Why Intelligent Machines will be the Key to Human-Centered Innovation

Editor’s Note: This post is part of our Big Ideas series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Sandro Kaulartz will be speaking at IIeX Europe 2020 in Amsterdam, Feb. 25-26. If you liked this article, you’ll LOVE IIeX Europe. Click here to learn more.

The Innovation Problem

Without a doubt, the ability to innovate in an agile and sustainable fashion has never been more essential to survive. Yet, in the current volatile business era constantly disrupted by technological shifts, new “out of the box” business models, and rapidly changing consumer culture, 90% of corporate innovations fail according to Mark Payne in “How to Kill a Unicorn”. With no one clear path to successful innovation, identifying the underlying reason for these failures is challenging. However, one critical root cause is certainly the tendency of large corporations to ground innovation endeavors on past successes. Instead of solving emerging real-world consumer problems, many innovation strategies are designed to focus on delivering efficiency, scalability, and profitability. And while the overarching goal is to develop “the next big thing” and true breakthrough innovations, most innovations actually result in minor improvements to existing products.

Traditionally the ability to scale quickly has been a growth engine for mature organizations. But now, in the new “economy of unscale”, empowered by technology, AI algorithms, and the consumer data explosion, small and agile challenger brands can effectively transform entire industries before the incumbent can even see it coming. We all know the poster children of category disruptors in the entertainment (e.g. Spotify), transportation (e.g. Uber) or hospitality (e.g. AirBnB) industries that eventually became early members of the ‘unicorn company’ club. We also see the phenomena of challenger brands entering categories that were long considered unassailable such as cosmetics, ready-made food, or banking with new, compelling business models.

Lead Users Can Shape a Human-Centered Innovation Culture

There are no easy answers to this complex innovation dilemma, but a truly human-centered innovation practice from the bottom up seems to be an obvious antidote. Academic innovation research has long shown that consumers themselves are the real pioneers behind breakthrough innovations. Those most engaged in a particular field; the ‘lead users’, have an inherent motivation to develop novel solutions and regularly create radically new products and services ahead of market demand. As the musician and producer James Murphy aptly put it “The best way to complain is to make things.” Those forerunning users adopt trends earlier than the majority and therefore set the horizon for the distant future in the category.

The idea of searching for Lead Users in a category to develop collaborative innovations is not a new concept. In fact, the Lead User method was pioneered by Professor von Hippel from MIT over 30 years ago. Since then, this approach has been studied and developed by hundreds of academics and practitioners. Their research has already long established a robust body of evidence that leads users to innovate ahead of general market demand. Sustainable innovators such as 3M (e.g. Post-It notes, Scotch Tape, Thinsulate) who manage to continuously grow through transformative innovations prove that the fusion of internal and external creative power, combined with knowledge and ideas from R&D departments and innovating Lead Users, is the key to transformative innovations. 

But adopting Lead User innovation principles goes beyond the problem definition as a result of claimed consumer needs from the market majority. It is designed with pioneering users in the lead role at the innovation stage to learn earlier about future market needs and discover their ideas, hacks or prototype solutions–their need-solution pairs. Lead users are hard to find and for that reason, the practical value of the Lead User method has long suffered from the time and cost required to identify them.

Using Machine Learning to Decode Novel Need-Solution Pairs

After exploring machine learning algorithms applied to data from millions of consumers, there is no doubt in my mind that the web is an incredibly rich user innovation ecosystem. It’s the place where users come together and collaborate to solve their emerging or niche needs. From do-it-yourself shampoos that prevent hair-loss, to complex cybersecurity solutions or a baby popsicle that nourishes the baby and soothes teething pain at the same time. Leveraging the latest advancements in machine learning for Natural Language Processing has made it practical to discover weak signals of changing need patterns with corresponding solutions from innovating users on a massive scale. Beyond, by synthesizing social and search data, we can now take the guess-work out of innovation strategies and correct the course by continuously analyzing trends and the innovation diffusion of novel concepts. Researchers now have easy access to the analytical toolkit and consumer data to turn the internet into an ongoing innovation cornucopia.

If you’re interested in learning more about how it works in practice, please join my talk at IIeX Europe next month.