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

Organizations are investing heavily in artificial intelligence projects. New models, new platforms, and new teams are emerging. The results are often disappointing.

It’s not because AI doesn’t work. It’s because the data infrastructure wasn’t set up properly before AI was implemented.

Where AI Projects Fail

Practical experience with projects shows that the most common cause of failure in an AI project is not the algorithm or the model, but the data.

Specifically:

  • Data is scattered across multiple sources without a consistent structure or management system
  • The database does not scale to the data volumes of AI workloads
  • The data quality is insufficient, and AI learns from incorrect data
  • Performance is insufficient for real-time AI applications
  • The SQL Server environment is overloaded and is eating into the budget that would be needed for AI investments

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

A Twofold Problem

An overloaded SQL Server environment slows down an AI project in two ways. It limits data availability and response times, and it eats into the budget that would be needed for AI investments.

If 2 million euros are spent on the SQL Server environment over five years and 600,000 euros of that could be optimized, that means 600,000 euros more for AI investments, without any increase in the budget.

Correct Order

Before making your next AI investment, there are two things you should do: assess the capacity and cost-effectiveness of your SQL Server environment, and ensure that your data is of sufficient quality and structure for use in models.

This doesn’t have to be a project that takes years. With our Data Value Discovery service, you’ll get a clear picture of the situation in just a few days.

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