As you have doubtless noticed, we are in the middle of a feeding frenzy about something called generative AI. Legions of hitherto normal people – and economists – are surfing a wave of irrational exuberance about its transformative potential. For anyone suffering from the fever, two antidotes are recommended. The first is the hype cycle monitor produced by consultants Gartner, which shows the technology currently perched on the "peak of inflated expectations", before a steep decline into the "trough of disillusionment". The other is Hofstadter's law, about the difficulty of estimating how long difficult tasks will take, which says that "It always takes longer than you expect, even when you take into account Hofstadter's law".

The sustained hype around machine learning (ML) applications in the business world has some reasonable roots. ML is already embedded in many business applications, as well as customer-facing services. Also, it just kind of sounds cool, right? As many an IT leader can tell you, though, excitement about a technology can lead to some unfulfilled and downright unrealistic expectations. So we asked a variety of ML and data science experts to share with us some of the tough truths that companies and teams commonly learn when they charge into production.

Artificial intelligence (AI) is generating both interest and investment from companies hoping to leverage the power of autonomous, self-learning solutions. The Pentagon recently earmarked $2 billion in funding to help the Defense Advanced Research Projects Agency (DARPA) push AI forward, and artificially intelligent solutions are dominating industry subsets such as medical imaging, where AI companies raised a combined $130 million worth of investments from March 2017 through June 2018. Information security deployments are also on the rise as IT teams leverage AI to defeat evolving attack methods, and recent data suggests that AI implementation could both boost gross domestic product (GDP) and generate new jobs. It's easy to see AI as a quick fix for everything from stagnating revenues to medical advancement to network protection. According to a recent survey from ESET, however, new increasing business expectations and misleading marketing terminology have generated significant hype around AI, to the point where 75 percent of IT decision-makers now see AI as the silver bullet for their security issues.

AI will revolutionize the world, or so sayeth Silicon Valley. But there are some potholes on the road to AI nirvana - starting with the people AI is supposed to help.

Whether you agree with experts who feel the "future of work" is still ahead, or is in fact already here, most organizations today are already experiencing significant levels of change. Technology is evolving, processes are becoming automated, and traditional, hierarchical organizational structures are being tested and re-evaluated in pursuit of more effective models. To navigate effectively in this landscape, employees and teams must develop new skills that allow them to adapt and work effectively through these changes, or risk getting left behind. The data from Deloitte, Gallup, the World Economic Forum (and many others) shows that the landscape is becoming more complex and uncertain, with an increased focus on collaboration in teams. As a result of this shift, it's time to reevaluate our perception of "soft skills"--communication, empathy, collaboration, creativity, emotional intelligence, and other skills traditionally viewed as secondary to technical ones--and consider them as core capabilities that are essential to competitive teams.