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Updated 21 Sep
Teams find AI fails in production because of missing context around the model, not the model itself
AI in Product
- Insight Partners and Arize argue that enterprise AI stalls on the context and tools around a model, not capability.
- Arize relays an OpenAI engineer's account of one system where roughly one conversation in every 30 to 50 stalled.
- Tomasz Tunguz counts $9.75 billion committed in a year to engineers sent to work inside customer teams.
- All five sources argue the same side, and one of them is a venture firm writing about its own market.
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First seen 24 Jan 2026
If you are a PM
- Before you blame the model for a stalled AI feature, list the context and tools it was missing. That is where these accounts point.
- Ask what one conversation in every 30 to 50 stalls on in your product. The pattern is usually missing context, not capability.
- Little for you here, beyond the screen that tells a person the assistant lacked context, instead of pretending it had an answer.
Who said it
Nielsen Norman Groupnngroup.com · 18 September 2026
The 3 Roles of Context for AI AgentsSummary: AI-agent power users curate 3 kinds of context: global (across tasks), local (task-specific), and ambient (raw streams like email).…