The participants from companies such as IBM, John Deere, Schneider Electric, etc. were discussing data-driven use cases in R&D, success stories and challenges. We heard about data facilitating investment decisions, in agriculture yield data driving acquisition recommendations, and insights from patents taken into consideration in product design and development.
I was expecting to hear from participants how their main concern is direct competitors outsmarting them in data acquisition and use. To the contrary, the vast majority of the participants in their remarks mentioned a company from a different industry. Google has been the most frequently mentioned company despite the discussion focusing on industries that were even a few years ago considered completely disconnected from the technology giants in the Bay Area.
It is well known how Google is disrupting the automotive industry with autonomous cars relying heavily on AI and mountains of data. Similarly, in logistics (Amazon with its Amazon Robotics acquired through Kiva Systems) and manufacturing (Google with the acquisition of Boston Dynamics who after a few years are parting ways) data together with AI and data science embraced by Google and other tech companies is making inroads in other traditional well entrenched industries. Google is also not shying away from life science data-driven opportunities which is being witnessed by its Life Sciences Group recently renamed to Verily. Not to mention tremendous opportunities that Google Glass or its future incarnations can potentially bring to R&D.
Based on all these facts it is no surprise that Google has been mentioned much more often than direct competitors or the economy. The company is a wolf waiting to feed on pigs. But it is not a bad wolf; if your company gets eaten, you should blame yourself for not being a visionary about what is possible with data, AI, and data science. The easiest way is to be passive and pretend that Google disrupting your industry is utopia or too far ahead.
And Google is not going to only affect other organizational units in your industry, it could also influence R&D. Consider, for example, that Google has already assembled product images and manuals. With additive manufacturing it could easily produce products by reverse engineering these materials and then sell the products online. From the vast amount of data it possesses, it can use AI to predict the need for products, willingness to pay and plug-ins that are going to sell. The only way to save your career is to overtake Google.