{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"ADAPT Insider","title":"Better baselines and end-to-end measures improve AI ROI, says Teachers Mutual Bank’s CIO","html":"","width":"100%","height":180,"duration":1416,"description":"Many AI programs can show momentum, but not what actually changed for the business.\nIn this ADAPT Insider conversation, Dan Chesterman, CIO at Teachers Mutual Bank Limited, argues that the issue usually starts earlier.\nOrganisations need a clearer view of what they are trying to improve, how that process performs today, and which outcome would prove the investment worked.\n \nKey takeaways:\nAI ROI becomes clearer when organisations start with a strong baseline and measure business improvement against it.\nUse cases, model choice, and governance need to match the decision or process being improved.\nEnd-to-end outcomes give a more useful picture of AI value than local productivity gains alone.\n \nROI starts with a baseline\n\nDan’s view is that AI performance is hard to judge when the starting point is vague.\nIf an organisation cannot describe current process performance, customer experience, or another measurable business outcome, it becomes much easier to celebrate implementation than improvement.\nThat is why he puts so much weight on baselining.\nOnce the current state is visible, AI can be measured against something real.\nThat also makes it easier to separate activity metrics from business metrics.\nToken consumption, model usage, and go-live dates may show movement, but they do not show value on their own.\n \n\nClear use cases matter more than broad AI ambition\n\nDan also points to overapplication as a common problem.\nAI can easily become a catch-all label for projects that have weak purpose, poor fit, or an inflated cost base.\nOrganisations can end up applying more expensive models than the task requires and calling that progress.\nStart with the use case, the decision, or the workflow that needs to improve.\nThen choose the level of intelligence, cost, and governance that fits it.\nSome tasks need prediction. Some need precision. Some need human judgement at key points.\nOnce that context is clear, AI becomes easier to apply with discipline.\n \nEnd-to-end outcomes matter...","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}