Contact center modernization: from IVR to conversational AI
A step-by-step migration model for retiring legacy IVR and moving to conversational AI without breaking CX. Covers platform selection, data readiness, integrations, agent experience, QA and cutover — proven across NICE, Genesys, Amazon Connect, Kore.ai and Five9 estates.
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What you'll learn
- How to sequence a multi-region IVR-to-conversational-AI migration in phases
- The 4-question platform decision tree (Genesys / NICE / Amazon Connect / Kore.ai / Five9 / Salesforce Agentforce)
- Data readiness — knowledge, CRM, entitlement and identity — before you go live
- Agent experience patterns that make agents faster, not more distracted
- Automated QA and containment SLOs that survive board scrutiny
- A cutover checklist that keeps SLAs green during the switch
What's covered
An excerpt of the full document. Request access above for the complete asset — including diagrams, templates and code where applicable.
- 01
Why IVR replacement fails
Wrong sequence, wrong platform, wrong data. The 5 patterns behind failed migrations.
- 02
The platform decision tree
A 4-question decision tree covering Amazon Connect, Genesys Cloud CX, NICE CXone, Microsoft Dynamics 365 CCaaS, Five9, Salesforce Agentforce, Google CCAI and Kore.ai.
- 03
Data & knowledge readiness
Knowledge structure, retrieval evaluation, CRM entitlements and identity resolution — the 4 pre-conditions to conversational AI.
- 04
Conversational design
Intent boundaries, disambiguation, containment budgets, escalation patterns, voice-first vs chat-first choices.
- 05
Agent experience
Assist, next-best-action, knowledge, real-time coaching — designed to reduce cognitive load, not add to it.
- 06
Automated QA and analytics
100% coverage QA, calibration workflows, agent coaching, containment vs CSAT-proxy SLOs.
- 07
Cutover playbook
Traffic ramp, fallback paths, SLO gates, war-room protocol — how to keep CSAT green during switch.
Questions enterprise readers ask
Which platform do you recommend?
We're platform-independent — the right answer depends on incumbent contracts, CRM alignment, region, and regulated-industry constraints. The playbook contains a 4-question decision tree that lands you on a shortlist of 2.
Can we phase this if we run 30+ contact centers?
Yes — the phasing model is designed exactly for multi-BU and multi-region estates. Chapter 7 covers wave selection, blast radius, and centralized-vs-federated design.
How is Kore.ai used in this?
As a conversational AI and agentic layer that sits above the CCaaS, delivering voice AI, chat, agent assist, knowledge and automated QA across every platform we implement.
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Resolves tier-1 customer service demand end to end across voice and digital.
35–60% of in-scope contacts resolved without a live agent
Books, reschedules and confirms appointments directly in the EHR.
50–75% of scheduling demand handled without staff
Explains claim status, EOB detail and denial reasons to members in channel.
35–55% of claims-status demand resolved in channel
Tracks, amends, cancels, reships and refunds within policy limits.
45–65% of order and returns contacts fully automated
Runs compliant early-stage collections conversations and sets up arrangements.
10–25% higher cure rate in early-stage arrears
Detects churn risk and runs the save conversation within approved offer limits.
8–20% higher save rate against a hold-out control
De-risks a CCaaS or platform cutover with automated discovery, testing and parity checks.
90%+ of legacy routing discovered and parity-tested before cutover
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Want to apply this to your program?
Book a working session with a pronix.ai strategy lead — we'll walk through how the ideas in playbook apply to your platform, industry and roadmap.