AI is no longer a future topic. It's here – in companies, in teams, in the way we work, make decisions, and lead. And yet: most businesses are only scratching the surface of what's possible.
In this article, I share my experiences with the impact of AI on the world of work – honestly, directly, and from the perspective of someone who implements AI in companies every day. You'll learn which changes are already noticeable, where the greatest untapped potential lies, what societal shifts are behind it, what's still hindering progress in Germany – and what concrete steps you can take to move forward instead of just watching.
Table of contents
AI is already here – most people just haven't realized it yet.
AI is already a reality in companies – but while individual employees quietly experiment on their own, teams, structures, and strategies are still dormant. The technology is there. Its use lags far behind.
Everyone for themselves – but no one together
AI tools are being used – but mostly quietly and in isolation. One employee uses ChatGPT for texts, another automates their emails, and a third has never even opened an AI tool. The result: a team at three different levels, yet still acting as if nothing has changed. The real leverage, however, lies not in individual usage, but in what happens when an entire team is on the same page and pulling in the same direction.
Neural networks and data analysis: The silent game changer in the background
What many don't see: The truly powerful AI applications have long been running in the background. Neural networks analyze customer data, recognize patterns in real time, and make predictions that previously required entire teams of analysts. For companies, this means: Those who structure their data cleanly and use the right systems have an informational advantage that translates directly into better decisions. Data analysis is no longer just an IT issue – it's a leadership responsibility.
Marketing of the future: Individual, automated, scalable
Personalized marketing used to be expensive, time-consuming, and only feasible for large budgets. Today, AI makes it possible to automatically tailor content, offers, and communication to specific target groups—or even individual people. Those who consistently use this technology no longer communicate with the masses, but rather reach each recipient where they are. This is no longer a luxury; it's the new standard by which customers measure companies.
What corporations are already doing – and what the rest still need to catch up on
Large corporations have an advantage – but not an insurmountable one. Their strengths include investment, infrastructure, and dedicated teams. The advantages of medium-sized businesses, freelancers, and startups are speed, flexibility, and the ability to make decisions without three layers of hierarchy. AI is one of the rare technologies that can make small units disproportionately powerful – if used consistently. The potential isn't limited to large corporations. It lies precisely where courageous individuals are willing to harness it.
Spotify's co-CEO Gustav Söderström explained on the Q4 earnings call 2025 that the company's most experienced developers have not written a single line of code manually since December – they now only generate and monitor code using AI. PPC Land Internally, this is done via a system called "Honk": Since mid-2024, over 1,500 AI-generated pull requests have already been merged into Spotify's production code – saving 60 to 90 percent of the time compared to manual development. Spotify Engineering This is not a future scenario. This is happening today.
Where large gaps still exist
The biggest obstacle to AI transformation is not the technology – it lies in teams that are not brought along and in knowledge that changes faster than it is shared.
The team isn't on board – AI knowledge remains isolated knowledge.
The problem is rarely a lack of will – it's the structure. Everyone on the team has their own tasks, their own priorities, their own daily routine. AI knowledge is acquired incidentally, but never systematically shared. Those who stumble across something learn by chance. Those who don't ask questions fall behind. Yet this is precisely the real leadership task: not just to lead from the front, but to actively empower the team – with clear formats, regular communication, and the understanding that AI competence doesn't happen automatically.
Knowledge becomes obsolete faster than it is passed on.
What's considered best practice today can be obsolete in three months. AI models are evolving at a pace that even experts struggle to keep up with. This doesn't mean learning is pointless—quite the opposite. It means that learning is becoming an ongoing process. Those who hoard knowledge and don't share it lose twice: the knowledge becomes outdated, and the team stagnates. Open knowledge sharing isn't just a nice-to-have in the AI era—it's essential for survival.
Focus trumps breadth: Those who want everything lose themselves.
The universal AI expert no longer exists – and never will. The field is too vast, too fast-paced, too diverse. What's needed instead are people who delve deeply into relevant topics that truly matter to their role and their company. Whether it's process automation, AI-powered marketing, or data-driven decision-making – depth trumps breadth. Those who try to understand everything end up not understanding anything well enough to apply it effectively.
Speed is not a loss of quality – it is the new standard.
Working on something for a long time was long considered a sign of diligence and quality. This equation no longer holds true. AI makes it possible to deliver in hours what used to take days – without compromising the result, often even with improved quality. Those who don't accept this not only lose time, but also competitiveness. Speed is no longer a shortcut. It's the expectation – from customers, from markets, and from your own organization.
IBM has been developing internal AI systems for development tasks for years – and even there, it's clear: those without a sound data strategy and sufficient cloud infrastructure simply cannot scale AI. Many medium-sized companies fail not because of AI itself, but because they lack the storage capacity, data structure, or internal expertise to use AI effectively. The tool is there – the foundation is missing.
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The societal change behind it
AI is not just changing processes – it is shifting role models, values, and the question of what truly makes a person irreplaceable in the workplace.
From doer to strategist – operational work is shifting
For decades, the most valuable person was the one who produced the most – the fastest, the most diligent, the one who worked the most hours. Those days are over. AI is taking over operational tasks at a pace that is impacting every industry. What remains is the work that machines cannot do: understanding context, providing direction, leading people, and assessing complex situations. The future does not belong to the best executor – but to the best thinker and creator.
A generation is taking the stage that is made for exactly this purpose.
There is a generation that grew up with uncertainty, knows information overload as the norm, and didn't have to learn digital tools – they've internalized them. This generation is now entering the workforce. And they bring something with them that will be a decisive advantage in the AI era: no entrenched thought patterns, no fear of technology, and a willingness to fundamentally rethink things. This isn't a threat to experienced leaders – it's an opportunity, if both sides are willing to learn from each other.
Experience and data as a competitive advantage
What AI cannot replace: real experience, seasoned judgment, and the ability to interpret data correctly. Numbers don't lie—but they also never tell the whole story. Anyone who has worked in an industry for years knows which questions to ask, which data truly matters, and where models reach their limits. Experience and AI are not mutually exclusive—they are the most powerful team a company can assemble.
Learning speed beats resumes
For a long time, an impressive resume was the most valuable asset in the job market. That's undergoing a fundamental change. What counts is no longer just what someone has learned – but how quickly they can learn new things. Someone who masters a new AI tool in three months, understands relevant processes, and applies their knowledge directly is more valuable than someone with five years of experience stuck in the past. Attitude, curiosity, and a willingness to learn will become the most important qualifications of the next decade.
The generation currently accused of lacking patience and work ethic is simultaneously the one that intuitively understands AI as a tool – not a threat. Those who aren't stuck working hours on operational tasks have mental space for ideas, strategy, and disruption. What's considered a weakness could be the decisive advantage of the next decade. The 9-to-5 model could also change. In the future, AI will be prepared in the evening, left to work overnight, and the results checked in the morning. This way, our day will continue.
What's slowing down change – the real problems
Germany has potential, talent and capital – but structural brakes that slow down the AI transformation, while other countries have long been in the fast lane.
Germany and digital infrastructure – a structural problem
Anyone in Germany who talks about AI has to confront an uncomfortable truth: the digital infrastructure is often inadequate. Slow networks, patchy digitalization in government agencies and businesses, and a lack of standards for data exchange – these aren't minor issues, but fundamental obstacles. AI needs data, connectivity, and digital infrastructure like an engine needs fuel. Those who cut corners at the foundation shouldn't be surprised when the top end fails. This applies whether it's within their own company, in public institutions and universities, or at home.
No time for ideas: Why internal professional development falls by the wayside
Day-to-day business consumes everything. Meetings, deadlines, operational tasks – at the end of the day, there's no energy left for what really matters: learning, experimenting, exchanging ideas. AI training is rarely prioritized internally because it doesn't deliver an immediate ROI that can be demonstrated in the next report. Yet, doing nothing is precisely the most expensive decision a company can make. If you don't give your team time to generate ideas, don't be surprised when none come.
Universities are lagging behind – AI is only a fringe topic
Most students at German universities today still learn according to structures designed before the AI revolution. AI is relegated to the sidelines, offered as an elective, or taught in outdated formats that bear little resemblance to the realities of the industry. The consequence: graduates emerge with a solid foundation – but without the practical AI knowledge that the market already demands. The gap between academic training and professional reality has rarely been as wide as it is now. All the more refreshing, then, to find a professorship that embraces AI and meaningfully adapts the learning journey.
Guidelines instead of progress: When compliance blocks innovation
In many companies, access to AI tools isn't a question of wanting them – it's a question of being allowed to. Data protection policies, IT security regulations, and internal compliance rules often block precisely the tools that would offer the greatest added value. What is well-intentioned becomes a brake on innovation. Of course, AI use needs clear guidelines – but those who send every advancement through ten levels of approval not only waste time, but also fall behind. Regulation and innovation don't have to be mutually exclusive – but they must finally engage in a genuine dialogue.
Anyone traveling on a business trip by train without stable Wi-Fi or a reliable mobile connection can neither collaborate nor effectively use AI tools. This isn't an isolated incident – it's a daily reality across Germany. How can digital transformation succeed if the infrastructure fails even for the simplest use case?
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What is being asked now
Those who wait for the perfect moment wait too long – AI transformation begins with a decision, not a perfect plan. At the beginning, nobody knows how. Those who are courageous and can persevere through uncertainty will simplify their own work.
Take the lead yourself and get the right people on board
Change doesn't wait for consensus. Anyone who truly wants to advance AI in their company must start themselves – visibly, consistently, and with conviction. This doesn't mean doing everything alone. It means involving the right people early on: allies who share the vision, decision-makers who release resources, and sparring partners who provide honest feedback. AI transformation isn't a solo project. But it does need someone to take the first step – and not wait until everyone is ready.
Trusted contacts instead of anonymous information overload
The internet is flooded with AI content – tutorials, opinions, hype, warnings, promises. Anyone trying to navigate this deluge alone quickly loses track and even faster loses motivation. What really helps: a reliable network of people you trust. Mentors, coaches, peers – people who not only share knowledge but also assess what's truly relevant to your situation. Trust trumps quantity. A good conversation with the right person is worth more than a hundred articles consumed.
Introduce knowledge regularly – change requires repetition
One-off workshops are ineffective. An inspiring presentation might have an impact for three days – then everyday life catches up with everyone again. Real change comes through repetition: regular formats where AI knowledge is shared, discussed, and directly applied. Whether weekly short updates, monthly workshops, or internal learning groups – continuity is crucial. Knowledge that has been heard once changes nothing. Knowledge that becomes habitual transforms an organization.
Big vision, strong nerves – AI transformation is not a sprint.
AI transformation is not linear. There are breakthroughs, setbacks, moments of overwhelm, and phases where nothing works as planned. Those who can't handle this give up too soon. What's needed is a clear vision—broad enough to motivate, concrete enough to navigate—and the resilience to persevere even when trial and error becomes the norm. The greatest progress doesn't happen despite setbacks; it happens because of the willingness to keep going.
You don't need a grand strategy to get started. All you need is a single, concrete use case from your own daily work – try something out, document the experience, and discuss it with someone. Anyone who has done this once will understand more than after reading ten articles. AI competence doesn't come from consumption – but from application, failure, and perseverance.
To the author:
Finn Poser He is an AI consultant at the Ihr Coaching Institute in Frankfurt and belongs to a generation that understands artificial intelligence as a design tool – not as a threat. As a business administration student and project manager at Siemens, he combines academic foundations with real-world business experience.
His focus is on concrete AI integration: tailor-made AI systems, practical workshops and use cases with immediate effect – from large language models to automation solutions and AI-supported decision-making processes.
He combines technological know-how with coaching expertise: How must leaders mentally prepare themselves to move forward in an AI-driven work environment – not just keep up? Finn Poser works with entrepreneurs, leaders, and startups precisely at this intersection of self-leadership and AI.
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