California Management Review
California Management Review is a premier academic management journal published at UC Berkeley
by Mark Esposito
In the past year alone, we have seen repeated headlines in the news saying that artificial intelligence (AI) will play a bigger and bigger role in our lives. And it’s fairly true. We now have smart gadgets at home that respond to voice commands; computer voices that pick up when we call a national customer service line; and bank fraud alerts that come from AI bots monitoring our accounts 24/7 for suspicious activity.
The takeaway from all this is the inevitability that companies will need to integrate AI technologies into their business strategy if they want to maintain or gain new advantages before their competitors do. Accenture recently found that 72% of surveyed senior executives agree that AI will be critical to their companies’ market differentiation. Yet, the business reality seems to suggest something entirely different. With the exception of GAFA (Google, Amazon, Facebook and Apple) and BAT (Baidu, Alibaba and Tencent), which all have the knowledge and financial means to explore the use of AI, the adoption rate among many Western companies and industries has been sluggish.A 2017 report from the Boston Consulting Group and the MIT Sloan Management Review found that only 20% of companies have begun integrating AI into their products and processes. Dan Ariely once said, “big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” This appears to also ring true for AI.
So, what can explain the slow uptake of AI among businesses? One reason, we believe, is unfamiliarity. Androids and self-driving cars are still what people imagine first when they think of what AI represents. But AI is much more than that. While it is easy to believe that AI can potentially be a mighty — and sometimes almighty — technology with the potential to transform companies, determining the actual role of AI within a company can be far more elusive: what specific functions can various AI products perform? What exactly are the benefits? What business issues can they really tackle? While brainstorming high-level AI applications can be exciting as a part of strategy exercises, once descended from the ladder of abstraction, working out how to integrate AI into existing operations demands a great deal of work and time.
Even when executives have a strong desire to employ AI into business activities, their busy schedules and other priorities often prevent them from being able or willing to divert the needed time, effort, and energy to go in depth into the operational details. Moreover, even if they do agree to spend the necessary resources, there is always a chance that executives are intimidated by the complicated technical aspect of AI. This poses a circular impasse: without an understanding of the technology, it is difficult to figure out what business objectives to pursue; but without knowing the specific aim(s) to be achieved, it is not easy to see what kind of AI technologies are needed and how they would work. But it can be done. Advanced industrial stalwarts like GE and BMW have already made the leap to integrating AI into their factories.
But what if you don’t have the same type of resources as a large multinational corporation? There’s good news. The cost of AI robotics is now less than $25,000 per machine. With the cost barrier falling, resting on your laurels is not an option as competitors will eventually move forward with AI capabilities. Fortunately, you don’t have to dive in head first in order to get your feet wet. For executives in a traditionally nontech company who are ready to get started, we offer four steps to help you begin:
Do reinvent the wheel
AI in and of itself accomplishes nothing. It is only useful when it supports your existing business. So to accommodate new AI implementations, executives must be ready to modify existing processes and workflows, if not to design entirely new ones. For instance, in farming, a decidedly nontech industry, AI is now used to help identify problems with crops, but it couldn’t have been done without tweaking a few things—like adding airplanes and cameras. The traditional method meant farmers and field hands had to check every field manually with limited tools, but now, AI programs process high-resolution photographs taken from several thousand feet above and are able to spot crop disease indicators weeks before the naked eye can see them.
Unlike the Internet or even blockchain, AI is not an infrastructure technology. In its current incarnation, AI is nothing more than a tool. And like all business tools, they are only effective when the right processes are built around it.
The general consensus among companies is that machines will never completely eliminate the human element, but a warning sign is out there for management: managers who use AI will replace the managers who don’t. Applying AI in a business setting is no small feat. Like learning any new skill, without knowing what are the first steps to take, it can be both disheartening and discouraging. And like learning something new, it is often much harder in the beginning. Starting an AI project is the same, we just have to persist.
About the Authors
Terence Tse is an Associate Professor at ESCP Europe London campus and Co-founder of Nexus FrontierTech, an Artificial Intelligence Studio. Terence has also worked as a consultant for Ernst & Young, and served as an independent consultant to a number of companies. He has published extensivelyon various topic of interests in academic publications and newspapers around the world. He has been interviewed by television channels including CCTV, Channel 2 of Greece, France 24, and NHK. Follow him on Twitter: (@terencecmtse)
Mark Esposito is a Socio- Economic Strategist and bestselling author, researching MegaTrends, Business Model Innovations and Competitiveness. He works at the interface between Business, Technology and Government and co-founded Nexus FrontierTech, an Artificial Intelligence Studio. He holds an appointment as Professor of Business and Economics at Hult International Business School and he is equally a faculty member at Harvard University since 2011. Follow him on Twitter: (@Exp_Mark)
Danny Goh is a serial entrepreneur and an early stage investor. He is the partner and Commercial Director of Nexus Frontier Tech, an AI advisory business with presence in London, Geneva, Boston and Tokyo to assist CEO and board members of different organisations to build innovative businesses taking full advantage of artificial intelligence technology.
Hajime Hotta is a serial entrepreneur and an early stage investor with a strong academic background in artificial intelligence (AI) and AI web applications. Received Ph.D with a study of neural networks. He was the inventor and product lead of AI-backed Ad Tech at Cirius Tech, which was acquired by Yahoo Japan, and also a CTO at Naked Tech, which was sold to Mixi Japan.