Interest in artificial intelligence (AI) is sky-high, and the technology is exponentially evolving at an explosive pace. How can organizations keep up, plan for, and (most importantly) reap the benefits AI promises?
Foundry put that question to its CIO Think Tank participants in a series of virtual roundtables in the spring of 2024. We learned firsthand about the use cases they were pursuing, the challenges they faced, and potential solutions. One reality quickly became clear: While AI requires a high-performance network to do it right, it also has the potential to deliver vastly improved network performance, resiliency, and ROI.
CIO Think Tank is a collaboration focused on sharing the ideas and expertise of top IT executives, IDC analysts, Foundry editors, and our exclusive vendor partners. The goal is to explore and shape the future of IT functions and emerging technologies.
Putting AI to use to make networks better
Based on the conversations with the CIOs, it became quickly clear that many enterprises are still in the planning stages of implementing AI, so there’s no need to panic if your vision isn’t set in stone yet. However, IT leaders universally expect big gains from AI in areas such as data analytics, employee productivity, and process automation. And they are largely depending on their network strategy and infrastructure to deliver those AI benefits.
The good news is that while enterprise networks are already highly reliable, AI stands to make them perform even better while simultaneously easing the operational burden on network staff.
Indeed, IDC’s 2023 Future of Connectedness Survey found that respondents expect AI and machine learning (ML) technologies to help them with network operations tasks (AIOps). About a third of them are counting on AIOps to help optimize network performance, enhance network security, and increase network automation, while more than a quarter also expect AI/ML to help speed up network problem resolution.
The output of the CIO Think Tank is similarly aligned with that thinking. As networks become increasingly interconnected with various cloud providers involved, it becomes more difficult to troubleshoot issues. “There’s a bunch of different translations of data as it goes through [the] pipeline, and trying to track problems can be very time intensive,” said Steven Nieland, VP of Software Engineering and Controls at Faith Technologies. “We hope AI can serve as another set of eyes there.”
Extending AI use cases and optimizing infrastructure
Beyond AIOps, Think Tank CIOs are also embedding AI into business applications, including chatbots and customer contact assistants, quality control, document analysis, and detecting anomalies in massive financial datasets.
Interestingly, however, participants do not all adhere to the basic advice of starting with a single project or low-hanging fruit. Many said they focus first on building a strong AI foundation, including a Center of Excellence (CoE), governance principles, and processes. “Use cases actually come last,” said Christopher LaCour, CIO of Propio Language Services.
The decision for where to run AI training and inference engines is another potential sticking point, one that relates to the requirement for a high-performance, resilient network that can handle all the AI chatter.
“There’s a lot of communication back and forth between these GPUs, so demand from the network has suddenly grown in terms of the bandwidth and latency,” said Praveen Jain, SVP and General Manager, AI Clusters and Cloud Ready Data Center, with Juniper.
Issues like sustainability also come into the decision-making process, given that running AI in your own data center has the potential to increase greenhouse gas emissions. Some fear unintended consequences, so they run AI only in a sandbox environment, while others are exclusively in the cloud—at least for now.
These are just a few of the takeaways from the CIO Think Tank Roadmap report. Read the full report to learn more about what CIOs think about AI issues, including trust, security, and staffing.
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