Send us a text In this episode of Sidecar Sync, Amith Nagarajan and Mallory Mejias dig into the startling findings of a recent MIT study: 95% of enterprise AI initiatives are failing to deliver measurable impact. Why are so many organizations investing in AI only to watch their efforts fizzle? Mallory and Amith unpack 10 core reasons for these failures—ranging from building chat-first features instead of end-to-end workflows, to skipping the unglamorous data cleanup that powers real results. ...
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Send us a text In this episode of Sidecar Sync, Amith Nagarajan and Mallory Mejias dig into the startling findings of a recent MIT study: 95% of enterprise AI initiatives are failing to deliver measurable impact. Why are so many organizations investing in AI only to watch their efforts fizzle? Mallory and Amith unpack 10 core reasons for these failures—ranging from building chat-first features instead of end-to-end workflows, to skipping the unglamorous data cleanup that powers real results. ...
100 Episodes Later: A Look Back at What We’ve Learned—and What’s Coming | 100
Sidecar Sync
48 minutes
3 months ago
100 Episodes Later: A Look Back at What We’ve Learned—and What’s Coming | 100
Send us a text In this special 100th episode of Sidecar Sync, co-hosts Amith Nagarajan and Mallory Mejias take a celebratory stroll down memory lane, unpacking the journey from the show’s inception in 2023 to today. They reflect on five distinct podcast eras, distill the top 10 AI insights echoed across episodes, and get candid about the surprising speed of tech transformation in the association space. You’ll hear how Mallory used Claude and Google Colab to distill hundreds of transcripts, wh...
Sidecar Sync
Send us a text In this episode of Sidecar Sync, Amith Nagarajan and Mallory Mejias dig into the startling findings of a recent MIT study: 95% of enterprise AI initiatives are failing to deliver measurable impact. Why are so many organizations investing in AI only to watch their efforts fizzle? Mallory and Amith unpack 10 core reasons for these failures—ranging from building chat-first features instead of end-to-end workflows, to skipping the unglamorous data cleanup that powers real results. ...
