Why AI Stack Recovery Changes Enterprise Resilience

As organizations adopt AI, recovery becomes about much more than restoring data. Modern AI applications introduce new layers of complexity, including agents, vector databases, embeddings, infrastructure, and interconnected dependencies that all need to be recovered together. In this STRIVE clip, Tim Zonca and Chris Mierzwa explore why enterprise resilience must evolve for the AI era. They discuss the growing complexity of AI stacks, why coherent recovery matters, and how AI can also help organizations better understand application dependencies across the enterprise. Key Takeaways * AI applications introduce new recovery challenges beyond traditional workloads * Effective recovery requires restoring applications, infrastructure, data, and state together * AI agents and distributed data create increasingly complex dependencies * AI can help organizations discover and map application relationships Coherent recovery becomes more important as enterprise AI adoption grows TL;DR Modern AI applications are more complex than traditional workloads. Organizations need resilience strategies that can recover the entire AI stack, not just the data. Why This Matters * Enterprise AI creates new operational dependencies * Recovery planning must evolve alongside AI adoption * AI can improve visibility into application and infrastructure relationships Who Is This For? * CIOs and CTOs * CISOs and Security Leaders * Enterprise Architects * Cloud and Infrastructure Teams * AI Platform and Engineering Teams FAQ Q: What is an AI stack? A: An AI stack includes the applications, infrastructure, data, models, agents, vector databases, and supporting services that work together to power AI workloads. Q: What is coherent recovery? A: Coherent recovery is the ability to restore applications, infrastructure, data, and their dependencies together so systems return to a trusted operational state. #FrontierAI #AIResilience #CyberResilience #EnterpriseAI #CloudResilience #STRIVE #Commvault