4 Insights About Azure AI Foundry That Will Change Your View of Enterprise AI
Madrid, November 2025
The business sector has moved beyond the initial phase of generative AI, which focused on increasing personal productivity with " copilot "-type tools. Now, the strategic challenge is to integrate this technology into the core of the organization to transform processes and create value for the customer. However, this progress is hampered by a fragmented ecosystem of tools, models, and platforms that generates security and governance risks.
At this crossroads, Microsoft presents Azure AI Foundry . But this isn't just another tool in the arsenal; it's the strategic answer to the key question: how do we move from scattered AI experiments to building an industrial-grade, secure, and value-generating capability?
Based on the presentation by Javier Lozano, CEO of Nanfor and member of the Board of Directors of IAMCP Spain , this analysis reveals four ideas that demonstrate why Foundry is much more than a catalog of models.
1. Forget isolated tools: Azure AI Foundry is a complete AI "factory".
The central metaphor that defines Azure AI Foundry is that of an industrial factory . This Microsoft vision unifies the entire AI development lifecycle in one place: from experimentation and development, through operations integration ( MLOps ), to production and observability . This governed and centralized approach mitigates the risks of " shadow AI" and transforms sporadic innovation into a managed and observable process.
The scale of this "factory" is astounding: it offers access to more than 11,000 models , a number that, according to Lozano himself, "is constantly increasing." Diversity is key, including models from Microsoft (Phi), Azure OpenAI , Meta, Mistral, and Cohere . Furthermore, Microsoft enables the deployment of Foundry solutions in on -premises environments , a decisive factor for industries with strict data residency requirements.
2. The end of the "one-size-fits-all" model: Choose the right AI for each task (and budget)
Azure AI Foundry provides a multi-model environment , meaning that businesses are not tied to a single AI architecture. This enables strategic optimization that goes beyond task simplicity, focusing on the balance between capacity and token efficiency. to control the economic impact .
For complex processes requiring deep reasoning, high-capacity models like GPT-4 can be deployed. But for high-volume internal workflows, the platform allows the use of small language models (SLMs) like F3, which prioritize efficiency and drastically reduce operating costs. As Javier Lozano pointed out:
"...perhaps for certain internal support service solutions, very large models are not needed, so other types of models are used, which are small language models..."
3. Beyond the chatbot : Build an "army" of agents that work together
The true potential of the platform lies not in creating isolated agents, but in building an "army" of specialists that collaborate to automate end-to-end workflows. The case of a "podcast agency" perfectly illustrates this:
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Manual transcription → Automatic voice agents.
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Content curation → RAG agents obtaining updated references.
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Metadata registration → Knowledge agents graphs that enrich data.
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Quality control → Compliance agents who enforce governance policies.
The key concept is multi-agent orchestration . It's not simply about automation, but about designing intelligent and reusable business processes that open the door to new business models .
4. Integration and Security : The silent advantage that changes everything
One of the biggest obstacles to enterprise-scale AI adoption is technological fragmentation . Azure AI Foundry 's answer is as simple as it is powerful. In the words of Javier Lozano:
"Instead of dispersion, we have integration"
By centralizing the AI lifecycle on the Azure platform, businesses automatically benefit from data protection, security, and governance. from Microsoft. This native integration is a critical differentiator for highly regulated sectors such as healthcare, banking, or energy.
Furthermore, it allows Microsoft's technology partners to create replicable intellectual property (IP) . Instead of starting from scratch, a partner can develop a specialized solution for a sector—such as industry or human resources—and scale it to multiple customers, making AI a sustainable business model.
Conclusion: Are you ready to build or just to experiment?
Azure AI Foundry isn't a lab for testing the latest AI model. It's an industrial platform for building, managing, and scaling secure, enterprise-grade AI solutions. It contrasts the "AI experimenter," who plays with isolated components, with the "AI builder," who designs integrated systems in a governed factory.
Your competitors are already building AI factories. Are you still just experimenting in the lab?
🧑 🏫 How to prepare to build an AI factory?
Adopting Azure AI Foundry involves more than just technology; it also requires strategic training . For organizations to effectively design, deploy, and scale AI solutions, it's crucial to have teams trained in the tools, models, and best practices of the Microsoft ecosystem.
In At Nanfor , we offer a Liaison as a Service (Licensing as a Service) , a comprehensive training solution that enables professionals to continuously update their skills in technologies such as Azure AI, MLOps , agent orchestration, and more. This license is ideal for companies looking to develop sustainable internal capabilities and stay at the forefront of innovation.
Contact us and discover how we can help you build your competitive advantage !
- Email: info@nanforiberica.com
- Phone: +34 91 031 66 78
- WhatsApp: +34 685 60 05 91
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