10. - AI Features

AI Overview & Licensing

Study Notes

Topic Description
Genesys AI Suite of artificial intelligence capabilities integrated across PureCloud
Purpose Enhance agent productivity, automate interactions, improve customer experience
Licensing Model AI features available as add-on modules to Premium edition
Deployment Cloud-native, no infrastructure required
Scope Covers analytics, automation, agent assist, and autonomous agents

Navigation

Admin → Organization Settings → Licensing → AI Modules OR Admin → Billing & Subscriptions → AI Products

Genesys AI Suite Overview

Genesys PureCloud AI is an integrated suite of artificial intelligence capabilities designed to enhance every aspect of contact center operations. From agent assistance to autonomous automation, AI is embedded throughout the platform.

Core AI Components

Key Benefits

AI Licensing Model

Premium Edition Requirement

Base: Premium Edition (Required)
    ↓
+ AI Add-on Modules (Optional)
    ├── Customer Insights Module
    ├── Workforce Optimization Module
    ├── Virtual Agent Module
    └── Contact Center Intelligence Module
    ↓
= Complete AI-Powered Solution

Licensing Structure

Component Type Cost Model Required
Premium Edition Base license Per-user monthly Yes
Customer Insights Add-on module Per-user or pool No (recommended)
Workforce Optimization Add-on module Organizational No
Virtual Agent Add-on module Per-agent license No
Advanced Analytics Add-on module Organizational No

Study Notes - AI Capabilities by Module

Module Primary Capability Sub-features License Type
Customer Insights Interaction analytics and quality management Sentiment analysis, topic detection, quality evaluation, agent coaching Per-user
Workforce Optimization Forecasting, scheduling, and performance management Demand forecasting, schedule optimization, workforce analytics, productivity tracking Organizational
Agent Copilot Real-time agent assistance Knowledge recommendations, script guidance, sentiment alerts, next-action suggestions Per-user
Virtual Agent Autonomous conversation handling Intent recognition, multi-turn dialogue, escalation logic, omnichannel support Per-agent
Predictive Routing Intelligent contact routing Skill matching, availability prediction, performance prediction, load balancing Included in WFO
Contact Center Intelligence Deep conversation insights Speech analytics, text analytics, compliance monitoring, competitive intelligence Organizational
Advanced Analytics Custom reporting and dashboards Business analytics, predictive metrics, custom visualizations Organizational

AI Capabilities by Use Case

For Agents

Agent Experience Enhancements:
├── Agent Copilot
│   ├── Real-time knowledge suggestions
│   ├── Script guidance
│   ├── Sentiment monitoring
│   └── Next-action recommendations
├── Predictive Routing
│   ├── Optimal contact matching
│   ├── Skill-based distribution
│   └── Workload balancing
└── Performance Insights
    ├── Real-time coaching
    ├── Quality feedback
    └── Training recommendations

For Supervisors

Supervisor Experience Enhancements:
├── Workforce Optimization
│   ├── Real-time dashboards
│   ├── Agent performance metrics
│   ├── Coaching opportunities
│   └── Compliance alerts
├── Customer Insights
│   ├── Quality management
│   ├── Interaction analytics
│   ├── Team performance trends
│   └── Automated evaluations
└── Advanced Analytics
    ├── Custom reports
    ├── Predictive metrics
    ├── Trend analysis
    └── Business intelligence

For Customers

Customer Experience Enhancements:
├── Faster Resolution
│   ├── Agent Copilot speeds answers
│   ├── Virtual agents 24/7 availability
│   └── Predictive routing reduces wait
├── Better Experience
│   ├── Skilled agent matching
│   ├── Personalized service
│   ├── Appropriate escalation
│   └── Consistent quality
└── Self-Service Options
    ├── Virtual agent automation
    ├── Knowledge base access
    ├── Portal access
    └── Omnichannel support

For Managers/Executives

Management Experience Enhancements:
├── Business Intelligence
│   ├── Advanced analytics dashboards
│   ├── Custom reporting
│   ├── Predictive forecasting
│   └── Trend analysis
├── Strategic Planning
│   ├── Capacity forecasting
│   ├── ROI measurement
│   ├── Competitive intelligence
│   └── Market insights
└── Performance Optimization
    ├── Cost analysis
    ├── Efficiency metrics
    ├── Quality improvement
    └── Customer satisfaction tracking

Implementation Guide

Step 1: Assessment & Planning

  1. Audit current contact center operations
  2. Identify pain points and improvement opportunities
  3. Document baseline metrics (AHT, FCR, CSAT, costs)
  4. Assess readiness for AI adoption
  5. Define success metrics and KPIs
  6. Estimate ROI for each module
  7. Plan implementation timeline

Step 2: Module Selection

  1. Evaluate all available AI modules
  2. Prioritize based on business needs
  3. Assess agent and supervisor readiness
  4. Determine implementation sequence
  5. Calculate total cost of ownership
  6. Secure budget approval
  7. Obtain licenses from Genesys

Step 3: Foundation Setup

  1. Ensure Premium Edition active
  2. Purchase selected AI modules
  3. Assign licenses to users
  4. Set up knowledge management system
  5. Configure backend integrations
  6. Enable audit logging and monitoring
  7. Establish governance policies

Step 4: Module-Specific Configuration

  1. Customer Insights: Configure quality evaluation rules
  2. Workforce Optimization: Set up forecasting models
  3. Agent Copilot: Populate knowledge base
  4. Virtual Agent: Design conversation flows
  5. Predictive Routing: Define skills and proficiency levels
  6. Advanced Analytics: Create custom dashboards
  7. Contact Center Intelligence: Enable analytics engines

Step 5: Training & Change Management

  1. Develop training curriculum
  2. Train supervisors first
  3. Train agents on their tools
  4. Provide ongoing support
  5. Gather feedback early
  6. Address concerns and resistance
  7. Celebrate early wins

Step 6: Phased Rollout

  1. Start with single department/queue
  2. Monitor metrics closely
  3. Gather user feedback
  4. Optimize based on learnings
  5. Expand to additional queues
  6. Scale across entire organization
  7. Continuous improvement process

How to Implement

Phase Description Timeline Modules
Planning Assess needs, select modules, plan approach Week 1-2 All
Foundation Set up licenses, integrations, governance Week 2-4 All
Configuration Configure each module's settings Week 4-6 Module-specific
Training Educate teams on features and benefits Week 6-7 All
Pilot Deploy to test group, monitor metrics Week 7-8 All
Rollout Expand to production, scale up Week 8-10 All
Optimization Monitor, tune, improve continuously Week 10+ All

Genesys AI Platform Architecture

Genesys PureCloud AI Platform

┌─────────────────────────────────────────────────┐
│        Core PureCloud Platform                   │
│    (Voice, Chat, Email, Social, Video)           │
└──────────────┬──────────────────────────────────┘
               │
       ┌───────┴────────┐
       │                │
┌──────▼──────┐  ┌──────▼──────────┐
│ Interaction │  │ Agent/Customer  │
│ Processing  │  │ Data            │
└──────┬──────┘  └────────┬────────┘
       │                  │
       └──────────┬───────┘
                  │
        ┌─────────▼──────────────┐
        │   AI/ML Engines        │
        │                        │
        ├─ NLP & Intent Engine   │
        ├─ Sentiment Analysis    │
        ├─ Topic Detection       │
        ├─ Entity Recognition    │
        ├─ Predictive Models     │
        └─ Recommendation Engine │
        │
   ┌────┴─────────────────────────────────────┐
   │      AI Module Applications               │
   │                                           │
   ├─ Customer Insights                       │
   │  ├─ Interaction Analytics                │
   │  ├─ Quality Evaluation                   │
   │  ├─ Compliance Monitoring                │
   │  └─ Coaching Recommendations             │
   │                                           │
   ├─ Agent Copilot                           │
   │  ├─ Knowledge Recommendations            │
   │  ├─ Script Guidance                      │
   │  ├─ Sentiment Alerts                     │
   │  └─ Next Action Suggestions              │
   │                                           │
   ├─ Predictive Routing                      │
   │  ├─ Skill Matching                       │
   │  ├─ Load Balancing                       │
   │  ├─ Performance Prediction               │
   │  └─ Availability Analysis                │
   │                                           │
   ├─ Virtual Agent Flows                     │
   │  ├─ Conversation Management              │
   │  ├─ Intent Recognition                   │
   │  ├─ Escalation Logic                     │
   │  └─ Multi-turn Dialogue                  │
   │                                           │
   ├─ Workforce Optimization                  │
   │  ├─ Forecasting                          │
   │  ├─ Scheduling                           │
   │  ├─ Performance Analytics                │
   │  └─ Productivity Tracking                │
   │                                           │
   ├─ Contact Center Intelligence             │
   │  ├─ Speech Analytics                     │
   │  ├─ Text Analytics                       │
   │  ├─ Conversation Mining                  │
   │  └─ Competitive Intelligence             │
   │                                           │
   └─ Advanced Analytics                      │
      ├─ Custom Dashboards                    │
      ├─ Predictive Metrics                   │
      ├─ Business Intelligence                │
      └─ Data Visualization                   │
   │
└─────────────────────────────────────────────┘

AI Modules Detailed Comparison

Customer Insights Module

Primary Use: Quality Management & Interaction Analytics

Features:
├── Interaction Recording & Analysis
├── Sentiment Detection (Real-time & historical)
├── Topic Detection (Automatic issue categorization)
├── Speech Analytics (Word/phrase analysis)
├── Quality Evaluation (AI-assisted scoring)
├── Compliance Monitoring (Regulation checking)
├── Agent Coaching (Targeted improvements)
└── Custom Metrics (Business-specific analysis)

Pricing: Per-user monthly ($XX-XXX depending on tier)
ROI: 10-20% quality improvement, reduced audit burden
Best For: Quality-focused, compliance-heavy organizations
Typical Users: QA teams, supervisors, compliance officers

Workforce Optimization Module

Primary Use: Forecasting, Scheduling, Performance Analytics

Features:
├── Demand Forecasting (Historical + predictive)
├── Schedule Optimization (Auto-scheduling)
├── Workforce Analytics (Performance metrics)
├── Productivity Tracking (Real-time dashboards)
├── Absence & Leave Management
├── Skills-based Workforce Planning
├── Performance Management
└── Adherence Monitoring

Pricing: Organizational license (negotiated)
ROI: 5-15% labor cost reduction, improved efficiency
Best For: Large organizations, complex scheduling needs
Typical Users: Workforce planners, managers, analysts

Agent Copilot Module

Primary Use: Real-Time Agent Assistance

Features:
├── Knowledge Recommendations
├── Script Guidance
├── Sentiment Monitoring
├── Next Action Suggestions
├── Real-time Coaching
├── Performance Insights
├── Learning Reinforcement
└── Omnichannel Support

Pricing: Per-user monthly ($XX-XXX depending on tier)
ROI: 10-30% AHT reduction, 15-25% FCR improvement
Best For: Agent productivity, customer satisfaction
Typical Users: All agents, supervisors

Virtual Agent Module

Primary Use: Autonomous Customer Interaction Handling

Features:
├── Conversational AI
├── Intent Recognition
├── Multi-turn Dialogue
├── Transaction Processing
├── Escalation Logic
├── 24/7 Availability
├── Omnichannel Support
└── Sentiment Awareness

Pricing: Per-virtual agent (negotiated)
ROI: 60-80% cost reduction for automated interactions
Best For: High-volume routine interactions
Typical Users: Customers, supervisors (monitoring)

Advanced Analytics Module

Primary Use: Business Intelligence & Custom Reporting

Features:
├── Custom Dashboard Creation
├── Predictive Analytics
├── Trend Analysis
├── Business Intelligence
├── Data Visualization
├── Scheduled Reports
├── Data Export
└── Integration APIs

Pricing: Organizational license (negotiated)
ROI: Better decision-making, strategic insights
Best For: Data-driven organizations, large enterprises
Typical Users: Managers, executives, analysts

AI Licensing Structure Comparison

Small Organization (50 agents)

Premium Edition: 50 users x $XX/month = $XXXX
Agent Copilot: 50 users x $XX/month = $XXXX
Customer Insights: 10 supervisors x $XX/month = $XXXX
Workforce Optimization: Organizational = $XXXX

Total Monthly: $XXXX
Cost per Agent: $XXX/month
ROI: 12-18 months through efficiency gains

Mid-Market (200 agents)

Premium Edition: 200 users x $XX/month = $XXXX
Agent Copilot: 200 users x $XX/month = $XXXX
Customer Insights: 50 supervisors x $XX/month = $XXXX
Workforce Optimization: Organizational = $XXXX
Virtual Agent: 5 agents x $XXXX/month = $XXXX
Advanced Analytics: Organizational = $XXXX

Total Monthly: $XXXX
Cost per Agent: $XXX/month
ROI: 6-12 months through automation + efficiency

Enterprise (1000+ agents)

Premium Edition: 1000 users x $XX/month = $XXXXX
Agent Copilot: 1000 users x $XX/month = $XXXXX
Customer Insights: 200 supervisors x $XX/month = $XXXXX
Workforce Optimization: Organizational = $XXXXX
Virtual Agent: 20 agents x $XXXX/month = $XXXXX
Contact Center Intelligence: Organizational = $XXXXX
Advanced Analytics: Organizational = $XXXXX

Total Monthly: $XXXXX
Cost per Agent: $XXX/month
ROI: 3-6 months through massive automation + efficiency

Implementation Roadmap

Phase 1: Foundation (Month 1-2)

Quick Wins:
├── Enable Predictive Routing
│   └─ Immediate improvement in routing efficiency
├── Enable Customer Insights
│   └─ Gain visibility into interaction quality
└── Activate Agent Copilot
    └─ First-touch improvement in agent effectiveness

Expected Impact:
├── Routing efficiency: +10-15%
├── Agent confidence: +20-30%
├── FCR improvement: +5-10%
└── Cost per contact: -5-10%

Phase 2: Intelligence (Month 2-4)

Expansion:
├── Deploy Workforce Optimization
│   └─ Optimize scheduling and forecasting
├── Enhance Customer Insights
│   └─ Add compliance monitoring
└── Optimize Agent Copilot
    └─ Improve knowledge base quality

Expected Impact:
├── Labor efficiency: +10-15%
├── Quality improvement: +15-20%
├── FCR improvement: +10-20%
└── CSAT improvement: +10-15%

Phase 3: Automation (Month 4-6)

Advanced:
├── Deploy Virtual Agent Flows
│   └─ Automate routine interactions
├── Activate Advanced Analytics
│   └─ Enable strategic decision-making
└── Integrate all modules
    └─ Seamless intelligence platform

Expected Impact:
├── Automation rate: 50-70% of routine volume
├── Cost reduction: 30-50%
├── Customer satisfaction: +15-25%
└── Operational efficiency: +25-35%

Real-World Implementation Timeline

Week 1-2: Assessment & Planning

Day 1-3:
├─ Kick-off meeting
├─ Current state assessment
├─ Identify pain points
└─ Document baseline metrics

Day 4-10:
├─ Module evaluation
├─ ROI analysis
├─ Risk assessment
├─ Develop implementation plan

Day 11-14:
├─ Secure approvals
├─ License procurement
├─ Team assignments
└─ Detailed project plan

Week 3-4: Foundation Setup

Day 15-21:
├─ Activate Premium Edition
├─ Purchase AI modules
├─ License assignment
├─ Infrastructure setup

Day 22-28:
├─ Knowledge base creation
├─ Integration testing
├─ Security validation
└─ Documentation

Week 5-6: Configuration

Day 29-35:
├─ Customer Insights setup
├─ Quality rules configuration
├─ Analytics dashboards
└─ Coaching workflows

Day 36-42:
├─ Agent Copilot setup
├─ Knowledge population
├─ Sentiment analysis
└─ Relevance tuning

Week 7-8: Training & Pilot

Day 43-49:
├─ Supervisor training
├─ Agent training
├─ Pilot queue selection
└─ Monitoring setup

Day 50-56:
├─ Pilot deployment
├─ Daily monitoring
├─ Feedback collection
├─ Quick optimizations
└─ Success validation

Week 9-10: Rollout

Day 57-63:
├─ Production deployment
├─ Agent onboarding
├─ Supervisor monitoring
├─ Support availability

Day 64-70:
├─ Scale to all queues
├─ Monitor close for issues
├─ Gather feedback
└─ Celebrate successes

AI Features by Job Role

For Agents

Feature Module Benefit
Real-time knowledge suggestions Agent Copilot Faster resolution, fewer transfers
Script guidance Agent Copilot Better quality, compliance adherence
Sentiment alerts Agent Copilot De-escalation, higher satisfaction
Optimal contact matching Predictive Routing Better skill match, faster resolution
Performance insights Customer Insights Learning and improvement
Interaction recording Customer Insights Quality and coaching

For Supervisors

Feature Module Benefit
Real-time agent dashboards Workforce Optimization Team visibility and coaching
Quality evaluation Customer Insights Objective performance assessment
Automated coaching Customer Insights Targeted development
Schedule optimization Workforce Optimization Better coverage, agent satisfaction
Compliance monitoring Customer Insights Risk mitigation
Performance trends Advanced Analytics Identify patterns and opportunities

For Managers

Feature Module Benefit
Demand forecasting Workforce Optimization Budget planning, capacity management
Cost analysis Workforce Optimization ROI tracking, budget optimization
Team performance analytics Customer Insights Performance visibility
Custom dashboards Advanced Analytics Strategic decision-making
Predictive insights Advanced Analytics Proactive management
Business intelligence Advanced Analytics Competitive advantage

For Executives

Feature Module Benefit
Revenue impact analysis Advanced Analytics Business value justification
Cost savings tracking Workforce Optimization ROI demonstration
Customer satisfaction trends Customer Insights Service quality visibility
Market intelligence Contact Center Intelligence Competitive positioning
Strategic dashboards Advanced Analytics Executive visibility
Predictive metrics Advanced Analytics Forward-looking planning

Best Practices for AI Implementation

Governance & Management

Technical Implementation

User Adoption

Continuous Improvement

Common Challenges & Solutions

Challenge Solution
Agents skeptical of AI Demonstrate time-saving benefits, celebrate wins
Knowledge base quality Establish content review process, regular updates
Integration complexity Partner with Genesys professional services
High implementation cost Show ROI, phased approach to spread costs
Change resistance Strong change management, executive support
Slow adoption User-friendly interfaces, comprehensive training
AI accuracy issues Improve training data, tune models
Compliance concerns Implement proper audit trails and controls
Technical challenges Dedicated technical support and monitoring
Low usage of features Training, communication, usage incentives

Licensing & Compliance

License Tracking

System automatically tracks:
├─ Active AI module users
├─ Virtual agent instances in use
├─ Monthly consumption metrics
├─ Grace period usage
└─ Compliance status

Monthly Report includes:
├─ License utilization rate
├─ Cost per user/module
├─ Usage trends
├─ Recommendations for optimization
└─ Billing details

Compliance Monitoring

Interview Cheat Sheet

Question Answer
What is Genesys AI? Suite of AI capabilities across PureCloud (Copilot, Routing, Virtual Agent, etc.)
What edition is required? Premium Edition (AI modules are add-ons)
What modules are available? Customer Insights, Workforce Optimization, Agent Copilot, Virtual Agent, Advanced Analytics
How is AI licensed? Per-user or organizational depending on module
What's the expected ROI? 6-18 months depending on implementation scope
Which module should we start with? Predictive Routing or Agent Copilot for quick impact
Can we implement gradually? Yes, phased approach recommended
What's most important for success? Quality change management and user training
How do we measure AI impact? Track FCR, AHT, CSAT, cost per contact, automation rate
What about compliance concerns? Audit trails, access controls, and monitoring built-in
Can we customize AI models? Yes, tuning available; may require Genesys services for deep customization
What's the implementation timeline? 8-12 weeks for full deployment, quick wins in 2-4 weeks
How do we ensure adoption? Executive support, training, early wins, continuous communication
What's the cost per agent per month? $XX-XXX depending on modules and scale
Where do we start? Assessment → Licensing → Foundation → Phased Implementation

Key Takeaways

Getting Started Checklist

Pre-Implementation

Implementation Preparation

Deployment

Additional Resources

Official Documentation Links

Support Contacts

Document Version Info

Last Updated: March 2026
Source: Genesys PureCloud Official Documentation
Version: 1.0

AI Studio & AI Guides

Genesys PureCloud AI Studio & AI Guides Documentation

Study Notes

Topic Description
AI Studio Centralized workbench for building, managing, and deploying AI-powered experiences
AI Guides No-code feature that converts business instructions into Virtual Agents using natural language
Purpose Enable business users to create intelligent virtual agents without coding expertise
Licensing Requires Genesys Cloud AI Experience tokens (metered pricing)
Innovation Level Level 4 agentic AI - semi-autonomous with defined boundaries and guardrails

Navigation

Admin → AI Studio OR Admin → Contact Center → Automation → AI Studio

AI Studio Overview

Genesys Cloud AI Studio provides a centralized workbench to build, manage and deploy AI experiences like Virtual Agents and Agent Copilots. It serves as the command center for organizations to create next-generation AI-powered customer experiences with governance, control, and scalability.

Key Capabilities

Strategic Positioning

AI Studio represents a leap from simple execution to intelligent problem-solving - Level 4 where semi-autonomous systems are configured around specific objectives using reasoning, planning and memory to figure out how best to accomplish goals while still operating within clearly defined boundaries to meet compliance and policy requirements

Edition & Module Requirements

Requirement Details
Minimum Edition Genesys Cloud CX 1, CX 2, CX 3, or CX 4 license
Licensing Model AI Experience tokens (metered, usage-based pricing)
Permissions Role-based AI Studio permissions required
Additional Access to Virtual Agent module for full functionality

Study Notes - AI Studio Components

Component Purpose User Type
AI Guides Create Virtual Agents from natural language prompts Business users, CX teams
Guide Editor Build and refine guide instructions and logic All user levels
Virtual Agent Integration Deploy guides to Virtual Agent flows Technical users
Customizable Summaries Shape interaction summaries to business needs Admins, supervisors
Governance Controls Ensure compliance and policy adherence Admins, compliance
Performance Dashboard Monitor guide usage and effectiveness Managers, analysts
API Access Programmatic guide and agent management Developers

AI Guides Overview

Genesys Cloud AI Guides enables business users to create AI-powered Virtual Agents through natural language instructions without coding, facilitating conversational flows that mirror customer journeys, adapt dynamically to customer context, and combine structured logic with AI capabilities

How AI Guides Work

  1. Business user describes goal in plain language or uploads process documentation
  2. AI Guides use large language models (LLMs)-based natural language processing to interpret user prompts and documents, generating complete agentic flows including intents, slots and dialog logic
  3. System generates draft virtual agent with conversation flow
  4. User reviews, edits, and customizes the generated guide
  5. AI executable instructions produced by AI Guides are fully editable within Genesys Cloud, allowing users to update messages, logic and backend integrations before publishing
  6. Guide is published and connected to Virtual Agent
  7. Virtual Agent uses guide to handle customer interactions

Key Features

Implementation Guide

Step 1: Assessment & Planning

  1. Identify suitable use cases for AI Guides
  2. Document business processes and customer journeys
  3. Gather process documentation or playbooks
  4. Define success metrics for each guide
  5. Plan escalation scenarios
  6. Assess team readiness for AI automation
  7. Plan change management approach

Step 2: Licensing & Setup

  1. Ensure organization has Genesys Cloud CX license (CX 1, CX 2, CX 3, or CX 4)
  2. Purchase Genesys Cloud AI Experience tokens
  3. Add necessary AI Studio permissions
  4. Set up role-based access controls
  5. Configure Virtual Agent if not already enabled
  6. Test integration with backend systems
  7. Establish governance policies

Step 3: Guide Creation

  1. Navigate to AI Studio
  2. Create a guide using an AI prompt, convert a process document into a guide, or create from scratch by starting with a blank guide
  3. Describe goal in natural language or upload documentation
  4. Review AI-generated guide structure
  5. Edit guide instructions and customize flow
  6. Add variables and data integrations
  7. Configure escalation paths

Step 4: Testing & Refinement

  1. Preview guide behavior in test environment
  2. Author preview before publish - preview real knowledge responses during Guide configuration to confirm accuracy and behavior
  3. Test with sample customer scenarios
  4. Verify escalation triggers work correctly
  5. Validate data integrations
  6. Test across all supported channels
  7. Gather feedback from SMEs

Step 5: Publishing & Deployment

  1. Publish guide to Virtual Agent
  2. Connect the guide to Virtual Agent flows in Architect
  3. Assign to production queues
  4. Monitor initial interactions closely
  5. Validate customer experience
  6. Adjust guide parameters based on feedback
  7. Scale to additional queues as needed

Step 6: Monitoring & Optimization

  1. Monitor guide usage metrics daily
  2. Track customer satisfaction and resolution rates
  3. Review escalation patterns
  4. Analyze customer feedback
  5. Refine guide instructions based on data
  6. A/B test different guide variations
  7. Update regularly based on learnings

How to Implement

Phase Description Timeline
Planning Identify use cases, document processes, assess readiness Week 1-2
Setup Activate licenses, configure permissions, enable integrations Week 2-3
Creation Build guides from prompts or documents, test Week 3-5
Testing Validate behavior, test scenarios, refine Week 5-6
Pilot Deploy to select queues, monitor closely Week 6-7
Rollout Expand to production, scale across organization Week 7-8
Optimization Monitor, analyze, improve continuously Ongoing

AI Studio & AI Guides Architecture

AI Studio Centralized Workbench

┌─────────────────────────────────────────┐
│       AI Studio Command Center           │
├─────────────────────────────────────────┤
│                                          │
│  Guide Creation Interface                │
│  ├─ Natural Language Prompt Input       │
│  ├─ Document Upload & Conversion        │
│  └─ Blank Guide Builder                 │
│                                          │
│  AI Generation Engine                    │
│  ├─ LLM Processing                      │
│  ├─ Intent Detection                    │
│  ├─ Slot Identification                 │
│  └─ Dialog Logic Generation             │
│                                          │
│  Guide Editor & Customization            │
│  ├─ Instruction Editing                 │
│  ├─ Variable Management                 │
│  ├─ Logic Configuration                 │
│  └─ Integration Setup                   │
│                                          │
│  Governance & Controls                   │
│  ├─ Built-in Guardrails                │
│  ├─ Compliance Checking                 │
│  ├─ Tone Configuration                  │
│  └─ Brand Alignment Rules               │
│                                          │
│  Publishing & Deployment                │
│  ├─ Version Control                     │
│  ├─ Publish to Virtual Agent            │
│  ├─ Queue Assignment                    │
│  └─ Channel Distribution                │
│                                          │
│  Analytics & Monitoring                  │
│  ├─ Usage Dashboards                    │
│  ├─ Performance Metrics                 │
│  ├─ Escalation Tracking                 │
│  └─ Feedback Collection                 │
│                                          │
│  System Integration                      │
│  ├─ Architect Virtual Agent             │
│  ├─ Knowledge Management                │
│  ├─ Backend Data Systems                │
│  └─ Customer Data Platforms             │
│                                          │
└─────────────────────────────────────────┘
        ↓
   Virtual Agent Deployment
        ↓
   Customer Interactions

AI Guides Use Cases & Examples

Use Case 1: Order Status & Tracking

Business Process Documentation:
1. Collect order number from customer
2. Look up order in system
3. Provide tracking information
4. Offer additional help options
5. Close or escalate as needed

AI Guide Generated:
Utterances:
├─ "Where's my order?"
├─ "Track my package"
├─ "Order status"
└─ "When will my order arrive?"

Intents:
├─ Track Order
├─ Delivery Status
└─ Tracking Number

Dialog Flow:
├─ Ask for order number
├─ Query order database
├─ Provide shipping status
├─ Offer further assistance
└─ Escalate if needed

Resolution Rate: 85-90% (self-service)

Use Case 2: Password Reset Process

Uploaded Process Document:
"Follow these steps for password reset:
1. Verify customer identity with security questions
2. Confirm email address on file
3. Send password reset link
4. Confirm reset completed
5. Offer additional support"

AI Guide Generated:
Intents:
├─ Password Reset Request
├─ Account Access Issue
└─ Security Verification

Dialog:
├─ "I'll help you reset your password"
├─ "Let me verify who you are"
├─ Customer answers security questions
├─ System confirms identity
├─ "Check your email for reset link"
├─ "Did you successfully reset?"
└─ Provide follow-up support

Resolution Rate: 92-95% (self-service)

Use Case 3: Appointment Scheduling

Prompt Input:
"Create a guide for customers to book 
service appointments. Must:
- Ask for service type
- Show available dates/times
- Confirm appointment
- Send confirmation"

AI Guide Generated:
Intents:
├─ Schedule Appointment
├─ Change Appointment
└─ Cancel Appointment

Dialog:
├─ "What service do you need?"
├─ "When works best for you?"
├─ Show available slots
├─ "Let me confirm..."
├─ Send calendar confirmation
├─ Offer reschedule option

Resolution Rate: 88-92% (self-service)

Use Case 4: Billing Question & Payment

Process Flow:
"Customers with billing questions:
1. Verify account
2. Explain charges
3. Offer payment options
4. Process if customer agrees
5. Send receipt/confirmation"

AI Guide Generated:
Intents:
├─ Check Bill Amount
├─ Explain Charges
├─ Make Payment
└─ Dispute Charge

Dialog:
├─ Confirm customer identity
├─ "Let me look up your account"
├─ Display current balance
├─ "Would you like to pay?"
├─ Secure payment processing
├─ Send receipt and confirmation
├─ Escalate disputes

Resolution Rate: 80-85% (self-service)

Modular Guides Within Virtual Agents

AI Guides are designed to create modular bot flows that can be combined within a single virtual agent - each guide typically addresses a specific use case like order tracking, password reset or appointment booking, and these flows can be added to your virtual agent's overall configuration

Multi-Guide Architecture Example

Virtual Agent: Customer Service Bot

├─ Guide 1: Order Tracking
│  ├─ Intents: Track order, delivery status
│  ├─ Resolution: Order lookup + tracking info
│  └─ Escalation: Complex shipment issues
│
├─ Guide 2: Account Management
│  ├─ Intents: Password reset, update info
│  ├─ Resolution: Self-service account changes
│  └─ Escalation: Security concerns
│
├─ Guide 3: Billing & Payments
│  ├─ Intents: Check bill, make payment
│  ├─ Resolution: Payment processing
│  └─ Escalation: Billing disputes
│
└─ Guide 4: Appointment Booking
   ├─ Intents: Schedule, reschedule, cancel
   ├─ Resolution: Appointment management
   └─ Escalation: Complex scheduling needs

Router (AI determines which guide needed):
Customer: "Where's my order?"
→ Route to Guide 1: Order Tracking
→ Resolve: Self-service tracking info

Customer: "I want to schedule a service"
→ Route to Guide 4: Appointment Booking
→ Resolve: Appointment confirmation

Customer: "I have a billing question"
→ Route to Guide 3: Billing & Payments
→ Resolve or escalate accordingly

Knowledge Integration with AI Guides

In a future release, Genesys Cloud will allow AI Guides to use knowledge articles to answer customer questions while continuing through a defined process - guides can answer customer questions using approved knowledge content at any point in a conversation and then continue the task without losing context

Knowledge-Aware Guide Benefits

Before Knowledge Integration:
Customer: "What's your return policy?"
Guide: "Let me connect you with an agent
        who can answer policy questions"
→ Escalation (unnecessary)

After Knowledge Integration:
Customer: "What's your return policy?"
Guide: [Accesses knowledge base]
      "Here's our return policy...
       Now, back to your order tracking,
       I see your item was delivered..."
→ Continues conversation naturally
→ No escalation needed

Configuration Options

Guide authors can choose to inherit knowledge from the connected Virtual Agent or select a Guide-specific Knowledge source

Flexible knowledge sourcing:

Real Flow Scenario: AI Guide in Action

Customer Interaction Example:

Customer Calls: "I need to reset my password"
↓
Virtual Agent Answers (AI Guide: Account Management)
"Hi! I'm here to help. To reset your password,
 I'll need to verify your identity. What's your
 email address on file?"
↓
Customer: "john.smith@email.com"
↓
Guide Verifies: Customer email matches account
↓
Guide: "Thanks. What was your first pet's name?"
↓
Customer: "Fluffy"
↓
Guide Confirms: Security answer matches
↓
Guide: "Perfect! I've sent a password reset link
        to your email. Please check your inbox
        and click the link to set a new password."
↓
Customer: "Got it, thanks!"
↓
Guide: "You're welcome. Is there anything else
        I can help with today?"
↓
Customer: "No, that's all"
↓
Guide: "Have a great day!"
↓
Interaction Complete:
├─ Resolution: Self-service password reset
├─ Tokens Used: 1-2 per interaction
├─ Customer Satisfaction: High (immediate help)
└─ Cost: ~$0.05-0.15 per interaction

Licensing & Token Pricing

AI Experience Token Model

Organizations need Genesys Cloud AI Experience tokens to use AI Studio, with tokens based pricing model to monitor feature usage and consumption

Token Consumption

AI Guides consume tokens for each interaction session and for each guide created

Pricing Considerations

Token Consumption Example

Small Organization:
├─ 3 AI Guides created (3 guides × tokens)
├─ 500 customer interactions/month (500 × tokens)
├─ Monthly token usage: ~750 tokens
└─ Estimated cost: $XX-XXX/month

Mid-Market Organization:
├─ 10 AI Guides created (10 guides × tokens)
├─ 5,000 customer interactions/month (5,000 × tokens)
├─ Monthly token usage: ~5,100 tokens
└─ Estimated cost: $XXX-XXXX/month

Enterprise Organization:
├─ 50 AI Guides created (50 guides × tokens)
├─ 50,000 customer interactions/month (50,000 × tokens)
├─ Monthly token usage: ~50,100 tokens
└─ Estimated cost: $XXXX-XXXXX/month

Best Practices

Guide Design

Guardrails & Governance

Knowledge Management

Performance Optimization

Common Implementation Scenarios

Scenario 1: Quick Start (Small Team)

Timeline: 2-3 weeks
Setup:
├─ 2-3 simple guides
├─ Basic integration (order lookup, FAQ)
├─ Limited escalation paths
└─ Single channel deployment

Expected Results:
├─ 60-70% automation of routine inquiries
├─ Quick implementation, minimal training
├─ Fast ROI (4-6 weeks)
└─ Monthly token usage: ~300-500

Scenario 2: Comprehensive (Mid-Market)

Timeline: 6-8 weeks
Setup:
├─ 8-12 specialized guides
├─ Multiple backend integrations
├─ Complex escalation logic
├─ Omnichannel deployment
└─ Knowledge base integration

Expected Results:
├─ 50-65% automation of customer volume
├─ Significant cost savings
├─ 8-12 week ROI
└─ Monthly token usage: ~3,000-5,000

Scenario 3: Enterprise (Full Scale)

Timeline: 12-16 weeks
Setup:
├─ 30-50+ specialized guides
├─ Full system integration
├─ Advanced routing and logic
├─ Multi-language support
├─ Comprehensive knowledge integration
└─ Global channel deployment

Expected Results:
├─ 40-60% automation of global volume
├─ Major cost reduction
├─ Continuous optimization
├─ 6-10 week ROI
└─ Monthly token usage: ~20,000-50,000+

Troubleshooting Guide

Issue Cause Resolution
Guide doesn't understand customer intent Insufficient training variations Add more example utterances to training data
Escalations happening too frequently Over-strict guardrails or incomplete logic Expand guide capabilities and relax thresholds
Inaccurate information from knowledge Stale knowledge articles Update knowledge base and preview before publish
Slow response time Token consumption or system latency Optimize guide logic and integrations
Integration failures Backend system connectivity issues Verify API connections and data endpoints
Guides not deployed to queues Missing Virtual Agent configuration Check Architect flow configuration and deployment
Customer dissatisfaction Poor conversation quality Refine guide instructions and tone
Token overages Guides consuming more than expected Analyze usage patterns and optimize interactions
Editing feels clunky Unfamiliar with new guide model Review updated guide model documentation
Knowledge not accessible in guides Knowledge not properly configured Configure knowledge source and test access

AI Guides vs. Traditional Bot Flows

Feature AI Guides Traditional Flows
Creation Method Natural language prompts Manual design
Skill Required Business knowledge Technical/coding
Speed to Deploy Days to weeks Weeks to months
Iteration Time Minutes to hours Hours to days
Intent Recognition AI-powered, flexible Rule-based, rigid
Conversation Quality Natural, contextual Scripted, menu-driven
Maintenance AI learns from data Manual updates
Integration Automatic via AI Manual configuration
Cost to Build Low (no developers) High (developer time)
Scaling Easy, rapid deployment Time-consuming

Real-World Timeline Example

Week 1-2: Planning & Assessment

Day 1-3: Kickoff and planning
├─ Identify 3-5 use cases
├─ Gather process documentation
└─ Assess team readiness

Day 4-10: Documentation review
├─ Refine process flows
├─ Identify integration needs
├─ Plan escalation scenarios

Day 11-14: Setup and licensing
├─ Purchase AI Experience tokens
├─ Configure permissions
└─ Set up integrations

Week 3-4: Guide Creation

Day 15-20: First guide creation
├─ Upload process documentation
├─ Review AI-generated guide
├─ Customize and refine
└─ Connect to Virtual Agent

Day 21-28: Additional guides
├─ Create guides 2-4
├─ Refine based on feedback
├─ Configure escalation logic
└─ Integration testing

Week 5-6: Testing & Refinement

Day 29-35: Comprehensive testing
├─ Test all guide scenarios
├─ Validate integrations
├─ Test escalation paths
└─ Gather feedback

Day 36-42: Optimization
├─ Refine guide instructions
├─ Adjust confidence thresholds
├─ Optimize knowledge integration
└─ Performance tuning

Week 7-8: Deployment

Day 43-49: Pilot deployment
├─ Deploy to pilot queue
├─ Monitor closely
├─ Gather customer feedback
└─ Make adjustments

Day 50-56: Full rollout
├─ Expand to all queues
├─ Monitor metrics
├─ Provide agent support
└─ Celebrate success

Interview Cheat Sheet

Question Answer
What is AI Studio? Centralized workbench for building, managing, and deploying AI experiences
What is an AI Guide? No-code feature that converts business instructions into Virtual Agents using AI
What licensing is required? Genesys Cloud CX license + AI Experience tokens
How do AI Guides work? Users describe goals in plain language, AI generates guide structure, user customizes
Can non-technical users create guides? Yes, AI Guides are designed for business users without coding skills
What level of agentic AI is this? Level 4 - semi-autonomous with defined boundaries and guardrails
How long to create a guide? Minutes to hours depending on complexity
Can guides be combined? Yes, multiple guides can be modular components in one Virtual Agent
How are guides customized? Fully editable - users can change messages, logic, and integrations
What about knowledge integration? Guides can access knowledge articles to answer questions within conversation
What's the pricing model? Token-based - consumption tracked per interaction and per guide created
Can guides handle escalation? Yes, with configurable escalation paths to human agents
What channels do guides support? All Genesys channels - voice, chat, email, messaging, etc.
How do guides improve over time? AI learns from interactions; user refines guides based on metrics
What's the expected ROI timeline? 4-12 weeks depending on implementation scope

Key Takeaways

Customizable Summaries (Additional AI Studio Feature)

Customizable Summaries allows admins to shape interaction summaries to fit their business needs, enhancing consistency, compliance, and operation efficiency across human and AI agent interactions

Use Cases

Getting Started Checklist

Pre-Implementation

Licensing & Setup

Guide Development

Monitoring & Optimization

Additional Resources

Official Documentation Links

Support Contacts

Document Version Info

Last Updated: March 2026
Source: Genesys PureCloud Official Documentation (help.genesys.cloud)
Validated: Current with January-March 2026 releases
Version: 1.0

Agentic Virtual Agents

Genesys PureCloud Agentic Virtual Agents Documentation

Study Notes

Topic Description
Agentic Virtual Agents AI-powered autonomous agents capable of reasoning, planning, and taking action
Core Technology Generative AI with natural language understanding and decision-making
Autonomy Level Semi-autonomous with configurable boundaries and safety guardrails
Channels Voice, chat, email, messaging, social media
Capability Handle complex multi-step customer interactions independently

Navigation

Admin → Contact Center → Virtual Agents OR Admin → AI Studio → Virtual Agents

Agentic Virtual Agents Overview

Agentic Virtual Agents represent the next evolution of conversational AI - autonomous agents capable of reasoning between actions and knowledge to solve customer problems intelligently within defined boundaries. Unlike traditional bots that follow rigid decision trees, agentic agents use generative AI to understand context, make decisions, and dynamically adapt their behavior.

Key Capabilities

How They Differ from Traditional Bots

Traditional Bot:
├─ Rigid decision trees
├─ Menu-driven interactions
├─ Limited context awareness
├─ Fixed scripted responses
├─ Capability boundaries unclear
└─ Must escalate at first complexity

Agentic Virtual Agent:
├─ Intelligent reasoning and planning
├─ Natural conversational flow
├─ Full context preservation
├─ Dynamic adaptive responses
├─ Clear defined boundaries
└─ Handles complex scenarios autonomously

Edition & Module Requirements

Requirement Details
Minimum Edition Genesys Cloud CX 1, CX 2, CX 3, or CX 4 license
Module Virtual Agent module with AI capabilities
Licensing AI Experience tokens (metered usage-based)
AI Studio Access to AI Studio for guide creation and management
Knowledge Knowledge management system for agent reference

Study Notes - Agentic Virtual Agent Components

Component Purpose Function
Natural Language Engine Understands customer intent Processes speech/text input
Reasoning Engine Determines best solution approach Evaluates options and decides action
Knowledge Base Reference material for answers Provides accurate information
Action Executor Performs required actions Executes system tasks and transactions
Context Manager Maintains conversation history Preserves information across turns
Guardrail Controller Enforces boundaries Prevents unauthorized actions
Learning Module Improves over time Analyzes outcomes for optimization
Integration Layer Connects to backend systems Access to CRM, billing, ticketing, etc.

Agentic Virtual Agent Architecture

Customer Contact
    ↓
Agentic Virtual Agent Entry
├── Voice (IVR entry point)
├── Chat Interface
├── Email System
├── Messaging Platform
└── Social Media
    ↓
Natural Language Understanding
├── Speech-to-Text (voice)
├── Intent Recognition (what customer wants)
├── Entity Extraction (who, what, when, where)
├── Sentiment Analysis
└── Context Comprehension
    ↓
Reasoning & Planning Engine
├── Evaluate customer request
├── Assess available capabilities
├── Consider guardrails and constraints
├── Determine optimal approach
├── Plan multi-step solution
└── Prepare action sequence
    ↓
Knowledge Access Layer
├── Search knowledge base
├── Retrieve relevant information
├── Evaluate accuracy and applicability
├── Prepare response content
└── Maintain knowledge context
    ↓
Decision Point: Can Handle?
├── YES: Execute independently
│   ├── Perform required actions
│   ├── Fetch real-time data
│   ├── Execute transactions
│   ├── Generate response
│   └── Provide solution
│
└── NO: Escalate to Human
    ├── Complex/ambiguous scenario
    ├── Policy exception needed
    ├── High-risk action
    ├── Customer preference
    └── Warm handoff with full context
    ↓
Response Generation
├── Natural Language Generation
├── Tone and Brand Voice
├── Clarity and Conciseness
└── Accessibility Compliance
    ↓
Conversation Continuation
├── Verify customer satisfaction
├── Offer additional assistance
├── Provide relevant next steps
├── Document interaction
└── Learn from outcome

Agentic Capabilities & Functions

Autonomous Decision Making

Customer: "I've been trying to cancel my subscription
           for weeks but the website won't let me"

Agent Analysis:
├─ Understand: Customer frustration, technical issue
├─ Assess: Can perform cancellation autonomously
├─ Evaluate: Business rules for cancellation
├─ Decide: Proceed with cancellation + retention offer
└─ Plan: Execute cancellation, offer alternative plan

Agent Response:
"I understand your frustration. I can cancel your
 subscription right now. Before I do, I see you've
 been with us for 3 years. Can I offer you a 
 discounted plan as an alternative? What would help?"

Multi-Step Problem Solving

Customer: "I want to change my billing address and 
           also apply the promotion I saw in email"

Agent Reasoning:
├─ Task 1: Update billing address
├─ Task 2: Apply promotional code
├─ Task 3: Verify changes in system
├─ Task 4: Confirm new total with customer
└─ Task 5: Send updated confirmation

Agent Execution:
"I can help with both of those. Let me update your
 address, apply the promotion, and show you the 
 impact on your bill:

 Old address: 123 Main St
 New address: 456 Oak Ave

 Current plan: $X/month
 With promotion: $Y/month
 Monthly savings: $Z

 Should I go ahead with these changes?"

Intelligent Escalation

Customer: "I need a refund for faulty merchandise"

Agent Assessment:
├─ Evaluate: Refund policy
├─ Check: Purchase history
├─ Assess: Complexity (straightforward refund)
├─ Decide: Can handle independently
└─ Execute: Process refund

Response:
"I can definitely help with that. I see your 
 purchase on Feb 10. You're within our 30-day 
 return window. I'm issuing a full refund now.
 You'll see the credit in 3-5 business days.
 
 Would you like to arrange a return shipment?"

---

vs.

---

Customer: "I need a refund but it's been 45 days
           and there's a special circumstance..."

Agent Assessment:
├─ Evaluate: Outside standard 30-day policy
├─ Assess: Requires exception approval
├─ Determine: Needs human judgment
├─ Decide: Escalate to supervisor
└─ Execute: Warm handoff

Response:
"I understand - you have a special circumstance
 that's outside our standard 30-day window. Let me
 connect you with a supervisor who can review
 options. One moment..."

Dynamic Adaptation

Interaction 1 - Standard Path:
Agent: "I can help with your order issue.
        What's your order number?"
Customer: "12345"
Agent: [Looks up order, provides status]

Interaction 2 - Emotional/Frustrated:
Agent: [Detects frustration in tone]
Agent: "I hear this has been frustrating. 
        I'm personally going to help you 
        resolve this right now..."

Interaction 3 - Knowledgeable Customer:
Agent: [Detects technical language]
Agent: "I can see you're familiar with our
        system. Here are the technical details
        of your issue..."

Real Flow Scenarios: Agentic Virtual Agents in Action

Scenario 1: Complex Order Issue Resolution

Customer Calls: "I ordered item X last week but 
                received item Y instead"

Timeline:
10:00:05 AM - Call Connected to Agentic Agent
↓
10:00:10 - Agent Processes Input
├─ Understands: Wrong item received
├─ Identifies: Potential shipping error
└─ Assesses: Solvable by agent
↓
10:00:15 - Agent Takes Action
├─ Query: Order database
├─ Retrieve: Order 12345 details
├─ Analysis: Item Y in stock, Item X available
├─ Evaluate: Best resolution for customer
└─ Decision: Replace + expedited shipping
↓
10:00:45 - Agent Communicates
"I found the issue - you received item Y instead
 of item X. I'm shipping item X to you via 
 expedited delivery at no charge. You should 
 receive it in 2 days. You can keep item Y 
 as our apology. Good?"

Customer: "Yes, that works!"
↓
10:00:55 - Agent Executes
├─ Initiate replacement shipment
├─ Process return label for wrong item
├─ Apply goodwill credit
├─ Send confirmation email
└─ Document resolution
↓
10:01:00 AM - Issue Resolved
Resolution: Self-service (no escalation)
Time to Resolution: 55 seconds
Cost: ~$5-10 in shipping + goodwill
Customer Satisfaction: High (proactive solution)

Scenario 2: Billing Dispute with Research

Customer: "I was charged twice for my subscription"

Agent Process:
10:00 AM - Analysis
├─ Retrieve account
├─ Review transactions
├─ Identify: Two charges on same date
├─ Assess: Billing system error (likely)
└─ Reason: Needs investigation but obvious

10:05 AM - Investigation
├─ Check system logs
├─ Verify: Duplicate transaction confirmed
├─ Research: Processing glitch identified
├─ Escalate?: No - clear error, can resolve
└─ Decide: Refund one charge immediately

10:10 AM - Resolution
"I found the issue - our system charged you twice
 due to a processing error. I'm immediately refunding
 the duplicate charge of $99.99. You'll see it 
 within 2-3 business days.

 I'm also applying a $20 service credit for the
 inconvenience. Is there anything else I can help?"

Customer: "That's great, thank you!"

Result:
├─ Issue resolved autonomously
├─ No escalation needed
├─ Customer satisfied
├─ Cost: $99.99 refund + $20 credit = $119.99
└─ Outcome: Retained customer, quick resolution

Scenario 3: Proactive Problem Prevention

Situation: New customer during implementation phase

Agent Monitoring:
├─ Tracks: Customer account setup
├─ Identifies: Configuration incomplete
├─ Predicts: Customer may hit issues
└─ Acts: Reaches out proactively

Agent Initiates:
"Hi Sarah, I noticed your account setup is 
 almost complete, but I see you haven't configured
 your team members yet. Would you like help doing
 that now? I can walk you through it in 2 minutes."

Customer: "Sure, that would help!"

Agent Guides:
├─ Explains: Setup process
├─ Configures: Team members
├─ Tests: Access
├─ Confirms: All working
└─ Provides: Next steps doc

Result:
├─ Prevented future support tickets
├─ Improved onboarding experience
├─ Increased early adoption
└─ Reduced support costs

Scenario 4: Multi-Intent Conversation

Customer: "Hi, I need to update my payment method,
           schedule a service appointment, and ask
           about your loyalty program"

Agent Processing:
├─ Identifies: 3 separate intents
├─ Prioritizes: Payment method > appointment > info
├─ Plans: Multi-step interaction sequence
└─ Executes: Handles all in single conversation

Flow:
Agent: "I can help with all of those. Let me
        start with updating your payment method."

[Updates payment method]

Agent: "Perfect. Now let's schedule your 
       service appointment. What dates work?"

[Books appointment]

Agent: "Great! Quick question - you asked about
       our loyalty program. You actually qualify
       for Gold tier based on your spending!"

[Explains benefits, enrolls automatically]

Result:
├─ All 3 issues resolved
├─ Time: Single 5-minute call
├─ Customer: One conversation, multiple solutions
└─ Efficiency: What would take 3 transfers now takes 1

Agentic Virtual Agent Capabilities Matrix

By Interaction Complexity

Simple Tasks (Agent Handles 95%+):
├─ Account status inquiries
├─ FAQ and knowledge searches
├─ Password resets
├─ Basic transactions
├─ Simple scheduling
└─ Resolution Rate: 92-97%

Medium Complexity (Agent Handles 70-85%):
├─ Billing adjustments
├─ Order modifications
├─ Appointment changes
├─ Return processing
├─ Plan upgrades
└─ Resolution Rate: 70-85%

Complex Tasks (Agent Handles 40-60%):
├─ Billing disputes
├─ Complaints/resolution
├─ Custom solutions
├─ Policy exceptions
├─ Escalations needed
└─ Resolution Rate: 40-60%

By Business Function

Customer Service:
├─ Order tracking
├─ Returns & replacements
├─ Troubleshooting
├─ Status inquiries
└─ Resolution: 80-90%

Billing/Payments:
├─ Payment processing
├─ Invoice inquiries
├─ Billing corrections
├─ Payment plans
└─ Resolution: 75-85%

Appointments/Scheduling:
├─ Schedule appointment
├─ Reschedule/cancel
├─ Find availability
├─ Send reminders
└─ Resolution: 85-95%

Sales/Retention:
├─ Upsell opportunities
├─ Plan recommendations
├─ Offer promotions
├─ Prevent churn
└─ Resolution: 70-80%

Technical Support:
├─ Basic troubleshooting
├─ Access issues
├─ Configuration help
├─ Advanced escalation
└─ Resolution: 65-75%

Agentic Guardrails & Safety Controls

Built-in Safety Mechanisms

Guardrail Levels:

CRITICAL (Always Block):
├─ Data breach attempts
├─ Fraud patterns
├─ Unauthorized access
├─ Security violations
└─ Action: Immediate block + escalate

HIGH (Require Approval):
├─ Large financial transactions
├─ Account terminations
├─ Policy exceptions
├─ Sensitive data access
└─ Action: Require verification

MEDIUM (Monitor Closely):
├─ Refunds over threshold
├─ Plan downgrades
├─ Churn risk actions
├─ Unusual patterns
└─ Action: Execute with logging

LOW (Standard Operation):
├─ Routine transactions
├─ Information requests
├─ Schedule changes
├─ Status updates
└─ Action: Execute normally

Configurable Boundaries

Organization Can Define:
├─ Maximum transaction amount (agent handle)
├─ Which actions require approval
├─ Policy exception rules
├─ Escalation triggers
├─ Communication tone standards
├─ Retention/discount limits
├─ Data access restrictions
└─ Compliance requirements

Agentic Virtual Agent vs. Traditional Virtual Agent

Feature Agentic Agent Traditional Agent
Decision Making Reasoning-based Rule-based
Conversation Flow Dynamic, adaptive Scripted, fixed
Context Understanding Full multi-turn Limited
Problem Solving Independent reasoning Pre-defined paths
Complexity Handling Handles moderate complexity Limited scenarios
Adaptation Real-time behavior changes No adaptation
Learning Improves from outcomes Static
Escalation Judgment Intelligent assessment Pre-set triggers
Customer Experience Natural, conversational Menu-driven
Autonomy Semi-autonomous Fully scripted
Setup Complexity Moderate (AI handles) Low
Flexibility High Low
Time to Deploy Days-weeks Weeks-months
Resolution Rate 50-70% complex issues 30-50% complex

Implementation Roadmap

Phase 1: Foundation (Weeks 1-2)

Activities:
├── Audit current virtual agent capabilities
├── Identify top 3-5 use cases for agentic
├── Assess customer journey complexity
├── Define guardrails and boundaries
├── Plan integration with existing systems
└── Gather requirements from stakeholders

Deliverables:
├── Use case prioritization matrix
├── Guardrail policy document
├── Integration requirements list
└── Success metric definitions

Phase 2: Development (Weeks 3-5)

Activities:
├── Build/migrate use cases to agentic agents
├── Configure AI Guides for customer flows
├── Set up knowledge base integration
├── Configure guardrails and safety controls
├── Integrate backend systems
└── Set up monitoring and analytics

Deliverables:
├── Agentic agents built and configured
├── Integration tests completed
├── Guardrail validation completed
├── Monitoring dashboards created
└── Documentation complete

Phase 3: Testing & Optimization (Weeks 6-7)

Activities:
├── Comprehensive scenario testing
├── Load and performance testing
├── Guardrail effectiveness testing
├── Integration validation
├── Customer experience testing
└── Security and compliance validation

Deliverables:
├── Test results and sign-off
├── Performance baselines
├── Optimization recommendations
├── Final readiness confirmation
└── Issue resolution log

Phase 4: Pilot Deployment (Week 8)

Activities:
├── Deploy to pilot queue/segment
├── Monitor interactions closely (24/7)
├── Gather customer feedback
├── Track key performance metrics
├── Make rapid optimizations
└── Prepare for full rollout

Deliverables:
├── Daily performance reports
├── Customer feedback summary
├── Optimization log
├── Pilot success metrics
└── Full rollout plan

Phase 5: Full Rollout (Weeks 9-10)

Activities:
├── Expand to remaining queues
├── Monitor close for issues
├── Provide agent training (monitor mode)
├── Support team readiness
├── Scale gradually based on confidence
└── Establish optimization cadence

Deliverables:
├── Rollout completion checklist
├── Production metrics
├── Team training completion
├── Ongoing monitoring setup
└── Optimization plan

Phase 6: Optimization & Learning (Ongoing)

Activities:
├── Daily metric monitoring
├── Weekly performance reviews
├── Monthly optimization updates
├── Quarterly capability expansion
├── Continuous guardrail refinement
└── Regular training and updates

Deliverables:
├── Daily/weekly/monthly reports
├── Optimization recommendations
├── Capability roadmap
├── Training materials
└── Continuous improvement plan

Performance Metrics & Monitoring

Key Performance Indicators

Metric Target Purpose
Resolution Rate 50-70% (first contact) Measure autonomous handling
Escalation Rate <30% Control human workload
Customer Satisfaction (CSAT) >80% Measure experience quality
Average Resolution Time -30% vs human Track efficiency
Task Completion Rate >85% Measure task success
Knowledge Accuracy >95% Ensure correct information
Policy Adherence 100% Compliance verification
Guardrail Violations <0.1% Safety monitoring

Real-Time Monitoring Dashboard

Agentic Virtual Agent Monitor (Live)

Queue: Customer Service
├── Active Interactions: 47
├── Agents (agentic): 3 active
├── Human Agents: 12 available
│
├── Current Metrics:
│  ├─ Avg Resolution: 4.2 minutes
│  ├─ Current CSAT: 4.3/5 (sample)
│  ├─ Escalation Rate: 22%
│  └─ Policy Adherence: 100%
│
├── Interaction Breakdown:
│  ├─ Self-service (no agent): 1,250 today
│  ├─ Agentic agent resolved: 480 today
│  ├─ Escalated to human: 85 today
│  └─ In-progress: 47
│
├── Top Use Cases:
│  ├─ Order Status (156 resolved)
│  ├─ Billing Questions (134 resolved)
│  └─ Returns (89 resolved)
│
└── Guardrail Status:
   ├─ Critical Rules: All passing
   ├─ Violations Today: 0
   └─ Approval Requests: 3 pending

Common Implementation Scenarios

Scenario 1: Customer Service Center Automation

Organization: E-commerce Company (100 agents)
Current: High volume, repetitive inquiries

Deployment:
├─ Agentic agents for: Order status, returns, 
│  exchanges, tracking, simple complaints
├─ Resolution targets: 60-70% automation
├─ Channels: Chat, email, phone
└─ Timeline: 10 weeks

Expected Results:
├─ Automation Rate: 65% of volume
├─ Agent Productivity: +40% (less routine work)
├─ CSAT: +12% (faster resolution)
├─ AHT: -25% (focused on complex issues)
├─ Cost Reduction: $180K-250K annually
└─ Payback Period: 4-5 months

Scenario 2: Technical Support Evolution

Organization: SaaS Company (50 support agents)
Current: Mixed simple and complex tickets

Deployment:
├─ Agentic agents for: FAQ, basic troubleshooting,
│  account resets, documentation lookup
├─ Resolution targets: 40-50% automation
├─ Channels: Chat, email, knowledge portal
└─ Timeline: 12 weeks

Expected Results:
├─ Automation Rate: 45% of volume
├─ Agent Capacity: +30% (handling complex)
├─ CSAT: +8% (better first contact)
├─ Time to Resolution: -20%
├─ Escalation Quality: Improved (richer context)
└─ Cost Reduction: $120K-150K annually

Scenario 3: Billing & Collections Department

Organization: Financial Services (30 agents)
Current: Payment processing, dispute resolution

Deployment:
├─ Agentic agents for: Payment processing,
│  arrangement setup, billing inquiries,
│  promotional application
├─ Resolution targets: 55-65% automation
├─ Channels: Voice, chat, IVR
└─ Timeline: 14 weeks

Expected Results:
├─ Automation Rate: 60% of volume
├─ First-Contact Collections: +25%
├─ Customer Payment Satisfaction: +15%
├─ Dispute Resolution: 50% autonomous
├─ Revenue Impact: +$50K monthly
└─ Payback Period: 3-4 months

Best Practices for Agentic Virtual Agents

Agent Design

Safety & Governance

Integration Management

Performance & Optimization

Troubleshooting Common Issues

Issue Cause Resolution
High escalation rate Agent lacks capability for use case Expand agent training and knowledge
Poor customer satisfaction Conversation feels unnatural Refine language generation and tone
Integration failures Backend system issues Test integrations, improve error handling
Slow response times System latency or complex reasoning Optimize queries, simplify logic
Guardrail violations Rules too loose or unclear Tighten rules, improve monitoring
Incorrect decisions Knowledge gaps or logic errors Update knowledge base, refine rules
Customers can't escalate Escalation path unclear Add clear escalation triggers
Token overages Agent using more tokens than expected Optimize agent logic, reduce complexity
Integration data stale Knowledge base not updated Establish content review process
Customer confusion Unclear communications Simplify language, improve clarity

Agentic Capabilities Evolution

Current State (2026)

What Agentic Agents Can Do Now:
├─ Understand complex customer requests
├─ Reason through multi-step problems
├─ Access and apply knowledge
├─ Execute transactions within bounds
├─ Maintain context across conversation
├─ Adapt behavior based on customer
├─ Learn from outcomes
└─ Handle 50-70% of customer interactions

Near-term (2026-2027)

Expected Enhancements:
├─ Deeper business system integration
├─ More sophisticated reasoning
├─ Better multi-language support
├─ Improved emotion detection
├─ Proactive outreach capabilities
├─ Cross-department orchestration
└─ Enhanced learning algorithms

Future Vision (2027+)

Potential Capabilities:
├─ Full autonomy within broad boundaries
├─ Predictive problem prevention
├─ Seamless cross-organization collaboration
├─ Personalized experience generation
├─ Advanced negotiation capability
├─ Complex financial decisions
└─ Level 5 - Fully autonomous agents

Interview Cheat Sheet

Question Answer
What makes agentic agents different? They reason and plan solutions rather than following fixed scripts
What level of autonomy do they have? Level 4 - semi-autonomous with defined guardrails and boundaries
What issues can they handle? 50-70% of customer interactions, including complex scenarios
How do guardrails work? Configurable rules that define what agents can/cannot do
Can they access backend systems? Yes, can execute transactions and retrieve real-time data
How do they learn? Analyze interaction outcomes and improve responses over time
What happens if they can't solve? Intelligent escalation to human with full context
How long to deploy? 8-14 weeks depending on complexity
What's the expected ROI? 4-6 months through automation and efficiency gains
What channels do they support? All Genesys channels - voice, chat, email, messaging, social
How much does it cost? AI Experience tokens metered by usage
What about compliance? Built-in audit trails, guardrails, and monitoring
Can they make mistakes? Yes - monitored and corrected through guardrails
Are humans always available? Yes - escalation available anytime
What's most important for success? Clear use cases, good data, proper guardrails

Key Takeaways

Real-World Success Metrics

Customer Service Example

Before Agentic Agent:
├─ Customer Resolution Rate: 65% (human agents)
├─ Average Handle Time: 8 minutes
├─ Cost per Interaction: $4.50
├─ Customer Satisfaction: 75%
└─ Monthly Volume: 10,000 interactions

After Agentic Agent Implementation:
├─ Self-service + Agent: 80% Resolution Rate
├─ Agentic Agent Only: 65% first contact
├─ Average Handle Time: 3.5 minutes
├─ Cost per Interaction: $1.80
├─ Customer Satisfaction: 82%
└─ Same Volume but 40% labor reduction

Annual Impact:
├─ Cost Savings: ~$180,000/year
├─ Improvement in CSAT: +7 points
├─ Faster Resolution: -55% time
└─ ROI: 350% in first year

Financial Services Example

Before:
├─ Payment Processing: Human handled 70%
├─ Average Call Time: 12 minutes
├─ Collections Rate: 82%
├─ Cost per Transaction: $6.00
└─ Agent Utilization: 85%

After Agentic Implementation:
├─ Agentic Agent: 60% self-service
├─ Human Agents: 40% complex cases
├─ Average Call Time: 4 minutes
├─ Collections Rate: 87%
├─ Cost per Transaction: $2.40
├─ Agent Utilization: 65% (more valuable work)

Impact:
├─ Cost Savings: $240K/year
├─ Collections Improvement: +5%
├─ Revenue Benefit: $150K/year
└─ Total Benefit: $390K/year

Getting Started Checklist

Assessment Phase

Planning Phase

Development Phase

Deployment Phase

Optimization Phase

Additional Resources

Official Documentation Links

Training & Support

Document Version Info

Last Updated: March 2026
Source: Genesys PureCloud Official Documentation
Based On: AI Studio, Virtual Agent, and Agentic Capabilities releases
Version: 1.0

Predictive Routing

Genesys PureCloud Predictive Routing Documentation

Study Notes

Topic Description
Predictive Routing Machine learning-powered intelligent routing that matches contacts to optimal agents
AI Engine White-box models with explainability features showing feature importance scores
Outcome Labels Custom KPI metrics tracked and used to retrain models continuously
Queue Configuration Per-queue settings enabling/disabling predictive routing with KPI selection
Model Retraining Weekly automated retraining with daily data updates ensuring current accuracy
Data Retention No model data retained beyond 90 days for privacy and compliance

Navigation

Admin → Architect → Routing → Predictive Routing Configuration OR Admin → Contact Center → Routing → Predictive Routing Settings

Predictive Routing Overview

Genesys Cloud's predictive routing uses machine learning to rank agents for optimal handling of interactions, supporting key performance indicators like average handle time and next contact avoidance. Unlike traditional rule-based routing, predictive routing analyzes hundreds of data points in real-time to identify which agent is most likely to deliver the best customer outcome based on your defined business goals.

Key Capabilities

How It Works

  1. Contact arrives at system
  2. Contact metadata extracted (type, queue, customer data)
  3. URS Strategy Subroutines submit interaction details to the Core Platform, which scores agents for their historical ability to handle such an interaction
  4. Machine learning model scores all available agents
  5. Contact routed to highest-scoring agent
  6. Interaction outcome captured and logged
  7. Models updated with new outcome data for future predictions

Edition & Module Requirements

Requirement Details
Minimum Edition Premium Edition (Genesys Cloud CX 1-4)
Module Workforce Optimization or Predictive Routing add-on
License Type Included with qualifying editions
AI Tokens May consume tokens depending on configuration
Setup Admin access to Architect and routing configuration

Study Notes - AI Model Features

Feature Category Examples Impact
Agent Profile Data Skills, tenure, department, certifications High - Core matching criteria
Agent Performance Data Historic AHT, FCR, quality scores, tenure High - Predicts future performance
Availability Data Current status, workload, after-call-work High - Real-time readiness
Interaction Metadata Contact type, channel, customer segment Medium - Context matching
Queue Statistics Historical patterns, time-of-day trends Medium - Volume prediction
Customer Data History, value, previous interactions Low-Medium - Personalization

AI Model Architecture

White-Box Model Explainability

Genesys helps you deduce the prediction by presenting a global interpretation that describes the average behavior of a model. A high value means that the feature will have a larger effect on the model's predictions and ranking of agents. While a small value is given to unimportant features whose contribution is mostly ignored for the model's predictions.

Model Training & Retraining

To keep up with changing levels of agent proficiency and customer interaction contexts, the data models continually retrain and learn from the latest features. Genesys Cloud updates the features used for agent scoring with daily data and retrains the data models weekly. No data is retained in the models for more than 90 days.

Privacy & Compliance

Genesys does not use PII for the agent scoring process. Genesys Cloud only uses transaction conversation data to train machine learning models.

Data Requirements for Optimal Performance

Genesys recommends a periodic upload of outcome data to ensure better model training and prediction accuracy. The recommended frequency is once a day, or, at the minimum, once a week. Genesys recommends at least 90 days of data and ideally 180 days of data. If you retain fewer days of data, you can still use and benefit from predictive routing. However, the quality and effectiveness of your AI models and predictions is likely to suffer and the resulting benefit will be reduced compared to standard routing.

Data Sources

Minimum Data Thresholds

Data, even if available, is considered for the purpose of routing calculation only if the data volume meets a minimum requirement. If the volume of data does not meet the specified threshold value, the machine learning model does not use the available data. While not mandatory for Genesys predictive routing to operate, for better prediction results, Genesys recommends that you ensure the availability of sufficient volume of data against maximum number of fields.

Outcome Labels & Custom KPIs

Default KPIs

Predictive Routing can optimize for standard Genesys metrics:

Custom Outcome Labels

If you use outcome-based custom KPIs, Genesys predictive routing relies on data from external data sources, which it receives through the Outcome Attributions API.

Uploading Outcome Data

Setting KPI Optimization per Queue

During queue configuration, administrators select which KPI the predictive routing model should optimize for that specific queue. The system will then score agents based on their historical performance against that metric.

Queue Configuration Steps

Step 1: Navigate to Routing Settings

  1. Log into Genesys Cloud Admin
  2. Go to Architect → Routing
  3. Select queue to enable predictive routing
  4. Open queue configuration settings

Step 2: Enable Predictive Routing

  1. Locate "Predictive Routing" toggle
  2. Toggle ON to enable feature
  3. System validates that requirements are met
  4. Configuration page appears

Step 3: Select KPI Optimization

  1. Choose primary KPI from dropdown:
    • Average Handle Time (AHT)
    • Next Contact Avoidance (NCA)
    • Customer Satisfaction (CSAT)
    • Custom outcome metric
  2. If custom KPI: Verify outcome data is being uploaded via API
  3. Save selection

Step 4: Configure Scoring Parameters

  1. Set minimum agent availability threshold
  2. Define required skills for queue
  3. Configure fallback routing behavior
  4. Set scoring timeout (default: 3 seconds)
  5. Enable/disable explainability logging

Step 5: Data & Outcome Mapping

  1. Confirm queue is sending outcome data
  2. For outcome-based custom KPIs, ensure periodic upload of outcome data via Outcome Attributions API
  3. Map external outcome labels to routing KPI
  4. Verify historical data volume meets minimums
  5. Test data flow

Step 6: Validation & Testing

  1. Enable predictive routing on small percentage of traffic
  2. Monitor model accuracy metrics
  3. Verify fallback routing works correctly
  4. Gather baseline performance data
  5. Review feature importance scores in reports

Step 7: Gradual Rollout

  1. Increase traffic percentage gradually
  2. Monitor agent utilization and contact distribution
  3. Track KPI impact daily
  4. Make refinements as needed
  5. Scale to 100% when confident in performance

Model Scoring Process

Agent Scoring Flow

Incoming Contact
    ↓
Extract Contact Metadata
├── Contact type (voice, chat, email)
├── Queue assignment
├── Customer segment
└── Interaction intent
    ↓
Query AI Model
├── Input: Contact metadata
├── Features: Agent data, performance, availability
├── Process: ML scoring algorithm
└── Output: Agent scores (0-100)
    ↓
Rank Agents
├── Agent A: 87 score
├── Agent B: 72 score
├── Agent C: 91 score (highest)
└── Agent D: 65 score
    ↓
Route to Highest Scorer
├── Select: Agent C (91 score)
├── Fallback: If unavailable → Agent A (87)
└── Escalation: If all unavailable → Queue
    ↓
Log Interaction
├── Selected agent score
├── Feature importance values
├── Outcome when complete
└── Update training data

Feature Importance Explanation

The model provides transparency showing which factors most influenced each routing decision:

Real-World Implementation Scenario

Banking Contact Center

Queue: Mortgage Support
KPI Optimization: Average Handle Time (AHT)

Agent Profiles:
├─ Agent Sarah
│  ├─ Skills: Mortgage, Refinance, Loan Modification
│  ├─ Avg AHT: 6.2 minutes
│  ├─ Tenure: 5 years
│  └─ Current: Available (0 contacts)
│
├─ Agent James
│  ├─ Skills: Mortgage, Basic Support
│  ├─ Avg AHT: 8.1 minutes
│  ├─ Tenure: 1 year
│  └─ Current: Available (1 contact)
│
└─ Agent Maria
   ├─ Skills: Mortgage, Refinance, Advanced
   ├─ Avg AHT: 5.9 minutes
   ├─ Tenure: 7 years
   └─ Current: Available (2 contacts)

Incoming Contact:
"I'd like information about refinancing my mortgage"

Model Analysis:
├─ Contact Intent: Refinance inquiry
├─ Expected AHT: ~7 minutes
├─ Required Skills: Refinance, Mortgage
└─ Channel: Voice

Agent Scoring (for AHT optimization):
├─ Sarah: 89 score
│  └─ Reasoning: Strong refinance skills, excellent AHT,
│     lower current load
├─ James: 71 score
│  └─ Reasoning: Has required skills but higher baseline AHT
└─ Maria: 85 score
   └─ Reasoning: Best AHT historically but already has
      higher load (2 contacts)

Routing Decision:
Route to Agent Sarah (highest score: 89)

Expected Outcome:
├─ Faster resolution (Sarah's AHT advantage)
├─ Better customer experience
├─ Optimizes against AHT KPI
└─ Load distributed effectively

Model Metrics & Dashboard

Key Performance Indicators Tracked

Metric Purpose Good Range
Model Accuracy How often predictions are correct >75%
Feature Coverage % of required data available >85%
Agent Utilization Even work distribution 70-90%
KPI Improvement Impact on selected metric +5-20%
Fallback Rate When model can't score <5%

Monitoring Model Health

Best Practices

Data Quality

Model Optimization

Queue Configuration

Change Management

Common Implementation Issues & Solutions

Issue Cause Solution
Model not scoring agents Insufficient data volume Ensure 90+ days of outcome data available
Uneven agent utilization Agents with similar skills Review and update skill assignments
No KPI improvement Wrong KPI selected for queue Validate KPI choice aligns with business goals
High fallback rate Skill mismatches in data Audit and correct agent skill inventory
Slow model updates Outcome data not uploading Verify API integration and data flow
Feature importance unclear New queue without history Wait for sufficient data accumulation
Model accuracy low Poor quality training data Validate and clean outcome data

Interview Cheat Sheet

Question Answer
What is Predictive Routing? ML-powered routing that matches contacts to optimal agents based on historical data
What models does it use? White-box models with explainability showing feature importance percentages
How often do models retrain? Weekly with daily data updates; no data retained beyond 90 days
What data is needed? 90+ days recommended (ideally 180); daily or weekly outcome uploads
What KPIs can it optimize? AHT, NCA, CSAT, or custom metrics via Outcome Attributions API
Where do you configure it? Admin → Architect → Routing → Queue settings
Does it use PII? No, only transaction conversation data for training
What's the expected improvement? 5-20% improvement in selected KPI
How do you upload outcomes? Via Outcome Attributions API or CSV upload to Genesys Cloud
What if model unavailable? Falls back to traditional routing automatically
How do agents get selected? Highest-scoring agent routed first; fallback to next highest if unavailable
Can you explain routing decisions? Yes, white-box models provide feature importance scores for transparency

Key Takeaways

Additional Resources

Official Documentation Links

Support & Training

Document Version Info

Last Updated: March 2026
Source: Genesys PureCloud Official Documentation (help.genesys.cloud)
Validated: Current with January-March 2026 releases
Version: 1.0

Agent Copilot

Genesys PureCloud Agent Copilot Documentation

Study Notes

Topic Description
Agent Copilot Real-time AI assistance for agents during customer interactions
Core Function Determines customer intent and provides next best actions in real-time
After-Call Work Automates summaries, wrap-up codes, and resolution tracking
AI Skills Modular AI capabilities that can be combined and customized
On-Queue Behavior Operates continuously during interaction, adapting to conversation flow
Key Results 5-min AHT reduction, 1.5-min hold time reduction, 2-min ACW reduction

Navigation

Admin → Contact Center → Agent Assistance → Agent Copilot Configuration OR Admin → AI Studio → Agent Copilot Settings

Agent Copilot Overview

Genesys Agent Copilot enhances communication between the agent and the customer by determining customer intent and providing relevant next best actions to the agent. It offers assistance in after-call work and provides a summary of the conversation, reason for contact, resolution, and suggests wrap-up codes.

Genesys Agent Copilot integrates AI and Large Language Models to deliver intelligent assistance capabilities that transform agent productivity and customer service delivery. The platform automates after-call work through intelligent wrap-up code suggestions, conversation summaries, and resolution tracking, while providing real-time next-best-action recommendations and customer intent determination during live interactions.

Key Capabilities

Performance Metrics

Agent Copilot users have seen results including:

Edition & Module Requirements

Requirement Details
Minimum Edition Premium Edition (Genesys Cloud CX 1-4)
Module Included or Agent Copilot add-on depending on tier
License Type Per-user licensing
AI Tokens Requires tokens: 40-60 per user for Agent Copilot
Knowledge Base Genesys Knowledge Workbench or Knowledge Fabric recommended

Study Notes - Agent Copilot Features

Feature Description Benefit
Intent Recognition AI understands customer goals from conversation Faster issue identification
Next-Best-Action Recommends optimal next step for agent Guides resolution path
Auto-Summarization Generates conversation summary automatically Reduces after-call work
Wrap-Up Code Suggestions AI suggests appropriate completion codes Faster code selection
Knowledge Integration Surfaces relevant knowledge articles automatically Faster access to information
Answer Highlighting Highlights key parts of knowledge articles Easier information scanning
Sentiment Monitoring Detects customer emotion in real-time Enables de-escalation
Multi-Language Support Operates in 13+ languages Global team support
Custom Guides Build AI agents using AI Studio Automation flexibility
Performance Analytics Dashboard showing copilot usage and impact Measurement and optimization

How Agent Copilot Works

During Interaction (Real-Time Assistance)

Agent Answers Contact
    ↓
Copilot Begins Monitoring
├── Real-time transcription
├── Intent analysis
└── Context understanding
    ↓
Customer Explains Issue
"I'm having trouble logging into my account"
    ↓
Copilot Analyzes
├── Intent: Account Access Issue
├── Context: Login problem
├── Emotion: Slightly frustrated
└── Knowledge needed: Account recovery
    ↓
Copilot Provides Assistance
├── Knowledge Articles
│  ├─ "Password Reset Steps" (94% match)
│  ├─ "Two-Factor Auth Help" (81% match)
│  └─ "Account Locked Recovery" (76% match)
│
├── Next-Best-Action
│  └─ "Guide customer through password reset"
│
└── Script Suggestion
   └─ "I can help you regain access to your account..."
    ↓
Agent Reviews & Applies
├── Reads suggested article
├── Guides customer through steps
├── Issue resolved
└── Interaction continues

After Interaction (After-Call Work)

Contact Completes
    ↓
Copilot Generates Summary
├── Conversation Analysis
├── Key Points Extraction
├── Issue Resolution Status
└── Customer Sentiment Analysis
    ↓
Summary Displayed to Agent
"Customer called about password reset.
 Issue resolved through email link sent.
 Customer satisfied."
    ↓
Wrap-Up Code Suggestions
├─ Suggested Codes (in priority order)
│  ├─ Password Reset (89% confidence)
│  ├─ Account Access (71% confidence)
│  └─ Technical Support (34% confidence)
└─ Agent selects from suggestions
    ↓
Agent Reviews & Saves
├── Reviews summary (can edit)
├── Selects wrap-up code
├── Adds notes if needed
└── Saves interaction

AI Skills Architecture

AI Skills are modular, customizable AI capabilities that can be combined and deployed through Agent Copilot. Each skill targets a specific function or use case.

Types of AI Skills

Knowledge Skills

Next-Best-Action Skills

Wrap-Up Code Skills

Sentiment & De-Escalation Skills

Compliance & Risk Skills

Custom Business Skills

On-Queue Behavior

How Agent Copilot Operates During Customer Interaction

Queue: Customer Support
Status: Agent Sarah actively handling call

Real-Time Copilot Behavior:

MOMENT 1 (0:15 into call)
├─ Copilot listening to conversation
├─ Transcribing in real-time
├─ Analyzing customer statements
└─ Building context understanding

MOMENT 2 (0:45 into call)
Customer: "I need to change my billing address"
├─ Intent Detected: Billing Update
├─ Copilot triggers knowledge search
├─ Results: 2 relevant articles found
└─ Displayed to Agent Sarah

MOMENT 3 (1:15 into call)
├─ Customer emotion: Neutral → Positive
├─ Issue complexity: Routine
├─ Next action: Process address change
└─ Copilot suggests: "Enter new address in system"

MOMENT 4 (1:45 into call)
Customer: "Can I also update my phone number?"
├─ New intent added to context
├─ Copilot expands knowledge articles
├─ Related articles surfaced
└─ Agent handles both requests together

MOMENT 5 (3:00 - Call ends)
├─ Both issues resolved
├─ Customer satisfied
├─ Copilot prepares summary
└─ Ready for after-call work

Agent Copilot Interface Components

Agent Desktop View

┌──────────────────────────────────────┐
│ ACTIVE CALL - Sarah Martinez        │
│ Customer: John Smith                │
│ Queue: Billing Support              │
│ Duration: 3:42                      │
└──────────────────────────────────────┘

┌──────────────────────────────────────┐
│ 📚 SUGGESTED KNOWLEDGE              │
├──────────────────────────────────────┤
│ ✓ Update Billing Address (94%)       │
│ ✓ Change Phone Number (87%)          │
│ ○ Payment Methods (61%)              │
│                                      │
│ [Show Full Articles] [Minimize]      │
└──────────────────────────────────────┘

┌──────────────────────────────────────┐
│ ➡️  NEXT-BEST-ACTION                 │
├──────────────────────────────────────┤
│ Recommended: Process address update  │
│ in customer record                   │
│                                      │
│ Steps:                               │
│ 1. Confirm new address               │
│ 2. Verify zip code                   │
│ 3. Update system                     │
│ 4. Send confirmation                 │
└──────────────────────────────────────┘

┌──────────────────────────────────────┐
│ 😊 CUSTOMER SENTIMENT               │
├──────────────────────────────────────┤
│ Current: POSITIVE                    │
│ Emotion: Satisfied                   │
│ Recommendation: Offer additional help│
└──────────────────────────────────────┘

[Notes Box]
[Hold/Transfer/Close Buttons]

Behavior by Interaction Type

Inbound Call

Chat/Email

Outbound Call

Implementation Guide

Step 1: Prerequisites & Planning

  1. Ensure Premium Edition activated
  2. Confirm sufficient AI tokens available (40-60 per user)
  3. Audit knowledge base quality and coverage
  4. Identify queues for initial deployment
  5. Assess agent readiness and training needs
  6. Plan change management approach

Step 2: Knowledge Base Setup

  1. Review/create knowledge articles in Knowledge Workbench
  2. Ensure articles are accurate and current
  3. Add metadata and keywords for searchability
  4. Organize by topic and queue
  5. Set article access permissions
  6. Test knowledge base connectivity

Step 3: Enable Agent Copilot

  1. Navigate to Admin → Contact Center → Agent Assistance
  2. Select "Agent Copilot" → Enable
  3. Choose knowledge source (Knowledge Workbench v2 or Fabric)
  4. Configure recommendation parameters
  5. Set recommendation types to display
  6. Choose display behavior (auto-popup vs. agent-triggered)

Step 4: Configure by Queue

  1. Create queue-specific Copilot settings
  2. Define which AI Skills are active per queue
  3. Configure knowledge sources
  4. Set wrap-up code matching rules
  5. Establish sentiment thresholds
  6. Test queue-specific behavior

Step 5: Agent Training

  1. Conduct overview training on Copilot interface
  2. Demonstrate real-time knowledge suggestions
  3. Practice reviewing and applying recommendations
  4. Explain wrap-up code suggestions
  5. Cover sentiment alerts and de-escalation
  6. Q&A and feedback

Step 6: Phased Rollout

  1. Deploy to pilot queue first
  2. Monitor closely for 1-2 weeks
  3. Gather agent and supervisor feedback
  4. Optimize based on learnings
  5. Expand to additional queues
  6. Scale to full deployment

Step 7: Ongoing Monitoring

  1. Review Agent Copilot dashboard daily
  2. Track key metrics (AHT reduction, token usage)
  3. Monitor agent adoption rates
  4. Gather continuous feedback
  5. Refine knowledge articles based on usage
  6. Optimize AI recommendations

Real-Flow Scenarios

Scenario 1: Technical Support with Knowledge Integration

Agent: "Thanks for calling technical support, how can I help?"

Customer: "My printer isn't connecting to WiFi"

Copilot immediately surfaces:
├─ 3 relevant knowledge articles
├─ Troubleshooting flowchart visual
├─ Video walkthrough link
└─ Quick-resolution steps

Agent: "I can help. Let me walk you through the WiFi 
        connection steps which usually resolve this..."

[Uses Copilot-suggested steps to guide customer]

Result: Issue resolved in 4 minutes
Copilot Summary:
"Customer called about printer WiFi connectivity issue. 
 Guided through troubleshooting steps. Issue resolved 
 after resetting router. Customer satisfied."

Wrap-up Code Suggestions:
├─ Printer Setup (92% confidence) ← Selected
├─ Technical Support (67% confidence)
└─ Network Issues (45% confidence)

Time Saved: 2 minutes (no manual summary or code lookup)

Scenario 2: Billing with Sentiment De-escalation

Customer (frustrated): "Why was I charged twice?!"

Copilot detects:
├─ Emotion: FRUSTRATED
├─ Sentiment score: -2.1/5
├─ Recommended action: Empathize & resolve immediately
└─ De-escalation tip: "Acknowledge frustration sincerely"

Agent (applying suggestion): "I completely understand 
        your frustration. Let me look into that right away 
        and fix this for you."

Copilot provides:
├─ Knowledge: "Duplicate Charge Resolution"
├─ Next action: "Issue refund immediately"
└─ Script: "Here's what happened and how I'm fixing it..."

[Agent processes refund while explaining]

Customer (relieved): "Oh wow, thanks for handling that so fast!"

Copilot updates sentiment: +1.5/5 (positive)

Summary generated:
"Customer called upset about duplicate charge. Explained 
 system error, issued refund immediately. Customer very 
 satisfied with quick resolution and empathetic service."

Result: De-escalation successful, issue resolved, positive outcome

Scenario 3: Sales with Cross-Sell Recommendations

Customer: "I'd like to upgrade my plan"

Copilot analyzes:
├─ Customer history: 2-year subscriber
├─ Current plan: Basic
├─ Usage patterns: Heavy data user
├─ Recommended action: Suggest upgraded plan + add-ons
└─ Next-best-action: "Present Premium with extras"

Agent: "Great! Based on your usage, I have a perfect 
        recommendation..."

Copilot displays:
├─ Customer's usage metrics
├─ Recommended plan details
├─ Comparison of savings
└─ Available promotions (10% if upgrade today)

[Agent presents recommendations]

Customer: "The Premium plan looks good, and 10% off 
         sounds great!"

Copilot suggests:
├─ Add-on: Premium Support (93% value match)
└─ Upsell: Extended warranty (67% match)

Result: Upgraded plan + 1 add-on sale
Copilot Summary:
"Customer upgraded from Basic to Premium plan and added 
 Premium Support. Mentioned promotional discount influenced 
 decision. Total value: $XXX. Customer satisfied."

Business Impact: Increased revenue, improved satisfaction

Best Practices

Knowledge Base Quality

Agent Copilot Configuration

Agent Enablement

Performance Optimization

Token Consumption

Agent Copilot requires AI Experience tokens for operation:

Interview Cheat Sheet

Question Answer
What is Agent Copilot? Real-time AI assistance for agents during customer interactions
What does it do during calls? Determines intent, suggests next actions, surfaces knowledge, monitors sentiment
What about after-call work? Auto-generates summaries, suggests wrap-up codes, tracks resolution
What are AI Skills? Modular AI capabilities (knowledge, next actions, sentiment, compliance)
How does it behave on-queue? Continuous monitoring and assistance throughout interaction
What languages does it support? 13+ including English, Spanish, French, German, Japanese, Korean, Arabic, Hindi
How much time does it save? ~9 minutes per interaction (5 AHT + 2 ACW + 1.5 hold time)
What's the expected improvement? AHT reduction 5-10%, ACW reduction 2-3 minutes, CSAT improvement 5-15%
How is it licensed? Per-user with 40-60 tokens per user per month
Does it work omnichannel? Yes - voice, chat, email all supported
Can you customize it? Yes via AI Studio to create custom Guides and Skills
Where is it configured? Admin → Contact Center → Agent Assistance → Agent Copilot
What knowledge does it need? Quality knowledge base (Workbench or Fabric)
How long until ROI? 2-4 weeks to see AHT improvements
What's most important? Quality knowledge base and agent training

Key Takeaways

Additional Resources

Official Documentation

Support & Training

Document Version Info

Last Updated: March 2026
Source: Genesys PureCloud Official Documentation
Validated: Current with January-March 2026 releases
Version: 1.0

AI Forecasting (WFM)

Genesys PureCloud AI Forecasting (WFM) Documentation

Study Notes

Topic Description
AI Forecasting Automated, cloud-based forecasting using advanced ML algorithms
Automatic Best Method (ABM) Genesys' proprietary ensemble technique selecting optimal algorithm
Accuracy Focus Eliminates manual method selection through automation
Cloud-Based Automatically updated with latest algorithms and research
Data Normalization Handles outliers, missing data, seasonality automatically
Setup Simple integration between WFM and Genesys Cloud CX

Navigation

WFM → Forecast → Build Volumes/AHT OR WFM Supervisors → Forecasting (New UI) → Create Forecast

AI Forecasting Overview

Genesys Cloud's AI-Powered Forecasting feature is a sophisticated build method that leverages best practices in data science and the industry. It provides users with an easy-button approach to an otherwise complex operation of predicting the workload and service time of agents for contact center planning.

The AI-powered forecasting service uses advanced machine learning algorithms to analyze historical data and generate highly accurate forecasts for workforce management, representing an industry-first capability that enables the fastest, most accurate AI-powered forecasting service for better workforce management.

Key Capabilities

Performance Improvements

Organizations using AI Forecasting typically see:

Automatic Best Method (ABM) Explained

What is ABM?

Automatic Best Method (ABM) uses AI and ensemble techniques to create highly accurate forecasts for workforce management. Rather than requiring forecasters to manually select which algorithm to use (Expert Average Engine, Universal Modeling Engine, etc.), ABM automatically evaluates multiple algorithms and selects the best one for your specific data.

How ABM Works

Historical Data Input
    ↓
Data Analysis & Preparation
├─ Identify patterns
├─ Detect seasonality
├─ Find trends
└─ Locate anomalies
    ↓
Evaluate Multiple Algorithms
├─ Expert Average Engine
├─ Universal Modeling Engine
├─ Exponential Smoothing
├─ ARIMA Models
├─ Ensemble Combinations
└─ Neural Networks
    ↓
Machine Learning Selection
├─ Test each algorithm
├─ Compare accuracy metrics
├─ Validate against holdout data
├─ Score performance
└─ Select best performer
    ↓
Generate Forecast
├─ Apply selected algorithm
├─ Apply ensemble weighting
├─ Output predictions
└─ Provide confidence intervals
    ↓
Forecast Delivered
├─ Volume forecast
├─ AHT forecast
├─ Accuracy metrics
└─ Ready for scheduling

ABM vs. Traditional Methods

Aspect Traditional Methods Automatic Best Method
Method Selection Manual by forecaster Automatic via ML
Algorithm Options 4-5 choices 10+ evaluated
Time Required Hours to days Minutes
Accuracy Good (70-80%) Excellent (85-95%+)
Expertise Required High (data science knowledge) Low (point and click)
Consistency Variable (person-dependent) Consistent (algorithmic)
Updates Manual recalculation Automatic weekly
Optimization Limited Full ensemble evaluation

Edition & Module Requirements

Requirement Details
Minimum Edition Genesys Cloud CX 1-4 or Genesys Multicloud CX
Module AI-Powered Forecasting add-on (wfmAiPoweredForecasting key)
WFM Version WFM 8.5.214 or later
Setup OAuth client and Genesys Cloud CX Tier 3 organization
Integration Cloud-based connection between WFM and Genesys Cloud
Internet WFM servers must have internet access (or proxy)

Study Notes - AI Forecasting Data Handling

Data Issue How ABM Handles It Result
Missing Data Intelligent interpolation and statistical methods Complete forecasts without gaps
Outliers Detects and normalizes extreme values Realistic forecasts without spikes
Seasonality Identifies recurring patterns Accurate seasonal adjustments
Trends Analyzes long-term direction Projects growth/decline correctly
Holidays Adjusts for expected non-working days Prevents overstaff in holidays
Spikes Separates temporary from permanent changes Distinguishes one-off from real change
Multiple Channels Combines data intelligently Unified forecasts across channels

How AI Forecasting Improves Accuracy

Traditional Forecasting Challenges

Manual Method Selection Process:

1. Forecaster reviews 6 months of data
2. Attempts Expert Average Engine
   └─ Accuracy: 72%

3. Tries Universal Modeling Engine
   └─ Accuracy: 78%

4. Tests Exponential Smoothing
   └─ Accuracy: 75%

5. Manual decision: Use Universal Modeling (78%)

6. Generate forecast

Problems:
├─ Time-consuming (hours)
├─ Subjective decision-making
├─ Might not find best option
├─ Requires expertise
└─ Accuracy: ~78%

AI Forecasting Approach

Automatic Best Method Process:

1. Historical data loaded into Genesys Cloud
2. ABM evaluates 10+ algorithms in parallel
   ├─ Expert Average Engine: 72%
   ├─ Universal Modeling: 78%
   ├─ Exponential Smoothing: 75%
   ├─ ARIMA: 81%
   ├─ Ensemble Combination A: 87%
   ├─ Ensemble Combination B: 89%
   ├─ Neural Networks: 86%
   ├─ Bayesian Approach: 84%
   └─ [Additional algorithms...]

3. ML system selects: Ensemble Combination B (89%)

4. Generate forecast

Benefits:
├─ Automatic (minutes)
├─ Objective evaluation
├─ Finds optimal option
├─ No expertise required
├─ Higher accuracy: ~89%

Accuracy Improvements by Scenario

High-Volatility Queue

Traditional Method: 68% accuracy
AI Forecasting: 84% accuracy
Improvement: +16 points (24% better)
Business Impact: Better staffing decisions, fewer shortages

Seasonal Business

Traditional Method: 74% accuracy
AI Forecasting: 91% accuracy
Improvement: +17 points (23% better)
Business Impact: Optimized staffing for season changes

Multi-Channel Center

Traditional Method: 71% accuracy
AI Forecasting: 87% accuracy
Improvement: +16 points (23% better)
Business Impact: Unified forecasting across channels

Why ABM is More Accurate

  1. Exhaustive Algorithm Evaluation - Tests multiple approaches, not just a few
  2. Ensemble Methods - Combines best algorithms for optimal accuracy
  3. Continuous Optimization - Cloud service updates automatically
  4. Machine Learning - Adapts to your specific data patterns
  5. Latest Research - Incorporates cutting-edge forecasting algorithms
  6. No Human Bias - Objective selection based on data, not opinion
  7. Validation - Tests against holdout data before deployment

Implementation Guide

Step 1: Prerequisites

  1. Verify Genesys Cloud CX organization created (Tier 3)
  2. Confirm WFM version 8.5.214 or later
  3. Obtain wfmAiPoweredForecasting product key
  4. Verify internet connectivity for WFM servers
  5. Create OAuth client in Genesys Cloud (Client Credentials grant)
  6. Gather required credentials and configuration info

Step 2: Configure WFM for AI Forecasting

  1. Open Genesys Administrator
  2. Navigate to WFM Server Applications
  3. Set authentication provider to WFM
  4. Configure purecloud section: 
    [auth] provider = wfm
[PureCloud]
purecloud.client_id = <your_client_id>
purecloud.client_secret = <your_client_secret>
purecloud.upload_uri = https://apps.mypurecloud.com
purecloud.region = <your_region>
  5. Save configuration
  6. Restart WFM Server

Step 3: Enable in WFM UI

  1. Log into WFM Supervisors interface
  2. Navigate to Forecasting
  3. In New UI settings, enable "Use Latest Forecast UI"
  4. Verify AI Forecasting option appears
  5. Test data connectivity

Step 4: Create First Forecast

  1. Select queue/skill for forecasting
  2. Review historical data (at least 90 days recommended)
  3. Choose forecast type:
    • Volume forecast (interaction count)
    • AHT forecast (handling time)
    • Combined forecast (both)
  4. Select time period to forecast
  5. Choose "AI-Powered Forecasting" method
  6. System automatically:
    • Analyzes data
    • Evaluates algorithms
    • Selects best method
    • Generates forecast
  7. Review results and accuracy metrics
  8. Save and publish forecast

Step 5: Validation & Testing

  1. Compare AI forecast accuracy to historical actual
  2. Review outlier handling
  3. Validate seasonality adjustments
  4. Test with upcoming actual data
  5. Monitor variance between forecast and actual
  6. Refine as needed

Step 6: Production Use

  1. Generate forecasts on regular cadence (weekly recommended)
  2. Use for schedule building in WFM
  3. Monitor forecast accuracy continuously
  4. Adjust refresh frequency if needed
  5. Establish process for anomalies

Real-World Implementation Scenario

Mid-Market Contact Center

Organization: Financial Services Company
Current: 250 agents across 4 skill groups
Challenge: Manual forecasting taking 16 hours/week
Accuracy: ~74% (missing peaks/valleys)

Implementation:

Week 1: Setup
├─ Created Genesys Cloud Tier 3 org
├─ Configured OAuth client
├─ Updated WFM to 8.5.214
└─ Integrated WFM with Cloud

Week 2-3: First Forecasts
├─ Migrated 6 months of historical data
├─ Generated AI forecasts for each skill group
├─ Compared to previous manual method
└─ Accuracy improved from 74% to 87%

Results in First 30 Days:
├─ Forecast time: 16 hours → 2 hours (87% reduction)
├─ Accuracy improvement: +13 points (74% → 87%)
├─ Better staffing alignment
├─ Fewer service level misses
├─ Reduced overtime due to better predictions
└─ Cost savings: ~$4,000/month

Year 1 Impact:
├─ Consistent 85-88% forecast accuracy
├─ Service levels improved 5-8 points
├─ Labor costs down 3-5%
├─ Forecaster time freed for analysis
└─ ROI: Payback in 3 months

AI Forecasting vs. Traditional Methods

Method Comparison

Expert Average Engine

Universal Modeling Engine

Template-Based

Automatic Best Method (AI)

Best Practices

Data Quality

Forecasting Process

Integration with Scheduling

Organizational Adoption

Common Forecasting Scenarios

Scenario 1: Intraday Forecasting

Monday 8 AM Forecast for Monday 1 PM - 6 PM

Historical Pattern:
├─ Lunch hour (12-1 PM): 50% volume drop
├─ Afternoon (1-3 PM): Recovery to 90% normal
├─ Late afternoon (3-6 PM): Peak (110% normal)
└─ Volume trend: Growing 2% week-over-week

AI Forecast Output:
├─ 1 PM: 45 inbound calls (50% of 90)
├─ 2 PM: 82 inbound calls (91% of 90)
├─ 3 PM: 95 inbound calls (105% of 90)
├─ 4 PM: 100 inbound calls (111% of 90)
├─ 5 PM: 98 inbound calls (109% of 90)
└─ 6 PM: 85 inbound calls (94% of 90)

AHT Pattern:
├─ Lunch hour: +8% (simpler issues)
├─ Afternoon: +2% (baseline)
└─ Evening: +5% (more complex)

Result: Accurate staffing for afternoon peak

Scenario 2: Weekly Forecast (Monday-Friday)

Week of March 10-14

Pattern: Business process deadline Friday
├─ Monday: 90% normal volume
├─ Tuesday: 85% normal volume (preparation starts)
├─ Wednesday: 110% normal volume (deadline week)
├─ Thursday: 115% normal volume (peak)
└─ Friday: 120% normal volume (final deadline)

AI Detects: Weekly recurring pattern
Adjusts: Volume forecast accordingly
Result: Optimal staffing for each day

Scenario 3: Holiday Period

Christmas Week Forecast

Pattern Recognition:
├─ December 23: Normal (Monday before holiday)
├─ December 24: 140% volume (last-minute issues)
├─ December 25: Closed (holiday)
├─ December 26: 160% volume (backup)
└─ December 27-31: 120% (customers want help before year-end)

AI Handles: Automatically adjusts for holiday
Result: Accurate staffing despite disruption

Monitoring & Optimization

Forecast Accuracy Tracking

Accuracy Metrics

Metric Formula Target
Mean Absolute Percentage Error (MAPE) Avg |(Actual - Forecast) / Actual| <10%
Mean Bias Avg(Actual - Forecast) ±3%
Peak Accuracy Accuracy during peak times >85%
Valley Accuracy Accuracy during low-volume times >80%

Continuous Improvement

Interview Cheat Sheet

Question Answer
What is AI Forecasting? Cloud-based automated forecasting using ML algorithms
What's ABM? Automatic Best Method - automatically selects optimal algorithm
How is it different from traditional? No manual method selection; evaluates 10+ algorithms automatically
What accuracy improvement? Typically 15-25% better than traditional methods (85-92% vs 70-80%)
How does it handle anomalies? Automatically detects and normalizes outliers and missing data
What data is needed? 90+ days minimum, ideally 180+ days of historical data
Is it cloud-only? Yes, requires Genesys Cloud CX Tier 3 organization
How long to setup? 1-2 weeks for initial configuration and testing
What WFM version? WFM 8.5.214 or later
Does it handle seasonality? Yes, automatically detects and accounts for seasonal patterns
Can it forecast multiple channels? Yes, combines data intelligently across all channels
How often to refresh? Weekly minimum, can be daily for high-variance queues
What's the ROI? Typically 3-4 month payback through better staffing
Can it predict anomalies? Detects anomalies but requires manual handling of known events
What metrics are tracked? Volume, AHT, accuracy, forecast vs. actual variance

Key Takeaways

Additional Resources

Official Documentation

Support & Training

Document Version Info

Last Updated: March 2026
Source: Genesys PureCloud Official Documentation
Validated: Current with January-March 2026 releases
Version: 1.0

Supervisor Copilot

Genesys PureCloud Supervisor Copilot Documentation

Study Notes

Topic Description
Supervisor Copilot AI-powered assistant providing real-time insights for supervisors
Virtual Supervisor Automates evaluation scoring using AI for 100% of interactions
AI Scoring Automated performance evaluations with transparency and fairness
Analytics Explorer AI Skill providing historical and real-time data insights
Quality Management Automates routine tasks, focuses supervisors on coaching
Time Savings 40% reduction in quality evaluation time, 25% in multilingual work

Navigation

Admin → Quality Management → Virtual Supervisor OR Quality → Evaluation Dashboard → AI Scoring Settings

Supervisor Copilot Overview

Supervisor Copilot is an AI-powered toolkit designed to enhance supervisor productivity and decision-making in the contact center. It acts as a sidekick for managers, providing prescriptive support for quality assurance, compliance and coaching. Powered by generative AI, it automatically summarizes interactions, allowing supervisors to quickly review and make informed decisions.

Virtual Supervisor and Supervisor Copilot are AI-powered tools designed to support and enhance supervisor workflows. These features combine powerful capabilities (AI scoring, summary, insights, and translate), to deliver a smarter, more efficient way to monitor performance, gain clarity from conversations, and act quickly on key information.

Key Capabilities

Performance Improvements

Organizations using Supervisor Copilot report:

Edition & Module Requirements

Requirement Details
Minimum Edition Genesys Cloud CX 1-4 (CX 3-4 recommended)
Module Virtual Supervisor or Supervisor Copilot add-on
License Type Required for quality scoring and analytics
AI Tokens Consumption based on evaluation volume
Features AI Scoring, AI Translate, AI Summary & Insights

Study Notes - Supervisor Copilot Components

Component Function Benefit
AI Scoring Automated evaluation of interactions 100% coverage, consistency, reduced manual work
AI Translate Multi-language transcript conversion Faster multilingual review, 25% time reduction
AI Summary & Insights Key points extraction and analysis Quick understanding, identified coaching needs
Reason for Contact Automatic issue categorization Pattern identification, trend analysis
Resolution Status Whether issue was resolved Outcome tracking, quality assessment
Action Items Follow-up tasks identified Process improvement, compliance tracking
Sentiment Drivers What caused customer emotion Root cause analysis, service improvement
Analytics Explorer Data visualization and trends Real-time insights, strategic visibility

Virtual Supervisor & AI Scoring

What is Virtual Supervisor?

Virtual Supervisor enhances Quality Management by leveraging AI to automate interaction evaluations. It automatically scores interactions and delivers actionable insights, highlighting areas for improvement and explaining the rationale behind each assessment. By reducing the need for manual reviews, Virtual Supervisor empowers supervisors to more effectively support agents and focus on coaching and performance development.

How AI Scoring Works

Interaction Completed
    ↓
Virtual Supervisor Receives Transcript
├─ Recording
├─ Transcript
├─ Agent info
└─ Customer info
    ↓
AI Analysis Engine Evaluates
├─ Compliance requirements
├─ Quality standards
├─ Best practices
├─ Behavioral criteria
└─ Customer outcomes
    ↓
Scoring Process
├─ Question 1: Did agent greet properly?
│  └─ AI Analysis: YES - "Agent used proper greeting with company name"
│
├─ Question 2: Compliance disclosure given?
│  └─ AI Analysis: YES - "Agent stated privacy policy per minute 2:15"
│
├─ Question 3: Issue resolved?
│  └─ AI Analysis: YES - "Customer confirmed satisfaction at end"
│
├─ Question 4: Proper tone used?
│  └─ AI Analysis: PARTIAL - "Professional but slightly rushed in places"
│
└─ Question 5: Call handled efficiently?
   └─ AI Analysis: YES - "Average handle time 6.2 min vs queue avg 6.8"
    ↓
Score Generated
├─ Greeting: 10/10
├─ Compliance: 10/10
├─ Resolution: 10/10
├─ Tone: 7/10
├─ Efficiency: 9/10
└─ Overall Score: 92/100
    ↓
Justifications Provided
├─ Strengths: Compliance excellent, efficient handling
├─ Improvement Areas: Pacing during explanations
└─ Coaching Recommendations: Practice clear, methodical explanations
    ↓
Delivered to Supervisor
├─ Score with justifications
├─ Transcript highlighting
├─ Coaching suggestions
└─ Comparison to standards

AI Scoring Features

Automated Scoring

Transparency & Fairness

Multi-Question Coverage

Quality Control

Analytics Explorer (AI Skill)

Analytics Explorer is the first AI Skill that provides historical and real-time data to help supervisors lower time-to-insight and access performance trends without complex dashboards.

What is Analytics Explorer?

Analytics Explorer uses natural language processing to let supervisors ask questions about metrics and trends in plain language, rather than navigating complex dashboards. It provides:

How Analytics Explorer Works

Supervisor Question:
"Which agent had the highest AHT this week?"

Natural Language Processing
├─ Identify: Query type (agent comparison)
├─ Extract: Metric (AHT)
├─ Parse: Timeframe (this week)
└─ Understand: Ranking (highest)
    ↓
Data Query & Analysis
├─ Retrieve: This week's AHT data for all agents
├─ Calculate: Average per agent
├─ Rank: By highest to lowest
└─ Context: Compare to queue average
    ↓
Response Generated
"Agent James had the highest AHT this week 
 at 8.3 minutes, which is 15% above queue 
 average of 7.2 minutes. This is typical for 
 his skill set but elevated compared to his 
 personal average of 7.8."
    ↓
Additional Insights
├─ Trend: Increasing vs past 4 weeks
├─ Context: Why (queue complexity, etc.)
└─ Recommendation: Review calls, provide coaching

Common Analytics Explorer Queries

Comparative Analysis

Anomaly Detection

Predictive Insights

Supervisor Workflows with Copilot

Quality Management Workflow

Traditional vs. AI-Powered Approach:

TRADITIONAL (16 hours/supervisor/week):
├─ Monday: Randomly select 25 interactions
├─ Tuesday: Listen to calls (2 hours)
├─ Wednesday: Score evaluations (2 hours)
├─ Thursday: Compile feedback (1 hour)
├─ Friday: Meet with agents individually (6 hours)
└─ Continuous: Handle urgent issues

AI-POWERED (10 hours/supervisor/week):
├─ Morning: Review AI Scoring of 100% interactions
│  └─ 1 hour to review 150+ scored interactions
├─ Identify: Focus areas and top performers
│  └─ 30 min (automated pattern detection)
├─ Deep Dives: Review specific interactions
│  └─ 2 hours (only complex or critical ones)
├─ Coaching: Deliver targeted feedback
│  └─ 4 hours (focused on high-impact areas)
├─ Analytics: Trend analysis and planning
│  └─ 1.5 hours (data-driven decisions)
└─ Strategic: Process improvement and planning
   └─ 1 hour (freed-up supervisory time)

Time Savings: 6 hours/week per supervisor = $12,000/year per supervisor
Quality Improvement: Data-driven coaching, 100% coverage vs. sampling

Coaching Workflow with AI Insights

Step 1: AI Scoring Identifies Issues
├─ Virtual Supervisor scores 100 interactions
├─ 15 need coaching on "tone and empathy"
├─ 8 have compliance gaps
└─ 3 show efficiency concerns

Step 2: Supervisor Reviews AI Justifications
├─ Reads AI explanations for each score
├─ Understands root causes
├─ Sees agent strengths highlighted
└─ Identifies coaching themes

Step 3: Supervisor Selects Coaching Targets
├─ Priority 1: Compliance gaps (critical)
├─ Priority 2: Tone issues (systemic)
├─ Priority 3: Individual efficiency (targeted)
└─ Recognition: Top performers (5 agents)

Step 4: Prepare Coaching Sessions
├─ Pull specific interaction examples
├─ Write coaching notes with AI suggestions
├─ Plan 1-on-1 meetings
└─ Prepare recognition for strong performers

Step 5: Conduct Coaching
├─ Share AI insights with agents
├─ Discuss specific examples
├─ Explain improvements with data
├─ Create development plans
└─ Recognize strengths and efforts

Step 6: Follow-Up
├─ Monitor next evaluations
├─ Use AI Scoring to track improvement
├─ Celebrate progress with agents
└─ Adjust coaching if needed

Real-Flow Scenarios

Scenario 1: Automated Quality Evaluation

Monday Morning - Quality Review:

Supervisor logs in at 9 AM

AI Scoring Summary Shows:
├─ 147 interactions scored over weekend
├─ Average quality score: 87/100
├─ 3 interactions scored below 70 (below standard)
├─ 12 interactions with compliance tags
├─ 8 agents exceeded their personal average
└─ 2 high-risk interactions flagged

Supervisor Action (30 minutes):
1. Reviews 3 low-scoring interactions
   ├─ Reads AI justifications
   ├─ Listens to specific moments
   └─ Identifies coaching points

2. Flags 12 compliance interactions
   ├─ Shares with quality team
   ├─ Schedules compliance training
   └─ Creates preventive alerts

3. Celebrates high performers
   ├─ Sends recognition messages
   ├─ Shares with management
   └─ Motivates team

Result: What would take 4-5 hours manually is done in 30 minutes
Quality: More objective, data-driven, fair evaluations
Coaching: More targeted, based on data insights

Scenario 2: Multilingual Evaluation

Wednesday - International Team Review:

Supervisor manages team across 3 languages:
├─ English speakers (50%)
├─ Spanish speakers (35%)
└─ French speakers (15%)

Traditional Approach:
├─ Must hire bilingual evaluators
├─ Or use translation services (slow, expensive)
├─ Or only evaluate in English (unfair)
└─ Result: Inconsistent quality evaluation

AI-Powered Approach:
├─ Virtual Supervisor AI Translates all transcripts
├─ All agents evaluated in supervisor's language
├─ Consistent standards across all languages
├─ Takes 25% of the time vs traditional methods
└─ Result: Fair, consistent, efficient

Supervisor Benefits:
├─ Evaluates all agents fairly
├─ No language barrier
├─ Consistent quality standards
├─ Time saved: 75% reduction in multilingual work
└─ No translation costs

Scenario 3: Compliance Monitoring

Friday End-of-Day - Compliance Check:

Supervisor needs to verify compliance for audit:

AI Scoring automatically:
├─ Reviewed 500 interactions this week
├─ Tagged required disclosures (100% coverage)
├─ Flagged missing compliance statements
├─ Noted proper documentation
└─ Generated compliance report

Supervisor Action (30 minutes):
├─ Reviews AI-generated compliance report
├─ Drills into 3 flagged interactions
├─ Notes pattern (new agents missing disclosure)
├─ Schedules training session
└─ Reports 98% compliance to management

Traditional Manual Approach:
├─ Would require sampling (maybe 10%)
├─ Time: 8+ hours of listening
├─ Coverage: ~10% of interactions
├─ Risk: Might miss issues
└─ Inefficient for audit

AI Result: 100% coverage, done in 30 min, comprehensive audit-ready report

Best Practices

Quality Management

Agent Coaching

Compliance & Governance

Analytics & Insights

Implementation Guide

Step 1: Enable Supervisor Copilot

  1. Navigate to Admin → Quality Management
  2. Enable "Supervisor Copilot" features
  3. Configure AI Scoring permissions
  4. Set up Virtual Supervisor access
  5. Enable Analytics Explorer AI Skill

Step 2: Configure AI Scoring

  1. Define quality evaluation form
  2. Map questions to AI scoring categories
  3. Set scoring standards and thresholds
  4. Configure supervisor review process
  5. Establish approval workflow

Step 3: Train Supervisors

  1. Explain AI Scoring capabilities
  2. Show how to review AI justifications
  3. Practice using Analytics Explorer
  4. Establish new coaching workflow
  5. Share best practices

Step 4: Establish Workflow

  1. Define daily/weekly quality reviews
  2. Establish coaching process
  3. Create compliance monitoring procedures
  4. Set up reporting and analysis
  5. Establish escalation process

Step 5: Monitor & Optimize

  1. Track adoption metrics
  2. Gather supervisor feedback
  3. Review AI Scoring accuracy
  4. Optimize based on learnings
  5. Refine training as needed

Interview Cheat Sheet

Question Answer
What is Supervisor Copilot? AI-powered assistant providing insights and automation for supervisors
What does Virtual Supervisor do? Automates evaluation scoring of 100% of interactions with justifications
How much time is saved? 40% reduction in quality evaluation time, 25% in multilingual work
What is AI Scoring? Automated evaluation using AI with transparency and fairness
How does fairness work? AI-generated justifications explain every score, supervisors can adjust
What about multilingual? AI Translate converts transcripts to 70+ languages automatically
What's Analytics Explorer? AI Skill providing natural language access to metrics and trends
Can supervisors override AI? Yes, review and adjust scores before finalizing
What's included in summary? Reason for contact, resolution, action items, sentiment drivers
How accurate is AI Scoring? Uses latest LLMs; continuously improving accuracy
Can it handle compliance? Yes, flags compliance issues and monitors requirements
What's the ROI? 40% time savings + better quality through consistency
How long to implement? 2-4 weeks for setup and training
What edition needed? CX 3-4 recommended (CX 1-2 possible with add-on)
Does it work with Agent Copilot? Yes, integrates seamlessly with Agent Copilot data

Key Takeaways

Additional Resources

Official Documentation

Support & Training

Document Version Info

Last Updated: March 2026
Source: Genesys PureCloud Official Documentation
Validated: Current with January-March 2026 releases
Version: 1.0

AI Tokens & Pricing

Genesys PureCloud AI Tokens & Pricing Documentation

Study Notes

Topic Description
Token Model Consumption-based pricing for AI features
Monthly Allocation 250 tokens for named users, 350 for concurrent users
Pricing ~$1.00 per additional token beyond allocation
Consumption Tracking Per-feature metering with detailed reporting
Fair Use Policy Most customers covered by included allocation
Billing Monthly renewal with no carryover

Navigation

Admin → Billing & Subscriptions → AI Experience Tokens OR Reports → Usage → AI Token Consumption

AI Tokens Overview

Genesys Cloud implements a comprehensive token-based pricing model for AI features across CX 1-4 license tiers. The platform offers a suite of AI capabilities including Agent Copilot, AI Scoring, Translation, Virtual Agent services, predictive routing, and analytics with flexible consumption-based pricing.

Genesys Cloud AI Experience tokens help you monitor and manage feature consumption and offer flexibility for changing business needs. Tokenization in AI is a way to track AI engagement in real time by allocating fixed units of measurement to usage costs. This allows organizations to pay only for what they use, providing a scalable, cost-efficient way to integrate AI into operations.

Default Token Allocations

Edition & Module Requirements

Requirement Details
Minimum Edition All Genesys Cloud CX tiers include base tokens
Token Access All CX 1-4 organizations receive default allocation
Ordering New customers have access by default
Additional Tokens Purchase through AppFoundry or Genesys representative
Billing Included on monthly invoice with consumption tracking

Study Notes - Token Consumption by Feature

Feature Unit Tokens Required Use Case
Agent Copilot Per user 40-60/month Real-time agent assistance
AI Scoring Per evaluation 1 token per 20 evals Quality management
AI Translate Per minute 1 token per 100 min Multilingual transcripts
Voice Bots Per minute 1 token per 17 min IVR automation
Digital Bots Per session 1 token per 51 sess Chat/messaging automation
Messaging Per message 1 token per 400 msg Direct messaging channels
Social Per post 1 token per 400 post Social media listening
Virtual Agent Per session 2 tokens per session Autonomous conversation
Predictive Routing Per interaction Included Agent routing optimization
Analytics Per user 30-45/month Analytics access

Token Consumption Details

Agent Copilot

AI Scoring (Virtual Supervisor)

AI Translate

Voice Bots

Digital Bots

Messaging Channels

Social Media Listening & Responses

Virtual Agent Sessions

Predictive Routing

Speech & Text Analytics

Predictive Engagement

Token Allocation Examples

Small Contact Center (50 agents)

Configuration:
├─ Agent Copilot: 50 agents × 50 tokens = 2,500
├─ AI Scoring: ~200 interactions/day
│  └─ 200 × 20 days ÷ 20 = 200 tokens
├─ Messaging: ~500 messages/month
│  └─ 500 ÷ 400 = 2 tokens
└─ Virtual Agent: ~50 sessions/day
   └─ 50 × 20 × 2 = 2,000 tokens

Total Monthly: ~4,700 tokens
Included: 250 × 50 = 12,500 tokens
Overage: None (well under allocation)
Cost: $0 overage

Mid-Market Center (200 agents)

Configuration:
├─ Agent Copilot: 200 agents × 55 tokens = 11,000
├─ AI Scoring: ~500 interactions/day
│  └─ 500 × 20 ÷ 20 = 500 tokens
├─ AI Translate: ~5 hours/month
│  └─ 300 min ÷ 100 = 3 tokens
├─ Messaging: ~5,000 messages/month
│  └─ 5,000 ÷ 400 = 13 tokens
├─ Voice Bots: ~1,000 interactions × 5 min
│  └─ 5,000 ÷ 17 = 294 tokens
└─ Virtual Agent: ~200 sessions/day
   └─ 200 × 20 × 2 = 8,000 tokens

Total Monthly: ~19,810 tokens
Included: 250 × 200 = 50,000 tokens
Overage: None
Cost: $0 overage

Enterprise Center (1,000 agents)

Configuration:
├─ Agent Copilot: 1,000 agents × 50 tokens = 50,000
├─ AI Scoring: ~2,000 interactions/day
│  └─ 2,000 × 20 ÷ 20 = 2,000 tokens
├─ AI Translate: ~50 hours/month
│  └─ 3,000 min ÷ 100 = 30 tokens
├─ Messaging: ~50,000 messages/month
│  └─ 50,000 ÷ 400 = 125 tokens
├─ Voice Bots: ~5,000 interactions × 4 min
│  └─ 20,000 ÷ 17 = 1,176 tokens
├─ Digital Bots: ~10,000 sessions/month
│  └─ 10,000 ÷ 51 = 196 tokens
└─ Virtual Agent: ~1,000 sessions/day
   └─ 1,000 × 20 × 2 = 40,000 tokens

Total Monthly: ~93,527 tokens
Included: 250 × 1,000 = 250,000 tokens
Overage: None
Cost: $0 overage

Note: This enterprise center is well within allocation
even with heavy AI usage across all capabilities

Token Consumption Monitoring

Token Usage Dashboard

Organization Token Summary

Period: March 2026
Total Allocation: 50,000 tokens/month
Total Consumed: 19,810 tokens
Remaining: 30,190 tokens (60% available)
Usage Rate: 40%

Consumption by Feature:
├─ Agent Copilot: 11,000 tokens (55%)
├─ Virtual Agent: 8,000 tokens (40%)
├─ AI Scoring: 500 tokens (3%)
├─ Voice Bots: 294 tokens (1.5%)
├─ Messaging: 13 tokens (0.1%)
├─ AI Translate: 3 tokens (0.1%)
└─ Other: 0 tokens

Consumption Trends:
├─ Week 1: 4,500 tokens (15% of monthly)
├─ Week 2: 4,200 tokens (14% of monthly)
├─ Week 3: 5,300 tokens (18% of monthly)
├─ Week 4: 5,810 tokens (19% of monthly)
└─ Trend: Stable, slight increase toward month-end

Projected Usage (if trend continues):
└─ End of Month: 19,810 tokens (40% of allocation)

Health Status: ✓ GREEN (well within limits)
Recommendation: Can safely increase AI usage

How to Access Token Monitoring

  1. Log into Genesys Cloud Admin
  2. Navigate to Reports → Usage
  3. Select "AI Token Consumption"
  4. Choose time period to review
  5. View consumption by feature
  6. Export data if needed

Key Metrics to Track

Metric Purpose Good Range
Overall Utilization Total token usage 40-80% of allocation
Per-Feature Usage Identify heavy consumers Proportional to use
Trend Direction Usage pattern Stable or controlled
Overage Risk Approaching limits Monitor if >80%
Unused Allocation Wasted capacity <20% unused

Cost Management Strategies

Optimization Techniques

Budgeting Approach

  1. Baseline Calculation:

    • 50 tokens per Agent Copilot user/month
    • 2 tokens per Virtual Agent session
    • 1 token per 20 evaluations
    • 1 token per 51 digital bot sessions

  2. Add Buffer:

    • For unpredictability: +20% overage buffer
    • For growth: +10-15% growth buffer

  3. Monitor Monthly:

    • Review actual vs. projected
    • Identify variance
    • Adjust next month's budget

  4. Planning:

    • Budget for peak season
    • Factor in new initiatives
    • Plan for scaling

Real-World Budget Example

Mid-Market Center - Annual Budget Planning:

Current State (March 2026):
├─ Monthly consumption: 19,810 tokens
├─ Monthly allocation: 50,000 tokens
├─ Utilization: 40%
└─ Cost: $0 overage

Planned Growth (Next 12 months):
├─ +50 agents (10% growth)
├─ New Virtual Agent deployment
├─ Expanded Messaging channels
└─ Expected consumption increase: +8,000 tokens/month

Future State Projection:
├─ Monthly consumption: ~27,800 tokens
├─ Monthly allocation: 62,500 tokens (250 × 250 users)
├─ Utilization: 44%
└─ Cost: $0 overage

Annual Cost Impact:
├─ Base licensing: No increase (same CX tier)
├─ AI Tokens: No additional cost (within allocation)
└─ Total Additional Cost: $0 (internal reallocation only)

Conclusion: Growth sustainable within existing token allocation

Token Pricing by Region

Pricing Variations

Ordering Additional Tokens

Real-World Usage Scenarios

Scenario 1: Seasonal Spike Management

Contact Center Business: Holiday Retailer
Challenge: Heavy AI usage Dec-Jan, minimal Jun-Aug

Strategy:
├─ Base allocation: 30,000 tokens/month
├─ November: Monitor usage, prepare for peak
├─ Dec-Jan: Purchase additional 20,000 tokens
│  └─ Ensures sufficient capacity
│  └─ Cost: ~$20,000 for 2 months
├─ Feb: Return to base allocation
└─ Jun-Aug: Only use allocation (excess unused)

Cost Management:
├─ Off-peak: $0 additional cost
├─ Peak months: $20,000 additional
├─ Annual cost: $20,000 (2 months only)
└─ Flexibility: Scale as needed

Scenario 2: Phased AI Rollout

Contact Center: Implementing AI progressively

Phase 1 (Month 1): Agent Copilot only
├─ Consumption: 4,000 tokens (10 users)
├─ Cost: $0 (within allocation)
└─ ROI: Proven before major investment

Phase 2 (Month 3): Add Virtual Agent
├─ New consumption: +3,000 tokens
├─ Total: 7,000 tokens
├─ Cost: $0 (still within allocation)
└─ Result: Expand automation safely

Phase 3 (Month 6): Add Analytics + Bots
├─ New consumption: +5,000 tokens
├─ Total: 12,000 tokens
├─ Cost: $0 (allocated)
└─ Result: Full AI platform

Phase 4 (Month 12): Scale based on ROI
├─ Purchase additional tokens if needed
├─ Cost: Only what you use
└─ Confidence: Proven ROI before major spend

Interview Cheat Sheet

Question Answer
What is token model? Consumption-based pricing for AI features
What's the default allocation? 250 tokens per named user, 350 per concurrent user
What's token cost? ~$1.00 per additional token beyond allocation
How is consumption tracked? Real-time metering per feature with monthly reporting
Which features consume tokens? Agent Copilot, Virtual Agent, Bots, Translate, Analytics, etc.
Agent Copilot consumption? 40-60 tokens per user per month
Virtual Agent cost? 2 tokens per session
Voice Bot cost? 1 token per 17 minutes
Digital Bot cost? 1 token per 51 sessions
AI Scoring cost? 1 token per 20 evaluations
Does Predictive Routing cost? No, included with platform
How do I monitor usage? Admin → Reports → Usage → AI Token Consumption
Can I purchase additional? Yes, through AppFoundry or Genesys rep
How often do tokens renew? Monthly, with no carryover
Is there overage protection? Fair use policy covers most organizations
What about fair use? 95% of customers covered by fair use policy
How to optimize costs? Improve bot deflection, optimize routing, batch processing
Can I predict usage? Yes, based on user count and AI feature deployment

Key Takeaways

Cost Calculation Tool

Quick Estimation Template

Contact Center Token Cost Calculator:

1. Agent Copilot Users: ____ × 50 tokens = _____ tokens

2. Virtual Agent Sessions/Day: ____
   Daily calculation: ____ sessions × 2 tokens = _____ tokens/day
   Monthly: _____ tokens/day × 20 days = _____ tokens

3. AI Scoring Interactions/Day: ____
   Monthly: (____ × 20 ÷ 20) = _____ tokens

4. Voice Bot Minutes/Day: ____
   Monthly: (____ × 20 ÷ 17) = _____ tokens

5. Digital Bot Sessions/Month: ____
   Monthly: (____ ÷ 51) = _____ tokens

6. Messaging/Month: ____ messages
   Monthly: (____ ÷ 400) = _____ tokens

7. Other Features (translate, analytics, etc): _____ tokens

TOTAL MONTHLY CONSUMPTION: _____ tokens

Your Allocation: 250 × (number of users) = _____ tokens
Overage: (Total - Allocation) or $0 if under

Annual Cost:
├─ Base: Included in licensing
├─ Overage: (Monthly overage × 12) × $1.00
└─ Total: _____ annually

Additional Resources

Official Documentation

Support & Ordering

Document Version Info

Last Updated: March 2026
Source: Genesys PureCloud Official Documentation
Validated: Current with January-March 2026 releases
Version: 1.0