Can you share a specific example from your organization where an AI investment directly competed with another critical IT priority? What was the outcome, and what would you do differently?

We implemented an Azure AI private ChatGPT instance, allowing the business to experiment with LLMs while ensuring data remained within our tenant and not exposed publicly or used for model training. Additionally, we deployed GitLab Duo to accelerate peer programming and remediate code vulnerabilities during testing.

In my previous role, AI was a board-level priority and received top-down support. It wasn’t a matter of competing with other initiatives; AI was an added investment, and other projects continued as planned. This approach helped eliminate many of the typical budget and resource conflicts. In my current role, I’m starting from scratch, there’s no AI or good data infrastructure yet. Previously, collaboration between IT and business was enabled by board support, allowing us to focus on solutions and data integration without significant internal conflict.

When we created our AI committee, our director of data and insights led it, which reduced her capacity to lead other insights projects. This resulted in some immediate priorities taking a hit, but it also led to valuable projects in data governance and security. While some initiatives were delayed, we’re now better positioned with foundational pieces in place, and I believe the long-term benefits outweigh the short-term productivity loss.

For us, AI initiatives were both competing and complementary to existing transformational plans. We’ve revisited all our plans to assess how AI fits in, what needs to change, and how to integrate AI into our architecture and implementation holistically. It’s all about positioning and integrating AI into the broader organizational work.

Last year, we undertook a large process automation project, what we used to call ERP, which directly competed with investments in experimental AI use case development. We also saw competition with foundational data and analytics (DNA) and infrastructure modernization, which are essential for developing secure data products and AI use cases. Ultimately, we made a major pivot toward AI. I don’t see these priorities as competitive, but rather as influenced by our strategic thrust on AI, which shapes our choices and includes experimentation.