Danish AI Lessons For SMEs Everywhere
Danish AI lessons for SME companies can be applied across the world.
Read more about the central lessons here.
- Get a summary of advice on how to use AI from 50+ Danish sources
- SME-owners can integrate ideas across all aspects of implementation and use
- The Danish ideas may include insights and advice beyond what you are used to reading
Small business owners and startup founders are constantly bombarded with different ways to “use AI.”
Sadly, much of the advice can be eye-roll-inducing as it targets larger organisations, making it more useless than useful. Sorting through it all for nuggets and insights is no small task.
I realised that early on in this new series of articles.
The series focuses in on AI guidance for SMEs and startups one country at a time, digging into what local industry bodies, business networks, experts, and solution providers are actually saying. For each article, I read through a range of sources that focus on AI advice for the startup and SME business segments. I then distil the most often occurring themes and advice into a range or topics in article form.
First stop: Denmark.
Why? Well, for one thing, I’m half-Danish. Another reason is that Denmark has a strong tradition of innovation, tight-knit industry networks, and a government that actively supports digital transformation.
Without further ado, here are central AI lessons and advice from Denmark to SMEs and startups everywhere
AI is a tool, not a magic fix
One of the most important mindset shifts for SMEs is to stop thinking of AI as a silver bullet. Danish experts stress that AI is a tool—powerful, yes, but still just a tool. Its value comes from improving existing processes or enabling new ways to deliver value, not from replacing the fundamentals of running a business.
The arrival of AI for knowledge workers is akin to the arrival of iron tools for craftspeople in the Iron Age—it can make you faster, more precise, and open up new possibilities, but only if you already know your craft.
Define clear ownership and roles
The first step to AI success is often clear roles and responsibilities. If everyone is responsible for AI adoption, no one really is. Smaller companies, which often run with lean teams, benefit from designating a specific person or small group to lead the charge.
Some Danish firms have created an “AI Lead” role to coordinate projects across the organisation, supported by departmental “AI Champions” who embed AI thinking into their specific areas. Others have added voluntary groups of “AI Enthusiasts” to keep momentum, share use cases, and inspire colleagues.
AI needs strategy and integration
AI is not a side project. It needs to be embedded into the company’s overarching strategy, with clear goals, resources, and directly linked to value creation.
This strategic anchoring makes expectations clear for employees, helps prioritise projects, and ensures that AI adoption isn’t lost among competing operational demands.
It also forces leaders to consider risk management and long-term scalability from the start.
However, leadership also needs to find a way to keep an eye on what is coming down the line with AI, in order to stay competitive by integrating best in class AI solutions and workflows.
Start small and immediately
For especially SMEs, the hardest step is often beginning. AI’s perceived complexity and unclear use cases can lead to endless planning and no action. Danish practitioners suggest starting with small, well-defined experiments—simple processes or tasks where AI can make a quick, measurable impact.
Some companies have used targeted workshops to identify AI-ready tasks, then worked directly with each team to try them out. Early, concrete wins build internal confidence and demonstrate real value, paving the way for more ambitious projects later.
Keep people front and centre
Even in a highly automated future, AI adoption is still about people. For SMEs, where culture and trust are key assets, employees need to feel confident using AI and understand how it benefits them.
That means creating an environment where experimentation is encouraged and mistakes are acceptable. It also means involving staff directly in the design of AI solutions and a focus on said solutions improving their day-to-day work experience and happiness.
Look for immediate impact areas
While long-term AI strategies matter, SMEs can benefit right now from practical, accessible applications:
- Customer service: AI-powered chatbots can handle common enquiries 24/7, freeing up staff time.
- Sales and marketing: AI tools can analyse data to spot patterns, refine targeting, and personalise content—boosting engagement without increasing headcount.
- Operations: From inventory management to demand forecasting, AI can reduce waste and improve efficiency, even for small-scale supply chains.
These use cases don’t require huge budgets and can deliver measurable results quickly.
Overcoming adoption barriers
Tight budgets, limited technical skills, and complex integration challenges can seem insurmountable barriers for adopting AI.
- Danish advice emphasises tackling these head-on by:
- Starting with free or low-cost AI tools and platforms.
- Identifying the right partners with expertise that lower integration costs and improves results.
- Using phased, modular adoption so integration happens in manageable steps.
This approach reduces risk while building the skills and systems needed for deeper AI adoption.
Making AI fit your business
AI works best when it’s shaped to fit the specific realities of your business—your customers, your data, and your processes. That means understanding your “data landscape” (what you have, where it is, how clean it is) and designing solutions with real users in mind.
Several Danish companies found that initial ideas for “one-size-fits-all” AI tools had to be re-engineered after talking to end users. The lesson: flexibility and domain expertise matter just as much as the technology itself.
Transparency and trust are musts
As AI systems make more decisions or influence critical processes, stakeholders will want to know how those decisions are made. This is particularly important in regulated industries such as healthcare, finance, or law.
The concept of “Explainable AI”—moving from a “black box” to a “glass box”—is becoming central. It not only builds user trust but can protect against misuse or errors. For SMEs, this might mean choosing tools that offer clear reporting and audit trails or being upfront with customers about when and how AI is used.
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