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. ...
The November Model Wave: DeepSeek 3.2, Gemini 3, Claude 4.5, Mistral 3, and OpenAI's Code Red Moment | 111
Sidecar Sync
49 minutes
1 month ago
The November Model Wave: DeepSeek 3.2, Gemini 3, Claude 4.5, Mistral 3, and OpenAI's Code Red Moment | 111
Send us a text This week on Sidecar Sync, Mallory Mejias and Amith Nagarajan unpack one of the most explosive weeks in AI so far—from the stunning debut of Google's Gemini 3 to the jaw-dropping image generation of Nano Banana Pro, and the best-in-class coding chops of Claude Opus 4.5. They also dive into Mistral 3 and DeepSeek 3.2, two powerful open-weight alternatives making waves in the AI space. With OpenAI allegedly declaring a Code Red in response, the co-hosts break down what this all m...
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. ...
