AI implementation: Security versus competitiveness, or why Google lost momentum in the AI race for a while

How much will waiting for "safe" AI cost you? Perhaps more than you can even imagine today. The implementation of artificial intelligence is no longer a question of innovation, but is becoming more a question of survival in the market. Yet many Czech and European companies are treading water. Management repeats mantras such as "we have to assess the risks," "we are waiting for a safe solution," "we are uncertain because of the AI Act"...

Meanwhile, however, competitors—often smaller and more flexible—are deploying AI tools without unnecessary delays and gaining a head start that will be very difficult to catch up with.

History repeats itself

We have seen a similar scenario many times before. Companies were afraid to connect their computers to the internet because of "hackers," "dangers," "loss of control," etc. Those who hesitated later paid dearly to catch up with digitization. Those who postponed the transition to the cloud are now dealing with slow systems. Those who waited for mobile applications to "become standard" lost customers who went elsewhere in the meantime.

The implementation of AI is the next technological wave—and it is faster than all the previous ones.

The longer companies wait, the more expensive the implementation will be and the greater the risk that they will never catch up with the technological gap that will arise in the meantime.

Security concerns are legitimate. But solutions already exist.

It is important to say this clearly: AI can be secure. It is not just about the technology itself, but how it is implemented. Today's enterprise tools already make this possible:

• isolated instance environment (the model runs separately from public data),

• access control and auditing,

• encryption of all communications,

• the option of on-premise or private cloud hosting,

• proprietary models trained on anonymized or synthetic data,

• and strict internal rules for working with sensitive information.

This is also possible in banking, for example, one of the most regulated industries.

Morgan Stanley implemented a GPT-based assistant for thousands of financial advisors. In doing so, it had to comply with extremely strict banking regulations. The result? Faster access to information, better customer service, and greater efficiency. And the bank is already rapidly scaling the project to other areas.

If it works in banking, it works almost everywhere.

How not to follow Google's example

Google, one of the technology leaders, also faced a dilemma between rapid deployment and security transparency.

Google has long been concerned about issues such as misinformation, bias, and reputational damage, which is why it has long adhered to a cautious strategy when deploying generative AI. While competitors such as OpenAI and Microsoft quickly launched their tools (ChatGPT, Copilot), Google postponed projects such as Gemini 3 and multimodal Astra, missing out on the first wave of adoption.

Strict European regulations and a complex internal structure further exacerbated the delays. But now it is accelerating significantly to catch up with the competition. Even a tech giant can pay the price for being too conservative. This is even more true for companies that do not have an unlimited budget.

How to implement AI safely, but without delays

The key is a combination of speed and governance.

• Start with pilot projects with clearly defined use cases and leverage cloud platforms with built-in monitoring and control tools (e.g., audit logs, access control). Set up an internal AI policy that addresses ethics, data protection, and transparency, but does not block experimentation.

• Use open-source sandboxes to test agents and automate model validation before deployment. This will ensure security and agility—without unnecessary delays.

• Security is important. But inaction is more dangerous.

Security policies have their place. Responsible management must address them. But excessive caution can become the biggest risk to a company. The real danger today is not AI. The real danger is losing competitiveness. Companies that already use AI gain an advantage every day they work with these tools. They accelerate, learn, and optimize processes. Conversely, the cost of waiting will be higher for companies in this case than the cost of a potential mistake.

Final recommendation for those who are still hesitating:

Find your internal data that does not contain sensitive information (or edit it if necessary) and use it to clearly determine what the right solution or answer is. This is important for training and measuring results. And then get started with your first POC.

“Simple as that.”