Prepare for your Agent Copilot launch

AI-generated summary

Success requires continuous tuning through weekly reviews of prompts, knowledge content, and dictionary refinements based on agent and supervisor feedback. Measurement should track adoption metrics (usage, confidence, feedback quality), efficiency indicators (After Call Work time, Average Handle Time, queue return speed), and experience metrics (note consistency, customer satisfaction, first contact resolution). Critical success factors include coaching supervisors before agents, aligning incentives with faster ACW and AHT reduction, ensuring agents use AI-generated notes as their primary documentation source rather than maintaining manual notes, and providing direct support during the adoption period

To get value quickly, complete the following steps before the official kick-off. Think in phases instead of a single large launch, and follow the 80/20 rule, that is, cover your most repetitive scenarios first, then expand.

Phase 1: Summaries and wrap-up support

Start with summarization and wrap-up codes. This is your easiest value story: less manual work after each call, more consistent notes, and agents back on the queue faster.
Begin with one queue, a focused set of use cases, and a representative group of agents.
Encourage agents to use Agent Copilot as their primary source of notes. Agents who keep taking manual notes during calls do not see time savings.
Review summaries for factual accuracy only, not for style.
Set up a trigger to push the summary into your system of record. Avoid copy and pasting.

For me information on how to configure summaries, see Customizable summaries overview.

Phase 2: Checklists and canned responses

Add checklists and predefined (canned) responses at key conversation points to guide agents through repetitive scenarios.

Phase 3: Key intents and simple rules

Configure your key intents and a small set of simple rules, starting with the scenarios that carry the highest volume.

Phase 4: Knowledge and answer generation

Review and update your key knowledge areas for accuracy and relevance, then turn on answer generation. Knowledge matters, but it should follow agent trust, clear content ownership, and a repeatable refinement cadence, so treat it as a later step rather than a starting point.

Throughout your rollout

Coach supervisors and team leaders before you coach agents. They set expectations and make feedback visible, which makes adoption consistent.
Tune continuously. Summary prompts, knowledge, your dictionary, and NLU rules all improve with active refinement, so set a weekly cadence to review them.
Add relevant domain-specific keywords and phrases to your dictionary to improve transcription accuracy, then refine your dictionary over the first two to three weeks, based on agent and team leader feedback. Transcription quality improves gradually as the dictionary develops. For more information, see Work with dictionary management.
Measure across adoption (usage, agent confidence, feedback quality), efficiency (ACW, AHT, time back to queue), and experience (note consistency, CSAT or NPS, transfers and first contact resolution). Tracking one or two hard metrics in isolation gives a misleading picture.
Align incentives with faster After Call Work and AHT. If agents can use the full ACW allowance regardless, there are no savings.
Be present with your agents and give them time to get comfortable with the system. Capture examples of how they benefit from AI-generated notes.

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