analytical. quantitative. tech.

In addition to regulatory changes, insurance companies and asset managers are particularly confronted with digital and analytical innovations such as artificial intelligence. This requires considerable investments in technological infrastructure, process control, and automation to generate growth in the markets and meet regulatory requirements.

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Digitisation poses major challenges for the chemicals, energy, real estate and manufacturing industries. At the same time however, it reveals opportunities for the more efficient use of human and natural resources in traditional business sectors and the possibility to develop new business sectors and models.

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The digital transformation has long since reached our healthcare market and will revolutionise the processes and value chains of the players. Machine learning, data-based computing methods and intelligent algorithms already have access to large amounts of data and support both medical care and the development and production of innovative medical products and therapeutic procedures in the pharmaceutical industry and medical technology. Traditional sector boundaries are being crossed, the patient is being focused on and One Health is becoming tangible.

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In the future, innovative algorithms and modern technologies will be steering our mobility, optimising transport processes in logistics and enabling new business models. These indications of tomorrow’s world are already clearly visible – with concepts for autonomous vehicles, intelligent charging infrastructure and digital platforms. The implementation starts now.

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Rapid technological change has greatly improved the processing and analyzing of large amounts of data. In turn, this opens up new opportunities for intelligent processes and products. Even more than in the past, successful measures require that use cases and technologies are brought together in a precise fit; be it in the application of artificial intelligence or the use of blockchain technology. At the same time, security issues must be considered from the get-go, not only concerning the technology itself, but also for example when training AI models.

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Public sector organizations are facing an increasing number of challenges, from driving data and digital transformation to improving processes and strengthening sustainability efforts. They are expected to deliver more while reducing costs. How can they meet – and exceed – these expectations?

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