In this episode of The Tech Trek, Amir Bormand sits down with Nirmal Ranganathan, CTO, Global Public Cloud at Rackspace, to dissect one of the hottest and most crucial topics in today’s tech landscape—trust in AI applications. They explore how enterprises can drive adoption of AI solutions, what key factors are needed to foster trust, and why guardrails, security, and change management play a pivotal role. Whether you're a developer, tech leader, or AI enthusiast, this episode dives deep into the challenges and opportunities shaping the future of AI adoption.Key TakeawaysTrust is the Cornerstone: For AI adoption to succeed, users must trust the output. Trust hinges on data quality, security, responsible use, and model transparency.Change Management Matters: Adoption in enterprises isn't about trends—it’s about clear processes, education, and user enablement.Guardrails Are Non-Negotiable: Especially when AI is exposed to external users, organizations need strong safety checks—think toxicity filters, bias mitigation, and strict data governance.Scaling AI = Scaling Costs: Unlike typical systems, scaling AI comes with heavy computational costs. Patterns like caching and model optimization are essential for sustainability.Prompt Engineering & Peer Learning: The secret to effective enterprise AI adoption is empowering users to master prompt engineering and fostering peer collaboration.Future of Adoption: 2025 might not yet be the year of mass AI production rollout, but the curve is gradually climbing—especially with evolving architectures and better model accuracy.Timestamped Highlights[00:00:00] Introduction to Nirmal Ranganathan & the importance of trust in AI[00:01:34] Why adoption is key—and why most tech projects fail due to lack of it[00:02:50] Three pillars of successful AI adoption: Trust, Change Management, Functionality[00:05:02] The trust barrier: Hallucinations, relevance, and grounding AI responses in enterprise knowledge[00:10:01] Why most AI projects are stuck in POCs—and what's preventing full-scale deployment[00:11:43] Technical guardrails: Security, scalability challenges, and compliance considerations[00:14:56] Cost & infrastructure challenges when scaling AI solutions to millions of users[00:17:52] How tech companies differ from enterprises in deploying AI—data privacy, safety checks, user unpredictability[00:20:00] The role of prompt engineering, peer learning, and experiential training in ensuring AI adoption success[00:22:16] What the future holds for AI adoption—and why the heavy lifting might get easierFeatured Quote "AI adoption compounds all of our existing challenges—and then multiplies them by five or ten times." — Nirmal RanganathanConnect with NirmalLinkedIn: https://www.linkedin.com/in/rnirmal/If you enjoyed this episode, please like, share, and subscribe! Don’t forget to follow the podcast to stay updated on future episodes.