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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

  • Automated method selection (no manual algorithm choosing required)
  • Analyzes historical data to determine best forecasting approach
  • Automatically detects and handles outliers and anomalies
  • Fills gaps in incomplete historical data
  • Accounts for seasonality and trends
  • Continuously evolving - automatically updated with latest algorithms
  • Cloud-based delivery ensures always-current capability
  • Supports both volume and AHT (Average Handling Time) forecasting
  • Works across all contact center channels
  • Integration with WFM for scheduling and planning

Performance Improvements

Organizations using AI Forecasting typically see:

  • 15-25% improvement in forecast accuracy over traditional methods
  • Reduced forecasting time (hours vs. weeks)
  • Elimination of subjective method selection
  • Better capacity planning and staffing accuracy
  • Improved service level achievement
  • Reduced labor costs through better predictions

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

  • Best for: Stable, consistent patterns
  • Accuracy: ~75%
  • Time: Manual selection (~1-2 hours)
  • Pros: Fast if chosen correctly
  • Cons: Misses complex patterns

Universal Modeling Engine

  • Best for: Moderate complexity
  • Accuracy: ~78%
  • Time: Manual selection (~2-3 hours)
  • Pros: Handles trends well
  • Cons: Not optimal for all scenarios

Template-Based

  • Best for: Similar queues
  • Accuracy: ~70%
  • Time: Minutes
  • Pros: Quick
  • Cons: Limited accuracy

Automatic Best Method (AI)

  • Best for: All scenarios
  • Accuracy: 85-92%
  • Time: Minutes (automatic)
  • Pros: Always optimal, no expertise needed
  • Cons: Requires cloud integration

Best Practices

Data Quality

  • Historical Data - Maintain at least 90 days, ideally 180+ days
  • Data Accuracy - Ensure interaction counts and AHT are correct
  • Clean Data - Remove obvious data entry errors
  • Consistent Definitions - Define queues/skills consistently
  • Regular Validation - Verify data quality before forecasting

Forecasting Process

  • Regular Cadence - Generate forecasts weekly at minimum
  • Validation - Compare forecasts to actuals and adjust
  • Anomaly Handling - Identify unusual events and exclude if needed
  • Multiple Methods - Consider different forecast scenarios
  • Document Changes - Track what changed between forecasts

Integration with Scheduling

  • Use Forecasts Immediately - Apply to schedule building
  • Schedule Alignment - Ensure schedules match forecast expectations
  • Adherence Tracking - Monitor agents vs. schedule accuracy
  • Feedback Loop - Use actual data to improve next forecast
  • Continuous Improvement - Refine process over time

Organizational Adoption

  • Stakeholder Buy-In - Get forecasting team support
  • Clear Communication - Explain benefits and changes
  • Training - Educate on new AI method
  • Quick Wins - Show improvements early
  • Continuous Monitoring - Track and share successes

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

  • Daily - Compare forecast to actual volume/AHT
  • Weekly - Calculate accuracy percentage
  • Monthly - Analyze trends and patterns
  • Quarterly - Review overall accuracy and improvements
  • Annually - Strategic assessment of forecasting effectiveness

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

  • Review Forecast-Actual Variance - Identify patterns of over/under forecasting
  • Adjust for New Patterns - Update forecasts as business changes
  • Incorporate Feedback - Use scheduling/staffing feedback
  • Test Scenarios - Model impact of business changes
  • Optimize Parameters - Fine-tune forecasting settings

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

  • Automatic Selection - No manual algorithm choosing; AI finds optimal method
  • High Accuracy - 85-92% typical accuracy vs. 70-80% traditional
  • Industry-Leading - Fastest, most accurate AI-powered forecasting available
  • Easy to Use - Simple point-and-click interface, no expertise needed
  • Intelligent Data Handling - Automatically manages outliers, seasonality, missing data
  • Cloud-Based - Always updated with latest algorithms
  • Faster Process - Hours/weeks reduced to minutes
  • Ensemble Methods - Combines multiple algorithms for optimal results
  • Continuous Learning - Improves recommendations over time
  • Proven ROI - 3-4 month payback through better staffing

Additional Resources

Official Documentation

  • Forecasting Overview: all.docs.genesys.com/PEC-WFM/GFrcstg
  • ABM Overview: help.mypurecloud.com/articles/automatic-best-method-forecast-method-overview/
  • AI-Powered Forecasting Setup: docs.genesys.com/Documentation/WM/latest/Admin/AIPwrdFrcst
  • Multicloud CX AI Forecasting: all.docs.genesys.com/PEC-WFM/PECAIPwrd

Support & Training

  • Genesys University: genesys.com/training
  • Community Forums: https://community.genesys.com
  • Technical Support: https://support.genesys.com

Document Version Info

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

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