How will Artificial Intelligence impact the German economy and labor market? Björn Münstermann, Senior Partner at McKinsey, discusses how Germany can position itself in times of technological transformation - and why it is time to move from hindsight to foresight.
Interviewed by Klara Marie Schroeder
AI can be a major lever to boost public sector productivity. Across the EU, we estimate an efficiency potential of around 100 billion euros by 2030.
The urgency is clear. Many public agencies will lose about 30 percent of their workforce by the end of the decade as large cohorts retire. This creates both pressure and opportunity to automate routine work and redesign services around digital tools.
The biggest potential lies in case handling, which makes up the bulk of administrative work. Other promising areas include forecasting and citizen interaction. In our client projects, we typically see productivity gains of around 30 percent, and in some processes even up to 70 percent when AI and generative AI are fully deployed.
A practical example comes from a government agency in an EU member state, recognized as one of the most advanced public institutions in Europe in the field of AI. In a high-volume process handling citizen benefit requests, they have achieved an 80 to 85 percent productivity improvement by using generative AI to interpret legal frameworks and draft compliant responses. It is not a pilot, but a system already in daily use.
AI can fundamentally change how governments make policy decisions by shifting from hindsight to foresight. Instead of analyzing the past, policymakers can use AI to model different scenarios and anticipate what is likely to happen next.
The core idea is to combine real-time data with simulations to test policy options before they are implemented. During the energy crisis following Russia’s invasion of Ukraine, for example, governments had to make rapid decisions on energy consumption and supply. AI-based forecasting models could have helped anticipate demand patterns and test alternative sourcing strategies in real time.
Similar approaches can support healthcare planning or labor market policy, particularly when it comes to demographic shifts and workforce forecasting. Across Europe, however, fewer than 15 percent of ministries currently integrate data across departments, which limits their ability to apply such tools effectively.
Progress is being made. Some governments have set up AI-driven policy labs that use simulations to evaluate the impact of new measures before rollout. Still, the potential is far greater. Strengthening data-sharing frameworks—such as Germany’s planned modernization of statistics law—will be essential. Over time, AI can help make policymaking more forward-looking, evidence-based, and responsive to social and economic change.
AI will first transform sectors where data is already a core asset. Financial services are leading the way, as most products and processes are digital by design. Manufacturing and other industrial sectors will follow quickly, driven by applications in predictive maintenance, quality control, and supply chain optimization.
Across the economy, the potential is enormous. Our research shows that up to 65 percent of working hours could be automated to some degree through AI and related technologies. This coincides with a looming workforce decline of up to 30 percent by 2030. Automation is therefore not optional but essential to sustain growth and competitiveness.
There is truth in that statement. As AI reduces the cost of knowledge creation to almost zero, traditional knowledge work will become less of a differentiator. Competitive advantage will increasingly come from applying that knowledge in the physical world.
That means skilled trades such as electricians,plumbers, and carpenters will remain in high demand. They are essential for maintaining and expanding the physical infrastructure that enables digital and AI-driven systems.
In that sense, these professions will not only endure but evolve. They form the backbone of the digital economy, translating technological progress into real-world impact.
The most important shift is from protection to preparation. Instead of focusing on how to shield jobs,governments and businesses should focus on equipping people with the skills they need for the age of AI. Lifelong learning, upskilling, and continuous capability building will be the decisive factors.
No single actor can achieve this alone. Public-private partnerships will be essential to scale training and reskilling programs fast enough. Governments can set frameworks and incentives,while businesses provide the platforms, tools, and content for practical training.
One example comes from a large European telco provider, which trained nearly 10,000 employees in just a few weeks through targeted AI-focused programs. When companies and policymakers align like this, they can cushion short-term disruptions and help workers adapt to long-term change.
At present, many still default to a protective mindset. The real opportunity lies in creating a culture that embraces learning and readiness for change.
That is a good question – and we do not have a clear answer yet. But I think bridging the transition requires a dual strategy: accelerating automation while investing heavily in skills. The talent gap is so large and immediate that organizations cannot rely on reskilling alone. Deploying AI at scale will be essential to keep workloads manageable and maintain service quality as the workforce shrinks.
At the same time, reskilling must remain a priority. Roughly one-third of today’s workforce could transition into new roles with targeted training. Another third will retire over the next decade, leaving a smaller labor pool. The rest will depend on how effectively we combine immigration, lifelong learning, and later retirement as complementary levers.
This is not a single-policy issue but a societal challenge. The countries that manage to align automation, education, and labor market reform will be best positioned to sustain growth through the AI transition.
However, the scarcity of talent is so immense, so urgent, and such a massive problem that there is really no choice but to automate and deploy AI at scale.
AI will affect advanced and emerging economies in very different ways. In developed markets, the first wave will come through individual productivity gains. As adoption expands across entire functions and processes, organizations will be able to redesign how work is done and offset the impact of aging populations.
For emerging economies, AI offers the potential to leap frog development stages that took advanced economies decades to complete.With the right access to data, infrastructure, and technology, they could move directly to more efficient and digital operating models—accelerating growth and even closing competitiveness gaps.
Ultimately, access to digital infrastructure and data will matter more than geography. Countries that build these foundations early will be best positioned to capture the next wave of AI-driven growth. In our client projects, we typically see productivity gains of around 30 percent, and in some processes even up to 70percent when AI and generative AI are fully deployed.
Björn Münstermann is a Senior Partner at McKinsey and leads McKinsey Technology in Germany. He also co-leads the Public Sector Practice in Germany and Austria and is a member of the McKinsey Digital leadership team. Björn works with leading organizations in both the private and public sectors to drive large-scale technology and AI transformations. He advises clients on how to modernize core systems, capture the full potential of digital and data, and strengthen technological competitiveness and resilience.
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