Customer Support And Training

Who this is for

AI Product Managers and cross-functional leads who need to launch and scale AI features with reliable customer support and effective user training.

Prerequisites

• Basic understanding of your AI product’s core use cases and limitations
• Familiarity with release management and feedback loops
• Ability to read basic support metrics (e.g., CSAT, time to resolution)

Why this matters

Great support and training turn AI curiosity into adoption and loyalty. As an AI PM, you’ll be asked to:

• Plan support capacity for AI launches and spikes
• Create playbooks for model-specific issues (e.g., hallucinations, bias, drift)
• Prepare knowledge bases, tooltips, and training for different user segments
• Instrument metrics to reduce tickets and increase time-to-value
• Close the loop: convert support insights into product improvements

Concept explained simply

Support helps users succeed after launch. Training helps them get value faster and avoid mistakes. For AI products, both must address uncertainty: model behavior can be non-deterministic and change with data or updates.

Mental model: The Support Runway

Picture a runway that aircraft use to take off safely. Before an AI feature “takes off,” you lay down:

• Clear expectations: what the AI can and cannot do
• Guides and training: how to get value in minutes, not weeks
• Support playbooks: what happens when things go wrong
• Feedback loop: how issues inform the next iteration

Key components

• Support channels: in-app help, email, chat, community, phone (for enterprise)
• Workflows: intake form → triage → diagnosis → resolution → follow-up
• SLAs and severity: Sev-1 (business-stopping) to Sev-4 (minor), with response/resolution targets
• Knowledge base: task guides, troubleshooting, release notes, known limitations
• Training types: quick-start guide, micro-courses, live webinars, admin enablement, train-the-trainer
• AI-specific considerations: model hallucinations, bias and safety, data privacy, versioning, release transparency, rollback plan

Metrics that matter

• CSAT (post-ticket satisfaction)
• FRT (First Response Time)
• TTR (Time to Resolution) and ART (Average Resolution Time)
• Deflection rate (self-serve resolutions/total attempts)
• Containment rate for bots (resolved without human handoff)
• Training adoption: course completions, feature activation post-training
• Time-to-value: time from signup to first successful task
• Issue recurrence rate: % of issues returning within 30 days

Worked examples

Example 1: Launching an AI writing assistant

• Pre-launch: publish “What it’s good at / not good at,” add in-product coach marks, create 10 macros for common issues (e.g., tone drift, repetitive output)
• Support: triage form asks for prompt, output sample, context, and version
• Training: a 15-minute Quick Start and a 45-minute Power User session
• Metrics: target FRT < 2 hours, 30% ticket deflection via KB

Example 2: Handling hallucinations in a search chatbot

• Playbook: verify source coverage → replicate with provided prompt → classify (data gap vs. retrieval error vs. model behavior) → respond with safe template + workaround
• Training: teach “evidence-first prompting” and how to request sources
• Follow-up: add missing sources to index; update KB article and release notes

Example 3: Enterprise rollout with admin training

• Enable IT/Admins: SSO setup guide, data retention policy, audit logs
• Change management: pilot champions, office hours, sandbox week
• Escalation: 24/7 path for Sev-1, weekly intel brief to stakeholders

Playbooks and templates

Step-by-step implementation

1. Map journeys: list top 5 tasks users try with your AI; identify likely failure modes.
2. Draft triage + macros: create 8–12 macros covering the top 80% of issues.
3. Build quick start: a 10–15 minute guide that gets users to first value.
4. Instrument: tag tickets by root cause; add CSAT; track deflection and TTR.
5. Pilot and iterate: dry-run with support agents; refine based on real tickets.
6. Enable champions: run train-the-trainer; set up office hours and FAQ cadence.

Exercises

Note: Everyone can take the exercises and quick test. Only logged-in users will have progress saved.

1. Exercise 1 (matches Ex1 below): Draft a one-page support and training plan for a new AI feature.
2. Exercise 2 (matches Ex2 below): Create a triage decision tree for an AI incident scenario.

Self-check checklist

• I defined severity levels and SLAs appropriate for AI incidents.
• I created at least 8 macros covering common AI issues.
• I produced a 15-minute quick start that leads to first value.
• I set up metrics for CSAT, FRT, TTR, deflection, and containment.
• I included a rollback and communication plan for risky AI changes.

Common mistakes and how to self-check

• Vague expectations: If users don’t know limits, tickets spike. Remedy: add “good at / not good at” to onboarding and KB.
• No reproducible context: Tickets stall without prompts/examples. Remedy: require prompt, output, task goal, and version in intake.
• Ignoring safety: Lack of bias/hallucination guidance causes trust loss. Remedy: safe response macros and escalation to evaluation team.
• Overloading support at launch: Remedy: pilot, soft launch, office hours, and staged rollout.
• Training that’s too long: Remedy: 10–15 minute quick start plus optional deep dives.
• No feedback loop: Remedy: tag root causes and share weekly insights with product/engineering.

Practical projects

• Ship a mini knowledge base: 5 articles, 10 macros, and a triage form tailored to your AI product.
• Design a 4-week enablement program: quick start, deep dive, office hours, champions, and a final assessment.
• Build a launch playbook: SLAs, escalation paths, rollback plan, release notes, and comms templates.

Mini challenge

In 10 lines or less, write a safe-response macro for when your AI tool refuses to answer due to low confidence. Include: empathy, brief reason, next step, and how the user can improve context.

Learning path

1. Draft your triage workflow and macros for top 5 issues.
2. Create a quick start and one deep-dive training.
3. Instrument support and training metrics.
4. Run a pilot with a small user cohort; iterate weekly.
5. Scale to broader audience with champions and office hours.

Next steps

• Complete the exercises to create your plan and triage tree.
• Take the quick test to confirm your understanding.
• Apply the plan to your next feature launch and track results for 2 sprints.