Customer service copilot inside the existing workflow

Context

A logistics provider's customer service team was handling thousands of tickets per day across shipment status, ETA queries, claims initiation, and exception handling. Earlier attempts at chatbots had failed because they sat outside the agent's existing tooling. Agents wouldn't context-switch into a separate AI tool — and any productivity gain on paper disappeared in the workflow break.

Approach

The AI was designed as a sidekick inside the existing ticketing UI, not a separate app. Suggested responses, draft text, and shipment data appeared inline with each ticket. The model was trained on the team's actual response patterns, so the suggestions fit the tone and approach the team already used.

Rollout was deliberately staged. The tool ran in shadow mode for two weeks — suggestions visible to a small group of agents but not surfaced to customers. After QA-graded sampling confirmed quality, the wider team got opt-in access. Only after stable QA results across two more weeks did the suggestions become the default first-draft response. Agent feedback shaped each phase.

Outcome

• Agents now handle roughly 60% more tickets per shift.
• QA-graded response quality has held stable or slightly improved through the rollout.
• New hires train against the AI-augmented workflow as the default — adoption is no longer a separate change-management problem.

Principle illustrated

Adoption. The technical model was solid in lab tests, but the determining factor was that it lived inside the workflow agents already used. Adoption shaped the design as much as accuracy did. The lesson: an AI feature that requires people to change tools tends not to land.

Used with permission from the client and shared here for illustrative purposes. Specific commercial details remain confidential and are available on request to qualified counterparties under NDA.

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