Scaling AI-Powered Support Platforms for 500K+ Global Users

Increased user satisfaction by 15 points and reduced support backlog by 30% through AI-driven automation workflows and strategic rollout of features without increasing operational costs.

Problem Statement

As Butternut AI’s user base crossed 500K+ globally, our support systems designed for early-stage scale, started showing signs of strain:

• Response times were getting longer.
• Users weren’t adopting new self-service features as intended.
• Support backlog was growing, eating into both time and morale.

The issue surfaced in data reviews and user feedback loops:

• NPS dropped by 8 points over one quarter.
• Chatbot abandonment rates increased by 22%.
• Support ticket resolution time rose by 18 hours on average.

Root Cause Analysis

We applied a Service Performance Assessment Framework that looked at People, Process, and Platform.

Key findings:

• Platform Limitation: The existing AI support model couldn’t handle concurrency at peak times.
• Process Gaps: Feature releases were being shipped, but adoption paths weren’t reinforced with clear onboarding or contextual prompts.
• People Factor: The support team spent a large portion of time on repetitive, low-complexity queries that could be automated.

The problem wasn’t just capacity, it was efficiency and adoption alignment.

Strategy Development

This time, instead of a workshop-heavy approach, I implemented a Design Sprint with Rapid Prototyping approach:

• Stakeholder Mapping: Identified core decision-makers: Product (feature owners), Engineering (platform capabilities), and Customer Experience (support workflows).
• Discovery Sessions: Used journey mapping of user support experiences to pinpoint drop-off and frustration points.
• Rapid Ideation: Focused on automation-first solutions, AI-model fine-tuning, and better in-product prompts to encourage self-service.

Implementation & Roadblocks

Initial attempt:

• Rolled out a highly automated AI-chat escalation model.
• Expected to reduce live agent load by 40%.

What went wrong:

• Too aggressive automation → Users felt “trapped” in chat loops without human escalation.
• Support tickets for escalations spiked temporarily (+12%).

Lessons Learned:

• AI needed human-in-the-loop failsafes for better escalation.
• Adoption required progressive introduction of AI tools, not an abrupt shift.

Final solution was a hybrid AI-powered support platform:

• Automation-first for repetitive queries, with clear human escalation paths.
• Iterative AI Feature Rollouts: Rolled out new features in cohorts to monitor adoption before full deployment.
• Proactive Self-Service Nudges: In-app contextual prompts (e.g., “Have you tried this?”) to guide users to solutions.

Monitoring tools:

• Real-time AI dashboard tracking escalations, resolution time, and feature adoption.
• Monthly CX retrospectives to refine scripts, prompts, and training data.

Outcomes

• +15 pts User Satisfaction: Users appreciated faster resolution and clearer human support when needed.
• −30% Support Backlog: Automation absorbed repetitive queries; human team focused on complex issues.
• Achieved efficiency without increasing staffing or budget.

Why it worked:

• Solution matched AI maturity to user comfort (gradual rollout).
• Built trust in automation through reliable human backup.
• Maintained clear feedback loops to adjust quickly.

Conclusion

This project reminded me that operational excellence is as much about adoption as it is about capability. Scaling support at a startup isn’t about replacing humans with AI, it’s about designing human-centered automation that builds user trust, preserves efficiency, and keeps pace with growth.