We’ve all fought the good fight to move our teams away from being ‘feature factories’ and toward becoming outcome-oriented powerhouses. We live by OKRs, North Star metrics, and focusing on ‘the why.’ But what happens when the very definition of a ‘good outcome’ changes almost overnight?
The rise of generative AI is putting immense pressure on our traditional goal-setting frameworks. An objective to ‘Increase user engagement by 15% through a better search interface’ might become obsolete when a new AI model can deliver the perfect answer instantly, reducing a 10-click journey to a single prompt. The goalposts aren’t just moving; they’re on a completely different field now.
This forces us to think beyond incremental improvements and ask more fundamental questions about the value we create. Are we still solving the right problem? Are our metrics capturing true user success or just legacy behavior? Keeping engineering efforts tightly coupled with this rapidly evolving customer feedback is more critical than ever. It’s about building a system, perhaps using tools like https://leera.io that link development work directly to user insights, allowing us to adapt our roadmap in real-time.
How are you adapting your goal-setting and success metrics to keep pace with the possibilities and user expectations being redefined by AI?