Gulyani says that many organizations are starting to address this by deploying high-capacity, low-latency, lossless data center fabrics tailored to AI. Nokia, he says, has worked with hyperscaler nScale and cloud provider CoreWeave on next-generation interconnect solutions, including 800G IP and optical networking. “Now is the time for the telecoms industry to rethink network design — prioritizing scalability, flexibility, and automation — to prevent them from becoming a bottleneck in AI strategies,” Gulyani says.
3. Cloud and hybrid storage are key parts of the puzzle
As AI workloads evolve, even organizations committed to on-premises infrastructure are leaning into public cloud and hybrid storage strategies. Anant Adya, executive vice president and head of Americas Delivery at Infosys, says that successful AI data center modernizing efforts entail “shifting workloads to the public cloud, and adopting hybrid storage. These moves boosted agility and cut energy use and cost.”
This blend of on-premises and cloud-based compute isn’t just about performance — it’s also about access. For organizations without massive infrastructure budgets, a hybrid approach can be the difference between riding in the AI wave or being left behind. Classroom365’s Friend has worked with customers to navigate these limitations. “A lot of the schools we help, especially in under-funded councils, don’t have the means to rebuild kit or hire local AI brains,” he says. “But they’re not excluded.”
