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 New Year AI Checkup: What We Got Right in 2025 and What’s Coming in 2026 | 115
Sidecar Sync
51 minutes
1 week ago
The New Year AI Checkup: What We Got Right in 2025 and What’s Coming in 2026 | 115
Send us a text In this annual predictions spectacular, Amith Nagarajan and Mallory Mejias kick off 2026 by looking back at how well their 2025 AI forecasts held up (spoiler: pretty darn well) and then boldly lay out what’s coming next for artificial intelligence, associations, and knowledge work. From real-world AI recruiting agents and voice-first interfaces to the rise of AI literacy as a job requirement, the shift from SEO to AEO, and the very real possibility of AI-native platforms disrup...
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. ...
