In this episode of AI x DevOps, Rohit sits down with Sailesh Panchal, CTO of TSB, a UK bank with around five million customers. Sailesh has spent over three decades leading and observing transformations: from building distributed systems in the mid-90s, to CTO at Travelex, to helping Lloyds Banking Group on its journey to cloud, to building his own fintech in the cloud and leading engineering transformations across several banks. The conversation is about why transformations fail before they start: most organizations don't actually know themselves. Sailesh walks through Transformation Intelligence, his approach to writing an organization down (its decisions, authority, jobs and values) in a structured grammar that both people and AI can read, how he used AI to mine hundreds of decisions out of existing documents, and MTDR, the open-source standard he has published for recording them. Then he asks: once AI makes execution instant, what is left for the organization to do? ⏱ Chapters: 00:00 - Introduction and Guest Background 03:48 - Rethinking Digital Transformation 12:18 - Transformation Intelligence and Organizational Context 24:13 - Mining Decisions with AI and Open Source 34:58 - Engineering Leadership in the AI Era 43:04 - Zero-Time Deployment and the Future of Organizations Here are some of our favourite takeaways: Know yourself before you transform: The real primitives are decisions, authority (and delegated authority), inheritance, jobs to be done, and value. Most organizations can't write these down, which is why partners end up doing the excavation work. Every consequential change is a transformation: Organizations run many transformations at once, from HR programs to cloud moves, and the discipline underneath them has to be consistent. Humans are the biggest probabilistic engine: Your systems may be deterministic, but governance depends on who shows up to the board that day. Writing decisions down is what makes them automatable. Mine your decisions with AI: Sailesh used AI to extract hundreds of decisions from existing documents into a reviewable, structured record, then into a graph you can project future states from. The human stays accountable for every one. DevOps was an organizational redesign disguised as an engineering practice: Deployment velocity is meaningless if the organization isn't ready. Features hide behind flags until the company catches up. Great engineers become great AI mentors: The skills that made someone a strong lead engineer (code review, mentoring, explaining why) are exactly the skills that make AI useful in engineering. The zero-time thought experiment: If you could execute everything instantly, what is disaster recovery? What does change approval mean? Start with a glass-tube pilot and redesign the roles around it. The next transformation isn't digital, it's organizational: Cloud made infrastructure programmable. AI can make the organizational model programmable, and the advantage goes to whoever can redesign their organization fastest, with the least risk. If you're leading a transformation and suspect the hard part isn't the technology, this one's for you. 🔗 MTDR, Sailesh's open standard for Transformation Decision Records: https://github.com/saileshpanchal/mtdr ✍️ Sailesh's writing on digital transformation: https://saileshpanchal.github.io/digital-transformation-advisory 🎧 Listen on Spotify: https://open.spotify.com/show/70tmILfTgaEy7o9ya1sJzC 🎧 Listen on Apple Podcasts: https://podcasts.apple.com/podcast/ai-x-devops-by-facets-cloud/id1813326497 #AIxDevOps #DigitalTransformation #AIinEngineering #PlatformEngineering #DevOps #EngineeringLeadership #AgenticAI #OpenSource #CTO #FinancialServices

In the 7th episode of the AI x DevOps podcast, host Rohit Raveendran sits down with Dr. Rahul Vilas Ghodke, SVP & Business Unit Leader – Global Technology Operations (APAC) at CGI, to explore what it really takes to move from AI assistants to AI coworkers in enterprise operations. Key topics covered: • Agentic Operations at Scale: Why successful AI-driven operations require decades of automation maturity before agents can be trusted with critical workflows. • Classic AI vs Generative AI: Understanding when machine learning and deterministic systems deliver better outcomes than LLMs. • Human-on-the-Loop Operations: How enterprises are balancing automation and oversight for critical infrastructure and business applications. • Making Tribal Knowledge Discoverable: The role of knowledge governance, prompt catalogs, and curated repositories in improving agent effectiveness. • Sovereign AI and Hybrid Infrastructure: The challenges of building interoperable AI infrastructure while avoiding vendor lock-in across clouds and hardware vendors.

In the 6th episode of the AI in DevOps podcast, host Rohit Raveendran sits down with Nathen Harvey, the lead at DORA at Google Cloud, to dissect the groundbreaking findings of the 2025 DORA Report. Key topics covered: • AI as an Amplifier: Learn why AI is categorized as an "amplifier" rather than a "magic wand," requiring solid existing practices to truly yield results. • The Platform Engineering Boom: A look into why 90% of survey respondents have now adopted platform engineering. • The J-Curve of Productivity: How to navigate the initial performance dip during a transformation to reach higher stability and efficiency. • AI-Centric UX: Discussing whether platforms should be redesigned to serve AI agents as primary users. • Measuring Success: Moving beyond static dashboards toward team reflection and experimentation to improve software delivery

In this episode of AI x DevOps, Rohit sits down with Görkem Ercan, CTO at Jozu, a company building a DevOps platform for AI agents and models. Görkem, a veteran with over two decades of software experience (including contributions to the Eclipse Foundation), explains why MLOps is fundamentally different from traditional, deterministic DevOps—leading to extreme pipeline fragmentation. Here are some of our favourite takeaways: • Standardization is Key: Why OCI is the recognized standard for packaging AI/ML artifacts, and how the Model Packs project (with ByteDance, Red Hat, and Docker) is defining the artifact structure. • Open Source Headaches: The critical challenge maintainers face when receiving large amounts of untested, verbose, AI-generated code. • LLM Economics: Discover why running small, fine-tuned LLMs in-house can be cheaper and provide more predictable, consistent results than generic large providers. • KitOps Solution: How KitOps creates an abstraction that allows data scientists to focus on training while leveraging existing DevOps platforms for deployment. Tune in now to understand the standardization movement reshaping the future of AI development!

Join host Rohit (Facets Cloud) in conversation with Sanjeev Ganjihal, Senior Specialist Solutions Architect - Containers at AWS and early Kubernetes expert. They discuss the rapid evolution of AI and DevOps, Kubernetes as the new operating system, generative AI in engineering, and the shifting landscape of roles like DevOps, SRE, and AIOps. Sanjeev shares practical advice on using AI assistants, agentic tools, self-hosted models, and the balancing act between automation, productivity, and upskilling in today’s cloud-native world.