How to stay competitive in the age of AI

Posted on
November 10, 2019

Below you can find an abridged version of the interview with the ATZ (automotive technology magazine) ATZ:

Q: In the future, automobile manufacturers will have to collect and evaluate an enormous amount of data in order to optimize their processes and set up new business models. Does data have the potential to protect the European automotive industry from a recession?

Big data and artificial intelligence techniques will not necessarily save the car industry from the next recession, whenever it occurs.

Rather, it will be possible to secure profitability and competitiveness in the medium and long term. I would by no means limit this assessment to the automotive industry, but rather apply it to all sectors to varying degrees.

Q: Is that your personal assessment?

No. This assessment is shared without exception by all well-known management consultancies and economic research institutes and is also expressed in national AI strategies, whether they were developed in the People's Republic of China or Canada.

Q: So if you close your mind to the Big Data trend, will you risk your competitiveness within a few years?

First of all, it helps to see Big Data and AI not as a trend, but as the new status quo that can be taken for granted just as much as lightweight construction or electric drives, for example. Nobody will question the importance of these two technologies - and the same applies to big data and artificial intelligence.

Q: What do you recommend to these companies? Should they collect as much data as possible?

You should start to develop and implement strategies for data-driven business models well in advance. Companies need to realize that this is a result of the increasing digitalization of the world of work, where fundamental advances in IT are the drivers.

Q: How do I introduce artificial intelligence in my company?

Quite simply: Initially you have to look at the underlying problem - be it a business process or a product and/or service improvement.

  • First, think about what you want to improve. Maybe you want to generate more leads for sales. Or improve quality control. Or increase sales in your online shop. Or you need better analyses and forecasts. In this phase it makes sense to be inspired by existing projects.
  • Next, think about where the necessary data comes from. You need as much historical data as possible in order to train an AI system and subsequently operate it. For example from your online shop, from your customer database, …
  • Which AI methodology can be used to achieve the goal is a question that only arises at the end. Research: Can you achieve your goal with AI? What solutions already exist? What is the current state of the art? Many things are easy to find on the net.

Always keep in mind: Artificial Intelligence is a technology and not a panacea. Its use is not an end in itself but must be targeted.

Q: At a time of upheaval and scepticism, your view of the opportunities for the European automotive industry sounds quite positive. What would you give to the doubters?

In the DACH region we unfortunately have a very underdeveloped self-confidence when it comes to digital topics. That, for example, with Sepp Hochreiter, the inventor of the AI architecture behind Siri and Alexa, and Sebastian Thrun, the pioneer of self-propelled cars, two of the most important players in modern AI are Germans, is only known to a tiny proportion of the population in this country.

The fact that these are by no means pleasant statistical outliers can also be proven by scientific publications, where Europe is still ahead. To a certain extent, the starting situation when it comes to the future of mobility is exactly the other way round than in the B2C sector: Europe, and Germany in particular, has by far the best base of companies and supply chains, which represent the ideal breeding ground for big data and AI.

Full interview at Springer Link (paywall): Link

Photos & Text by Clemens Wasner, EnliteAI.

Posted on
November 10, 2019
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Interviews
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