The AI Contact Center Handbook
A practical field guide to customer experience in the age of agentic AI — chapter by chapter, for teams evaluating AB Support.
Appendix F — CX Math and Operational Models
The four pieces of math a CX leader should be able to sketch on a whiteboard — Erlang C with a worked example, Erlang O concurrency, LLM inference economics with model routing and caching, containment arithmetic, and blended cost per contact.
Appendix E — Vendor Market Guide
The 2026 CCaaS and CPaaS market map, a seven-vendor comparison, a hidden-cost checklist that catches the two-to-three-times multiplier on published pricing, and the reference-check questions worth asking.
Appendix D — Agentic AI Developer Toolkit (MCP)
A working map of the Model Context Protocol (MCP) — what it is, curated MCP servers by category for contact center use, three architectural composition patterns, and the checklist for building your own MCP server safely.
Appendix C — Security & Compliance Frameworks
PCI DSS v4.0 in twelve steps, the merchant-level thresholds that decide validation obligations, and a working comparison of GDPR, CCPA/CPRA, TCPA, LGPD, and APPI for any multinational contact center.
Appendix B — Readiness Checklists
Diagnostic tools for the voice AI transformation: a 20-question readiness assessment, a 25-question vendor security screen, a DPIA template, five survey designs, an executive readiness agenda, and the weekly operating rhythm for a live AI program.
Appendix A — Glossary
A single alphabetical A–Z glossary covering the contact center industry, AI, and compliance — every acronym you will hear in a CCaaS bake-off, a QBR, or a security review, defined in one sentence.
The Toolkit — Six Appendices
Six practical reference tools from The AI Contact Center Handbook — glossary, readiness checklists, compliance frameworks, MCP developer toolkit, vendor market guide, and CX math.
Chapter 12 — Strategic Vendor Selection and Implementation
The four vendor camps, the phased rollout playbook, contract terms that actually matter, the shift to outcome-based pricing, and a twelve-month calendar of what goes right and wrong.
Chapter 9 — Keeping AI out of PCI Scope
Why post-call redaction fails PCI DSS v4.0 audits, how the LLM Scope Rule silently pulls modern contact-center systems into scope, and the pause-and-resume architecture with network-level DTMF masking the industry has converged on.
Chapter 11 — The Changing Job Market and New Enterprise Roles
Which contact center jobs shrink, which grow, and how today's agents actually bridge into the new roles. The workforce transition as a role-definition problem, not a headcount problem.
Chapter 10 — Guardian Agents and Trust by Design
The AI that watches the AI. Guardian Agents, voice biometrics, deepfake detection, and the multi-layer trust architecture behind AI-heavy contact centers in 2026.
Chapter 7 — Hyper-Personalization and Omnichannel Journeys
Chapter 7 of the AI Contact Center Handbook — why omnichannel became multichannel, what optichannel replaces it with, live journey analytics, real-time speech-to-speech translation, and the trust line that separates helpful personalization from creepy surveillance.
Part 5 — Execution
Part 5 of The AI Contact Center Handbook — where the book stops describing the terrain and starts telling you what to do: trust architecture, the changing workforce, and vendor selection.
Part 3 — Quality & Measurement
Part 3 of the AI Contact Center Handbook — how quality assurance, workforce engagement, and personalization change when AI does the talking. Covers WEM (Ch 6) and hyper-personalization plus omnichannel journeys (Ch 7).
Chapter 5 — The Experience Orchestration Platform (XOP)
Chapter 5 walkthrough from The AI Contact Center Handbook. The XOP is the sub-second decision layer above the CCaaS, CRM, and CDP — the brain that fuses live signals and CAMARA network APIs into a real-time answer.