Quick Answer
For search, voice, and "just tell me what to do".
Products don't announce when they become obsolete - they quietly decline while you're focused elsewhere. AI can monitor market signals, customer feedback, competitive moves, and search patterns to detect when products need updating, repositioning, or retirement. This early warning system lets you act before sales collapse rather than after.
Key Takeaways:
- Obsolescence happens gradually, then suddenly
- Early detection enables proactive response
- AI can monitor many signals simultaneously
- Regular product audits prevent surprise declines
- Retirement decisions are easier with data
Playbook
Define signals that indicate declining relevance
Set up AI monitoring for those signals across products
Create thresholds that trigger review or action
Regularly audit product portfolio for health
Make update or retirement decisions based on data
Common Pitfalls
- Ignoring early warning signs until it's too late
- Monitoring metrics that don't predict obsolescence
- Emotional attachment to products that should be retired
- Waiting for sales to crash before acting
Metrics to Track
Relevance score trend by product
Early obsolescence signal detection rate
Time from signal to action
Revenue saved by early intervention
Product retirement vs. rescue ratio
FAQ
What signals indicate obsolescence?
Declining search volume, negative review trends, competitor innovations, customer questions about alternatives, and decreasing conversion rates.
When should I retire vs. update a product?
Update if the core value proposition remains valid. Retire if the fundamental need has changed or better solutions have emerged that you can't match.
How often should I audit products?
Continuous monitoring with quarterly deep reviews. Markets change faster than annual reviews can catch.
Related Reading
Next: browse the hub or explore AI Operations.