From Model to Mission-Critical App: Why AI Adoption is Harder Than It Looks

AI models are only the beginning. The real challenge? Turning those models into reliable, scalable enterprise applications—especially for organizations aiming to build sovereign AI strategies.

In this interview, Aleph Alpha CEO Jonas Andrulis breaks down the practical gaps between training models and embedding them into real-world solutions. Fine-tuning alone isn’t enough. Retrieval-Augmented Generation (RAG) helps, but only if your data is documented, accessible, and understandable to the model. And when the data comes from out-of-distribution sources—unfamiliar languages, formats, or specialized domains—both techniques struggle.

💡 Key Takeaways:

✅ Combining RAG + fine-tuning is often the only viable path.

✅ Enterprises need frameworks—not just models—to bridge gaps in documentation, data quality, and process transparency.

✅ Sovereign AI success demands flexibility: working across open-source models, local hardware, and regulatory constraints.

✅ Innovation like tokenizer-free architectures could drastically cut costs and carbon footprint—a critical factor for sustainable AI.

🌍 Aleph Alpha’s approach? Act as a translator and connector between cutting-edge AI research and practical enterprise deployment. They don’t aim to build “the next Llama”—they aim to help companies build their own sovereign AI with the right tools, processes, and governance.

⚠️ Why this matters: As AI moves from chatbots to agentic systems capable of autonomous multi-step workflows, the complexity will only grow. Enterprises need trusted frameworks to turn AI ambition into operational reality.

👉 Read the full article to explore the future of sovereign AI and the frameworks that could define it: https://www.theregister.com/2025/02/23/aleph_alpha_sovereign_ai/

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