The 5 Things Your AI Unit Needs to Do
AI remains an early-stage technology application. The potential is vast, but how can managers cut through the AI hyperbole? One approach to bringing more discipline to your efforts in AI is to focus on five things: scouting AI technology, applications, and partners; experimenting AI technology and applications; supporting business units in applying AI technology; getting the entire organization to understand AI; and attracting and retaining AI-savvy talent.
Not a day goes by without the announcement of the appointment of a new VP of Artificial Intelligence (AI), a Chief Data Scientist, or a Director of AI Research. While the enthusiasm is undeniable, the reality is that AI remains an early-stage technology application. The potential is vast, but how managers cut through the AI hyperbole to use its power to deliver growth?
In our consulting work, we often encounter managers who struggle to convert AI experiments into strategic programs which can then be implemented. Michael Stern (not his real name), for instance, is the Head of Digital for a German Mittelstand office equipment company. Michael is used to starting new projects in emerging areas, but feels unable to fully understand what can AI can do for his business. He started a few experiments using IBM Watson, and these produced some clear, small tactical gains. Now Michael is stuck on how to proceed further. How can he create cross-functional teams where data experts work with product teams? And how will they pick project ideas that produce real ROI? Michael wonders if his firm even knows what new business models can be explored with their existing datasets — let alone which new ones might be made possible by AI.
Michael is not alone. As more and more companies invest in AI-driven units, many newly appointed managers face these challenges – especially in companies with little or no previous experience with cognitive technologies. Part of the trouble: in many companies, the role of these teams is undefined. Very little research has been done to design the mission and scope of these new units.
At the European Center for Strategic Innovation (ECSI), we examined numerous corporate AI initiatives among large organizations, and identified five key roles that can help AI units to develop the right mission and scope of work to succeed.
1. Scouting AI technology, applications, and partners. This role is about setting up a core team of “AI sensors” in charge of monitoring new trends, identifying disruptive technologies, and networking with innovative players — mainly startups. The automobile-parts supplier Bosch and the tech and engineering powerhouse Siemens are two prime examples of this. With a planned investment of $300 million, Bosch has established three AI corporate centers focused on IoT and other AI-related fields in Germany, India, and Palo Alto. Siemens, similarly, has included AI in the company’s list of innovation fields to be monitored through its network of innovation outposts with offices in California, China, and Germany.
2. Experimenting with AI technology and applications. This role is about understanding through quick, small AI pilots how to develop or adopt cognitive technologies to the company’s business and operational models. Although off-the-shelf AI tools and open-sourced systems are available, they have limited transformational potential compared to customized ones. At Deutsche Telekom, the development of its own AI solutions is an important priority. Instead of buying AI chatbots from vendors, Deutsche Telekom has its own developer teams. With the support of partners, they design, train, and fine-tune AI solutions for the company.
Rather than concentrating efforts on a single big win, AI units and teams should embrace a portfolio approach to their experiments. The power of AI should be tested across functions and business areas. There are three types of experiments that are worth paying particular attention to…
