What’s the most challenging conversation you’ve had about balancing AI initiatives against foundational IT needs? What resistance did you encounter?
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Our situation is unique; our company builds power generation solutions for data centers, and we are experiencing rapid growth. Although the company has been conservative, there is executive support for AI initiatives. We started with small pilots to prove value and measure ROI. Alongside core IT projects like SAP and CRM upgrades, we assess incremental investments based on ROI. We also consider the risk of not providing AI tools, as employees may use public models and expose company data. This risk perspective has driven us to invest in broader AI tool access.
Balancing AI with foundational IT needs is challenging due to the hype around AI and the pressure to keep up. However, we have more pressing structural needs. Justifying AI investment is difficult, but ignoring it seems misguided. The most significant barrier is the people side—business users want AI but lack understanding of how to proceed. Our strategy is to be slow followers, focusing on education and awareness before making significant investments. We want to ensure clear benefits before moving forward.
Our experience is similar. We are conservative and recognize the hype, but we need solid use cases to justify expensive AI tools. We are experimenting with AI enablement within existing applications, such as Oracle, Workday, and Google Gemini, and observing how these embedded capabilities add value. We expect use cases to evolve over time, but we are still in the early stages.
We are in an interim phase where vendors are embedding AI into their solutions. While these out-of-the-box solutions are promoted as simple, they work best when enriched with data from other systems. Managing this integration is a challenge.