What's next?
What's next
The AI Agent has been trained, tested, deployed, and you've analyzed its initial results. It's handling real conversations, potentially taking actions, producing measurable outcomes, and generating a baseline of performance data.
The focus now shifts from launch to optimization.
The rollout framework becomes a repeatable process for optimizing the AI Agent, applied continuously in smaller, targeted iterations.
Think of it as a flywheel: Train, Test, Deploy, Analyze.
After initial deployment, you re-enter the loop at the analyze stage:
1. Analyze
Use performance data to identify enhancement opportunities
Post-launch, you need to keep track of your AI Agent's performance so you can see what's working well and what needs to be improved.
Focus on macro patterns, not individual queries. Look for:
- Topics with frequent handoffs or low resolution (often linked to content gaps).
- Follow-up questions that indicate lack of clarity.
- Segments or channels with lower satisfaction.
The goal is to identify improvements that will significantly increase resolution, reduce volume on the human side, or expand the AI Agent's coverage.
Fin has built-in tools to surface these insights:
- Topics ExplorerAutomatically organizes every conversation into topics and subtopics to highlight emerging issues.
- Customer Experience ScoreEvaluates and scores every interaction across sentiment, resolution, and service quality.
- Optimize DashboardSurfaces AI-generated suggestions for immediate improvements.
2. Train
Refine coverage, clarity, behavior, or task handling
Based on what's surfaced in analysis, make targeted updates to the AI Agent's knowledge and behavior.
This might include:
- Adding content to improve topic coverage.
- Updating guidance to clarify tone, policies, or handover rules.
- Connecting new data sources for personalization.
- Configuring structured task flows for scenarios that go beyond informational queries (e.g., cancellations or refunds).
The goal is to expand what your AI Agent can handle autonomously while delivering the same high-quality support as your human team.
With Fin, your team can manage training through a centralized system:
- Fin GuidanceEnables you to define policy, tone, and behavior.
- Content TargetingPersonalize responses based on plan, location, or usage.
- Fin TasksAllows you to automate complex flows like refunds or cancellations.
3. Test
Validate changes with simulated or historical data
Before deploying updates, validate them against real scenarios to reduce risk and prevent costly rollbacks.
Run batch tests, preview new behaviors, and stress-test updates across customer types and channels.
Fin has built-in testing tools that let your team validate changes before they go live:
- Fin PreviewTo test how updates will appear to customers in live conversations.
- Answer InspectionTo show how Fin generated each response, including which content or attributes were used.
- Answer RatingTo assess response quality and prioritize areas for improvement.
4. Deploy
Once updates are tested, roll them out in a controlled, measured way.
Deployment decisions typically include:
- Which channels (email, chat, social, etc.).
- Which audiences (by geography, plan, or customer type).
Your team can configure how updates are deployed using Fin's built-in controls:
- Channel expansione.g., Fin over email, Fin for phone.
- Audience targetingFor precise rollout.
- AI CategoriesTo automatically and seamlessly route conversations to the right inbox.
Once you've deployed, move back to the Analyze stage and monitor performance closely. Use early signals to guide the next round of improvements.
Chapter 4: Scale it
Once the improvement loop is running, it becomes the foundation for scale – the next priority.
That means:
- Rewiring customer journeys for seamless, AI-first service delivery.
- Shifting from experimentation to formal roles and governance.
- Elevating support from a cost center to a strategic growth lever.
Coming soon.