8 Stages of the AI "Emotional Rollercoaster"

Feelings are running hot right now when it comes to generative AI (GenAI). Within the business community, many employees (and execs) are nervous with excitement and anticipation. Others are more fearful and skeptical of how this “thing” will play out, which leads us to an important question: Are enterprises too emotional when it comes to adopting nascent technologies?

[Read also: The 3 biggest GenAI threats (Plus 1 other risk) and how to fend them off]

Historically, organizations have been hesitant to embrace the latest innovations until there’s a clear roadmap for adoption. Most take a responsible and pragmatic view, allowing governance, standards, and stewardship to heavily influence their IT strategy. But in the absence of such guidelines, how should CIOs, CTOs, and CISOs approach GenAI without falling for the hype?

At Tanium, we’ve identified a clear pattern of corporate technology adoption that (unsurprisingly) mirrors Gartner’s familiar Hype Cycle, but with one important distinction — the emotional factor. Like it or not, we, as IT professionals, use emotion and sentiment to influence our technology choices. The consumerization of IT has simply accelerated this, and GenAI is merely the latest example.

The Emotional Hype Cycle explained

So, what is the Emotional Hype Cycle? And how can it be used to guide our IT strategy when it comes to GenAI? Let’s take a look at the eight stages of the curve to witness how much of an emotional rollercoaster it can be for enterprises and employees:

1. Triggered: A new innovation breaks with the norm, disrupts old modes of thinking, and triggers interest, intrigue, wonder, and foreboding. AI has been around since the 1950s, but AI in Real Life (AI IRL) has triggered a new wave of possibilities (and human reactions).
2. Hyped: Overexcitement sprouts forth with a widespread embracement of the technology’s potential. Early publicity produces a number of success stories that are shared excitedly, but they’re often accompanied by scores of (lesser-known) failures.
3. Rejection: The hype’s peak is over as organizations face setbacks and instances of misuse. Prohibition takes over with user access becoming restricted as organizations manage the uncertainties. Employees are left in a holding pattern or out in the cold entirely. Shadow IT emerges.
4. Doubt: A healthier degree of caution, doubt, and skepticism arises as the technology is assessed in the round — objectively. Consideration is given to ethical factors (such as bias) as well as privacy and protection. Hesitancy is the default emotion.
5. Comfort: There is a sense of gradual acceptance as organizations build their own test beds and case studies in controlled environments. Access is limited to a few, but these “pilots” are helpful for managing excitement and expectation.
6. Habitual: Businesses get into the rhythm of using and applying the technology thanks to the establishment of common standards and governance. Fair and effective user policies and working patterns emerge, setting the foundation for enterprise-wide rollouts.
7. Social: The technology becomes part of the social fabric of the organization with adoption now widespread. Critically, there is sense of connectedness and alignment between IT and the wider business when it comes to the technology’s strategic role.
8. Creative: This is a transformative stage for users and the enterprise as people use the technology to innovate and operate in productive new ways. This is a rewarding phase for all parties and an early indicator of technology maturity.

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Are you looking to manage the risks of generative AI adoption right now? If so, download our latest cheat sheet, which highlights the top ten AI risks to control and the three best ways to balance speed versus security in machine learning and AI engineering.