% of AI leaders report that their CEOs are satisfied with the returns. A high-quality training programme must incorporate these specific figures to help leaders benchmark their own spending and manage stakeholder expectations.
In plain terms, after the party comes the hangover: companies excitedly invest in a trendy AI tool, then realise it’s not trivial to get a return on that investment. The attention is shifting from “Wow, look what AI could do!” to “Okay, how do we make it actually work for us?”. For a business leader, the implication is that success is not found in the act of adoption itself, but in the precision of the implementation and the maturity of the underlying infrastructure.