AI Training · Customer Support & CX
AI Training for Customer Support & CX Teams
Support teams face a growing queue, knowledge scattered across four different places, and customers who expect a clear answer right now, in whatever channel and language they're using.
This training helps your team draft, summarize, classify, and coach with AI, and improve service quality as a result — without ever hiding an escalation or handing a high-stakes resolution to a model.
- modules
- 6
- hours
- 13
Some of the 500+ brands we've worked with
Selected brands we’ve worked with across industries.
MeteksanGlobalCase Study
VisperaGlobalCase Study
Why this training
Why AI for this team?
It's 9am and the queue already has four hundred tickets sitting in it. A password reset. A billing dispute that's been open a week. A customer typing in all caps because the last agent never actually answered the question. Whoever picks up ticket one still has to read the history and find the right policy. Then they decide whether this is a five-minute reply or a case that needs someone with more authority.
AI drafts a response from approved knowledge, summarizes long ticket history, and shifts tone across channels without inventing a policy — but a refund, an account change, or a safety call stays with a person, a boundary set before anyone touches a live ticket. Agents check their own drafts, team leads run QA against a documented rubric, and every workflow keeps a named human owner. Teams wanting an autonomous support bot instead should see our AI Agents training; other programs sit in the AI training index.
The queue grows faster than the roster
AI prepares a grounded first draft and case summary so agents spend their saved time resolving the issue, not rebuilding context.
Why does the same answer keep drifting?
AI turns scattered policies and release notes into one consistent answer without inventing details absent from the source.
A technically correct answer that still sounds cold
A correct reply still costs a customer when it sounds cold, defensive, or generic — tone control matters as much as accuracy.
Translation isn't the multilingual problem
AI speeds a multilingual draft, but a trained reviewer protects product language, local context, and the customer's intent.
Stop sampling five random tickets and calling it QA
A documented rubric and structured classification reveal patterns; a model's score informs coaching, but performance calls stay human.
Syllabus
Training syllabus
AI foundations for customer support and CX
120 minBeginner
A working model of what these tools do well and where they fail. The limits become concrete through support work. Teams name safe-assistance tasks and the high-stakes calls that stay human, then define the check every response needs before it reaches a customer.
- How a language model generates a support reply, in plain terms
- Hallucination and false confidence
- What AI may assist with, and which resolutions stay a human call
- Grounding an answer in approved policy and product knowledge
- A review-before-send habit for every response that leaves the desk
Response drafting with tone and empathy
120 minBeginner
Participants build reusable prompts for email, chat, social, and call-center follow-up, aimed at concise diagnosis, empathetic language, and an escalation path that stays visible instead of a polished reply that hides an unresolved problem.
- A reusable prompt structure for a support response
- Drafting a clear first response from case context
- Tone adjusted without weakening the policy or the accuracy behind it
- Empathetic language that doesn't invent certainty the case doesn't have
- One resolution, many channels
- A last check before the draft goes out, every time
Knowledge-base content and multilingual support
150 minIntermediate
Knowledge only helps once agents and customers can find and understand it. Teams practice drafting from approved source material articles, macros, and localized drafts while keeping terminology, caveats, and final publication ownership intact.
- Approved source material, converted into a help-center article draft
- Building and maintaining a response macro from a policy document
- A gap or a duplicate in the knowledge base, found before a customer does
- A technical explanation, rewritten for a different reading level
- Drafting multilingual support content against a fixed terminology list
- Human review for local meaning and policy accuracy before publication approval
Summarization and escalation by classification
150 minIntermediate
A long history and a messy queue get reduced to what's usable: the issue, what's been tried, the customer's state, and the next step. Participants set categories and escalation triggers that speed up routing. AI must never close a high-stakes case or quietly hide the need for a human owner.
- A long ticket or conversation history, summarized into what's usable
- Intent, urgency, product area, and sentiment, classified in one pass
- Drafting a structured handover note for the next team
- An escalation trigger with a named owner attached to it
- What AI never resolves
- Sharing the escalation notice with the customer as well as the receiving team
QA and service-quality coaching
120 minIntermediate
A QA rubric becomes a consistent review aid instead of an opaque score handed down from above. Team leads learn to summarize a pattern, prepare a coaching conversation, and turn a repeat failure into a knowledge or workflow fix, without handing employment decisions to a model.
- A QA rubric for accuracy, tone, process, and resolution quality
- A conversation, reviewed against a documented rubric instead of a hunch
- Summarizing a recurring strength or a coaching opportunity
- Evidence-based coaching notes, prepared for the team lead before the conversation
- Turning a repeat failure into a knowledge-base or workflow change
- Performance decisions stay human
Customer-data governance and team rollout
120 minAdvanced
Daily practice for handling customer data, KVKK, approved tools, access, and auditability, closing with a pilot plan that names its workflow, its reviewers, its escalation path, and the quality measure that tells you it's working.
- Personal data and customer identifiers inside a support conversation
- KVKK-aware handling of an example or transcript, including attachments
- Where consumer tools and enterprise tools send your data differently
- Ownership, logs, audit trail
- Picking a low-risk pilot workflow and a baseline quality measure
- A rollout plan with named reviewers and escalation paths, plus regular calibration
Outcomes
Outcomes & audience
What you will learn
- Draft a reply, checked before sending
- Match tone to the moment
- Turn source material into a knowledge-base article, plus a reusable macro
- Draft a multilingual reply that keeps the original meaning
- Turn a messy thread into a clean handover
- A high-stakes call that stays human
- Prepare coaching notes from rubric scores, real transcripts included
- Handle transcripts the KVKK-aware way
Who should attend
- Customer support agents and senior agents
- Contact-center and customer-service team leads
- Customer-experience and service-design teams
- Quality-assurance and coaching specialists, including trainers
- Knowledge-management and help-center content owners
- Support-operations and escalation managers
Format
Training format
Two days, about 13 hours total, split into half-day blocks if queue coverage is tight. Onsite and live-online sessions both use your own approved examples and service standards.
- Format
- Onsite or live online
- Duration
- 2 days (about 13 hours, can be split into half-day sessions)
- Group size
- Up to 20 participants per group
- Language
- English or Turkish
- Materials
- Prompt library, QA rubric, escalation worksheet, and practice workbook
- Certificate
- Certificate of completion
Keep exploring
Related programs
Next step
Build this around your queue
Questions
Frequently asked questions
Who is this built for on a support team?
Frontline agents, team leads, contact-center managers, CX teams, QA and coaching specialists, knowledge owners, and support-ops all get something from this. We can group a cohort by role so frontline practice and leadership review use relevant material. Knowledge work gets its own examples too.
Do participants need coding or technical experience?
No. There's no coding or data-science prerequisite. You need to know your own channels, policies, knowledge sources, and escalation paths, because the exercises get built around that context.
How long is this, and how do you deliver it?
About 13 hours, usually across two days or in half-day blocks that protect queue coverage, delivered onsite or live online with guided practice and review included either way.
Can you build this around our actual support operation?
Yes. Before the program we go through your channels, approved tools, knowledge sources, QA rubric, and escalation model, and the exercises use sanitized or synthetic examples shaped around your own products and standards.
What tools do we practise on?
The core skills carry across mainstream tools like ChatGPT, Microsoft Copilot, and Gemini, subject to whatever your organization has already approved. If the goal is building an autonomous assistant rather than improving how the team works day to day, that's a separate build discipline covered in our AI Agents program.
Can AI make a refund or account decision, including a safety call?
The program treats AI as drafting, summarization, classification, and review support. A refund, an account change, or a safety call stays with an authorized person. The escalation path stays visible even when the draft sounds confident.
How is customer data and KVKK handled during training?
The material is synthetic or anonymized, with full sanitization before anyone opens it. The governance module then covers which tickets may enter an approved tool and which may not, how access and logging carry accountability, and the point at which privacy or legal has to be brought in. This is operational training. Legal questions go to your own counsel.
How is pricing worked out, and what's left after the training ends?
Group size, delivery format, customization depth, and location set the number, so each proposal is built for the specific team. Your people keep the working prompt library, the QA rubric, the escalation worksheet, and the rollout plan built during the sessions.