Timo Lindström, CEO, DB Pro Oy & DB Pro Services Oy.

Organizations are investing heavily in AI projects: new models, new platforms, new teams. The results are often disappointing—not because AI doesn’t work, but because the data infrastructure hasn’t been set up properly.

A database is often the engine of an AI project. If the engine is overloaded, misadjusted, or too expensive to run, the car won’t run properly.

Where AI Projects Fail

One common observation is that some AI projects fail before they ever make it into production. The technical reasons are often related to data quality and data architecture, rather than to models or algorithms.

Specific obstacles include the fragmentation of data across multiple sources without a unified structure, the inability of databases to scale to the data volumes required by AI projects without massive infrastructure investments, and slow data access, which makes real-time AI applications impossible.

These aren’t AI problems. They are database and data platform problems.

Microsoft SQL Server Environment as the Foundation for an AI Project

Microsoft SQL Server is the most common database solution used by organizations. When an AI project is launched, the data is usually stored in Microsoft SQL Server, and the condition of the SQL Server environment determines the scalability of the AI project.

An overloaded, improperly sized, or excessively expensive SQL Server environment hinders an AI project in two ways: it limits data availability and response times, and it eats into the budget that would otherwise be needed for AI investments.

If 2 million euros were spent on the SQL Server environment over five years and 600,000 euros of that could be optimized, that would mean 600,000 euros more for AI investments.

First, get the basics right

AI is no substitute for a well-built data infrastructure. It requires one.

Before an organization invests in AI projects, it should ensure that its database environment is properly sized, cost-effective, and scalable. This does not mean a years-long rebuild project, but rather an analytical review of the current environment and concrete steps.

→ Make your database environment AI-ready: www.sqlgovernor.com