AI Is Dominating Business — But Most Companies Are Losing Money on It
Artificial Intelligence is currently dominating the business world, and that is a fact.
However, despite all the hype, most companies are not making money from AI yet. In many cases, they are actually losing it.
Irresponsible implementations, lack of control, no verification of results, the first financial penalties, data chaos, and implementation chaos — this is the real picture of how AI is being introduced in companies today. The technology is powerful, but the way it is often deployed creates more problems than value.
That is why in this episode we focus on the most common risks related to AI adoption and on how organizations can start using AI in a safer and more responsible way.
A Simple Question: Is Anyone in Your Company Using AI?
Let’s begin with an honest question: is anyone in your company using AI?
Ask this question internally and listen carefully to the answers. If everyone says that no one is using AI, there is a very high chance that this is simply not true.
The reality is that most employees are already using AI tools in their daily work. They rely on AI for analysis, reports, content creation, research, and various operational tasks. Many of them use popular tools such as ChatGPT, Copilot, cloud-based AI solutions, or models provided by companies like Google, Anthropic, or others.
They upload fragments of offers, internal reports, analytical documents, and sometimes even personal or sensitive data. This often happens completely outside official company systems.
Shadow AI and the Loss of Control
This phenomenon is known as Shadow AI. It describes a situation in which employees use AI tools without the company’s knowledge and without approved, company-managed solutions.
The consequences of this are serious. Organizations lose control over their data and processes, and this loss of control can lead to major security, legal, and compliance risks. According to Gartner, Shadow IT and Shadow AI are becoming one of the main causes of security incidents related to IT infrastructure and AI usage.
The Illusion That AI Will Fix Existing Chaos
One of the most common mistakes companies make is adding AI on top of existing organizational chaos.
Many organizations treat AI as a magical solution that will automatically streamline processes, fix inefficiencies, and “do the work for them.” While AI can support and automate certain activities, it cannot repair broken processes.
If a company already struggles with unclear workflows, scattered data, and a lack of ownership, introducing AI will only accelerate the chaos instead of reducing it. Before AI can truly help, organizations need to clean up their own processes, understand how they work, and take responsibility for them.
Only then does it make sense to introduce AI in places where its value is clear and measurable.
Responsibility Does Not Belong to AI
Another critical issue is the lack of responsibility. There is a dangerous assumption that if AI generates something, it must be correct simply because it is AI.
This is not true. AI does not take responsibility for its outputs. Responsibility always lies with the organization and, internally, with the employees using the tools. Externally, the company remains accountable to its clients, partners, and regulators.
This principle is fundamental to managing AI properly. It applies not only to AI but to every tool and process used within a business.
Sensitive Data and Regulatory Risks
Consider the example of sensitive or personal data. A very simple but important question should be asked: do employees know where the data they upload into AI tools actually goes?
In most cases, they do not. This is a serious issue from the perspective of GDPR, contractual obligations, and legal requirements. Not all data can be transferred to third countries, such as the United States, where many AI systems are hosted and where data processing often takes place.
Organizations must understand where data flows, educate employees about these risks, and clearly communicate why data protection matters. Without this awareness and control, companies expose themselves to significant legal, administrative, and financial consequences.
Trusting AI Without Verification
Another major risk is failing to verify AI-generated results.
AI outputs often sound professional and convincing. This makes it easy to trust them automatically. However, AI systems can hallucinate, provide outdated information, or generate incorrect facts. Many AI models have a knowledge cut-off date, which means they simply do not know about recent events.
If companies rely on AI-generated content without verification, they risk losing credibility, facing penalties, or damaging relationships with customers. Deloitte describes this as the risk of making poor business decisions based on unverified AI outputs.
The problem is not that AI makes mistakes. The real problem is that no one checks its work.
Buying AI Tools Without a Clear Plan
Another common mistake is purchasing AI tools without a clear purpose. The idea of “let’s buy AI and see what happens” rarely leads to success.
This does not mean that every AI initiative needs a complex strategy. However, organizations should always start by identifying a concrete problem, deciding how they want to solve it, and only then selecting the appropriate tool.
Otherwise, AI becomes just another interesting gadget that is used briefly and then forgotten.
How to Introduce AI in a Safer Way
Safe AI adoption starts with basic risk management. Companies should know what tools they are using, what data is involved, and who is responsible for decisions and outcomes. This approach aligns with frameworks such as those proposed by NIST.
Instead of banning AI usage, organizations should focus on managing it wisely. Employees already use AI, and competitors are doing the same. AI can significantly increase productivity if used properly.
The key is to talk to employees, understand their needs, and provide secure, business-grade AI tools. It is essential to ensure that these tools respect data privacy and do not use company data for model training. This should always be verified in the terms and conditions of the tools being used.
Security, Control, and Transparency
Security must remain a top priority. Companies need to secure AI accounts properly, understand where data is stored and processed, and update privacy policies if data flows change.
They should also ensure that employees use company-managed accounts and that access is controlled. Organizations must know exactly how AI tools are used, what data they process, and for what purpose.
Final Thoughts
AI is undoubtedly a powerful opportunity and a major growth driver for business. However, with great power comes great responsibility.
AI is not just about automation or productivity boosts. It requires processes, governance, awareness, and security to be implemented properly. When done thoughtfully, AI can accelerate business growth. When done carelessly, it creates new risks and problems.
In this episode, we discussed the most common risks related to AI adoption, situations in which AI should not be implemented, and the key principles of safe and responsible AI usage.
I hope this episode was helpful. Thank you for reading, and see you next time.
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