From AI Hype to AI Architecture: Making Practical Infrastructure Decisions

Pressure to act on AI is rising, but moving from an early experiment to a reliable production deployment is often more difficult, costly, and complex than expected. When infrastructure decisions come before clear business outcomes, data requirements, security controls, and an operating model, promising pilots can quickly create long-term technical debt.

Join TierPoint’s SVP of Product Development, Greg Ahlheim, and cloud and AI industry expert and thought leader David Linthicum for a practical fireside chat on turning AI ambition into sound architecture. They’ll examine why enterprise AI initiatives stall, what changes when workloads move into production, and how cost, security, latency, compliance, data location, and integration should shape infrastructure choices.

You’ll learn how to evaluate public cloud, private cloud, specialized AI infrastructure, edge, colocation, and managed services without locking into assumptions too early, and how to choose an approach that supports both immediate projects and long-term platform strategy.

• Why AI pilots often struggle to become reliable, scalable production deployments
• How to focus AI use cases on measurable business value
• What to assess before selecting infrastructure, including workload behavior, data sets, performance needs, security, and data residency
• When public cloud makes sense, and when private, hybrid, or managed infrastructure may offer a better operational and financial fit
• How early architecture choices can increase costs, create technical debt, and constrain future AI initiatives
• How to build an AI infrastructure roadmap that supports current projects and broader 2027–2028 business and technology priorities