A Stack Overflow Data Science director explains the current AI adoption bottleneck: AI tools are capable but lack the right context to be truly useful in real workflows. The core problem is context engineering — AI needs access to siloed data across email, Slack, Jira, and other tools, but connecting all those sources requires significant setup effort that rarely feels worth it for occasional tasks. Enterprise proprietary data compounds the problem since AI labs can't train on it. The conversation covers how AI gets distracted by irrelevant context, the cost in tokens and time this causes, and practical advice for context engineering: observe what information you'd actually use, document it, build iteratively, and test. The discussion also touches on AI creativity, tool connectivity gaps, and a balanced view of AI as just another tool with strengths and weaknesses.
