Building an AI Platform with Databricks

Many companies have AI and generative AI have already been successfully tested. Chatbots, assistants, and the first AI agents demonstrate the potential of these technologies.

The real challenge, however, begins when successful pilot projects are to be transformed into productive enterprise AI solutions. What works in a controlled demo must be operated safely, cost-effectively, and scalably in everyday business operations. Neither a powerful model nor a good prompt is sufficient for this. Companies need an AI platform that data, Governance, and AI are united on a common foundation.

An AI platform provides the technical foundation for developing, testing, deploying, and continuously operating AI applications and agents. It integrates enterprise data, AI models, governance, permissions, and operational processes on a common foundation.

Unlike individual AI tools or isolated pilot projects, an AI platform is designed for repeatable, enterprise-wide use. This means that data access, security requirements, quality controls, and monitoring do not have to be completely rebuilt from scratch for every new use case.

A modern AI platform thus also lays the foundation for secure self-service. Business units can independently develop and use analytics and AI applications, while IT establishes central standards for security, data access, governance, and compliance. Innovation and control are not treated as opposites, but are integrated on a shared platform.

AI projects rarely fail because of the chosen LLM (Large Language Model). While it matters whether GPT, Claude, Gemini, or a specialized open-source model is used, the real challenges lie in the foundation:

• Data Access: Corporate data is distributed across SAP, data lakes, data warehouses, and line-of-business applications.
• Governance: Permissions, lineage, and quality rules are often not defined consistently.
• Business Context: AI agents require domain-specific definitions, roles, and processes to be able to correctly contextualize decisions.
• Operating model: Cost control, monitoring, and responsibilities are often not yet adequately regulated.
• Scaling: Each new AI use case is built individually, rather than being developed on a shared platform.

Rebuilding these foundations from scratch for every single AI use case is neither cost-effective nor scalable. When teams develop their own data copies, prompt logic, authorization models, and monitoring approaches, isolated solutions quickly emerge. What initially appears flexible leads, in the long run, to a fragmented AI landscape with higher operational costs and greater risks.

• Faster implementation of new AI use cases, because data, governance, and AI are brought together on a single platform.
• Secure self-service for business units, so that analytics and AI applications can be developed independently without compromising central governance.
• Scalable AI agents that access consistent enterprise data and thereby deliver reliable results for business units and business processes.

Economic value is not created by having as many separate AI projects as possible. What matters is an AI platform on which new applications can be efficiently developed, operated in a controlled manner, and continuously expanded.