The AI hype cycle has made it harder to have pragmatic conversations about where these tools actually help. Here's our current take, based on implementations we've done and seen.
AI excels at tasks with clear inputs, tolerance for imperfection, and high volume. Document classification, initial draft generation, and pattern recognition in large datasets are genuinely valuable applications.
AI struggles—and often fails invisibly—with tasks requiring consistency, nuanced judgment, or accountability. Anywhere you need to audit decisions or guarantee specific outcomes, approach AI integration cautiously.
The most successful AI implementations we've seen treat AI as a tool that augments human capability rather than replaces human judgment. This framing leads to better architecture decisions and more realistic expectations.