The Center for Data Innovation spoke with Dalith Steiger, managing partner and co-founder of SwissCognitive, an artificial intelligence knowledge-sharing hub based in Switzerland. Steiger discussed how cognitive platforms can build bridges between businesses and how this can foster the development and use of AI within the economy.
Eline Chivot: What is the vision and mission you set out to achieve with SwissCognitive?
Steiger: Having been in IT for years, I had the opportunity to explore this landscape and learn about the business objectives of big and mid-sized companies. This was during the golden age of the financial industry, where I started my career as a software developer. Things have changed since then. I realized that the Swiss ecosystem was not going to survive based on that sole level of luxury, by relying on potatoes, chocolates and watches—and on the financial industry. Our businesses are at risk of becoming redundant. We must find ways to ensure the sustainability of our system.
Being half-Israeli, half-Swiss, and knowing Israel as a startup nation, I began to wonder why Switzerland wasn’t. This country has all the assets required for this. We have money and venture capital. Next to offering a high quality of life, Switzerland has a highly-skilled workforce, the best universities, and is home to powerful research centers—for example from IBM, Oracle, Disney. Why is Google investing so much to send talents to Switzerland? Because here, employees are well-paid and enjoy high living standards and security in an open and safe society. Brain power is something that businesses are still willing to pay for, and will be critical to achieve sustainability in a digital economy.
My intrinsic wish was to energize our economy. Switzerland needs a wake-up call. Companies need to be told that we have all assets to be first movers and be in a pole position. Being the mother of two teenage girls, I feel that we owe this to the next generation. I want them to have interesting jobs and lead interesting lives.
Chivot: Could you give me a concrete example of what businesses can do to remain sustainable in the digital economy?
Steiger: First, smart technologies developed by startups and other companies must be brought into business models. Those technologies can be used in all those industries where we already lead. For instance, Switzerland has a strong pharmaceutical industry.
Second, another small step that we can take to best seize our brain power and be competitive on a global scale is to integrate the profile of the “digital employee” into our businesses. As companies are becoming digital enterprises, employees need to be empowered and enabled to become “digital workers” through digital capabilities and skills.
Outsourcing strategies are a good example of why this is important. Companies that have been hiring teams in Eastern Europe or Asia to support their call centers are now struggling with the result that I would qualify as “same mess for less:” it’s cheaper, but it’s not better. Meanwhile, these companies haven’t been innovating, knowledge and investment have been driven away, and we have been losing some of our brain assets to other countries. We should rebuild those supporting call centers at home, through smart technologies and by setting up teams of digital employees for the first level of support, thereby moving away from traditional IT business models. Systems will be faster, more productive, more sustainable, and less expensive.
Some jobs have been lost to other countries, but back home we will still need highly skilled teams to develop, support and maintain the applications used elsewhere. These are the new jobs that should be created in Switzerland and Europe. Obviously, the skills required will be different, but this is giving us a new chance. As we’re still an attractive country in which it is safe to do business, U.S. companies would start outsourcing the first level of support to call centers to Switzerland. Those companies know that we can be trusted with their data, not just their money.
Chivot: How can cognitive platforms such as SwissCognitive support change in this direction?
Steiger: The cognitive ecosystem in general is still in its emerging phase but is evolving rapidly. Our hub serves as a non-biased facilitator between industries, organizations, governments. We provide a neutral environment for knowledge and experience sharing, for strategy development, and discussing opportunities and challenges related to smart technology integration.
Someone needs to show companies how change can happen and how to do this in practice, because there is much reluctance from C-level executives. This is why we’re not there yet when it comes to the use of AI and new technologies.
The current mindset is about getting the yearly bonus and avoiding risk and failure. Sharing and opening up with others is difficult for businesses, because this is not compatible with what they think their interactions should be about—competition. At SwissCognitive, we are building bridges between them: it’s about sharing for success and being transparent, fostering an “experience culture.”
Another reason why it is difficult to legitimize the use of smart technologies in businesses and support change is that although technologies such as AI are everywhere in our daily lives and have already shown their huge potential, those benefits also need to be explained. Many business leaders worry about the risks associated with it and don’t take taking up new technologies to transform their teams and businesses. Fear is a bad business advisor. Our experience has shown how the exchange of knowledge and experiences across industries is key to alleviating fear and prejudices, building awareness, and fostering the development and use AI within businesses. It is important to include everyone in the conversation and explore the possibilities of AI this way. We need to start looking at technologies and AI as augmented competence rather than something that would replace humans.
We are bringing the topic of “practical AI” to many boardrooms and we’re working with cross-industry representatives, which leads to very useful outcomes. For example, we once brought together for a workshop an insurance company, a bank, a hospital, a building security company and a railway company—to talk about huge incidents. They saw how they could use each other’s solutions and apply them to their own sector. This can speed up the adoption of best practices and processes within each sector and, in turn, these companies add value to their own.
Chivot: What does your network of companies say about the obstacles to the development and use of AI in their business?
Steiger: Companies struggle with low data quality, which leads to low quality results and causes many innovation bottlenecks.
We once organized a workshop with about 22 attendees. After a while, one of them admitted that in his company, although a global, powerful business, they did not have access to data structured in a way that would allow it to experiment with proofs of concept. To which other participants responded that their companies struggled with that issue as well. This led to an exchange of views and best practices on how to improve data structure.
It takes courage to acknowledge and share internal corporate problems, and we often notice that some easily imagine others are ahead of them in the use of smart technologies and AI.
In addition, we often hear that obstacles are not due to the inadequacy of technologies available, but to a pure and simple lack of communication. Whether or not technologies and processes work efficiently depends on that factor. And this is very much a human factor. A company once told me how one of their teams had to develop a process using data from the accounting department. Due to a limited server space, this team was provided with the data in a low-resolution format, which they were not able to use. The transfer had to be done all over again—just because the specifics had not been communicated clearly.
Data needs to be available in good quality and shared in appropriate formats. This requires communicating about what the technology used, such as an algorithm, can and cannot do in practice. That can be revealed through conversations within and across industries, so all can be on the same page—and this is what SwissCognitive facilitates. Companies that do not share experience and knowledge are missing many opportunities to innovate. How can a startup assess the potential of a business idea, and develop the right solutions and algorithms if they don’t have access to data?
Insufficient funding is seen as an obstacle as well. We’re trying to change the mind-sets of companies that are not moving forward because of a lack of investment. They need to realize that investing in smart technologies and AI and transforming their businesses accordingly will not lead to immediate returns for shareholders. Change takes time to materialize in profits. We cannot wait for governments to allocate money to this. Besides, Europe’s investment efforts will not be enough.
One more brake to innovation that companies are facing is too much regulation. Of course, it is important to have some set rules in this space. But it’s a very difficult discussion. Sharing data to develop algorithms can accelerate breakthroughs within the healthcare industry, for example, thereby strongly benefiting patients. In my view, by looking at smart technologies and data through our “ancient economic lens”, my generation has been over-regulating, thereby restricting the speed of innovation in some way. We have made those decisions for the next generation, yet I see how the youth experiences, lives with and shares data: they have a totally different view of data, a totally different need and definition of what is private and what isn’t.
Chivot: As part of contributing to solving challenges, SwissCognitive includes initiatives for jobs in AI. Why is this particularly important? Can you tell us about the latest campaign you’ve launched for this?
Steiger: Part of our goal when involving all stakeholders to discuss the use of AI is to alleviate fears, emphasizing opportunities, and acknowledging the negative externalities but through a healthy, constructive discussion. A strong, recurring theme within the AI conversation is about the loss of jobs as a result of the growing use of automated processes. It is one of the biggest concerns in the world of AI. But we believe in the need to move forward by seizing the opportunities that this can also lead to. AI is not just about jobs being lost. It is mostly tasks that will be replaced. Some jobs will be gone, sure, but we’ve been through this before in times of industrial revolutions. When addressing the next generation’s professional aspirations, we need to be open and honest about the jobs that we know will be redundant and likely disappear.
With this in mind, we consider it more constructive to find solutions and build a conversation that isn’t about what we’re losing and fearing.
To convey this message concretely, we extracted data from our LinkedIn profiles, and got over a thousand AI-related jobs—real, actual jobs. These did not come out of our imagination, they are not for robots, and they already exist! Weeks in a row, every day, we published a new job description on our website and social media. In fact, what we did was using data to show that data itself isn’t a threat, and to show what happens if we use more of it. And this was only an extract from our Linkedin, so there are certainly many more opportunities out there.