How to Prepare Your Data Foundation for AI

3. Audit data infrastructure to understand fragmentation

Once they have a preview of the reliability issues that may emerge with AI, organizations should audit their own data infrastructure to see how fragmented it has become. 

Mark and the team at Limestone have discovered that the average mid-market company is working with anywhere between 7 and 12 disconnected systems.

Implementing AI on top of a disconnected foundation will only amplify reliability problems at enterprise scale. Incorrect, inconsistent results will not only inhibit effective decision-making for organizational users but also undermine their trust in AI.

4. Build unified data infrastructure to give AI what it needs

Following the audit of their data architecture, organizations should focus on the core infrastructure changes required for implementation.

At the heart of this transformation, Mark shares that organizations must establish data pipelines that automatically consolidate data into a centralized repository, such as a data lake. He also emphasizes the importance of implementing vector database capabilities. These core components work together to create unified, consistent, and accessible data, helping to provide AI with the business context required to function reliably. 

While traditional data transformation initiatives can stretch across months of complex planning and execution, Mark highlights that a focused approach can help tackle these foundational challenges in a fraction of the time. For example, Limestone works with clients to tackle these tasks in 4–6 week sprints to accelerate the path to AI readiness. Unlike traditional consultants who take 3-4 months just for assessment, Limestone delivers working infrastructure in the time it takes others to write a proposal.

Victory in the AI arena

The pressure to implement AI is high — but organizations should not let the urgency rush them into improper implementation. A job done quickly but wrong is still a job done wrong. 

If organizations can create a connected data foundation, their AI tools will be more reliable across the business, fed by unified and consistent data. 

But leave fragmentation unchecked, and deploying AI across enterprise systems will inevitably fail, wasting significant time and investment dollars. Meanwhile, more foundationally sound competitors will enjoy immediate operational improvements and surge ahead. 

In the AI era, the future belongs to those who build on solid foundations. Start building yours today.