Remember welding robots? When they arrived on factory floors in the 1980s and 90s, the change was impossible to miss: large machines, cordoned-off areas, new safety regulations. Artificial Intelligence is different. AI has no physical presence – the transformation happens on screens, in workflows, in the way people think. That’s precisely what makes it so hard to grasp. And that’s why a gap is opening up in the SME sector: 42 percent of companies expect AI to soon become central to their business model – yet many are still struggling with the basics.
AI Transformation: Why the Change Remains Invisible
AI has no physical presence. It doesn’t require floor space, manual safety guards, or visibly installed equipment. An employee who uses an LLM for a summary in the morning looks exactly the same as one who doesn’t. The transformation takes place on screens, in workflows, in the way people do their work and how they think while doing it.
This invisibility makes it difficult to categorise. We still think in hardware terms: which smartphone has the best camera, even though the differences from last year’s model are barely perceptible. The truly relevant question – which software and AI applications are we actually using on our smartphones – is one we ask far less often. Yet software has become far more important than hardware. Paradoxical, isn’t it?
A Recurring Pattern – The AI Disruption
The history of technological upheaval reveals a recurring pattern. When Gutenberg invented the printing press, he made an entire profession obsolete within a few decades. The copyists in scriptoria – whether in monasteries or in the commercial workshops that served universities and wealthy patrons – had spent years perfecting their craft. A single printing press suddenly produced 3,600 pages per day. The copyists who refused to adapt disappeared. Those who learned to operate the new machines became printers, typesetters, or publishers.
Another consequence of the printing press, whose technology later extended to other print products: literacy rates rose.
The same pattern with welding robots: protests, fears, then adaptation. Manual welders became robot operators, programmers, quality controllers. Manufacturing became more efficient; jobs changed, but they didn’t disappear entirely.
Disruption and AI Adoption in 2026
The Algae on the Lake
There is one characteristic that sets AI apart from previous technological leaps: the speed at which the change unfolds. The printing press – technologically unremarkable by today’s standards – took decades to spread across Europe. The Industrial Revolution – from the steam engine through electrification to Ford’s assembly line in 1913 – spanned a century and a half and brought profound social upheaval.
With AI, the timeframe is considerably shorter.
Imagine you pass a lake on your way to work in the morning. The sun glitters idyllically on the water. Perhaps on second glance you notice a few green patches near the shore. Nothing remarkable. In the evening, on your way home, the entire lake is covered with algae. No more glittering sunset. Exponential growth works like this: seemingly nothing for a long time, then suddenly everything – and all at once.
With AI, we may still be at the “few green patches” stage. The technology is advancing rapidly, but for many companies it feels as though there’s still plenty of time to consider AI deployment. That can change faster than most expect – and this applies equally to use cases as well as to the quality and capabilities of AI itself.
AI Adoption in SMEs: Where Do You Stand?
Three typical phases – and how to overcome the most common barriers
Other priorities, limited resources, uncertainty about the right approach.
Risk: The gap to experimenting companies keeps growing.
Employees using ChatGPT & Co., mostly for text generation (67%) and translations (44%).
Challenge: How do experiments become systematic usage?
AI not seen as an IT topic, but as a change in how work gets done.
Advantage: Ready for the future – 42% expect AI to be central in 5 years.
For many SMEs, it still feels like there’s plenty of time to think about AI adoption. That can change faster than most expect – and this applies equally to the use cases as well as to the quality and capabilities of AI itself.
AI Adoption in SMEs: A Look at the Facts
New studies on AI adoption are published almost daily. We found the DigiMit² project particularly interesting, which surveyed companies in northern Rhineland-Palatinate. The results are likely representative of many regions across Germany.
Of the 176 companies surveyed, only 8.5 percent currently view AI as central to their business model. In five years, however, 42 percent expect it to play a central role – a remarkable gap between present and expectation.
Regarding current usage: more than a quarter already deploy AI tools, and roughly half are testing applications in pilot projects. However, use cases have so far concentrated on low-threshold applications such as text generation (67 percent) and translation (44 percent). The more advanced possibilities – AI as a sparring partner for complex decisions, as an analytical tool for business data, as support in product development – remain largely untapped. The full potential is not yet being realised.
An interesting observation: it’s not always the large companies that lead on AI. Small, agile firms can leverage AI as a genuine multiplier. A small team with systematic AI support can already handle tasks that previously required twenty or more employees. The resource constraints that traditionally disadvantaged smaller companies against corporations are being partially dismantled by AI.
AI Integration: Different Paths Through Transformation
In practice, we observe different approaches to the topic of AI. The transitions are fluid, and most companies move between various positions.
Some companies adopt a wait-and-see stance. They observe developments, register the headlines, but decide not to act yet. The reasons are varied and often understandable: other priorities, limited resources, uncertainty about the right approach. The challenge with this position is that the gap to companies already experimenting steadily widens.
Other companies are actively experimenting. They try out tools, let employees work with ChatGPT or similar applications, gather initial experiences. That’s a good start. The question is how experiments become systematic use that avoids frustration and inefficiencies.
And a select few have already begun integrating AI strategically into their processes. They understand AI not as an isolated IT topic, but as a change in the way work is organised and performed.
AI Implementation in 2026: The Real Challenge
The biggest hurdle in AI implementation is not the technology. The tools are getting better, more accessible, and cheaper all the time. The real challenge lies elsewhere.
The DigiMit² study names the obstacles specifically: lack of expertise, time constraints, data protection requirements, insufficient data quality. And above all: the difficulty of integrating AI meaningfully into everyday business operations. Companies want guided pilot projects, practical training, clarity on legal questions.
None of this is a technology problem. It’s a change management problem.
AI implementation means changing working methods. It means bringing along people who may have reservations and often fears. It means adapting processes that have functioned for years. It means creating a culture where AI use becomes second nature.
Anyone who wants to introduce AI like a software update – pay, install, done – will fail. The companies that successfully use AI have understood that it’s about more than tools. It’s about how teams collaborate, how knowledge is shared, how decisions are made.
Developing an AI Strategy: What Matters Now
The question is not whether AI will change your market. The question is how you prepare for it.
This doesn’t mean frantic panic or rushed large-scale projects. Our principle of “AI without Big Bang” isn’t just a slogan – it’s a methodology: systematic, step by step, with a clear focus on practical benefit.
A few guiding thoughts:
Make visible what is invisible. Do you know how your employees already use AI today? Which tools are in use? Where potential lies? Most companies have no overview of what’s already happening – often “shadow AI” exists, meaning AI use that occurs without clear guidelines and bypasses the IT team. This not only creates risks but also prevents the company from learning from these experiences.
Start small, learn systematically. A pilot project in an area that delivers quick results. Gather experiences, document them, discuss them so knowledge can spread – then scale. No overnight transformation, but a methodical approach.
People before tools. The best AI solution is useless if employees cannot or will not use it. Change management is not an optional add-on; it’s the core of any successful AI implementation.
Don’t do everything alone. The study makes it clear: companies need practical support. Guided pilot projects, not PowerPoint strategies from an ivory tower. Someone who understands how AI works in concrete day-to-day operations.
The Right Time for AI Implementation
The algae on the lake are multiplying. Not dramatically overnight, but steadily. Companies that begin systematically engaging with AI today will be in a different position in two years than those still waiting.
The good news: you don’t have to go this path alone. At NordAGI, we guide SMEs through AI transformation – not with abstract strategy papers, but with practical support in day-to-day operations. We call it “Leadership-First”: empower leaders first, then bring teams along, then scale.
Because ultimately, it’s not the technology that determines the success of your AI implementation. It’s the people who use it.
Would you like to know where your company stands on AI integration?
