From Models to Reality: Seagate's Colin Presly on What It Takes to Run AI Reliably at Scale

As AI moves from experimentation into production, the data layer — not just models and compute — is deciding who wins. Seagate's VP of Customer Engineering brings the view from the world's largest cloud and data-center operators to Seoul.

Everyone is talking about models. The companies actually running AI in production are talking about data. At AI Summit Seoul & Expo 2026, Colin Presly, Vice President of Customer Engineering at Seagate Technology, will make the case that as AI becomes part of everyday operations, success is shaped less by raw compute and more by the ability to manage growing volumes of data reliably and efficiently in the real world.

The session

In "From Models to Reality: What It Takes to Run AI Reliably at Scale," Presly explores how production AI is reshaping data-center infrastructure design. In production settings, decisions about how data is stored, accessed, and managed increasingly determine system performance, operational cost, and scalability — across both training and inference. Gaps in the data layer create friction that limits the effective use of expensive compute and complicates AI operations over time.

Drawing on patterns observed across a wide range of global cloud and data-center environments, the session examines why data architecture and lifecycle management are becoming central to AI system design — and what "AI-ready" can practically mean when AI is no longer an isolated project but part of how the business runs.

Why it matters now

The timing could not be sharper. The global AI factory buildout is accelerating — NVIDIA and Korea's SK hynix just announced a multiyear partnership on next-generation memory for AI infrastructure, and sovereign AI data centers are scaling toward gigawatt capacity across the region. As capital pours into compute, the organizations that win will be the ones whose data foundations can keep up. Presly's session gives technology leaders a grounded framework for that conversation.

About the speaker

Colin Presly brings over 25 years of hard-drive technology and customer experience to the stage, spanning R&D, precision equipment engineering, the CTO office, and global customer-facing technical organizations. Through regular engagement with the world's largest cloud and data-center operators, he has a front-row view of how infrastructure requirements are evolving as AI moves from experimentation into production. He holds a Master of Engineering from Imperial College London.

Key Takeaways

The data layer is the new bottleneck — why storage, access, and lifecycle decisions now shape AI performance and cost as much as models and GPUs.
Patterns from global operators — what leading cloud and data-center environments are doing differently as AI workloads scale.
A practical definition of "AI-ready" — what infrastructure readiness actually means when AI becomes everyday operations.