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Artificial Intelligence (AI) is discussed everywhere today, often with very different levels of understanding. As a result, AI is sometimes portrayed as a universal solution and sometimes as a major threat.
In reality, artificial intelligence already has — and will continue to have — a significant impact on society, including employment, the economy, education, and service delivery. Based on my professional experience in Data, Analytics & AI and my studies at the MIT Sloan School of Management, I have observed that AI progress is highly uneven across domains.
Over the last 70 years, we have seen multiple AI hypes and as many so called AI winters. It started in 1956 with the Dartmouth Summer Research Project on Artificial Intelligence. It attracted a lot of funding to enable automatic translation. But it failed and afters DARPA’s cutbacks to academic AI research in general in 1974, we had the first AI winter. In 1980, the first expert system was developed at Carnegie Mellon for DEC. It kick-started many investments, but in 1987 the LISP machine market collapsed, and most expert systems were abandoned soon after. Next AI winter. In 1996, IBM DeepBlue was the first computer winning against the chess world champion Garri Kasparow. IBM and others invested again. In 2011, IBM won the quiz Jeopardy in a stunning fashion. But both successes are based on huge computing power and no intelligence. IBM Watson never achieved the promised results and was downgraded accordingly. The 2022 release of ChatGPT started the latest hype.
In 2020/2021, I attend the MIT Sloan School of Management course on “Artifical Intelligence: Implications for Business Strategy”. In my role, I had the opportunity to participate in many discussions around AI and see applications of it left and right. Progress is not equally distributed.
Obviously, startup’s raise venture capital with the boldest imagination possible. It is the same situation as we have seen in the internet bubble in the late 90ies. Most startup’s will go bust, a lot of venture capital will be lost, but I expect a few winners. Google is a winner of the internet bubble. I do not know the winner of the AI bubble. If I would, I would not require a job for salary.
I do not see anything that lets me believe in general artificial intelligence. Therefore, the impact depends very much on if AI can support specific domains.
In articles written five years ago, authors expected driver jobs to become redundant. It is far away. But there are less jobs in translation offices already today. I expect a shift in the job market and more jobs at the end. It was the same with all earlier shifts. Today, there are less jobs available as horse coachman, telegraph operator, typesetter, home weaver, elevator operator; but many more in new domains. In fact, the advent of AI does not destroy jobs but rather addresses a lack of service coverage: call centers overwhelmed, unable to handle huge numbers of calls; 24/7 service coverage which is too expensive to fund; problems arriving at service desks and taking far too long to resolve etc.
Artificial Intelligence has fundamentally changed how we create and interact with content. Generative models can produce high-quality text, images, and even videos in seconds, reducing the cost and time of content production dramatically. At the same time, personalization algorithms ensure that users receive tailored information, which increases engagement but also raises questions about authenticity and bias. In short, content is no longer scarce—its abundance requires new strategies for curation, trust, and ethical use. (This paragraph is written with GenAI, whereas I wrote the rest of the article.)
In conclusion, we are aware that Artificial intelligence is going to impact our lives with far-reaching change. Lets use it to our benefit and learn to master AI rather than living in fear of it. Researchers, IT specialists and mathematicians etc. have an interest in creating something complementary, which would allow humanity to evolve.
Artificial intelligence creates business value when it is applied to specific, well-defined business problems and integrated into existing processes rather than treated as a standalone technology. Today, AI delivers the greatest value by improving operational efficiency, enhancing service quality, and supporting better decision-making in areas such as customer interaction, document handling, forecasting, and quality assurance. Successful organizations start with use cases where data is available, outcomes are measurable, and human oversight remains central. Experience shows that sustainable value emerges when AI initiatives are embedded in a broader data and business strategy, supported by strong governance, reliable data foundations, and clear ownership across business and IT.


Head of Business Line Data, Analytics & AI
Meet Markus Grob, our Data, Analytics & AI leader specializing in Cloud Data Analytics Platforms, AI Applications & DataAI Strategy/Governance. Contact Markus to discuss how he can help propel your DataAI initiatives forward.

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