Treat AI Like Human Agents: What Support Leaders Can Learn from Chime
AI in support isn't a silver bullet. It's a system. Chime shows that if you hold AI to the same standards as your human team, you can improve outcomes without grinding your customer experience for the sake of "containment."
By treating its chatbot and voice bot like full-fledged agents - with the same QA, policies, and governance - Chime has its bots handling around 70% of customer support. In some channels, CSAT is up by more than 50%, and automated resolution has climbed almost 40%.
Hold AI to the Same Standard as Human Agents
Chime runs its AI agents through the exact QA process used for humans. Every interaction generates a transcript. Every transcript goes through QA. The bots follow the same SOPs and policies as live agents and have the same level of empowerment to resolve issues.
This shifts AI from a black box to an auditable, coachable teammate. The team can pinpoint where the model underperforms, feed those insights to their GenAI partners, and iterate without guessing.
Make Your Metrics Non-Negotiable
Vendors love containment and cost. Chime optimized for two "hero metrics": automated resolution rate and CSAT - at the same time. "It's not that hard to make one rise at the expense of the other. Our real challenge to them was we need both."
They use separate vendors for chat and voice to keep optionality and treat both as strategic partners. That required re-training vendor instincts. Any hidden friction that blocked handoff to a live agent was unwound and used as a coaching moment to re-align on the North Star.
The "Art and Science" of Escalation
Chime started with simpler, lower-risk flows (many inherited from the legacy IVR) and expanded gradually. Data now guides what to automate, when to escalate, and which channel performs better for a given issue type. Some tasks simply work better on the phone than in chat - and vice versa.
As bots took on more volume, contacts to human agents dropped from roughly 50% to about 20%. Human teams now focus on higher-complexity, longer-running work - where they add the most value.
Prevent Hallucinations by Controlling the Inputs
Before rollout, Chime consolidated content into a single source of truth. That clean foundation reduced hallucinations and let the team catch issues early during QA sprints - before members ever saw the bot.
The result is an ecosystem of data across channels, agents, and bots that shows where customers fall off the happy path, how quickly they're recovered, and what happens next.
Crawl-Walk-Run Rollout
Chime staged deployment carefully: internal employees first, then BPO agents, then 1% of members, then 100%. They spent five months preparing. Once the first 1% went live, it took two months to reach full rollout - fast, because the groundwork was solid.
Copy This Playbook
- Unify SOPs, policies, and knowledge into a single source of truth. Give bots the same policy surface area as humans.
- Instrument everything. Store transcripts. Run the same QA scorecards across human and AI interactions.
- Set two hero metrics and refuse trade-offs: automated resolution rate and CSAT. Review both in every vendor meeting.
- Demand vendor alignment. Block any tactic that adds friction to live-agent access just to goose automation numbers.
- Start with low-risk, high-volume intents. Expand quarterly based on data, not gut feel.
- Route by channel fit. Some issues belong in voice; others perform better in chat.
- Stand up a safety layer: redaction, access controls, and a content governance process to reduce hallucinations.
- Roll out in stages: internal → BPO → small member cohort → full GA. Gate each stage on measured outcomes.
- Re-scope agent work. Point humans at complex, emotional, or high-risk interactions where judgment matters most.
Questions to Put in Front of Your Vendors
- Can we export all bot transcripts and score them with our QA tooling?
- How do you optimize for resolution and CSAT simultaneously - without throttling handoff to live agents?
- What controls prevent unapproved changes that add friction to escalation?
- How are PII handling, redaction, and access permissions enforced across channels?
- What's your process for fast iteration when QA flags a model behavior issue?
Compliance and Governance Basics
If your industry is sensitive (finance, healthcare, public sector), connect your program to an AI risk framework from day one. It gives your legal, risk, and support teams a shared playbook.
Key Outcomes Chime Reported
- Bots handle ~70% of support volume.
- CSAT up by 50%+ in some channels.
- Automated resolution up nearly 40%.
- Human agent share reduced from ~50% to ~20%, focusing people on complex cases.
What This Means for Customer Support Teams
Your AI agents should be held to the same bar as your best human agents. Same rules. Same QA. Same accountability. If a vendor can't support that, you'll pay for it in hidden friction and lost trust.
Start small, measure hard, and scale only when both resolution and CSAT rise together. That's the signal you've built a system - not just dropped in a bot.
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