Architecting Enterprise AI for Generative and Agentic Systems - with Ranjan Sinha of IBM - Emerj Artificial Intelligence Research

Executive Summary

Maintaining legacy IT platforms has become a massive financial and operational drag. Research from Pegasystems Inc., in partnership with research firm Savanta, found that the average global enterprise wastes more than USD $370 million each year on technical debt. The primary reason for this technical debt cited in the study is that legacy transformation projects are slow, resource-intensive, and repeatedly fail, resulting in annual losses of around $56 million USD to maintain and integrate outdated systems. In critical public-sector infrastructure, a U.S. Department of Transportation review of AI for Intelligent Transportation Systems finds that legacy platforms — limited by restricted computational power, constrained data storage, and weak system documentation — create integration and compatibility challenges that raise infrastructure costs and prevent agencies from unlocking the full potential of AI-enabled safety and operational improvements. Health care shows the same pattern. A 2011 editorial in Applied Clinical Informatics reports that health IT projects “fail at a rate up to 70% of the time,” when failure is defined to include cost overruns, delays, unmet objectives, or project abandonment. The author links these outcomes to organizational complexity, resource constraints, and weak governance structures — arguing that trust, safety, and regulatory alignment must be addressed before digital and AI-driven systems can move from pilot programs into routine clinical use. In a recent episode of Emerj’s ‘Vision to Value in Enterprise AI’ video podcast, Emerj Editorial Director Matthew DeMello sat down with Ranjan Sinha, IBM Fellow, Vice President and Chief Technology Officer for watsonx at IBM Research AI, to discuss the fundamental shift in how enterprises need to approach AI infrastructure. Their conversation highlights two critical insights for enterprise adoption of agentic AI: Making AI-ready infrastructure a priority: As quantum computing becomes mor