The evolving scope of digital transformation: a conversation with Markus Enderlein

It is fast becoming a truth universally acknowledged that digital transformation has generally been accelerated by the COVID-19 pandemic. However, definitions of digital transformation vary between individuals, organizations and sectors. I caught up with Markus Enderlein from INFOMOTION GmbH to talk more about what he is seeing in and beyond the insurance sector on digital transformation.

Markus, what are you seeing with your clients in both insurance and non-insurance around digital transformation?

I don’t think that there are huge differences between insurance and other industries. We have noticed a few things. First, I think decisions are being made faster. Customers are prioritizing more, and this means that we are seeing more consistency. We have also noticed that many internal processes are now running faster, and projects have been started more quickly. The budgeting, cooperation and focus have all developed more quickly and pragmatically. I think this is particularly true for what we might call the digital workplace, across our whole client base. There are a lot of projects in the field of data management. My feeling is that this is because many companies have noticed a lack of mature data management in the context of digital workplaces or digital channels for customers. There is a huge and pragmatic focus on output, orientation, and short-termism.

What are your views on the skills and capabilities needed to drive change?

The analogy I use is muscles: they are always there but develop more when you train. If I look at digitalization in insurance at the moment, then I think it is fair to say that the muscle is there, but it is not particularly well trained. Let’s stick with that analogy, because I think in the insurance industry, especially in the area of organization, we might say that the biceps and triceps aren’t really working well together. In other words, specialist areas are not working together, and the whole system isn’t running as smoothly as it should. You also have legacy architecture, which is a bit like trying to train with an anvil strapped to your leg! We are seeing a lot of automation, especially in customer-facing processes. However, the back-office processes are still fairly basic, and there is room for improvement in data quality.

What AI use cases are you seeing?

In insurance, there is no question that AI is an accelerator. However, that only applies to a few business units. We might call those lighthouses or torchbearers: they’re up at the front with the torch, showing everyone else the way. It creates a ‘pull’ effect, and others follow. However, AI is very much driven by data, and that’s having some interesting effects. We see insurance companies that start an AI project and then realize that their silos are not helping, so they start to work on their data. This drives strong democratization of data, but indirectly. I therefore think AI is both a direct and an indirect accelerator for digitization, and it is definitely having an effect. There is still a long way to go in the industry, but I have confidence that we will get there.

Are there particular skills issues in insurance?

In almost all industries, including insurance, my perception is that companies are increasingly taking on fresh young blood. They have recognized that they can now manage a good skills mix, and there are some really good people there: young, dynamic, who bring good experience, or up-to-date knowledge. The problem, however, is that we don’t really have data science teams. There is nobody to work with the citizen data scientists. That causes problems with understanding and learning.

BaFin, the German regulatory authority, has just published a paper concerning supervisory principles for the use of algorithms in decision-making processes of financial companies. How does regulation impact the AI initiatives you are seeing?

The concern about regulation is not there at first. I see a lot of excitement among insurers who want to use AI. It is only when individual projects become more concrete that concerns arise, especially from governance staff. And rightly so, because the protection of personal data is a very sensitive issue. But there are also concerns about the infrastructure because the cloud is still a critical topic, especially after the cyber-attacks we have seen in the news in recent months. The question is: Can I control the infrastructure myself? Or do I outsource it and, with it, control over its proper operation? How do I ensure that the infrastructure is not simply "switched off" for critical business processes? In the business departments, however, such considerations do not play a major role. There is certainly still a lack of awareness of the potential pitfalls here. But the good news is that the specialists are aware of this.

Innovation at Scale - The longer view

I want to thank Markus for sharing his insights on a topic that is dominating discussions and decisions. The SAS global study team is exploring accelerated transformation over the summer of 2021. Tune in to study updates here. We’d love to hear about your experience - please leave a comment below and let’s keep asking the right questions.