Reduce burnout on my support team
A practical plan that spots burnout risks in workload, schedules, and emotional strain and offers changes a team lead can make.
The prompt
Help me reduce burnout on my support team.
Team size and setup: [agents, shifts, channels, remote or on-site]
Workload signals: [tickets per agent, backlog trend, overtime, after-hours work]
Emotional load: [how often agents handle angry or abusive customers, sensitive topics]
People signals: [sick days, attrition, engagement survey results, what agents have told me]
What I control: [scheduling, queue assignment, targets, breaks, small budget, process changes]
What I don't control: [headcount, pay, product issues]
Do this:
1. Identify the likely burnout drivers from what I've shared: workload, lack of control, constant exposure to anger, unfair metrics, monotony, no recovery time, or feeling unheard. Rank them by evidence.
2. Suggest changes within my control, such as rotating off high-stress queues, protected off-queue time, decompression breaks after hard contacts, a clear policy for ending abusive conversations, adjusting targets that reward speed over quality, and recognition for good outcomes.
3. Suggest what to escalate to my manager, with the business case (attrition cost, quality impact).
4. Give me questions for 1:1s to check in on wellbeing without making it awkward.
5. Give me a 30-day plan and 3 indicators to track.
If anyone mentions thoughts of self-harm or a crisis, point them to professional support, your employee assistance program if available, or local crisis services right away. This is not a substitute for HR or mental health professionals.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Leadership & Management prompts
What to fill in
- agents, shifts, channels, remote or on-site
- tickets per agent, backlog trend, overtime, after-hours work
- headcount, pay, product issues
How this prompt is built
This prompt follows the RTF structure (Role, Task, Format). RTF tells the model who to be, what to do and what shape the answer should take. It is the quickest structure that still gives noticeably better results than a bare request.
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