Mastering AI-Powered Financial Forecasting in Dynamics 365
You're under pressure. Your forecasts are questioned in every board meeting. Stakeholders demand accuracy, but legacy tools deliver guesswork. You're stuck between outdated spreadsheets and overpromising solutions that fail to integrate. The cost of error? Wasted capital, eroded credibility, and missed promotions. Manual forecasting is no longer defensible. With market volatility accelerating and margins tightening, finance professionals who rely on intuition or static models are being left behind. The future belongs to those who harness AI-driven insights within enterprise platforms they already use. And that platform is Dynamics 365. Mastering AI-Powered Financial Forecasting in Dynamics 365 is your proven pathway from uncertainty to authority. This is not theory. It’s a battle-tested methodology that transforms how financial leaders plan, predict, and present - turning reactive reporting into strategic foresight. Imagine delivering quarterly forecasts with 94% accuracy, backed by AI models that continuously learn from your organisation’s data. That’s what happened for Priya M., Senior Financial Analyst at a Fortune 500 manufacturing firm. After applying this framework, she reduced forecasting cycle time by 60% and presented a board-ready financial model that secured $12M in operational funding. This course is designed for ambitious finance leaders, ERP analysts, and Dynamics practitioners who refuse to trade integrity for speed. You’ll go from idea to AI-augmented forecasting capability in 30 days, with a fully documented, auditable, and scalable forecasting workflow - complete with a Certificate of Completion issued by The Art of Service to validate your expertise. You’ll gain the clarity, confidence, and competitive edge to not only survive but lead in the new era of predictive finance. Here’s how this course is structured to help you get there.Course Format & Delivery Details This is not a passive learning experience. Mastering AI-Powered Financial Forecasting in Dynamics 365 is a self-paced, on-demand learning environment with immediate online access, engineered for professionals who value precision, flexibility, and results. Designed for Real-World Integration
This course is 100% self-paced, with no fixed dates, live sessions, or timed commitments. You’ll gain access to the full curriculum instantly after confirmation, allowing you to progress according to your schedule - whether that’s 30 minutes per day or an intensive weekend immersion. - Typical completion time: 4 weeks with 3–5 hours per week
- Learners report implementing core forecasting workflows within 10 days
- Immediate application: Apply each module directly to your live Dynamics 365 environment
Unlimited, Future-Proof Access
You’re not buying a fleeting lesson. You’re investing in a permanent asset. This course includes: - Lifetime access to all course materials
- Ongoing updates as Microsoft releases new AI features in Dynamics 365
- Continuous content enhancements at no extra cost
- 24/7 global access from any device - fully mobile-friendly
Direct, Role-Specific Support
Every learner receives structured guidance through integrated progress checkpoints, expert-authored implementation templates, and direct access to instructor-curated troubleshooting frameworks. You’re never on your own. - Dedicated Q&A pathways for Dynamics configuration issues
- Standard operating procedures for common forecasting challenges
- Guided workflows for data cleansing, model calibration, and AI integration
Career-Validated Certification
Upon completion, you’ll earn a Certificate of Completion issued by The Art of Service - a globally recognised credential trusted by thousands of professionals across finance, IT, and enterprise risk. This certification enhances your profile on LinkedIn, internal review cycles, and promotion discussions. - Certificate includes unique verification ID
- Aligned with enterprise AI governance standards
- Recognised by hiring managers in Fortune 500 and mid-market firms
Risk-Free Enrollment with Full Confidence
We understand the stakes. That’s why this course comes with a 100% satisfaction guarantee. If you complete the first two modules and feel the content does not meet your professional standards, you’re entitled to a full refund - no questions asked. - No hidden fees. One-time straightforward pricing.
- Secure checkout with Visa, Mastercard, and PayPal
- After enrollment, you’ll receive a confirmation email, with access details sent separately once your course package is fully provisioned
This Works Even If…
…you’re not a data scientist, …you’ve never configured an AI model before, …you work in a heavily regulated industry, or …your Dynamics instance has years of legacy data. The step-by-step implementation logic is built for real-world complexity, not ideal scenarios. Thousands of professionals across manufacturing, healthcare, logistics, and finance have used this methodology to turn Dynamics 365 into a predictive engine. The tools are already in your stack - you just haven’t been shown how to unlock them. This is your leverage. Your advantage. Your path to being the one person in the room who doesn’t just report the numbers - but predicts them.
Module 1: Foundations of AI-Driven Financial Forecasting - Understanding the evolution of financial forecasting
- Limitations of traditional forecasting in ERP environments
- Introduction to AI and machine learning in financial planning
- Key benefits of AI-powered forecasting in Dynamics 365
- Differentiating between rule-based and AI-driven forecasting
- The role of predictive analytics in modern finance
- Overview of Microsoft’s AI strategy in Dynamics 365
- Identifying organisational readiness for AI forecasting
- Mapping forecasting pain points to AI capabilities
- Establishing forecasting KPIs and success metrics
Module 2: Dynamics 365 Finance Architecture for AI - Core components of Dynamics 365 Finance
- Understanding the data model for financial forecasting
- Master data management for forecasting accuracy
- Chart of accounts structuring for AI consumption
- Dimensions and segment configuration
- Integration between General Ledger and forecasting modules
- Ledger allocation rules and their impact on forecasts
- Configuring fiscal calendars and period alignment
- Role of financial parameters in forecast setup
- Managing intercompany transactions in forecasting models
- Currency setup and financial consolidation considerations
- Understanding the reconciliation logic between actuals and forecasts
- Best practices for setting up financial hierarchies
- Extensibility points in Dynamics 365 for custom forecasting
- Security roles and data access in financial forecasting
Module 3: Data Preparation and Cleansing for AI Models - Importance of clean data in AI forecasting
- Identifying common data anomalies in financial systems
- Handling missing, duplicated, or inconsistent values
- Outlier detection and correction techniques
- Using Power Query for data transformation
- Automating data quality checks in Dynamics 365
- Setting up data validation rules for financial entries
- Creating audit trails for data preparation steps
- Temporal alignment of financial data
- Time series data formatting for forecasting models
- Normalising data for multi-entity comparisons
- Scaling and standardising financial metrics
- Handling seasonality and trend adjustments
- Creating derived forecasting variables
- Mapping historical data to AI model input requirements
Module 4: Introduction to AI Builder and Forecasting Tools - Overview of Microsoft AI Builder capabilities
- AI Builder licensing and permissions in Dynamics 365
- Exploring prebuilt vs custom AI models
- Using Prediction models for financial outcomes
- Setting up Object Detection for document processing
- Text classification for financial categorisation
- Choosing the right AI model for forecasting use cases
- Understanding confidence scores and model reliability
- Model training lifecycle in AI Builder
- Interpreting AI model outputs for financial decisions
- Monitoring model performance over time
- Retraining models with new financial data
- Exporting AI model results to financial reports
- Integration of AI Builder with Power Automate
- Best practices for model naming and documentation
Module 5: Building Your First AI Forecasting Model - Selecting a forecasting use case for initial implementation
- Defining input variables and target outcomes
- Connecting AI Builder to financial datasets
- Training a model on historical revenue data
- Training a model on expense patterns
- Setting prediction thresholds and sensitivity
- Validating model accuracy with test datasets
- Interpreting feature importance in financial predictions
- Adjusting model weightings based on business logic
- Exporting predicted values to Excel for review
- Creating model performance reports
- Setting up model refresh schedules
- Version control for AI forecasting models
- Documenting model assumptions and limitations
- Generating audit-ready model documentation
Module 6: Advanced Forecasting with Power BI Integration - Connecting Dynamics 365 to Power BI
- Setting up live connections for real-time forecasting
- Import vs DirectQuery modes for financial data
- Building forecasting dashboards in Power BI
- Visualising AI predictions alongside actuals
- Using decomposition trees for variance analysis
- Applying DAX measures for forecast calculations
- Time intelligence functions in forecasting models
- Creating dynamic forecasting scenarios
- Scenario comparison using What-If analysis
- Drill-through capabilities from dashboard to transaction
- Scheduling automated report distribution
- Configuring row-level security for forecasts
- Embedding Power BI dashboards in Dynamics 365
- Publishing to Power BI Service for enterprise access
Module 7: Financial Forecasting Workflow Automation - Introduction to Power Automate in forecasting
- Designing a monthly forecasting workflow
- Automating data extraction from Dynamics 365
- Triggering AI model predictions on schedule
- Emailing forecast results to stakeholders
- Conditional logic for exception handling
- Approvals workflow for forecast validation
- Updating budget registers with forecast outputs
- Pushing forecast adjustments back to Dynamics
- Logging workflow execution and errors
- Monitoring automation health in Power Platform
- Using gateways for on-premises data sources
- Handling large volume data transfers securely
- Performance optimisation for financial flows
- Documenting workflow dependencies and ownership
Module 8: Cash Flow and Liquidity Forecasting - Configuring cash flow forecasting in Dynamics 365
- Setting up cash flow worksheet templates
- Configuring liquidity analysis parameters
- Linking customer and vendor payment schedules
- Projecting cash inflows from sales pipelines
- Forecasting cash outflows for procurement
- Importing bank statement data for reconciliation
- Synchronising with third-party treasury systems
- Modelling short-term borrowing requirements
- Simulating worst-case liquidity scenarios
- Generating cash runway reports
- Integrating AI predictions into cash flow models
- Validating forecasts against bank cash positions
- Setting up alerts for negative cash flow
- Creating liquidity stress-test dashboards
Module 9: Revenue and Sales Forecasting - Configuring sales forecasting in Dynamics 365 Sales
- Linking opportunity stages to revenue probability
- Setting up forecast categories and hierarchy
- Rolling up forecasts by team, product, region
- Aligning sales forecasts with financial planning
- Integrating CRM data with General Ledger
- Using AI to predict opportunity closure dates
- Forecasting revenue by product line
- Modelling seasonal demand fluctuations
- Adjusting forecasts based on market signals
- Validating AI predictions against actual bookings
- Handling forecast overrides and manual adjustments
- Generating variance reports for sales leadership
- Setting up forecast collaboration workflows
- Documenting assumptions for audit purposes
Module 10: Expense and Budget Forecasting - Configuring budget planning in Dynamics 365
- Setting up budget cycles and versions
- Defining budget dimensions and control rules
- Creating forecast templates for department budgets
- Rolling forward prior year actuals
- Incorporating inflation and growth factors
- Using AI to predict departmental spend patterns
- Forecasting headcount-related expenses
- Modelling capital expenditure trends
- Integrating procurement forecasts into budgets
- Setting up budget alerts and thresholds
- Conducting budget vs forecast variance analysis
- Exporting budget forecasts to executive reports
- Managing budget revisions and approvals
- Aligning budget forecasts with strategic planning
Module 11: Demand and Inventory Forecasting - Connecting financial forecasts to supply chain data
- Using AI to predict product demand
- Configuring master planning parameters
- Setting up forecast models in Supply Chain Management
- Linking demand forecasts to procurement planning
- Forecasting inventory carrying costs
- Predicting stockout risks and overage costs
- Modelling safety stock requirements
- Integrating with warehouse management systems
- Forecasting production capacity needs
- Aligning manufacturing output with sales forecasts
- Using Power BI for inventory health monitoring
- Generating inventory turnover reports
- Simulating just-in-time inventory scenarios
- Optimising reorder points with AI predictions
Module 12: Scenario Planning and Forecast Modelling - Creating multiple forecast scenarios in Dynamics 365
- Best practices for scenario naming and structure
- Building optimistic, baseline, and pessimistic models
- Modelling impact of market disruptions
- Simulating M&A or divestiture scenarios
- Forecasting impact of pricing changes
- Modelling workforce restructuring outcomes
- Analysing impact of currency fluctuations
- Stress-testing forecasts under inflation shocks
- Using AI to compare scenario outcomes
- Automating scenario roll-up for consolidation
- Presenting scenarios in executive dashboards
- Exporting scenario outputs to board reports
- Version control for forecast models
- Documenting scenario assumptions and triggers
Module 13: Forecast Validation and Accuracy Monitoring - Setting up forecasting accuracy benchmarks
- Calculating Mean Absolute Percentage Error (MAPE)
- Tracking forecast bias over time
- Using Power BI to visualise forecast performance
- Setting up forecast deviation alerts
- Conducting root cause analysis on forecast errors
- Identifying systematic over- or under-forecasting
- Reviewing model inputs for data integrity issues
- Adjusting models based on performance feedback
- Creating monthly forecast performance reports
- Incorporating feedback from business units
- Updating assumptions based on actual results
- Documenting accuracy improvements over time
- Establishing continuous improvement cycles
- Presenting forecast reliability to audit committees
Module 14: Governance, Compliance, and Audit Readiness - Establishing forecasting governance framework
- Defining roles and responsibilities for forecast owners
- Creating standard operating procedures for forecasting
- Documenting model development and validation steps
- Ensuring compliance with SOX and financial regulations
- Setting up audit trails for forecast changes
- Implementing change management processes
- Version control for forecasting models
- Secure access to forecasting data and models
- Data retention policies for model history
- Handling model updates in regulated environments
- Preparing audit packages for external review
- Reconciling AI forecasts with official financial statements
- Training internal audit teams on AI forecasting
- Creating governance dashboards for oversight
Module 15: Enterprise Integration and Scalability - Scaling forecasting models across legal entities
- Consolidating forecasts from subsidiaries
- Handling multi-currency and multi-GAAP environments
- Integrating with enterprise performance management (EPM) tools
- Connecting to Azure Data Lake for big data forecasting
- Using Dataverse as a central forecasting repository
- API integration with third-party planning systems
- Building custom connectors for external data
- Orchestrating forecasting across hybrid environments
- Managing version consistency in global deployments
- Training regional finance teams on central models
- Creating language-specific forecasting templates
- Aligning local forecasts with corporate strategy
- Monitoring global forecast health in real time
- Establishing escalation paths for forecasting issues
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from all modules
- Completing the practical forecasting project
- Submitting documentation for verification
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Leveraging your credential in job interviews and promotions
- Building a portfolio of forecasting deliverables
- Presenting your forecasting model to leadership
- Documenting ROI from AI forecasting implementation
- Creating a business case for wider AI adoption
- Joining the community of certified practitioners
- Accessing advanced learning pathways in AI and finance
- Staying updated with new Dynamics 365 AI features
- Planning your next forecasting initiative
- Understanding the evolution of financial forecasting
- Limitations of traditional forecasting in ERP environments
- Introduction to AI and machine learning in financial planning
- Key benefits of AI-powered forecasting in Dynamics 365
- Differentiating between rule-based and AI-driven forecasting
- The role of predictive analytics in modern finance
- Overview of Microsoft’s AI strategy in Dynamics 365
- Identifying organisational readiness for AI forecasting
- Mapping forecasting pain points to AI capabilities
- Establishing forecasting KPIs and success metrics
Module 2: Dynamics 365 Finance Architecture for AI - Core components of Dynamics 365 Finance
- Understanding the data model for financial forecasting
- Master data management for forecasting accuracy
- Chart of accounts structuring for AI consumption
- Dimensions and segment configuration
- Integration between General Ledger and forecasting modules
- Ledger allocation rules and their impact on forecasts
- Configuring fiscal calendars and period alignment
- Role of financial parameters in forecast setup
- Managing intercompany transactions in forecasting models
- Currency setup and financial consolidation considerations
- Understanding the reconciliation logic between actuals and forecasts
- Best practices for setting up financial hierarchies
- Extensibility points in Dynamics 365 for custom forecasting
- Security roles and data access in financial forecasting
Module 3: Data Preparation and Cleansing for AI Models - Importance of clean data in AI forecasting
- Identifying common data anomalies in financial systems
- Handling missing, duplicated, or inconsistent values
- Outlier detection and correction techniques
- Using Power Query for data transformation
- Automating data quality checks in Dynamics 365
- Setting up data validation rules for financial entries
- Creating audit trails for data preparation steps
- Temporal alignment of financial data
- Time series data formatting for forecasting models
- Normalising data for multi-entity comparisons
- Scaling and standardising financial metrics
- Handling seasonality and trend adjustments
- Creating derived forecasting variables
- Mapping historical data to AI model input requirements
Module 4: Introduction to AI Builder and Forecasting Tools - Overview of Microsoft AI Builder capabilities
- AI Builder licensing and permissions in Dynamics 365
- Exploring prebuilt vs custom AI models
- Using Prediction models for financial outcomes
- Setting up Object Detection for document processing
- Text classification for financial categorisation
- Choosing the right AI model for forecasting use cases
- Understanding confidence scores and model reliability
- Model training lifecycle in AI Builder
- Interpreting AI model outputs for financial decisions
- Monitoring model performance over time
- Retraining models with new financial data
- Exporting AI model results to financial reports
- Integration of AI Builder with Power Automate
- Best practices for model naming and documentation
Module 5: Building Your First AI Forecasting Model - Selecting a forecasting use case for initial implementation
- Defining input variables and target outcomes
- Connecting AI Builder to financial datasets
- Training a model on historical revenue data
- Training a model on expense patterns
- Setting prediction thresholds and sensitivity
- Validating model accuracy with test datasets
- Interpreting feature importance in financial predictions
- Adjusting model weightings based on business logic
- Exporting predicted values to Excel for review
- Creating model performance reports
- Setting up model refresh schedules
- Version control for AI forecasting models
- Documenting model assumptions and limitations
- Generating audit-ready model documentation
Module 6: Advanced Forecasting with Power BI Integration - Connecting Dynamics 365 to Power BI
- Setting up live connections for real-time forecasting
- Import vs DirectQuery modes for financial data
- Building forecasting dashboards in Power BI
- Visualising AI predictions alongside actuals
- Using decomposition trees for variance analysis
- Applying DAX measures for forecast calculations
- Time intelligence functions in forecasting models
- Creating dynamic forecasting scenarios
- Scenario comparison using What-If analysis
- Drill-through capabilities from dashboard to transaction
- Scheduling automated report distribution
- Configuring row-level security for forecasts
- Embedding Power BI dashboards in Dynamics 365
- Publishing to Power BI Service for enterprise access
Module 7: Financial Forecasting Workflow Automation - Introduction to Power Automate in forecasting
- Designing a monthly forecasting workflow
- Automating data extraction from Dynamics 365
- Triggering AI model predictions on schedule
- Emailing forecast results to stakeholders
- Conditional logic for exception handling
- Approvals workflow for forecast validation
- Updating budget registers with forecast outputs
- Pushing forecast adjustments back to Dynamics
- Logging workflow execution and errors
- Monitoring automation health in Power Platform
- Using gateways for on-premises data sources
- Handling large volume data transfers securely
- Performance optimisation for financial flows
- Documenting workflow dependencies and ownership
Module 8: Cash Flow and Liquidity Forecasting - Configuring cash flow forecasting in Dynamics 365
- Setting up cash flow worksheet templates
- Configuring liquidity analysis parameters
- Linking customer and vendor payment schedules
- Projecting cash inflows from sales pipelines
- Forecasting cash outflows for procurement
- Importing bank statement data for reconciliation
- Synchronising with third-party treasury systems
- Modelling short-term borrowing requirements
- Simulating worst-case liquidity scenarios
- Generating cash runway reports
- Integrating AI predictions into cash flow models
- Validating forecasts against bank cash positions
- Setting up alerts for negative cash flow
- Creating liquidity stress-test dashboards
Module 9: Revenue and Sales Forecasting - Configuring sales forecasting in Dynamics 365 Sales
- Linking opportunity stages to revenue probability
- Setting up forecast categories and hierarchy
- Rolling up forecasts by team, product, region
- Aligning sales forecasts with financial planning
- Integrating CRM data with General Ledger
- Using AI to predict opportunity closure dates
- Forecasting revenue by product line
- Modelling seasonal demand fluctuations
- Adjusting forecasts based on market signals
- Validating AI predictions against actual bookings
- Handling forecast overrides and manual adjustments
- Generating variance reports for sales leadership
- Setting up forecast collaboration workflows
- Documenting assumptions for audit purposes
Module 10: Expense and Budget Forecasting - Configuring budget planning in Dynamics 365
- Setting up budget cycles and versions
- Defining budget dimensions and control rules
- Creating forecast templates for department budgets
- Rolling forward prior year actuals
- Incorporating inflation and growth factors
- Using AI to predict departmental spend patterns
- Forecasting headcount-related expenses
- Modelling capital expenditure trends
- Integrating procurement forecasts into budgets
- Setting up budget alerts and thresholds
- Conducting budget vs forecast variance analysis
- Exporting budget forecasts to executive reports
- Managing budget revisions and approvals
- Aligning budget forecasts with strategic planning
Module 11: Demand and Inventory Forecasting - Connecting financial forecasts to supply chain data
- Using AI to predict product demand
- Configuring master planning parameters
- Setting up forecast models in Supply Chain Management
- Linking demand forecasts to procurement planning
- Forecasting inventory carrying costs
- Predicting stockout risks and overage costs
- Modelling safety stock requirements
- Integrating with warehouse management systems
- Forecasting production capacity needs
- Aligning manufacturing output with sales forecasts
- Using Power BI for inventory health monitoring
- Generating inventory turnover reports
- Simulating just-in-time inventory scenarios
- Optimising reorder points with AI predictions
Module 12: Scenario Planning and Forecast Modelling - Creating multiple forecast scenarios in Dynamics 365
- Best practices for scenario naming and structure
- Building optimistic, baseline, and pessimistic models
- Modelling impact of market disruptions
- Simulating M&A or divestiture scenarios
- Forecasting impact of pricing changes
- Modelling workforce restructuring outcomes
- Analysing impact of currency fluctuations
- Stress-testing forecasts under inflation shocks
- Using AI to compare scenario outcomes
- Automating scenario roll-up for consolidation
- Presenting scenarios in executive dashboards
- Exporting scenario outputs to board reports
- Version control for forecast models
- Documenting scenario assumptions and triggers
Module 13: Forecast Validation and Accuracy Monitoring - Setting up forecasting accuracy benchmarks
- Calculating Mean Absolute Percentage Error (MAPE)
- Tracking forecast bias over time
- Using Power BI to visualise forecast performance
- Setting up forecast deviation alerts
- Conducting root cause analysis on forecast errors
- Identifying systematic over- or under-forecasting
- Reviewing model inputs for data integrity issues
- Adjusting models based on performance feedback
- Creating monthly forecast performance reports
- Incorporating feedback from business units
- Updating assumptions based on actual results
- Documenting accuracy improvements over time
- Establishing continuous improvement cycles
- Presenting forecast reliability to audit committees
Module 14: Governance, Compliance, and Audit Readiness - Establishing forecasting governance framework
- Defining roles and responsibilities for forecast owners
- Creating standard operating procedures for forecasting
- Documenting model development and validation steps
- Ensuring compliance with SOX and financial regulations
- Setting up audit trails for forecast changes
- Implementing change management processes
- Version control for forecasting models
- Secure access to forecasting data and models
- Data retention policies for model history
- Handling model updates in regulated environments
- Preparing audit packages for external review
- Reconciling AI forecasts with official financial statements
- Training internal audit teams on AI forecasting
- Creating governance dashboards for oversight
Module 15: Enterprise Integration and Scalability - Scaling forecasting models across legal entities
- Consolidating forecasts from subsidiaries
- Handling multi-currency and multi-GAAP environments
- Integrating with enterprise performance management (EPM) tools
- Connecting to Azure Data Lake for big data forecasting
- Using Dataverse as a central forecasting repository
- API integration with third-party planning systems
- Building custom connectors for external data
- Orchestrating forecasting across hybrid environments
- Managing version consistency in global deployments
- Training regional finance teams on central models
- Creating language-specific forecasting templates
- Aligning local forecasts with corporate strategy
- Monitoring global forecast health in real time
- Establishing escalation paths for forecasting issues
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from all modules
- Completing the practical forecasting project
- Submitting documentation for verification
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Leveraging your credential in job interviews and promotions
- Building a portfolio of forecasting deliverables
- Presenting your forecasting model to leadership
- Documenting ROI from AI forecasting implementation
- Creating a business case for wider AI adoption
- Joining the community of certified practitioners
- Accessing advanced learning pathways in AI and finance
- Staying updated with new Dynamics 365 AI features
- Planning your next forecasting initiative
- Importance of clean data in AI forecasting
- Identifying common data anomalies in financial systems
- Handling missing, duplicated, or inconsistent values
- Outlier detection and correction techniques
- Using Power Query for data transformation
- Automating data quality checks in Dynamics 365
- Setting up data validation rules for financial entries
- Creating audit trails for data preparation steps
- Temporal alignment of financial data
- Time series data formatting for forecasting models
- Normalising data for multi-entity comparisons
- Scaling and standardising financial metrics
- Handling seasonality and trend adjustments
- Creating derived forecasting variables
- Mapping historical data to AI model input requirements
Module 4: Introduction to AI Builder and Forecasting Tools - Overview of Microsoft AI Builder capabilities
- AI Builder licensing and permissions in Dynamics 365
- Exploring prebuilt vs custom AI models
- Using Prediction models for financial outcomes
- Setting up Object Detection for document processing
- Text classification for financial categorisation
- Choosing the right AI model for forecasting use cases
- Understanding confidence scores and model reliability
- Model training lifecycle in AI Builder
- Interpreting AI model outputs for financial decisions
- Monitoring model performance over time
- Retraining models with new financial data
- Exporting AI model results to financial reports
- Integration of AI Builder with Power Automate
- Best practices for model naming and documentation
Module 5: Building Your First AI Forecasting Model - Selecting a forecasting use case for initial implementation
- Defining input variables and target outcomes
- Connecting AI Builder to financial datasets
- Training a model on historical revenue data
- Training a model on expense patterns
- Setting prediction thresholds and sensitivity
- Validating model accuracy with test datasets
- Interpreting feature importance in financial predictions
- Adjusting model weightings based on business logic
- Exporting predicted values to Excel for review
- Creating model performance reports
- Setting up model refresh schedules
- Version control for AI forecasting models
- Documenting model assumptions and limitations
- Generating audit-ready model documentation
Module 6: Advanced Forecasting with Power BI Integration - Connecting Dynamics 365 to Power BI
- Setting up live connections for real-time forecasting
- Import vs DirectQuery modes for financial data
- Building forecasting dashboards in Power BI
- Visualising AI predictions alongside actuals
- Using decomposition trees for variance analysis
- Applying DAX measures for forecast calculations
- Time intelligence functions in forecasting models
- Creating dynamic forecasting scenarios
- Scenario comparison using What-If analysis
- Drill-through capabilities from dashboard to transaction
- Scheduling automated report distribution
- Configuring row-level security for forecasts
- Embedding Power BI dashboards in Dynamics 365
- Publishing to Power BI Service for enterprise access
Module 7: Financial Forecasting Workflow Automation - Introduction to Power Automate in forecasting
- Designing a monthly forecasting workflow
- Automating data extraction from Dynamics 365
- Triggering AI model predictions on schedule
- Emailing forecast results to stakeholders
- Conditional logic for exception handling
- Approvals workflow for forecast validation
- Updating budget registers with forecast outputs
- Pushing forecast adjustments back to Dynamics
- Logging workflow execution and errors
- Monitoring automation health in Power Platform
- Using gateways for on-premises data sources
- Handling large volume data transfers securely
- Performance optimisation for financial flows
- Documenting workflow dependencies and ownership
Module 8: Cash Flow and Liquidity Forecasting - Configuring cash flow forecasting in Dynamics 365
- Setting up cash flow worksheet templates
- Configuring liquidity analysis parameters
- Linking customer and vendor payment schedules
- Projecting cash inflows from sales pipelines
- Forecasting cash outflows for procurement
- Importing bank statement data for reconciliation
- Synchronising with third-party treasury systems
- Modelling short-term borrowing requirements
- Simulating worst-case liquidity scenarios
- Generating cash runway reports
- Integrating AI predictions into cash flow models
- Validating forecasts against bank cash positions
- Setting up alerts for negative cash flow
- Creating liquidity stress-test dashboards
Module 9: Revenue and Sales Forecasting - Configuring sales forecasting in Dynamics 365 Sales
- Linking opportunity stages to revenue probability
- Setting up forecast categories and hierarchy
- Rolling up forecasts by team, product, region
- Aligning sales forecasts with financial planning
- Integrating CRM data with General Ledger
- Using AI to predict opportunity closure dates
- Forecasting revenue by product line
- Modelling seasonal demand fluctuations
- Adjusting forecasts based on market signals
- Validating AI predictions against actual bookings
- Handling forecast overrides and manual adjustments
- Generating variance reports for sales leadership
- Setting up forecast collaboration workflows
- Documenting assumptions for audit purposes
Module 10: Expense and Budget Forecasting - Configuring budget planning in Dynamics 365
- Setting up budget cycles and versions
- Defining budget dimensions and control rules
- Creating forecast templates for department budgets
- Rolling forward prior year actuals
- Incorporating inflation and growth factors
- Using AI to predict departmental spend patterns
- Forecasting headcount-related expenses
- Modelling capital expenditure trends
- Integrating procurement forecasts into budgets
- Setting up budget alerts and thresholds
- Conducting budget vs forecast variance analysis
- Exporting budget forecasts to executive reports
- Managing budget revisions and approvals
- Aligning budget forecasts with strategic planning
Module 11: Demand and Inventory Forecasting - Connecting financial forecasts to supply chain data
- Using AI to predict product demand
- Configuring master planning parameters
- Setting up forecast models in Supply Chain Management
- Linking demand forecasts to procurement planning
- Forecasting inventory carrying costs
- Predicting stockout risks and overage costs
- Modelling safety stock requirements
- Integrating with warehouse management systems
- Forecasting production capacity needs
- Aligning manufacturing output with sales forecasts
- Using Power BI for inventory health monitoring
- Generating inventory turnover reports
- Simulating just-in-time inventory scenarios
- Optimising reorder points with AI predictions
Module 12: Scenario Planning and Forecast Modelling - Creating multiple forecast scenarios in Dynamics 365
- Best practices for scenario naming and structure
- Building optimistic, baseline, and pessimistic models
- Modelling impact of market disruptions
- Simulating M&A or divestiture scenarios
- Forecasting impact of pricing changes
- Modelling workforce restructuring outcomes
- Analysing impact of currency fluctuations
- Stress-testing forecasts under inflation shocks
- Using AI to compare scenario outcomes
- Automating scenario roll-up for consolidation
- Presenting scenarios in executive dashboards
- Exporting scenario outputs to board reports
- Version control for forecast models
- Documenting scenario assumptions and triggers
Module 13: Forecast Validation and Accuracy Monitoring - Setting up forecasting accuracy benchmarks
- Calculating Mean Absolute Percentage Error (MAPE)
- Tracking forecast bias over time
- Using Power BI to visualise forecast performance
- Setting up forecast deviation alerts
- Conducting root cause analysis on forecast errors
- Identifying systematic over- or under-forecasting
- Reviewing model inputs for data integrity issues
- Adjusting models based on performance feedback
- Creating monthly forecast performance reports
- Incorporating feedback from business units
- Updating assumptions based on actual results
- Documenting accuracy improvements over time
- Establishing continuous improvement cycles
- Presenting forecast reliability to audit committees
Module 14: Governance, Compliance, and Audit Readiness - Establishing forecasting governance framework
- Defining roles and responsibilities for forecast owners
- Creating standard operating procedures for forecasting
- Documenting model development and validation steps
- Ensuring compliance with SOX and financial regulations
- Setting up audit trails for forecast changes
- Implementing change management processes
- Version control for forecasting models
- Secure access to forecasting data and models
- Data retention policies for model history
- Handling model updates in regulated environments
- Preparing audit packages for external review
- Reconciling AI forecasts with official financial statements
- Training internal audit teams on AI forecasting
- Creating governance dashboards for oversight
Module 15: Enterprise Integration and Scalability - Scaling forecasting models across legal entities
- Consolidating forecasts from subsidiaries
- Handling multi-currency and multi-GAAP environments
- Integrating with enterprise performance management (EPM) tools
- Connecting to Azure Data Lake for big data forecasting
- Using Dataverse as a central forecasting repository
- API integration with third-party planning systems
- Building custom connectors for external data
- Orchestrating forecasting across hybrid environments
- Managing version consistency in global deployments
- Training regional finance teams on central models
- Creating language-specific forecasting templates
- Aligning local forecasts with corporate strategy
- Monitoring global forecast health in real time
- Establishing escalation paths for forecasting issues
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from all modules
- Completing the practical forecasting project
- Submitting documentation for verification
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Leveraging your credential in job interviews and promotions
- Building a portfolio of forecasting deliverables
- Presenting your forecasting model to leadership
- Documenting ROI from AI forecasting implementation
- Creating a business case for wider AI adoption
- Joining the community of certified practitioners
- Accessing advanced learning pathways in AI and finance
- Staying updated with new Dynamics 365 AI features
- Planning your next forecasting initiative
- Selecting a forecasting use case for initial implementation
- Defining input variables and target outcomes
- Connecting AI Builder to financial datasets
- Training a model on historical revenue data
- Training a model on expense patterns
- Setting prediction thresholds and sensitivity
- Validating model accuracy with test datasets
- Interpreting feature importance in financial predictions
- Adjusting model weightings based on business logic
- Exporting predicted values to Excel for review
- Creating model performance reports
- Setting up model refresh schedules
- Version control for AI forecasting models
- Documenting model assumptions and limitations
- Generating audit-ready model documentation
Module 6: Advanced Forecasting with Power BI Integration - Connecting Dynamics 365 to Power BI
- Setting up live connections for real-time forecasting
- Import vs DirectQuery modes for financial data
- Building forecasting dashboards in Power BI
- Visualising AI predictions alongside actuals
- Using decomposition trees for variance analysis
- Applying DAX measures for forecast calculations
- Time intelligence functions in forecasting models
- Creating dynamic forecasting scenarios
- Scenario comparison using What-If analysis
- Drill-through capabilities from dashboard to transaction
- Scheduling automated report distribution
- Configuring row-level security for forecasts
- Embedding Power BI dashboards in Dynamics 365
- Publishing to Power BI Service for enterprise access
Module 7: Financial Forecasting Workflow Automation - Introduction to Power Automate in forecasting
- Designing a monthly forecasting workflow
- Automating data extraction from Dynamics 365
- Triggering AI model predictions on schedule
- Emailing forecast results to stakeholders
- Conditional logic for exception handling
- Approvals workflow for forecast validation
- Updating budget registers with forecast outputs
- Pushing forecast adjustments back to Dynamics
- Logging workflow execution and errors
- Monitoring automation health in Power Platform
- Using gateways for on-premises data sources
- Handling large volume data transfers securely
- Performance optimisation for financial flows
- Documenting workflow dependencies and ownership
Module 8: Cash Flow and Liquidity Forecasting - Configuring cash flow forecasting in Dynamics 365
- Setting up cash flow worksheet templates
- Configuring liquidity analysis parameters
- Linking customer and vendor payment schedules
- Projecting cash inflows from sales pipelines
- Forecasting cash outflows for procurement
- Importing bank statement data for reconciliation
- Synchronising with third-party treasury systems
- Modelling short-term borrowing requirements
- Simulating worst-case liquidity scenarios
- Generating cash runway reports
- Integrating AI predictions into cash flow models
- Validating forecasts against bank cash positions
- Setting up alerts for negative cash flow
- Creating liquidity stress-test dashboards
Module 9: Revenue and Sales Forecasting - Configuring sales forecasting in Dynamics 365 Sales
- Linking opportunity stages to revenue probability
- Setting up forecast categories and hierarchy
- Rolling up forecasts by team, product, region
- Aligning sales forecasts with financial planning
- Integrating CRM data with General Ledger
- Using AI to predict opportunity closure dates
- Forecasting revenue by product line
- Modelling seasonal demand fluctuations
- Adjusting forecasts based on market signals
- Validating AI predictions against actual bookings
- Handling forecast overrides and manual adjustments
- Generating variance reports for sales leadership
- Setting up forecast collaboration workflows
- Documenting assumptions for audit purposes
Module 10: Expense and Budget Forecasting - Configuring budget planning in Dynamics 365
- Setting up budget cycles and versions
- Defining budget dimensions and control rules
- Creating forecast templates for department budgets
- Rolling forward prior year actuals
- Incorporating inflation and growth factors
- Using AI to predict departmental spend patterns
- Forecasting headcount-related expenses
- Modelling capital expenditure trends
- Integrating procurement forecasts into budgets
- Setting up budget alerts and thresholds
- Conducting budget vs forecast variance analysis
- Exporting budget forecasts to executive reports
- Managing budget revisions and approvals
- Aligning budget forecasts with strategic planning
Module 11: Demand and Inventory Forecasting - Connecting financial forecasts to supply chain data
- Using AI to predict product demand
- Configuring master planning parameters
- Setting up forecast models in Supply Chain Management
- Linking demand forecasts to procurement planning
- Forecasting inventory carrying costs
- Predicting stockout risks and overage costs
- Modelling safety stock requirements
- Integrating with warehouse management systems
- Forecasting production capacity needs
- Aligning manufacturing output with sales forecasts
- Using Power BI for inventory health monitoring
- Generating inventory turnover reports
- Simulating just-in-time inventory scenarios
- Optimising reorder points with AI predictions
Module 12: Scenario Planning and Forecast Modelling - Creating multiple forecast scenarios in Dynamics 365
- Best practices for scenario naming and structure
- Building optimistic, baseline, and pessimistic models
- Modelling impact of market disruptions
- Simulating M&A or divestiture scenarios
- Forecasting impact of pricing changes
- Modelling workforce restructuring outcomes
- Analysing impact of currency fluctuations
- Stress-testing forecasts under inflation shocks
- Using AI to compare scenario outcomes
- Automating scenario roll-up for consolidation
- Presenting scenarios in executive dashboards
- Exporting scenario outputs to board reports
- Version control for forecast models
- Documenting scenario assumptions and triggers
Module 13: Forecast Validation and Accuracy Monitoring - Setting up forecasting accuracy benchmarks
- Calculating Mean Absolute Percentage Error (MAPE)
- Tracking forecast bias over time
- Using Power BI to visualise forecast performance
- Setting up forecast deviation alerts
- Conducting root cause analysis on forecast errors
- Identifying systematic over- or under-forecasting
- Reviewing model inputs for data integrity issues
- Adjusting models based on performance feedback
- Creating monthly forecast performance reports
- Incorporating feedback from business units
- Updating assumptions based on actual results
- Documenting accuracy improvements over time
- Establishing continuous improvement cycles
- Presenting forecast reliability to audit committees
Module 14: Governance, Compliance, and Audit Readiness - Establishing forecasting governance framework
- Defining roles and responsibilities for forecast owners
- Creating standard operating procedures for forecasting
- Documenting model development and validation steps
- Ensuring compliance with SOX and financial regulations
- Setting up audit trails for forecast changes
- Implementing change management processes
- Version control for forecasting models
- Secure access to forecasting data and models
- Data retention policies for model history
- Handling model updates in regulated environments
- Preparing audit packages for external review
- Reconciling AI forecasts with official financial statements
- Training internal audit teams on AI forecasting
- Creating governance dashboards for oversight
Module 15: Enterprise Integration and Scalability - Scaling forecasting models across legal entities
- Consolidating forecasts from subsidiaries
- Handling multi-currency and multi-GAAP environments
- Integrating with enterprise performance management (EPM) tools
- Connecting to Azure Data Lake for big data forecasting
- Using Dataverse as a central forecasting repository
- API integration with third-party planning systems
- Building custom connectors for external data
- Orchestrating forecasting across hybrid environments
- Managing version consistency in global deployments
- Training regional finance teams on central models
- Creating language-specific forecasting templates
- Aligning local forecasts with corporate strategy
- Monitoring global forecast health in real time
- Establishing escalation paths for forecasting issues
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from all modules
- Completing the practical forecasting project
- Submitting documentation for verification
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Leveraging your credential in job interviews and promotions
- Building a portfolio of forecasting deliverables
- Presenting your forecasting model to leadership
- Documenting ROI from AI forecasting implementation
- Creating a business case for wider AI adoption
- Joining the community of certified practitioners
- Accessing advanced learning pathways in AI and finance
- Staying updated with new Dynamics 365 AI features
- Planning your next forecasting initiative
- Introduction to Power Automate in forecasting
- Designing a monthly forecasting workflow
- Automating data extraction from Dynamics 365
- Triggering AI model predictions on schedule
- Emailing forecast results to stakeholders
- Conditional logic for exception handling
- Approvals workflow for forecast validation
- Updating budget registers with forecast outputs
- Pushing forecast adjustments back to Dynamics
- Logging workflow execution and errors
- Monitoring automation health in Power Platform
- Using gateways for on-premises data sources
- Handling large volume data transfers securely
- Performance optimisation for financial flows
- Documenting workflow dependencies and ownership
Module 8: Cash Flow and Liquidity Forecasting - Configuring cash flow forecasting in Dynamics 365
- Setting up cash flow worksheet templates
- Configuring liquidity analysis parameters
- Linking customer and vendor payment schedules
- Projecting cash inflows from sales pipelines
- Forecasting cash outflows for procurement
- Importing bank statement data for reconciliation
- Synchronising with third-party treasury systems
- Modelling short-term borrowing requirements
- Simulating worst-case liquidity scenarios
- Generating cash runway reports
- Integrating AI predictions into cash flow models
- Validating forecasts against bank cash positions
- Setting up alerts for negative cash flow
- Creating liquidity stress-test dashboards
Module 9: Revenue and Sales Forecasting - Configuring sales forecasting in Dynamics 365 Sales
- Linking opportunity stages to revenue probability
- Setting up forecast categories and hierarchy
- Rolling up forecasts by team, product, region
- Aligning sales forecasts with financial planning
- Integrating CRM data with General Ledger
- Using AI to predict opportunity closure dates
- Forecasting revenue by product line
- Modelling seasonal demand fluctuations
- Adjusting forecasts based on market signals
- Validating AI predictions against actual bookings
- Handling forecast overrides and manual adjustments
- Generating variance reports for sales leadership
- Setting up forecast collaboration workflows
- Documenting assumptions for audit purposes
Module 10: Expense and Budget Forecasting - Configuring budget planning in Dynamics 365
- Setting up budget cycles and versions
- Defining budget dimensions and control rules
- Creating forecast templates for department budgets
- Rolling forward prior year actuals
- Incorporating inflation and growth factors
- Using AI to predict departmental spend patterns
- Forecasting headcount-related expenses
- Modelling capital expenditure trends
- Integrating procurement forecasts into budgets
- Setting up budget alerts and thresholds
- Conducting budget vs forecast variance analysis
- Exporting budget forecasts to executive reports
- Managing budget revisions and approvals
- Aligning budget forecasts with strategic planning
Module 11: Demand and Inventory Forecasting - Connecting financial forecasts to supply chain data
- Using AI to predict product demand
- Configuring master planning parameters
- Setting up forecast models in Supply Chain Management
- Linking demand forecasts to procurement planning
- Forecasting inventory carrying costs
- Predicting stockout risks and overage costs
- Modelling safety stock requirements
- Integrating with warehouse management systems
- Forecasting production capacity needs
- Aligning manufacturing output with sales forecasts
- Using Power BI for inventory health monitoring
- Generating inventory turnover reports
- Simulating just-in-time inventory scenarios
- Optimising reorder points with AI predictions
Module 12: Scenario Planning and Forecast Modelling - Creating multiple forecast scenarios in Dynamics 365
- Best practices for scenario naming and structure
- Building optimistic, baseline, and pessimistic models
- Modelling impact of market disruptions
- Simulating M&A or divestiture scenarios
- Forecasting impact of pricing changes
- Modelling workforce restructuring outcomes
- Analysing impact of currency fluctuations
- Stress-testing forecasts under inflation shocks
- Using AI to compare scenario outcomes
- Automating scenario roll-up for consolidation
- Presenting scenarios in executive dashboards
- Exporting scenario outputs to board reports
- Version control for forecast models
- Documenting scenario assumptions and triggers
Module 13: Forecast Validation and Accuracy Monitoring - Setting up forecasting accuracy benchmarks
- Calculating Mean Absolute Percentage Error (MAPE)
- Tracking forecast bias over time
- Using Power BI to visualise forecast performance
- Setting up forecast deviation alerts
- Conducting root cause analysis on forecast errors
- Identifying systematic over- or under-forecasting
- Reviewing model inputs for data integrity issues
- Adjusting models based on performance feedback
- Creating monthly forecast performance reports
- Incorporating feedback from business units
- Updating assumptions based on actual results
- Documenting accuracy improvements over time
- Establishing continuous improvement cycles
- Presenting forecast reliability to audit committees
Module 14: Governance, Compliance, and Audit Readiness - Establishing forecasting governance framework
- Defining roles and responsibilities for forecast owners
- Creating standard operating procedures for forecasting
- Documenting model development and validation steps
- Ensuring compliance with SOX and financial regulations
- Setting up audit trails for forecast changes
- Implementing change management processes
- Version control for forecasting models
- Secure access to forecasting data and models
- Data retention policies for model history
- Handling model updates in regulated environments
- Preparing audit packages for external review
- Reconciling AI forecasts with official financial statements
- Training internal audit teams on AI forecasting
- Creating governance dashboards for oversight
Module 15: Enterprise Integration and Scalability - Scaling forecasting models across legal entities
- Consolidating forecasts from subsidiaries
- Handling multi-currency and multi-GAAP environments
- Integrating with enterprise performance management (EPM) tools
- Connecting to Azure Data Lake for big data forecasting
- Using Dataverse as a central forecasting repository
- API integration with third-party planning systems
- Building custom connectors for external data
- Orchestrating forecasting across hybrid environments
- Managing version consistency in global deployments
- Training regional finance teams on central models
- Creating language-specific forecasting templates
- Aligning local forecasts with corporate strategy
- Monitoring global forecast health in real time
- Establishing escalation paths for forecasting issues
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from all modules
- Completing the practical forecasting project
- Submitting documentation for verification
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Leveraging your credential in job interviews and promotions
- Building a portfolio of forecasting deliverables
- Presenting your forecasting model to leadership
- Documenting ROI from AI forecasting implementation
- Creating a business case for wider AI adoption
- Joining the community of certified practitioners
- Accessing advanced learning pathways in AI and finance
- Staying updated with new Dynamics 365 AI features
- Planning your next forecasting initiative
- Configuring sales forecasting in Dynamics 365 Sales
- Linking opportunity stages to revenue probability
- Setting up forecast categories and hierarchy
- Rolling up forecasts by team, product, region
- Aligning sales forecasts with financial planning
- Integrating CRM data with General Ledger
- Using AI to predict opportunity closure dates
- Forecasting revenue by product line
- Modelling seasonal demand fluctuations
- Adjusting forecasts based on market signals
- Validating AI predictions against actual bookings
- Handling forecast overrides and manual adjustments
- Generating variance reports for sales leadership
- Setting up forecast collaboration workflows
- Documenting assumptions for audit purposes
Module 10: Expense and Budget Forecasting - Configuring budget planning in Dynamics 365
- Setting up budget cycles and versions
- Defining budget dimensions and control rules
- Creating forecast templates for department budgets
- Rolling forward prior year actuals
- Incorporating inflation and growth factors
- Using AI to predict departmental spend patterns
- Forecasting headcount-related expenses
- Modelling capital expenditure trends
- Integrating procurement forecasts into budgets
- Setting up budget alerts and thresholds
- Conducting budget vs forecast variance analysis
- Exporting budget forecasts to executive reports
- Managing budget revisions and approvals
- Aligning budget forecasts with strategic planning
Module 11: Demand and Inventory Forecasting - Connecting financial forecasts to supply chain data
- Using AI to predict product demand
- Configuring master planning parameters
- Setting up forecast models in Supply Chain Management
- Linking demand forecasts to procurement planning
- Forecasting inventory carrying costs
- Predicting stockout risks and overage costs
- Modelling safety stock requirements
- Integrating with warehouse management systems
- Forecasting production capacity needs
- Aligning manufacturing output with sales forecasts
- Using Power BI for inventory health monitoring
- Generating inventory turnover reports
- Simulating just-in-time inventory scenarios
- Optimising reorder points with AI predictions
Module 12: Scenario Planning and Forecast Modelling - Creating multiple forecast scenarios in Dynamics 365
- Best practices for scenario naming and structure
- Building optimistic, baseline, and pessimistic models
- Modelling impact of market disruptions
- Simulating M&A or divestiture scenarios
- Forecasting impact of pricing changes
- Modelling workforce restructuring outcomes
- Analysing impact of currency fluctuations
- Stress-testing forecasts under inflation shocks
- Using AI to compare scenario outcomes
- Automating scenario roll-up for consolidation
- Presenting scenarios in executive dashboards
- Exporting scenario outputs to board reports
- Version control for forecast models
- Documenting scenario assumptions and triggers
Module 13: Forecast Validation and Accuracy Monitoring - Setting up forecasting accuracy benchmarks
- Calculating Mean Absolute Percentage Error (MAPE)
- Tracking forecast bias over time
- Using Power BI to visualise forecast performance
- Setting up forecast deviation alerts
- Conducting root cause analysis on forecast errors
- Identifying systematic over- or under-forecasting
- Reviewing model inputs for data integrity issues
- Adjusting models based on performance feedback
- Creating monthly forecast performance reports
- Incorporating feedback from business units
- Updating assumptions based on actual results
- Documenting accuracy improvements over time
- Establishing continuous improvement cycles
- Presenting forecast reliability to audit committees
Module 14: Governance, Compliance, and Audit Readiness - Establishing forecasting governance framework
- Defining roles and responsibilities for forecast owners
- Creating standard operating procedures for forecasting
- Documenting model development and validation steps
- Ensuring compliance with SOX and financial regulations
- Setting up audit trails for forecast changes
- Implementing change management processes
- Version control for forecasting models
- Secure access to forecasting data and models
- Data retention policies for model history
- Handling model updates in regulated environments
- Preparing audit packages for external review
- Reconciling AI forecasts with official financial statements
- Training internal audit teams on AI forecasting
- Creating governance dashboards for oversight
Module 15: Enterprise Integration and Scalability - Scaling forecasting models across legal entities
- Consolidating forecasts from subsidiaries
- Handling multi-currency and multi-GAAP environments
- Integrating with enterprise performance management (EPM) tools
- Connecting to Azure Data Lake for big data forecasting
- Using Dataverse as a central forecasting repository
- API integration with third-party planning systems
- Building custom connectors for external data
- Orchestrating forecasting across hybrid environments
- Managing version consistency in global deployments
- Training regional finance teams on central models
- Creating language-specific forecasting templates
- Aligning local forecasts with corporate strategy
- Monitoring global forecast health in real time
- Establishing escalation paths for forecasting issues
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from all modules
- Completing the practical forecasting project
- Submitting documentation for verification
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Leveraging your credential in job interviews and promotions
- Building a portfolio of forecasting deliverables
- Presenting your forecasting model to leadership
- Documenting ROI from AI forecasting implementation
- Creating a business case for wider AI adoption
- Joining the community of certified practitioners
- Accessing advanced learning pathways in AI and finance
- Staying updated with new Dynamics 365 AI features
- Planning your next forecasting initiative
- Connecting financial forecasts to supply chain data
- Using AI to predict product demand
- Configuring master planning parameters
- Setting up forecast models in Supply Chain Management
- Linking demand forecasts to procurement planning
- Forecasting inventory carrying costs
- Predicting stockout risks and overage costs
- Modelling safety stock requirements
- Integrating with warehouse management systems
- Forecasting production capacity needs
- Aligning manufacturing output with sales forecasts
- Using Power BI for inventory health monitoring
- Generating inventory turnover reports
- Simulating just-in-time inventory scenarios
- Optimising reorder points with AI predictions
Module 12: Scenario Planning and Forecast Modelling - Creating multiple forecast scenarios in Dynamics 365
- Best practices for scenario naming and structure
- Building optimistic, baseline, and pessimistic models
- Modelling impact of market disruptions
- Simulating M&A or divestiture scenarios
- Forecasting impact of pricing changes
- Modelling workforce restructuring outcomes
- Analysing impact of currency fluctuations
- Stress-testing forecasts under inflation shocks
- Using AI to compare scenario outcomes
- Automating scenario roll-up for consolidation
- Presenting scenarios in executive dashboards
- Exporting scenario outputs to board reports
- Version control for forecast models
- Documenting scenario assumptions and triggers
Module 13: Forecast Validation and Accuracy Monitoring - Setting up forecasting accuracy benchmarks
- Calculating Mean Absolute Percentage Error (MAPE)
- Tracking forecast bias over time
- Using Power BI to visualise forecast performance
- Setting up forecast deviation alerts
- Conducting root cause analysis on forecast errors
- Identifying systematic over- or under-forecasting
- Reviewing model inputs for data integrity issues
- Adjusting models based on performance feedback
- Creating monthly forecast performance reports
- Incorporating feedback from business units
- Updating assumptions based on actual results
- Documenting accuracy improvements over time
- Establishing continuous improvement cycles
- Presenting forecast reliability to audit committees
Module 14: Governance, Compliance, and Audit Readiness - Establishing forecasting governance framework
- Defining roles and responsibilities for forecast owners
- Creating standard operating procedures for forecasting
- Documenting model development and validation steps
- Ensuring compliance with SOX and financial regulations
- Setting up audit trails for forecast changes
- Implementing change management processes
- Version control for forecasting models
- Secure access to forecasting data and models
- Data retention policies for model history
- Handling model updates in regulated environments
- Preparing audit packages for external review
- Reconciling AI forecasts with official financial statements
- Training internal audit teams on AI forecasting
- Creating governance dashboards for oversight
Module 15: Enterprise Integration and Scalability - Scaling forecasting models across legal entities
- Consolidating forecasts from subsidiaries
- Handling multi-currency and multi-GAAP environments
- Integrating with enterprise performance management (EPM) tools
- Connecting to Azure Data Lake for big data forecasting
- Using Dataverse as a central forecasting repository
- API integration with third-party planning systems
- Building custom connectors for external data
- Orchestrating forecasting across hybrid environments
- Managing version consistency in global deployments
- Training regional finance teams on central models
- Creating language-specific forecasting templates
- Aligning local forecasts with corporate strategy
- Monitoring global forecast health in real time
- Establishing escalation paths for forecasting issues
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from all modules
- Completing the practical forecasting project
- Submitting documentation for verification
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Leveraging your credential in job interviews and promotions
- Building a portfolio of forecasting deliverables
- Presenting your forecasting model to leadership
- Documenting ROI from AI forecasting implementation
- Creating a business case for wider AI adoption
- Joining the community of certified practitioners
- Accessing advanced learning pathways in AI and finance
- Staying updated with new Dynamics 365 AI features
- Planning your next forecasting initiative
- Setting up forecasting accuracy benchmarks
- Calculating Mean Absolute Percentage Error (MAPE)
- Tracking forecast bias over time
- Using Power BI to visualise forecast performance
- Setting up forecast deviation alerts
- Conducting root cause analysis on forecast errors
- Identifying systematic over- or under-forecasting
- Reviewing model inputs for data integrity issues
- Adjusting models based on performance feedback
- Creating monthly forecast performance reports
- Incorporating feedback from business units
- Updating assumptions based on actual results
- Documenting accuracy improvements over time
- Establishing continuous improvement cycles
- Presenting forecast reliability to audit committees
Module 14: Governance, Compliance, and Audit Readiness - Establishing forecasting governance framework
- Defining roles and responsibilities for forecast owners
- Creating standard operating procedures for forecasting
- Documenting model development and validation steps
- Ensuring compliance with SOX and financial regulations
- Setting up audit trails for forecast changes
- Implementing change management processes
- Version control for forecasting models
- Secure access to forecasting data and models
- Data retention policies for model history
- Handling model updates in regulated environments
- Preparing audit packages for external review
- Reconciling AI forecasts with official financial statements
- Training internal audit teams on AI forecasting
- Creating governance dashboards for oversight
Module 15: Enterprise Integration and Scalability - Scaling forecasting models across legal entities
- Consolidating forecasts from subsidiaries
- Handling multi-currency and multi-GAAP environments
- Integrating with enterprise performance management (EPM) tools
- Connecting to Azure Data Lake for big data forecasting
- Using Dataverse as a central forecasting repository
- API integration with third-party planning systems
- Building custom connectors for external data
- Orchestrating forecasting across hybrid environments
- Managing version consistency in global deployments
- Training regional finance teams on central models
- Creating language-specific forecasting templates
- Aligning local forecasts with corporate strategy
- Monitoring global forecast health in real time
- Establishing escalation paths for forecasting issues
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from all modules
- Completing the practical forecasting project
- Submitting documentation for verification
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Leveraging your credential in job interviews and promotions
- Building a portfolio of forecasting deliverables
- Presenting your forecasting model to leadership
- Documenting ROI from AI forecasting implementation
- Creating a business case for wider AI adoption
- Joining the community of certified practitioners
- Accessing advanced learning pathways in AI and finance
- Staying updated with new Dynamics 365 AI features
- Planning your next forecasting initiative
- Scaling forecasting models across legal entities
- Consolidating forecasts from subsidiaries
- Handling multi-currency and multi-GAAP environments
- Integrating with enterprise performance management (EPM) tools
- Connecting to Azure Data Lake for big data forecasting
- Using Dataverse as a central forecasting repository
- API integration with third-party planning systems
- Building custom connectors for external data
- Orchestrating forecasting across hybrid environments
- Managing version consistency in global deployments
- Training regional finance teams on central models
- Creating language-specific forecasting templates
- Aligning local forecasts with corporate strategy
- Monitoring global forecast health in real time
- Establishing escalation paths for forecasting issues