Using AI in business with legal certainty: orientation instead of overreaction
Many companies have reached a critical point with artificial intelligence: the pressure to adopt it is rising, teams are already experimenting, but there is still no reliable line to follow. Are we even allowed to use AI? Where are the legal risks? Who is accountable? And how do we prevent a gain in efficiency from turning into a reputational or compliance problem?
This is exactly where companies need orientation. The decisive question is no longer whether AI matters. It is already part of everyday work. The real question is how to use AI in a way that creates value without losing sight of governance, quality, and legal certainty.
Important note: I am not a lawyer, and this article is not legal advice. A binding legal assessment can only be provided by a qualified attorney in the specific individual case. This article is intended to offer guidance to companies and cannot replace legal counsel.
Key takeaways
- AI is already a reality inside companies, often without clear governance.
- AI-generated content is not automatically legally unproblematic.
- Responsibility always remains with the company, not with the model.
- Clear guidelines, approval paths, and human review are what really matter.
- A controlled start is more productive than either bans or blind overreaction.
Where does the pressure come from?
AI is already everywhere. Companies such as OpenAI, Anthropic, and Grok keep raising expectations. They shape what seems possible and create the familiar moments of surprise. At the same time, established software vendors like Microsoft, Adobe, SAP, and others are rapidly weaving AI into products companies already use every day. In many cases, opting out has become so difficult that it is no longer a truly deliberate decision. That is precisely why freedom of choice inside companies is shrinking. An organization can ban or tightly regulate specific tools, of course. But AI often enters through products that have been in use for years, far beyond any conscious governance. Many companies do not even know which AI capabilities are already circulating in their software landscape.
The real problem is not the technology, but its uncontrolled use
That is why AI has already arrived in many companies, just not as a properly introduced system, but as an informal tool. Employees use generative AI for texts, research, presentations, images, summaries, and code. That is understandable. It becomes problematic when use begins before rules, approvals, and responsibilities have been clarified.
This creates a dangerous in-between state: the company is already using AI, but no longer knows exactly with which data, in which processes, under which approvals, and with which risks. That is the state that later costs time, trust, and sometimes money.
The most common misconception: if AI created it, it must be harmless
Especially with generative digital content, it may seem logical to assume that an AI-generated text, image, or video is automatically unproblematic under copyright law because it was not simply copied but newly generated. Many people still behave as though copying were the only possible copyright issue. It is not that simple.
Companies need to separate two questions very clearly: does an AI-generated result qualify for copyright protection at all? And separately from that: may this result actually be used and published in its specific form? Even if an AI output does not automatically qualify as a classic copyrighted work, its use can still infringe third-party rights or create other legal problems.
What is actually protected by copyright?
The short answer is this: not every AI output is automatically protected by copyright. In Germany, copyright arises from human creativity. The decisive question is therefore not whether AI was used, but how much human creative contribution is reflected in the final result. Which leads to the next question: how creative must a human prompt be for the resulting work to be protectable?
Copyright in software development
Copyright and licensing in software development are not fringe topics. Quite the opposite. And despite what many assume, copyright plays a particularly important role in open-source software. To release software under common open-source licenses, authorship across all its parts must be clear. Every author must agree to that license. Commercial software, too, requires a manufacturer to trace all parts of the software bill of materials back to their origin in order to charge license fees or clarify liability. Under the Cyber Resilience Act, this becomes a legal obligation for nearly all products with digital elements on the EU market. The Cyber Resilience Act becomes fully applicable on December 11, 2027. So yes, it matters very much whether you can explain authorship and provenance for AI-generated source code.
For companies, responsibility and transparency are what count
In a serious case, a company cannot excuse itself by saying that the AI produced the content. A model does not publish. An organization does. That is exactly where responsibility lies.
AI-generated content can come too close to third-party works, contain trademark-sensitive elements, infringe personality rights, or simply be unlawful in substance. The risk is not limited to copyright. Data protection, trade secrets, trademark law, personality rights, unfair competition law, and in some cases even criminal law can all become relevant.
What is AI actually allowed to do?
Providers of AI systems must still answer difficult questions about which ethical and legal principles they are expected to follow. Recently, two debates have made this especially visible. The first concerns song lyrics: training artificial intelligence on copyrighted lyrics without a license is legally contested and increasingly being treated by courts as unlawful. The second concerns deepfakes. AI providers differ noticeably when it comes to generating ethically questionable and potentially criminal content that infringes the rights of others. In both cases, the responsibility of those who create and distribute the content with the help of AI remains untouched. Anyone who has an AI recite copyrighted lyrics and then publishes them may violate copyright law. The same applies to those who use deepfakes to violate the rights of others.
Between prohibition and overreaction lies the productive path
Many companies react to this uncertainty with one of two extremes. Either AI is broadly blocked. Or teams are simply allowed to experiment and the rules are meant to follow later. Ban it or ignore it. A blanket ban prevents learning and pushes use into gray areas. An uncontrolled release approach creates speed without guardrails. Both are the opposite of responsible AI adoption.
What companies need instead is a controlled entry point: prioritized use cases, clear guidelines, defined responsibilities, and a shared understanding of what is allowed, what is sensible, and what requires review. Not everything at once.
And what about time to market?
Yes, speed matters. The early bird catches the worm. The fast outpace the slow. Time to market is real. But good companies think more broadly than that. Speed, quality, security, ethical conduct, sustainability. If you remove one factor from the equation, you also accept responsibility for the consequences. Taking risks consciously is part of prudent leadership and has a legitimate place in modern business. The same should apply to artificial intelligence. Everyone wants to be the early bird. Nobody wants to be the early worm.
Where companies should begin
- With a clear assessment of which AI use cases are genuinely relevant to the business.
- With guidelines for data, content, tools, and approvals.
- With an evaluation of the risks created by each use case.
- With the principle that sensitive content and external publications must always be reviewed by humans.
- With a realistic roadmap instead of tool-driven activism.
The biggest mistake is often talking about tools too early and governance too late. AI only becomes a reliable business tool when value, risk, and responsibility are considered together.
Why external guidance can be useful
In many companies, the biggest hurdle is not a lack of interest, but a lack of translation between business, organization, technology, and risk. IT looks at systems. Business units look at usefulness. Management looks at impact and liability. Marketing looks at speed. Compliance looks at rules. That is exactly why AI adoption so often fails not because of the technology, but because of missing integration.
SilverQ helps companies turn diffuse AI pressure into a clear and defensible way forward. Not with generic future promises, but with a structured view of business reality, processes, risks, and implementation. The goal is not to introduce AI somehow. The goal is to use AI in a way that fits the company and creates real value.
Conclusion
It is often difficult for companies to even identify where AI is already present. But they do not need to adopt it blindly, nor block it out of uncertainty. Inaction is often just the seemingly safer version of an uncontrolled status quo. Unrestricted release on the other hand is beyond responsible leadership.
Anyone who wants to use AI well needs clarity before speed and structure before overreaction. That is exactly where the opportunity lies: companies that create orientation now can use AI productively without losing control or trust. If you introduce AI deliberately today, you are far more likely to remain in control of your processes tomorrow.
If your company wants to build a sustainable line on AI, SilverQ can help you prioritize meaningful use cases, classify risks properly, and integrate AI responsibly into processes and decisions.
Introduce AI in business responsibly
Would you like to use AI in your company in a sensible way, assess risks clearly, and build reliable guardrails for processes and decisions? SilverQ supports a pragmatic and responsible introduction.