Adopt AI or Die: The Strategic Reality for 2026
The hype cycle is over. We are now in the deployment phase.
Three years ago, Artificial Intelligence was about generating funny images. Today, at Glarium, we are integrating private LLMs (Large Language Models) that automate 70% of customer support tickets and predict supply chain failures before they happen.
The question isn’t if you should use AI, but how fast you can deploy it safely.
It’s Not About Replacing Humans
The fear is real: “Will AI take my job?” The answer is nuanced: AI won’t replace you. A human using AI will.
In our software development process, AI acts as a force multiplier. It writes the boilerplate code, allowing our senior engineers to focus on complex architecture and business logic. The result? We ship faster.
The Security Elephant in the Room
Most CEOs I talk to are terrified of one thing: Data Leaks. “If I put my data into ChatGPT, does OpenAI train on it?”
This is why Custom AI Implementation is critical. You cannot rely on public, free tools for sensitive data.
Our Approach to Secure AI: RAG
We build systems using RAG (Retrieval-Augmented Generation).
- Your Data stays yours: We index your PDFs, databases, and emails in a secure, private vector database.
- The AI is a translator: The LLM processes the query but doesn’t “learn” or store your secrets.
- Traceability: You know exactly where the answer came from.
FAQ: AI Implementation
Q: Is AI expensive to implement? A: Not anymore. API costs have dropped 90% in the last year. The main investment is in cleaning your data.
Q: Can AI work offline? A: Yes. We can deploy smaller, open-source models (like Llama 3) directly on your own servers.
Conclusion
Waiting to adopt AI is like waiting to adopt the internet in 1998. The gap between AI-native companies and traditional ones is widening every day.
