{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"ADAPT Insider","title":"Enterprise AI requires an operating model built for uncertainty","html":"","width":"100%","height":180,"duration":1050,"description":"Enterprise AI pilots often succeed under controlled conditions, then break when messy data, edge cases and dependent systems enter the journey.\nIn this first episode of ADAPT Insider’s AI Economics Series, Escaping the Pilot Trap and the Demo God Curse, Vijayan Seenisamy, Author of The Pilot Trap | Enterprise Agentic AI Delivery, joins ADAPT’s Byron Connolly to explore why organisations manage probabilistic AI through operating models designed for deterministic software.\nThe gap surfaces when a successful demonstration reaches production.\nAI systems change with data, context and user behaviour, requiring continuous monitoring, clear ownership and business cases that account for reliability across the entire journey.\n\nKey takeaways:\nTreat AI readiness as a continuous management responsibility rather than a gate the organisation passes once.\nTest the messy end-to-end journey instead of relying on an individual agent or carefully curated demonstration.\nFund experiments as experiments and assign one owner to the reliability of the entire chain.\n\nAI requires an operating model built for change\n\nMany enterprise AI experiments run through operating models designed for traditional software.\nCode is tested, defects are fixed and the system is expected to behave consistently in production.\nAI behaves differently. Its performance shifts as data, context and user behaviour change.\nVijayan describes the distinction as construction versus weather. A building can be completed and signed off. An AI system requires ongoing observation, correction and governance.\nWithout that operating discipline, a pilot may perform well in controlled conditions while remaining unprepared for production.\n\nA perfect demo proves potential, not resilience\n\nA polished demonstration can create false confidence when its conditions are mistaken for evidence of production readiness.\nDemo data is clean, prompts follow a happy path and the environment is arranged to help the agent succeed.\nProduction...","thumbnail_url":"https://img.transistorcdn.com/t0Jtrt9eZN4kwrPnSzLt-aLKB9f-tVVg1JGt9pql9t4/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS84ODM3/NGVhYmU2ZjljNWIx/NTliY2NlMTAzZDRl/NGNmOS5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}