Growth idea action plan
AI vs non-AI activation benchmark
Measure AI feature usage against the core product path and by customer segment so the team sees whether the AI is helping the right users or merely attracting the wrong curiosity.
Why this can grow a startup
AI usage can look encouraging while still hiding a product mistake. A team may see a lot of interaction and assume the feature is working, when the real pattern is that non-ICP users are playing with it more than the buyers the product actually needs. Benchmarking AI usage against the normal activation path and across segments forces a harder question: is the AI improving the product's real job or only creating extra attention around itself? That view helps teams tune positioning, onboarding, and roadmap bets before they build too much around vanity engagement.
Ian's take
From scaling consumer platforms across MENA and Southeast Asia, my default is to distrust growth work that only looks good in a slide. My bias is to treat this as a small market test first. Make the audience narrow, make the promise concrete, and let the first real response decide whether it deserves more work. I would run it small enough to learn quickly, then only scale the parts that real users repeat, save, reply to, or buy from. For this tactic, I would watch one clear growth signal before putting more time or budget behind it.
Action plan
- Define one narrow startup segment where ai vs non-ai activation benchmark can create a measurable lift.
- Turn the tactic into one offer, page, campaign, or workflow for the Analytics and Activation channel.
- Use the evidence from newsletter.posthog.com to set the first version of the message, format, and audience.
- Launch a small test for 7 to 14 days with one success metric: one measurable growth signal.
- Review the result, keep the winning message, remove weak variants, and turn the learning into a repeatable growth playbook.
Source-backed example
PostHog found marketers and product managers used Max AI more often than product engineers, which pushed the team to rethink who the assistant was actually serving.
Source: PostHog Newsletter (newsletter.posthog.com)
GrowthDex source hub: PostHog Newsletter
Last checked: 2026-05-26
Adjacent tactics in the same lane
If this page is close to your problem, these tactic pages usually belong in the same working set.
- Workflow-first AI demand validation same source · 1 shared channel · 1 shared stage
- AI install wizard for 90-second setup same source · 1 shared channel · 1 shared stage
- Suggested prompts in the empty AI state same source · 1 shared channel · 1 shared stage
- Task-based model routing for AI speed same source · 1 shared channel · 1 shared stage
Related GrowthDex essays
- The AI feature only feels smart after the first useful minute ai products, activation, product UX
Read GrowthDex essays
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Why this is worth your time
GrowthDex starts with tactics that founders, marketers, and product teams have actually tried. Each essay turns the evidence into a practical move you can test without pretending one case study is a guarantee.
Ian Goh has helped grow consumer platforms across Southeast Asia, India, and MENA. His work includes scaling Tiki to 100M+ users, doubling BIGO's MENA revenue in 7 months, and increasing OYO's direct booking share across 6 Southeast Asian markets.
- Helped scale Tiki to 100M+ users.
- Doubled BIGO's MENA revenue in 7 months.
- Raised OYO's direct booking share by 50% across 6 Southeast Asian markets.
Want help turning this into a growth system?
If you want someone to pressure-test this against your real market, Ian works with founders on growth, market entry, and operator-led distribution.
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