AI Adoption

Nearly 50% of businesses in Germany believe AI is not relevant for them. This is one result of a recent study by PwC (german only). They surveyed managers in 500 businesses, large and small. If you look into the details you will see that especially the Small and Midsized (SME) companies with less than 500 employees and/or less than 1 Mrd € in revenue are skeptical.

And there is another worrying fact. The danger of losing market share to competitors with (more effective) use of AI is described by only 12% of respondents as very large, 19% as large and 36% as medium-sized. It seems that many companies have not yet fully reflected the opportunities offered by AI – including those of their competitors.

This is especially worrying if you look at another study done by BcG. They have surveyed 2700 managers in seven countries (Austria, China, Germany, France, Japan, Switzerland, and the United States) looking at the adoption rate of AI. For Germany they see 49% of the companies as active AI players. Very similar to the PwC results. But looking at China they see 85% of the companies as active players. And this is true across all industries.

I was involved in the development of the German AI strategy last year. One of the key focus areas of the strategy is the transfer of AI knowledge to SMEs and the development of an AI startup ecosystem. The results from the studies show how importent it is to execute on this now. appliedai is a great example how this can be done. The Cyber Valley initiative will also focus more on this important aspect. But we have to do more, fast. And not only in Germany. The BCG study also shows the same challenges for France, Austria , Switzerland.

One Key area addressed in both studies is the responsibility of the senior management to drive AI adoption. It is a critical success factor.

In the PwC study 70 % of the decision-makers see responsibility for AI above all with IT , only 57 % with senior management. This might explain why nearly half of the businesses don’t think AI is relevant if it is not on top of mind for the senior management.

We have to find ways to adress this. There might be different reasons why AI is not on the list of top priorities for C- Level. Access to people who can explain AI (in the business context) and discuss the relevance it will have for businesses (in a non-technical way) might be one. Other companies might have full order books and see no reason to change. But their competitors might see this differently, and it will be very hard to catch up once you are behind. It is a very different game this time.

Which brings us to the other insight I find very interesting. The BCG study shows a correlation between the length of the innovation cycles in companies and how active they are with AI adoption. The shorter the innovation cycle the more active (and successful) they are with AI. And these cycles are much shorter in China compared to Germany or Europe.

The World Economic Forum’s 2018 Global Competitiveness Report recently placed Germany at the top of the list in “innovation capability,” ahead of the United States, China, and many others, based on its extensive academic research network and large volume of patents. Led in no small measure by tech-friendly sectors such as the automobile industry, German companies have historically learned to prosper by following a traditional approach to R&D—one that is deliberately incremental, thoroughly planned and research-driven, and frowns on trial and error. It is almost in these companies’ DNA to take the long view and work diligently behind the scenes to perfect solutions to hard problems before launching them. But when it comes to technologies like AI, past innovation performance is no guarantee of future results. In fact, quite the opposite is true. (Source BcG)

So what does this mean? AI is fundamentally disruptive. The proven approaches for innovation are not working anymore. Becoming an active AI player requires a different management culture and different innovation approach: to pilot early, to test, to learn—and to fail along the way. Breaking up the silos within companies is another important success factor for active AI companies. AI needs multidisciplinary teams as the BcG study clearly points put. And again China is far ahead of Europe and the US in this aspect as well.

What is the conclusion?
In Germany (and Europe) we need more education and discussion on AI especially with senior management. These discussion should not be limited to AI as a new technology. AI is the enabler for completely new business models, it will change most of the business processes we know today. This is a strategic discussion about the future of every business – whatever industry you are in.

On the other hand we just have to start. Every business can do this. Simply begin with first (small) pilots. Look for startups and see what can be done today. Have partners come in and find the low hanging fruits (aka AI use cases) to get started – even if you don’t have the know-how internally. It is about speed and there is no time to wait until all questions have been answered. It appears to be a far more successful strategy for senior managers is to carefully choose a small number of initial use cases, build a modest, agile interdisciplinary team to run them, and just get started.
Successful pilot use cases can act as lighthouses, helping to build a cultural acceptance of AI that is fundamental to broader AI implementation.

In another article I will discuss in more detail how to approach an AI strategy as well as AI adoption in businesses.