The infrastructure mistake that kills AI pilots: Why sandboxes can't reach enterprise data centers скачать в хорошем качестве

The infrastructure mistake that kills AI pilots: Why sandboxes can't reach enterprise data centers

Most enterprise AI pilots fail at scale because they're architected in cloud sandboxes that can't translate to production data centers. Linda Yao, VP and GM of Hybrid Cloud and AI Solutions at Lenovo, explains why infrastructure architecture coherence matters more than use cases or data quality when moving from pilot to production. Lenovo cut parts planning from 6 hours to 90 seconds and achieved 80% faster marketing content production through systematic AI deployment. But Linda's core insight challenges conventional wisdom: the architecture you choose for pilots determines whether they'll ever reach enterprise scale. CHAPTERS: 00:00 Introduction 03:04 Three waves of enterprise AI adoption: from high frequency trading to generative AI 06:11 Why FOMO is driving faster AI adoption across enterprises 08:23 Hybrid deployment preferences replacing cloud first mentality 09:47 Five stage AI adoption methodology and why ongoing monitoring is the failure point 12:15 Infrastructure architecture mismatch: the real reason pilots don't scale 15:27 Evaluating AI use cases and choosing the right deployment strategy 18:24 Learning from deployment scars: architecture coherence requirements 21:02 Navigating the AI vendor landscape with build, leverage, partner philosophy 24:03 Merger and acquisition strategy for AI capabilities 29:38 AI for social impact: diagnostic tools and special education applications 34:39 Balancing commercial and mission driven AI work 36:47 AI and sustainability: liquid cooling for GPU infrastructure 39:12 What AI won't replace in three years 40:55 Setting clear objectives for every AI deployment ABOUT LINDA YAO: Linda Yao is VP and General Manager of Hybrid Cloud and AI Solutions at Lenovo, a Fortune 200 technology company serving enterprises in over 180 markets. She has led AI adoption across multiple waves, from big data analytics in financial services to machine learning at Boeing to generative and agentic AI at Lenovo. KEY FRAMEWORKS MENTIONED: Five stage AI adoption methodology: discovery, advisory and planning, fast start, deploy and scale, ongoing management Four pillar readiness assessment: security, data quality, people capability, technology infrastructure Build, leverage, partner philosophy for full stack integration AI library of battle tested use cases with validated deployment architectures RELATED RESOURCES: Lenovo Global CIO Report on AI adoption Linda's work on AI factories for healthcare diagnostics in the Middle East Bridge Academy computer vision project for special education in Hong Kong Subscribe for more conversations with AI leaders building production systems at enterprise scale. CONNECT WITH LINDA YAO: LinkedIn:   / lindayao   #EnterpriseAI #AIDeployment #HybridCloud #AIInfrastructure #AIAdoption