{"href":"https://api.simplecast.com/oembed?url=https%3A%2F%2Fthenewstack.simplecast.com%2Fepisodes%2Fwhy-ai-engineering-needs-old-school-discipline-At9M7eQV","width":444,"version":"1.0","type":"rich","title":"Why AI engineering needs old-school discipline","thumbnail_width":300,"thumbnail_url":"https://image.simplecastcdn.com/images/1425ebfd-95bd-4a66-b963-a0b885c75680/bb688835-10e4-4197-b01f-34221ccb5d38/tns-makers-logo-simplecast.jpg","thumbnail_height":300,"provider_url":"https://simplecast.com","provider_name":"Simplecast","html":"<iframe src=\"https://player.simplecast.com/aa027765-248d-4d8b-b412-09c3ea3ac37a\" height=\"200\" width=\"100%\" title=\"Why AI engineering needs old-school discipline\" frameborder=\"0\" scrolling=\"no\"></iframe>","height":200,"description":"In this episode of The New Stack Makers, Nimisha Asthagiri of ThoughtWorks explores why many AI initiatives stall between proof of concept and production. A key issue is that organizations focus on speed—asking how to move faster—rather than rethinking what new capabilities AI actually enables. Successful companies take a systems-thinking approach, investing in organizational literacy and aligning teams around meaningful use cases instead of retrofitting AI into existing workflows."}