Live from IBM Think 2026, experts discuss the maturation of enterprise AI, moving from siloed applications to integrated, end-to-end solutions. The panel explores the rising trust in AI for strategic decisions, the evolving role of the Chief AI Officer (CAIO), and the state of AI investment, arguing that the field is broadening to solve specific business problems.
This episode of Mixture of Experts breaks down IBM's enterprise-focused Granite 4.1 and Project Bob, Google DeepMind's DiLoCo distributed training method, the inference-efficient DeepSeek V4 model, and IBM's strategy for achieving quantum advantage through strategic partnerships.
Steven Sinofsky, Aaron Levie, and Martin Casado of a16z dissect the reality of AI adoption within large enterprises. They explore the significant gap between Silicon Valley's developer-centric culture and the complex, legacy-driven world of established organizations, explaining why many top-down AI initiatives fail. The discussion introduces a key architectural shift—treating AI agents as users rather than integrated software—and analyzes the immense integration, security, and data challenges that agents face. Ultimately, they argue that AI, rather than eliminating jobs, will create new ones by increasing system complexity and enabling professionals to operate at a higher level of abstraction.
Kishore Subramanian, CTO of Propel Software, discusses how AI is revolutionizing physical product development. He explains how Propel's AI platform, built on Salesforce's Agentforce 360, uses agentic AI to "shift left" quality control, reviewing engineering changes to prevent costly downstream errors in hardware, high-tech, and med-tech manufacturing. The conversation also covers best practices for deploying enterprise-grade AI agents and the surprising benefits of yoga and meditation for creative problem-solving in tech.
SAP CTO Philipp Herzig discusses the company's AI-driven transformation, focusing on three core pillars: generative UI, AI-native business processes, and a unified data layer. He explores the primary challenges to enterprise AI adoption—scale, data fragmentation, and security—while emphasizing the critical role of verifiability and "agent mining" in creating reliable, compounding value. Herzig also details the limitations of LLMs for predictive analytics on tabular data and introduces SAP's alternative, Relational Pre-trained Transformers (RPT1).
Matthew Meckes from AWS makes a compelling case for Java's central role in the future of enterprise AI. This talk explores how Spring AI empowers developers to build robust, production-ready agentic applications by integrating LLMs with existing Java services, moving beyond proofs-of-concept to solve real-world business problems.