The most common mistake founders make is thinking that they are the ones who brought AI into the company.
Humans are creative and comfortable creatures. From my perspective, these two things most often motivate us to change our work habits. AI has combined both of them and offered the possibility to creatively and efficiently "handle" the uninteresting part of the work. The entry of AI into the company and its data often occurs without control, without directive, and without the necessary education.
Most of our clients today do not face the question of whether to use AI. They face another problem, AI has already started to coexist natively within the company, but no one is managing it.
Employees have started building their own solutions for various automations, small internal tools, and have developed their own workflows. Everyone is solving their problem in their own sandbox. Everyone is optimizing their work. The result? Local efficiency. Global chaos.
Shadow AI as the New Standard
A similar pattern is starting to repeat among our clients today, leading to the emergence of so-called "shadow AI", which are tools and solutions that operate outside official systems, without clearly defined governance or control. The reasons are quite pragmatic.
Centralized systems tend to be slow, security restrictions significantly hinder experimentation, and the pressure for efficiency is immediate. Therefore, employees are finding their own ways to simplify their work. The result is a paradoxical situation where smaller companies, especially in e-commerce, are fully experimenting with AI and advancing by leaps and bounds without considering long-term impacts, while large corporations often stagnate or even lack a real overview of how AI is actually used in their organization.
This problem is not unique. According to various studies, today more than half of employees use AI tools without official company approval, and approximately 52% would not even admit it to their superior. In other words, AI is not entering organizations through managed adoption but through the natural quiet behavior of people.
The Problem is No Longer Adoption, but Management
Companies that have allowed AI into the organization without clear management are now entering a new phase. They have dozens of different tools at their disposal, but often cannot assess their quality, lack clearly defined ownership, and especially a unified strategy on how to work with them further. A typical situation looks like, for example, a PPC specialist creates their own AI tool that works very well for their needs. However, the problem arises when the company starts to rely on it. Who will maintain it long-term? What happens if that person leaves? Is the solution safe and scalable? Without answers to these questions, even a well-functioning innovation quickly turns into a risk.
Governance as a Key Step
Companies are naturally moving into a phase where they need to start systematically managing their AI activities. This means auditing existing solutions, redefining standards, setting clear ownership, and at the same time centralizing those tools that are critical to the business. It's not about stopping innovation or slowing down experimentation. On the contrary, the goal is to grasp these activities so that they are sustainable, safe, and scalable in the long term.
Security vs. Innovation?
Large organizations today face another fundamental conflict, the need to innovate on one hand and the need to protect data on the other. The result is often two extremes: either a complete ban on the use of AI or, conversely, its uncontrolled and untransparent use across the company. Neither of these approaches is sustainable in the long term. Companies that are moving forward sustainably in this area are therefore looking for a balanced approach: setting clear rules, creating a safe environment for experimentation, and at the same time ensuring controlled access to data.
The Biggest Mistake is Replacing a Process with a Single Tool
Many companies today are looking for one universal tool that could solve everything. However, this approach does not prove effective in the practice of our colleagues and clients. A more effective path is exactly the opposite, breaking existing processes into smaller parts and gradually optimizing them where it makes the most sense. In practice, this means, for example, using AI in data analysis in research, in creating initial designs in design, or in personalization in marketing. Instead of a one-time revolution, it is more of a gradual evolution that brings sustainable results.
Future: Orchestration, Not Tools
Today's AI strategy is not primarily about selecting specific tools. Much more important is the ability to connect, effectively manage, and scale these tools across the entire organization. Companies that succeed in this area will not be those with the most tools, but those that can meaningfully integrate AI into their processes so that it brings value not only to the business but also to the people who work with it.
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