{"id":13308,"date":"2025-09-01T05:54:46","date_gmt":"2025-09-01T04:54:46","guid":{"rendered":"https:\/\/www.moresophy.com\/?p=13308"},"modified":"2025-11-25T07:18:41","modified_gmt":"2025-11-25T07:18:41","slug":"demographic-change-as-a-driver","status":"publish","type":"post","link":"https:\/\/www.moresophy.com\/en\/blog\/data-driven-business\/demographic-change-as-a-driver","title":{"rendered":"Demographic Change as a Driver"},"content":{"rendered":"\t\t
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Friederike Scholz<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Demographic Change
as a Driver<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Why Automation Is Not an Option, but a Necessity and How AI Can Help<\/h3>

It is rolling slowly but inexorably: the turning point in the labor market. Driven by demographic change, the baby boomer generation is retiring – and with it a hell of a lot of skilled workers. And with them an enormous amount of knowledge and experience.<\/p>

And then you stand there as a medium-sized company and realize that 86 percent of German companies are fighting with you for the same shrinking group of skilled workers. And yes, it is finite.<\/p>

This is not a vision of the future, this is reality in 2025.<\/p>

While companies used to have the luxury of philosophizing about automation, today they are faced with a simple truth: automation has gone from being a choice to a necessity.<\/p>

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The Bare Figures of Demographic Change: Hello Reality!<\/h3>

The statistics speak for themselves: Germany is at the forefront of the skills shortage in Europe. With 86 percent of companies affected<\/a>, we even top the global average of 74 percent. An area in which people are reluctant to take the lead. These figures conceal a dramatic development that many decision-makers have apparently not yet fully grasped.<\/p>

Due to demographic factors, the labor supply will continue to decline significantly in the coming years, as – as mentioned at the beginning – the group of so-called baby boomers is retiring. In June last year, the largest age group among the working-age population in Germany was those aged 55 to under 65 – a full 13.1 million people<\/a>. Let that melt in your mouth: Thirteen million people. Just under a quarter of the working-age population.<\/p>

Of these 13.1 million, around 7.8 million were employed and subject to social security contributions. This means that by 2035 there will probably be around 7 million fewer experienced skilled workers available.<\/p>

It is easy to ignore the fact that the average age of Germans has also risen: to 44.9 years <\/a>in 2024 – in 1994 it was 39.8 years.<\/p>

What does that mean in concrete terms?<\/p>

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From an Optional Extra to a Necessity: The Paradigm Shift<\/h3>

Just 10 years ago, people could afford the luxury of merely philosophizing about automation. Even then, few people faced up to the impending changes. Today, the theoretical vision of the future has become a bitter reality: Jobs can simply no longer be filled.<\/p>

This change is fundamental. Automation is no longer just the magic word for nerds, forward thinkers and entrepreneurs. Automation is a question of survival. Sounds harsh, but it is. Companies that do not automate will simply no longer be competitive – not only because competition is becoming more efficient, but also because they simply can no longer find employees.<\/p>

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The Next Generation Won’t Wait: Changing Customer Expectations<\/h3>

And while some companies are still considering whether they should really automate now, the next generation of customers has already made up their minds: they expect fast, digital solutions. They expect customized services – across all industries – and show their displeasure when companies fail to meet these expectations.<\/p>

The young generation, which is currently entering the market as customers and employees, has a completely different behavior than “the rest of us”. These candidates cannot be won over without automation and these customers cannot be won over without significantly faster response times. And please, don’t come up with fragmented solutions now. You don’t just have to answer simple questions, you have to be able to work on a case-by-case basis. Frustration tolerance has dropped considerably and I think – at least in part – rightly so.<\/p>

Why? Because there is another way.<\/p>

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AI as a Game Changer for Demographic Change: More Than Just Efficiency<\/h3>

Artificial intelligence is not just a tool for increasing efficiency – it is the key to overcoming the demographic challenge. At the same time, this is where the wheat is separated from the chaff. As explained in one of our last blog articles<\/a>, many AI projects fail to make the transition from pilot project to integration into everyday working life. This is partly due to the fact that GenAI cannot do everything (frustration is inevitable because the results cannot be used) and partly because the specific tasks that are fundamental from the outset, including fixed KPIs for monitoring success, are often missing.<\/p>

The solution is not more AI, but more intelligent AI. It’s about understanding the specifics of your data and your tasks in context. Special features that are different for every company, that are different in every situation or technical constellation.<\/p>

The true potential then lies in the intelligent integration of AI solutions into existing business processes. Only then will the benefits become directly tangible and your employees will not react with rejection, but will be happy that all the manual fiddly tasks and searches now run “just like that” in the background.<\/p>

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The Areas with the Greatest Potential<\/h3>

Now I could rattle off all kinds of sectors and areas here, but we can also get to the point more simply:<\/p>

The greatest potential lies where there are interfaces. Interfaces between different systems, between different data pools or between people in different roles. <\/em><\/p>

This is where the most common – and avoidable – problems lie: Abortions, delays, misunderstandings.<\/p>

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A Practical Example<\/h3>

One of your key account customers calls with a problem. You want to help them as quickly and effectively as possible, so you need some background information. So you have to query various systems, search for documents and perhaps even contact colleagues. Each of these interfaces is a potential source of error, each step is a potential waste of time and each unfounded answer potentially leads to frustration for your customer. AI can dramatically reduce these frictional losses.<\/p>

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SMEs: Smart Solutions Instead of Complex Architectures<\/h3>

Not every company needs the AI architecture of a large corporation. While they have the resources for large, fundamental solutions, medium-sized companies can take a smarter approach: a much more needs-oriented approach and a much closer look at their own processes.<\/p>

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The Future Is Now: We Take You Along<\/h3>
  1. Start with the interfaces
    <\/strong>Identify the interfaces in your processes – between systems, departments, people. Which tasks or individual steps are the most time-consuming or cost the most time? This is where the greatest potential for automation lies. <\/span><\/li>
  2. Check whether your data is AI-ready
    <\/strong>A comprehensive data analysis in the form of an <\/span>    audit<\/a> gives you a holistic view of your information and shows which of the defined tasks can be solved with your data.<\/span><\/li>
  3. Start with a pilot project
    <\/strong>Choose a manageable process with high automation potential and clear benefits for the start. Gain initial experience here before transferring what you have learned to other areas. <\/span><\/li>
  4. Define clear success criteria<\/strong>
    Set measurable KPIs from the outset: How much time do you save? By what percentage will error rates be reduced? How does employee or customer satisfaction improve? Clear metrics create acceptance and trust in AI projects.<\/li>
  5. Rely on hybrid intelligence<\/a><\/strong>
    Pure GenAI solutions will fail. The future lies in the intelligent combination of analytical and generative intelligence –     DAPHY\u00ae<\/sup><\/a> makes it possible.<\/li>
  6. Invest in the right skills
    <\/strong>Over 20 years of AI experience cannot be replaced by three months of ChatGPT training. Look for partners who really understand how artificial intelligence solves problems in a reliable, transparent and trustworthy way.<\/li><\/ol>

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    The Added Value Lies in the Data \u2013 and Therefore in Your Hands<\/h3>

    The real rapid developments will no longer take place in AI research – they will happen in practical applications. Now. AI is already smart enough. Unleash its potential and integrate lean, trustworthy AI into your company. Value is then created based on your data. Companies need to tap into this potential now.<\/p>

    Demographic change is the catalyst for a long overdue transformation. The future belongs to the companies that take the first step today.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Friederike Scholz<\/a><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Senior Customer Success Manager<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Friederike Scholz has been helping clients to derive real benefits from new technologies for over 20 years. At MORESOPHY, she supports customers in the targeted planning and successful introduction of AI solutions and acts as an interface to sales and product. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    More articles from Data-Driven Business<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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