AI doesn’t operate in a vacuum. Models need data. Data needs infrastructure. And infrastructure needs to put information in the right place, with enough performance, security and reliability to support the business.
If that sounds obvious, then how does one explain why so many organizations rush ahead with AI projects before ensuring their data foundations are aligned for success?
Nature Fresh Farms took a different approach. The company, which operates 250 acres of greenhouse space in the United States and Canada, collects data on roughly 3 million plants every day, tracking factors such as light exposure, irrigation, humidity, nutrients and production volume — and uses AI to analyze all of it.
But AI isn’t particularly useful if the data has to travel somewhere else before a decision can be made. So, the company uses local computing infrastructure to analyze information at the edge, enabling AI workloads to manage everything from lighting and irrigation to produce quality.
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Enterprise AI Success Starts With the Data Foundation
The insurance company Genworth, meanwhile, is taking a pragmatic approach to modernization, using cloud-native infrastructure where it can deliver new capabilities while continuing to operate some mature workloads in its own data centers.
Genworth evaluates workloads individually rather than assuming everything belongs in the cloud, and AI has become part of that calculation. As Genworth considers its infrastructure strategy, it’s also thinking about how today’s modernization decisions can position the company for AI initiatives down the road. The company has already begun applying Microsoft 365 Copilot to business workflows, for example, while rethinking its end-to-end processes around AI and automation.
These two very different companies came to different conclusions about their infrastructure, but the process was similar: Start with the desired outcome, determine how AI might help get there, then build the data foundation AI needs to do its work.
