Customer Support Operations With AI
Use AI for triage, drafting and self service in a support team without hurting customers or agents
A taste of a lesson
Since launching our support bot, deflection is up but complaints are also up. What is going on?
A deflected contact is not necessarily a solved problem. Some customers who leave the bot without reaching a person simply gave up, and come back angrier later or complain elsewhere. Check three things: repeat contacts within a week from people who used the bot, how easy it is to reach a person, and a sample of bot conversations reviewed for correct answers. Often a few topics cause most failures and should go back to agents. Exercise: pull twenty bot conversations that ended without escalation and mark which ones actually solved the issue.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Map the support workflow and choose where AI helps at each stage
- Set handoff rules that get customers to a person when needed
- Measure resolution and satisfaction, not just deflection
- Roll out agent assist before customer facing automation
- Support agents through changes to their work
Lesson plan
- 1 Mapping the support workflow Lay out intake, handling, escalation and follow up before adding AI. Start
- 2 Triage and routing Use AI to classify, prioritise and route tickets accurately. Start
- 3 Agent assist Give agents grounded draft replies and summaries they review and edit. Start
- 4 Self service with a way out Automate common questions while keeping an easy route to a person. Start
- 5 Measuring what matters Build a dashboard that shows whether customers' problems are solved. Start
- 6 People and change Plan the human side of AI in support for agents and customers. Start
Try asking
About this tutor
For support team leads, operations managers and experienced agents running a support function with several people. You will learn where AI fits across the support workflow: classifying and routing tickets, suggesting replies for agents to edit, summarising long threads, self service answers for common questions, and spotting trends. We give equal weight to the operational side: escalation paths, when a human must take over, measuring resolution and satisfaction rather than deflection alone, protecting customer data, and supporting agents whose jobs change. Lessons use your own ticket categories and metrics. You finish with a workflow map showing where AI helps, a set of handoff rules, and a measurement plan for the first months.
Reviews
4.3
3 ratingsSample
- Bianca M.Sample
The deflection versus resolution lesson explained exactly what went wrong with our first bot. We moved two topics back to agents and complaints dropped.
- Nkechi A.Sample
Practical and fair to agents, which I appreciated as a team lead. The edit rate metric is now on our weekly dashboard.
- Tomasz K.Sample
Strong on handoff rules. Starting with agent assist was the right call for us. Wanted more on multilingual support.
About the teacher
Risk, vendors, data and support: the unglamorous work that makes AI safe to rely on
9 tutors 493 lessons taught Sample
I teach the parts of business AI that decide whether a project survives contact with reality: data quality, vendor choices, contracts, security habits and customer support operations. My background is in IT operations and service management, which taught me to ask what happens when something goes wrong before asking what happens when it goes right. I teach with checklists, worked...
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