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The AI Hype Trap: A Realistic Guide to Avoid It

Generative AI has made many people super excited by portraying an imaginary futuristic world where technologies like self-driving cars and helpful virtual assistants could transform and redefine everything.

However, along with that auspicious vision and enormous hype also comes impractically high hopes and unrealistic expectations from investors,  scientists, and tech leaders themselves regarding what AI is capable of achieving in the very near term, which means not all of those hopes are guaranteed to fully materialise on schedule because of the too much hype and sci-fi aura around AI.

This enthusiasm can cause investors, engineers, and leaders to inaccurately perceive the pragmatic truth about the technology, thereby fundamentally misunderstanding its genuinely impressive albeit relatively nascent and limited current capabilities. This creates a situation where people and markets could wind up deeply disappointed when technology fails to meet those sky-high expectations. ( Gartner provides a name for this in its hype curve: Disillusionment).

Financial and resource risks with hype

But the disillusionment doesn’t prevent that in the first phase, all of that excessive hype could result in misguided investments where organisations spend lots of budgets on AI technologies, tools, and platforms without objectively understanding at a deep level the considerable practical effort still required to properly implement. This is to validate and integrate AI alongside human workers to generate a decent, timely return on that substantial investment given present constraints.

Moreover, leadership might overestimate and take for granted how quick and easy it will supposedly be to leverage AI to transform workflows without headaches or hiccups. This inadvertently takes attention, workforce, and financial resources away from other more straightforward process improvement solutions and basic ways to boost productivity.

Creating the gap between reality and hype  

On top of making it hard for organisations, employees, and markets to grasp limitations, the general amount of publicity and hype around AI in all areas of business and tech can make it extra tricky for individual organisations to accurately envision, quantify and communicate what the proper practical near term applications and uses are within their specific business operations and unique circumstances because there is often a massive canyon sized gap between the cutting edge accomplishments and future-looking promises portrayed about A, changing everything across media, conferences, podcasts and tech advertisements versus its actual capabilities operating under constraints.

In this way, gigantic disparity and contradiction understandably seriously confuse employees at all levels, managers, and investors, who hold unrealistic expectations and hope that AI will instantly solve long-standing challenges and take their organisation’s performance to the next level very soon.

My two cents?

Before jumping into AI, connect with trusted experts who can guide you based on experience. Run small tests with providers to see how the technology performs for daily business situations. Openly ask peers about the lessons they learned while trying to use AI, This “wisdom” from others who went first is precious.

Also, make time to teach employees and investors what AI can achieve versus 5 years from now. Share realistic examples of what AI has done in other companies like yours. ( and gain knowledge too...)

Admit current weaknesses and set proper expectations on costs, speed, and mistakes. Be honest about what AI can’t do well. Set people’s hopes right about how much you estimate this will cost the company, how long improvement takes, and how many errors to expect at first.

 As an authentic and truthful leader, show the technology as it is today, no exaggeration. Moving forward as a team and being aware of limits builds trust and patience for the step-by-step progress.



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