Percentage of users actively using a specific product feature within a given period.
Users Using Feature / Total Active Users × 100
Higher is better · Unit: percentage
How to Improve
Use in‑product announcements and guided tours when launching new features. Target specific user segments who would benefit most from each feature. Build contextual tooltips that introduce features at the moment of need. Create video tutorials and knowledge base articles that lower the learning curve. Track adoption by cohort to understand which features drive retention.
Ehsan's Analysis
AI feature adoption has an unusual pattern: new AI features get massive initial trial (50-70% of users try them) followed by rapid dropoff (only 10-20% use them after 30 days). Notion AI reportedly saw 60% of users try AI writing within the first week of launch, but only 15% used it consistently after one month. This "novelty → abandonment" curve is unique to AI and does not match traditional feature adoption patterns. The AI features with sustained adoption (Grammarly's corrections, Copilot's completions) share one trait: they require zero user initiation. The AI acts automatically, and the user accepts or rejects. Features requiring the user to invoke AI ("click this button to generate") consistently show the novelty → abandonment curve. The AI feature adoption lesson: integrate AI into the existing workflow as an automatic suggestion, not as a tool the user must remember to activate. Auto-completion, not generation buttons, is the sustainable adoption pattern.
EJ
Ehsan Jahandarpour
AI Growth Strategist & Fractional CMO
Forbes Top 20 Growth Hacker · TEDx Speaker · 716 Academic Citations · Ex-Microsoft · CMO at FirstWave (ASX:FCT) · Forbes Communications Council
Frequently Asked Questions
What is a good Feature Adoption Rate for AI/ML companies at Series B stage?
The median Feature Adoption Rate for AI/ML companies at the Series B stage is 25%. Top‑quartile companies (75th percentile) significantly outperform this baseline. The most important factor is consistent improvement over time rather than hitting any single target number.
How does Feature Adoption Rate differ by company stage in AI/ML?
Feature Adoption Rate typically improves as AI/ML companies mature from seed through growth stage. Earlier‑stage companies should benchmark against stage‑appropriate peers rather than comparing themselves to mature companies.
How often should AI/ML companies measure Feature Adoption Rate?
AI/ML companies at the Series B stage should track Feature Adoption Rate monthly with quarterly deep‑dive analysis. Set up automated dashboards and alerts for significant deviations from your baseline.
What factors most impact Feature Adoption Rate in the AI/ML sector?
In AI/ML, the primary factors impacting Feature Adoption Rate include product‑market fit maturity, competitive landscape intensity, customer segmentation strategy, pricing optimization, and operational efficiency. Series B‑stage companies should focus on the one or two highest‑leverage factors rather than trying to optimize everything simultaneously.
How does Feature Adoption Rate for AI/ML compare to cross‑industry benchmarks?
AI/ML Feature Adoption Rate benchmarks can differ significantly from cross‑industry averages due to factors specific to the AI/ML vertical including customer acquisition dynamics, competitive intensity, and typical deal sizes. Always compare against industry‑specific benchmarks rather than broad averages for meaningful insights.