Turning a slow, error-prone AI setup into a guided flow that lifted adoption of a top Sybill revenue driver by 83%.
OVERVIEW
One of Sybill's biggest bets is using AI to remove a sales rep's most tedious daily task: manually updating the CRM after every call. It's a top revenue driver and a key competitive differentiator, yet adoption was stalling. The setup was so complex that many teams dropped off before ever reaching value, leaving revenue on the table on the exact feature meant to pull customers up into the paid tiers.
I led the diagnosis and end-to-end redesign of that setup. By reframing the core mental model and rebuilding the flow around it, I lifted feature adoption by 83% and helped convert more customers onto Sybill's paid Business plan.
MY CONTRIBUTION
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🎉
- Led cross-functional partners to change direction by reframing the core mental model.
- Defined foundational components for Sybill's AI feature set and expanded the design system.
- Partnered with engineering to solve technical constraints and design for trust in AI
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IMPACT
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🎉
83% increase in paid-org adoption
within 5 months after launch
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🎉
77% faster setup
45 → 7 minutes
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🎉
58% more existing customers activated
getting the value they'd previously skipped
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🎉
Drove new revenue two ways
- Stronger SLG demos created on-call “aha” moments that converted prospects to the Business plan
- Existing customers who finally adopted Autofill expanded seat counts
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EMPHASIZE
The real problem wasn't the feature. Teams never finished setup.
Low adoption directly suppressed the revenue this feature was meant to drive into Sybill's paid tiers.
The team knew adoption was low but not why. The early assumption was a functionality gap, that the feature lacked something teams needed.
To diagnose, I did research by talking to customers, analyzing user behaviors, and worked closely with the CS and sales teams to see where users actually got stuck.
Old version
Research pointed somewhere other than functionality: the real blocker was the drop off during setup, so the feature never got the chance to deliver value.
From user research, we discovered the following friction points during feature set up :
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😵💫
High-effort setup
Setting up required extensive manual work to create and configure CRM fields in Sybill
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😕
Prompt engineering burden
Each field needed a custom AI prompt to get team-specific outcome, forcing busy users to act like prompt engineers.
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😕
Error-prone configuration
Every field had to be mapped and typed manually, so a single mismatch could break the sync
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😵
Lack of activation
Users landed on a bare settings page with no guidance on what to set up, or that setup had even started.
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