Mastering AI-Driven Financial Forecasting in Microsoft Dynamics 365
You’re not just managing spreadsheets anymore. You're responsible for predicting the financial pulse of your organisation, and one inaccurate forecast can trigger misaligned budgets, wrong strategic calls, or even investor concern. The pressure is real. Traditional forecasting methods are too slow, too manual, too blind to inflection points. You need better tools, sharper insight, and credible authority to back your numbers. But what if you could stop guessing and start anticipating? What if your forecasts weren’t just passable, but board-confident, powered by AI inside the system you already use every day - Microsoft Dynamics 365? That’s exactly what Mastering AI-Driven Financial Forecasting in Microsoft Dynamics 365 delivers. This isn’t abstract theory. It’s a results-driven blueprint for transforming reactive financial planning into proactive, data-rich strategy. By the end of this course, you’ll go from uncertain to empowered, producing accurate, AI-enhanced forecasts in under 30 days - complete with a fully documented, audit-ready financial model and a board-ready proposal that positions you as the strategic leader your CFO expects. Take Sarah Lim, Senior Financial Analyst at a global logistics firm. After implementing the frameworks in this course, she reduced forecast variance by 67% in Q3 and presented a predictive cash flow model that secured executive buy-in for a EUR 8.2M expansion. Her comment? “For the first time, Finance wasn’t behind the curve. We were leading it.” You don’t need a data science degree. You need applied, system-specific knowledge - precise instruction on how to unlock, direct, and trust AI inside Dynamics 365's financial modules. No generic overviews. No fluff. Just the exact methods, configuration steps, and validation protocols used by top-performing finance teams. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced, On-Demand, Always Accessible
This course is designed for professionals like you - already in motion, already delivering, and needing to learn without disruption. From the moment you enrol, you gain self-paced access to the entire curriculum. There are no scheduled sessions, no deadlines, and no time pressure. Progress at the intensity that fits your schedule. Most learners complete the core workflow in 14 to 21 hours, and begin applying key models within the first 72 hours. Many report producing their first AI-optimised forecast in under one week. Lifetime Access With Ongoing Updates
Your investment isn’t just for today. You receive lifetime access to all course materials, including every future update. As Microsoft releases new AI capabilities within Dynamics 365 Finance and Supply Chain, we continuously refine this course to reflect real-world changes. No annual renewals. No extra fees. You stay current - automatically. Available Where You Work
Access the full curriculum from any device - desktop, tablet, or mobile. Whether you're reviewing forecasting frameworks during a flight or configuring predictive settings on-site, the course adapts to your workflow. All content is indexed for fast search, revision, and reference. Direct, Applied Support from Industry Practitioners
You’re not learning in isolation. This course includes structured instructor guidance through a private support portal. Submit process-specific queries, configuration challenges, or implementation roadblocks - and receive targeted, system-aware responses from certified Dynamics 365 architects with 10+ years in financial AI deployment. Global Payment Options & Zero Hidden Fees
Pricing is straightforward, transparent, and one-time. There are no recurring charges, surprise fees, or tiered pricing. The full curriculum, support access, and certification are included. We accept Visa, Mastercard, and PayPal - processed securely with bank-level encryption. Recognised Certification Upon Completion
Upon finishing the course, you’ll earn a Certificate of Completion issued by The Art of Service. This credential is trusted by over 40,000 professionals worldwide and signals mastery in applied AI integration within enterprise systems. Many learners add it to their LinkedIn profile the same day - with immediate visibility from recruiters and hiring managers in fintech, consulting, and digital transformation. Risk-Free Investment with Full Guarantee
If you complete the first three modules and don’t believe this course will deliver measurable value to your forecasting accuracy or strategic influence, simply request a refund. No forms. No hoops. No questions asked. This is your assurance that the content not only meets, but exceeds, the standard of professional upskilling. Enrolment Confirmation & Access Flow
After enrolment, you’ll receive a confirmation email acknowledging your registration. Once your access credentials are finalised, a separate email will provide secure login details to the learning environment. This ensures your experience is seamless, tested, and fully operational from the start. This Works Even If:
- You have limited AI experience beyond basic reporting functions
- Your organisation uses an older version of Dynamics 365 Finance
- You’re not in a central finance HQ role but operate in a regional or divisional capacity
- You’ve tried AI tools before and found them unstable or hard to validate
- You need to collaborate with IT or data teams but lack formal influence
We’ve built this course for real-world constraints - not idealised environments. With process checklists, exportable templates, and version-compatibility notes, you’ll be able to apply every concept regardless of your starting point.
Module 1: Foundations of AI in Financial Forecasting - Understanding the evolution of financial forecasting in enterprise systems
- Defining artificial intelligence in the context of financial planning
- Key limitations of traditional forecasting models in dynamic markets
- How AI enhances accuracy, responsiveness, and scenario agility
- The role of machine learning versus rule-based automation
- Core principles of predictive analytics in finance
- Overview of Microsoft Dynamics 365 Finance and Supply Chain capabilities
- Distinguishing supported AI features from third-party integrations
- Prerequisites for enabling AI in your financial forecasting landscape
- Mapping business drivers to financial outcomes using causal variables
- Understanding time-series forecasting fundamentals
- The importance of data freshness and update cycles
- Introduction to responsible AI in financial systems
- Establishing trust and auditability in AI-generated forecasts
- Identifying organisational readiness for AI adoption
Module 2: Dynamics 365 Architecture for Financial Intelligence - Core components of the Dynamics 365 financial module stack
- Data model structure: General ledger, dimensions, and financial entities
- Integration between Accounts Payable, Receivable, and forecasting engines
- How the Common Data Model supports cross-module forecasting
- Configuring financial dimensions for predictive analysis
- Data entity relationships in the forecasting data pipeline
- Understanding the role of the Dynamics 365 metadata framework
- Accessing financial data via query APIs and data management tools
- Overview of the Finance and Operations application layer
- Security roles and data access permissions for forecasting users
- Setting up user profiles with appropriate AI feature access
- Organisational hierarchies and their impact on forecast aggregation
- Currency and consolidation models in multi-entity environments
- Periodic and fiscal calendar alignment for forecasting accuracy
- Data versioning and historical record preservation
Module 3: Enabling and Configuring AI Features in Dynamics 365 - Locating AI settings in the Dynamics 365 Finance workspace
- Prerequisites for activating AI-driven forecasting tools
- Enabling predictive insights for cash flow forecasting
- Configuring data connectors for external financial sources
- Setting up data retention policies for AI training datasets
- Defining data filters and exclusion rules for forecast inputs
- How to calibrate AI model refresh intervals
- Configuring the AI prediction threshold for anomaly detection
- Setting confidence intervals and forecast reliability tolerances
- Enabling ML models for revenue, expense, and working capital projections
- Activating automatic seasonality detection in time-series models
- Configuring holiday adjustment rules in forecasting algorithms
- Setting up financial forecast alerts and deviation triggers
- Managing AI model lifecycle: Training, evaluation, and deployment
- Understanding model drift detection and retraining schedules
Module 4: Data Preparation for AI-Enhanced Forecasting - Assessing data quality for predictive modelling
- Identifying missing, outlier, and inconsistent financial records
- Techniques for handling zero-balance periods in time series
- Smoothing irregular data using aggregation and transformation
- Standardising currency, units, and account codes across entities
- Aligning historical records with current chart of account structures
- Using data cleansing rules in the Data Management Framework
- Validating imported data against predefined financial thresholds
- Defining relevant forecast drivers: Volume, price, headcount, etc.
- Mapping non-financial inputs to financial outcomes
- Creating derived metrics for predictive input (e.g. AR days, burn rate)
- Time alignment of external market indicators with internal data
- Leveraging budget and actuals data as training inputs
- Handling ledger adjustments and reversals in training sets
- Setting data sampling frequency: Daily, weekly, monthly
Module 5: Building Predictive Forecast Models - Navigating the Forecasting workspace in Dynamics 365
- Creating a new AI-driven forecast project
- Selecting appropriate forecast variables: Revenue, expenses, cash
- Choosing between additive and multiplicative seasonality models
- Specifying forecast horizon: Short-term, mid-term, long-term
- Defining model input time ranges and backtesting windows
- Incorporating known future events into forecast assumptions
- Modelling the impact of contracts, renewals, and sales pipelines
- Configuring predictive models for intercompany transactions
- Applying moving average and exponential smoothing techniques
- Using ARIMA and ETS models within Dynamics 365 tools
- Enabling automatic model selection for optimal accuracy
- Setting constraints for forecast outputs (minimum, maximum, growth caps)
- Incorporating inflation or exchange rate trends as external factors
- Building scenario-specific models for M&A or divestiture planning
Module 6: Validating and Interpreting AI Forecast Outputs - Accessing and reviewing the AI forecast generation log
- Interpreting model accuracy metrics: MAPE, RMSE, MAE
- Understanding prediction confidence bands and uncertainty ranges
- Visualising forecast outputs using built-in charting tools
- Comparing AI forecasts against historical performance
- Backtesting models against known outcomes for validation
- Using holdout datasets to test model generalisability
- Identifying overfitting and underfitting in financial models
- Analysing model residuals for pattern detection
- Validating predictive ability across different business units
- Assessing forecast stability under changing market conditions
- Detecting anomalies and outliers in predicted series
- Reviewing model variable importance and feature weights
- Validating forecast logic with finance stakeholders
- Documenting model assumptions and limitations for audit use
Module 7: Scenario Planning and What-If Analysis - Creating custom forecast scenarios: Optimistic, pessimistic, base
- Duplicating and modifying existing models for sensitivity testing
- Adjusting growth rates, cost structures, and volume assumptions
- Assessing the impact of delayed payments or early collections
- Modelling the effect of new hires or restructuring plans
- Simulating market downturns or supply chain disruptions
- Testing pricing strategy changes on revenue streams
- Evaluating the financial impact of capital expenditures
- Running scenario comparisons using dashboard overlays
- Exporting scenario outputs for executive presentations
- Defining trigger points for scenario activation
- Automating scenario generation based on KPI deviations
- Integrating risk probabilities into scenario weighting
- Using scenario planners for board-level decision support
- Documenting assumptions for regulatory and audit purposes
Module 8: Cash Flow Forecasting with AI - Setting up AI-powered cash flow forecasting in Dynamics 365
- Selecting relevant data sources: Bank feeds, AP, AR, payroll
- Configuring dynamic cash position projections
- Forecasting operating, investing, and financing cash flows
- Predicting short-term liquidity gaps and surpluses
- Incorporating payment term variability and supplier patterns
- Modelling customer payment behaviour and collection risks
- Automating forecasting of recurring and non-recurring items
- Integrating bank reconciliation data for accuracy improvement
- Forecasting cash needs for dividend payments and debt servicing
- Aligning cash forecasts with treasury and investment plans
- Monitoring forecast-to-actual variance in cash positions
- Detecting cash flow anomalies before they become crises
- Creating rolling 13-week cash flow models with AI support
- Exporting cash flow forecasts to liquidity dashboards
Module 9: Revenue Forecasting Using Predictive Analytics - Configuring AI models for revenue forecasting by product line
- Linking sales pipeline data from Dynamics 365 Sales to forecasts
- Weighting opportunities by probability and stage maturity
- Incorporating seasonality into subscription and licence revenue
- Modelling contract renewals and expansion revenue
- Forecasting revenue for project-based and time-and-materials work
- Adjusting forecasts for currency fluctuations and pricing changes
- Handling multi-year contracts with variable deliverables
- Incorporating customer churn and retention rates into predictions
- Using historical win rates to refine forecast accuracy
- Validating forecasts against CRM conversion benchmarks
- Forecasting revenue for new markets and product launches
- Aligning revenue forecasts with general ledger recognition rules
- Creating regional revenue breakdowns with local drivers
- Documenting forecast rationale for revenue assurance audits
Module 10: Expense and Cost Forecasting Optimisation - Building AI models for operating expense forecasting
- Separating fixed, variable, and semi-variable cost behaviours
- Forecasting payroll costs with headcount and benefit inputs
- Predicting utility and overhead escalation patterns
- Modelling one-time and project-based expenses
- Forecasting procurement and vendor spend trends
- Incorporating purchase order data into expense models
- Predicting travel and entertainment spend based on historical usage
- Using budget vs actual data to refine forecast assumptions
- Forecasting depreciation and amortisation schedules
- Modelling R&D and capitalised expense pipelines
- Adjusting forecasts for inflation or commodity price shifts
- Creating zero-based forecasting iterations using AI support
- Monitoring forecast accuracy across cost centres
- Linking cost forecasts to performance dashboards
Module 11: Integrating Forecast Data into Financial Statements - Mapping forecast outputs to income statement line items
- Translating revenue and expense forecasts into P&L projections
- Generating AI-supported balance sheet estimates
- Forecasting working capital components: AR, inventory, AP
- Projecting debt and equity movements based on forecasts
- Creating rolling financial statements using updated forecasts
- Automating financial report generation from forecast models
- Linking forecasts to budgeting and planning cycles
- Aligning forecast data with GAAP and IFRS reporting standards
- Setting audit trails for forecast-influenced financial entries
- Using forecast scenarios to stress test financial health
- Exporting forecast-based reports for external disclosure
- Reconciling forecast assumptions with management commentary
- Presenting forecasted statements to audit committees
- Versioning forecasted financials for regulatory submissions
Module 12: Dashboard Visualisation and Executive Reporting - Designing executive dashboards using Power BI integration
- Creating visual KPI summaries from forecast models
- Displaying forecast accuracy trends over time
- Highlighting forecast deviations and alert thresholds
- Using heat maps to visualise regional performance risks
- Building interactive drill-down reports for detailed analysis
- Exporting dashboards to PDF or PowerPoint for board meetings
- Setting up automated dashboard refresh cycles
- Configuring role-based dashboard views for stakeholders
- Incorporating commentary and assumptions into visuals
- Using forecast overlays for strategic planning sessions
- Embedding dashboard widgets into intranet portals
- Sharing secure links with remote executives and advisors
- Creating printable one-page forecast snapshots
- Documenting dashboard logic for compliance purposes
Module 13: AI Forecast Governance and Audit Readiness - Establishing ownership and accountability for AI forecasts
- Creating a forecasting governance charter
- Documenting model inputs, assumptions, and configurations
- Implementing version control for forecast models
- Setting up model approval workflows for changes
- Conducting peer reviews of forecast logic and outputs
- Archiving historical forecast versions for audit trails
- Ensuring compliance with SOX, GDPR, and financial regulations
- Generating model validation reports for internal audit
- Preparing for external auditor inquiries about AI use
- Managing access controls for forecast data and tools
- Logging user actions within the forecasting environment
- Establishing escalation paths for forecast discrepancies
- Training finance teams on responsible forecasting practices
- Auditing model performance against business outcomes
Module 14: Change Management and Stakeholder Adoption - Communicating the value of AI forecasting to non-technical leaders
- Managing resistance from teams used to manual processes
- Running pilot projects to demonstrate forecast accuracy gains
- Training controllers and analysts on new forecasting methods
- Creating user guides and quick-reference job aids
- Setting up feedback loops for continuous improvement
- Defining key adoption metrics and tracking progress
- Involving IT teams in integration and support planning
- Establishing a centre of excellence for financial forecasting
- Scheduling regular forecast review cadences
- Onboarding new team members using standardised playbooks
- Measuring user proficiency and confidence post-implementation
- Recognising and rewarding successful forecast accuracy
- Scaling best practices across divisions and geographies
- Creating a roadmap for ongoing forecasting maturity
Module 15: Advanced Integration with External Systems - Connecting Dynamics 365 to ERP and CRM systems
- Integrating with Excel for ad hoc analysis and reporting
- Using Power Automate to streamline data flows
- Importing macroeconomic indicators from external sources
- Linking to bank APIs for real-time transaction feeds
- Syncing with HR systems for headcount forecasting
- Connecting to project management tools for cost tracking
- Using Azure Logic Apps for complex data pipelines
- Setting up data synchronisation schedules and monitors
- Handling data transformation during integration
- Validating data consistency across systems
- Managing error handling and reconciliation processes
- Ensuring compliance in cross-system data sharing
- Documenting integration architecture for IT review
- Using integration for real-time forecast updates
Module 16: Certification Readiness and Real-World Project - Reviewing all core concepts for certification exam preparation
- Navigating the final assessment structure and format
- Practising scenario-based questions with detailed feedback
- Building a complete AI-driven forecast for a sample organisation
- Selecting appropriate models based on business context
- Configuring the forecasting environment from scratch
- Importing and cleansing sample financial datasets
- Generating multi-scenario forecasts with commentary
- Creating executive dashboards and visual outputs
- Documenting all assumptions, configurations, and results
- Submitting the project for review against rubric criteria
- Receiving structured feedback on strengths and improvements
- Preparing for real-world implementation in your organisation
- Developing a 30-day rollout plan for your team
- Earning your Certificate of Completion issued by The Art of Service
- Understanding the evolution of financial forecasting in enterprise systems
- Defining artificial intelligence in the context of financial planning
- Key limitations of traditional forecasting models in dynamic markets
- How AI enhances accuracy, responsiveness, and scenario agility
- The role of machine learning versus rule-based automation
- Core principles of predictive analytics in finance
- Overview of Microsoft Dynamics 365 Finance and Supply Chain capabilities
- Distinguishing supported AI features from third-party integrations
- Prerequisites for enabling AI in your financial forecasting landscape
- Mapping business drivers to financial outcomes using causal variables
- Understanding time-series forecasting fundamentals
- The importance of data freshness and update cycles
- Introduction to responsible AI in financial systems
- Establishing trust and auditability in AI-generated forecasts
- Identifying organisational readiness for AI adoption
Module 2: Dynamics 365 Architecture for Financial Intelligence - Core components of the Dynamics 365 financial module stack
- Data model structure: General ledger, dimensions, and financial entities
- Integration between Accounts Payable, Receivable, and forecasting engines
- How the Common Data Model supports cross-module forecasting
- Configuring financial dimensions for predictive analysis
- Data entity relationships in the forecasting data pipeline
- Understanding the role of the Dynamics 365 metadata framework
- Accessing financial data via query APIs and data management tools
- Overview of the Finance and Operations application layer
- Security roles and data access permissions for forecasting users
- Setting up user profiles with appropriate AI feature access
- Organisational hierarchies and their impact on forecast aggregation
- Currency and consolidation models in multi-entity environments
- Periodic and fiscal calendar alignment for forecasting accuracy
- Data versioning and historical record preservation
Module 3: Enabling and Configuring AI Features in Dynamics 365 - Locating AI settings in the Dynamics 365 Finance workspace
- Prerequisites for activating AI-driven forecasting tools
- Enabling predictive insights for cash flow forecasting
- Configuring data connectors for external financial sources
- Setting up data retention policies for AI training datasets
- Defining data filters and exclusion rules for forecast inputs
- How to calibrate AI model refresh intervals
- Configuring the AI prediction threshold for anomaly detection
- Setting confidence intervals and forecast reliability tolerances
- Enabling ML models for revenue, expense, and working capital projections
- Activating automatic seasonality detection in time-series models
- Configuring holiday adjustment rules in forecasting algorithms
- Setting up financial forecast alerts and deviation triggers
- Managing AI model lifecycle: Training, evaluation, and deployment
- Understanding model drift detection and retraining schedules
Module 4: Data Preparation for AI-Enhanced Forecasting - Assessing data quality for predictive modelling
- Identifying missing, outlier, and inconsistent financial records
- Techniques for handling zero-balance periods in time series
- Smoothing irregular data using aggregation and transformation
- Standardising currency, units, and account codes across entities
- Aligning historical records with current chart of account structures
- Using data cleansing rules in the Data Management Framework
- Validating imported data against predefined financial thresholds
- Defining relevant forecast drivers: Volume, price, headcount, etc.
- Mapping non-financial inputs to financial outcomes
- Creating derived metrics for predictive input (e.g. AR days, burn rate)
- Time alignment of external market indicators with internal data
- Leveraging budget and actuals data as training inputs
- Handling ledger adjustments and reversals in training sets
- Setting data sampling frequency: Daily, weekly, monthly
Module 5: Building Predictive Forecast Models - Navigating the Forecasting workspace in Dynamics 365
- Creating a new AI-driven forecast project
- Selecting appropriate forecast variables: Revenue, expenses, cash
- Choosing between additive and multiplicative seasonality models
- Specifying forecast horizon: Short-term, mid-term, long-term
- Defining model input time ranges and backtesting windows
- Incorporating known future events into forecast assumptions
- Modelling the impact of contracts, renewals, and sales pipelines
- Configuring predictive models for intercompany transactions
- Applying moving average and exponential smoothing techniques
- Using ARIMA and ETS models within Dynamics 365 tools
- Enabling automatic model selection for optimal accuracy
- Setting constraints for forecast outputs (minimum, maximum, growth caps)
- Incorporating inflation or exchange rate trends as external factors
- Building scenario-specific models for M&A or divestiture planning
Module 6: Validating and Interpreting AI Forecast Outputs - Accessing and reviewing the AI forecast generation log
- Interpreting model accuracy metrics: MAPE, RMSE, MAE
- Understanding prediction confidence bands and uncertainty ranges
- Visualising forecast outputs using built-in charting tools
- Comparing AI forecasts against historical performance
- Backtesting models against known outcomes for validation
- Using holdout datasets to test model generalisability
- Identifying overfitting and underfitting in financial models
- Analysing model residuals for pattern detection
- Validating predictive ability across different business units
- Assessing forecast stability under changing market conditions
- Detecting anomalies and outliers in predicted series
- Reviewing model variable importance and feature weights
- Validating forecast logic with finance stakeholders
- Documenting model assumptions and limitations for audit use
Module 7: Scenario Planning and What-If Analysis - Creating custom forecast scenarios: Optimistic, pessimistic, base
- Duplicating and modifying existing models for sensitivity testing
- Adjusting growth rates, cost structures, and volume assumptions
- Assessing the impact of delayed payments or early collections
- Modelling the effect of new hires or restructuring plans
- Simulating market downturns or supply chain disruptions
- Testing pricing strategy changes on revenue streams
- Evaluating the financial impact of capital expenditures
- Running scenario comparisons using dashboard overlays
- Exporting scenario outputs for executive presentations
- Defining trigger points for scenario activation
- Automating scenario generation based on KPI deviations
- Integrating risk probabilities into scenario weighting
- Using scenario planners for board-level decision support
- Documenting assumptions for regulatory and audit purposes
Module 8: Cash Flow Forecasting with AI - Setting up AI-powered cash flow forecasting in Dynamics 365
- Selecting relevant data sources: Bank feeds, AP, AR, payroll
- Configuring dynamic cash position projections
- Forecasting operating, investing, and financing cash flows
- Predicting short-term liquidity gaps and surpluses
- Incorporating payment term variability and supplier patterns
- Modelling customer payment behaviour and collection risks
- Automating forecasting of recurring and non-recurring items
- Integrating bank reconciliation data for accuracy improvement
- Forecasting cash needs for dividend payments and debt servicing
- Aligning cash forecasts with treasury and investment plans
- Monitoring forecast-to-actual variance in cash positions
- Detecting cash flow anomalies before they become crises
- Creating rolling 13-week cash flow models with AI support
- Exporting cash flow forecasts to liquidity dashboards
Module 9: Revenue Forecasting Using Predictive Analytics - Configuring AI models for revenue forecasting by product line
- Linking sales pipeline data from Dynamics 365 Sales to forecasts
- Weighting opportunities by probability and stage maturity
- Incorporating seasonality into subscription and licence revenue
- Modelling contract renewals and expansion revenue
- Forecasting revenue for project-based and time-and-materials work
- Adjusting forecasts for currency fluctuations and pricing changes
- Handling multi-year contracts with variable deliverables
- Incorporating customer churn and retention rates into predictions
- Using historical win rates to refine forecast accuracy
- Validating forecasts against CRM conversion benchmarks
- Forecasting revenue for new markets and product launches
- Aligning revenue forecasts with general ledger recognition rules
- Creating regional revenue breakdowns with local drivers
- Documenting forecast rationale for revenue assurance audits
Module 10: Expense and Cost Forecasting Optimisation - Building AI models for operating expense forecasting
- Separating fixed, variable, and semi-variable cost behaviours
- Forecasting payroll costs with headcount and benefit inputs
- Predicting utility and overhead escalation patterns
- Modelling one-time and project-based expenses
- Forecasting procurement and vendor spend trends
- Incorporating purchase order data into expense models
- Predicting travel and entertainment spend based on historical usage
- Using budget vs actual data to refine forecast assumptions
- Forecasting depreciation and amortisation schedules
- Modelling R&D and capitalised expense pipelines
- Adjusting forecasts for inflation or commodity price shifts
- Creating zero-based forecasting iterations using AI support
- Monitoring forecast accuracy across cost centres
- Linking cost forecasts to performance dashboards
Module 11: Integrating Forecast Data into Financial Statements - Mapping forecast outputs to income statement line items
- Translating revenue and expense forecasts into P&L projections
- Generating AI-supported balance sheet estimates
- Forecasting working capital components: AR, inventory, AP
- Projecting debt and equity movements based on forecasts
- Creating rolling financial statements using updated forecasts
- Automating financial report generation from forecast models
- Linking forecasts to budgeting and planning cycles
- Aligning forecast data with GAAP and IFRS reporting standards
- Setting audit trails for forecast-influenced financial entries
- Using forecast scenarios to stress test financial health
- Exporting forecast-based reports for external disclosure
- Reconciling forecast assumptions with management commentary
- Presenting forecasted statements to audit committees
- Versioning forecasted financials for regulatory submissions
Module 12: Dashboard Visualisation and Executive Reporting - Designing executive dashboards using Power BI integration
- Creating visual KPI summaries from forecast models
- Displaying forecast accuracy trends over time
- Highlighting forecast deviations and alert thresholds
- Using heat maps to visualise regional performance risks
- Building interactive drill-down reports for detailed analysis
- Exporting dashboards to PDF or PowerPoint for board meetings
- Setting up automated dashboard refresh cycles
- Configuring role-based dashboard views for stakeholders
- Incorporating commentary and assumptions into visuals
- Using forecast overlays for strategic planning sessions
- Embedding dashboard widgets into intranet portals
- Sharing secure links with remote executives and advisors
- Creating printable one-page forecast snapshots
- Documenting dashboard logic for compliance purposes
Module 13: AI Forecast Governance and Audit Readiness - Establishing ownership and accountability for AI forecasts
- Creating a forecasting governance charter
- Documenting model inputs, assumptions, and configurations
- Implementing version control for forecast models
- Setting up model approval workflows for changes
- Conducting peer reviews of forecast logic and outputs
- Archiving historical forecast versions for audit trails
- Ensuring compliance with SOX, GDPR, and financial regulations
- Generating model validation reports for internal audit
- Preparing for external auditor inquiries about AI use
- Managing access controls for forecast data and tools
- Logging user actions within the forecasting environment
- Establishing escalation paths for forecast discrepancies
- Training finance teams on responsible forecasting practices
- Auditing model performance against business outcomes
Module 14: Change Management and Stakeholder Adoption - Communicating the value of AI forecasting to non-technical leaders
- Managing resistance from teams used to manual processes
- Running pilot projects to demonstrate forecast accuracy gains
- Training controllers and analysts on new forecasting methods
- Creating user guides and quick-reference job aids
- Setting up feedback loops for continuous improvement
- Defining key adoption metrics and tracking progress
- Involving IT teams in integration and support planning
- Establishing a centre of excellence for financial forecasting
- Scheduling regular forecast review cadences
- Onboarding new team members using standardised playbooks
- Measuring user proficiency and confidence post-implementation
- Recognising and rewarding successful forecast accuracy
- Scaling best practices across divisions and geographies
- Creating a roadmap for ongoing forecasting maturity
Module 15: Advanced Integration with External Systems - Connecting Dynamics 365 to ERP and CRM systems
- Integrating with Excel for ad hoc analysis and reporting
- Using Power Automate to streamline data flows
- Importing macroeconomic indicators from external sources
- Linking to bank APIs for real-time transaction feeds
- Syncing with HR systems for headcount forecasting
- Connecting to project management tools for cost tracking
- Using Azure Logic Apps for complex data pipelines
- Setting up data synchronisation schedules and monitors
- Handling data transformation during integration
- Validating data consistency across systems
- Managing error handling and reconciliation processes
- Ensuring compliance in cross-system data sharing
- Documenting integration architecture for IT review
- Using integration for real-time forecast updates
Module 16: Certification Readiness and Real-World Project - Reviewing all core concepts for certification exam preparation
- Navigating the final assessment structure and format
- Practising scenario-based questions with detailed feedback
- Building a complete AI-driven forecast for a sample organisation
- Selecting appropriate models based on business context
- Configuring the forecasting environment from scratch
- Importing and cleansing sample financial datasets
- Generating multi-scenario forecasts with commentary
- Creating executive dashboards and visual outputs
- Documenting all assumptions, configurations, and results
- Submitting the project for review against rubric criteria
- Receiving structured feedback on strengths and improvements
- Preparing for real-world implementation in your organisation
- Developing a 30-day rollout plan for your team
- Earning your Certificate of Completion issued by The Art of Service
- Locating AI settings in the Dynamics 365 Finance workspace
- Prerequisites for activating AI-driven forecasting tools
- Enabling predictive insights for cash flow forecasting
- Configuring data connectors for external financial sources
- Setting up data retention policies for AI training datasets
- Defining data filters and exclusion rules for forecast inputs
- How to calibrate AI model refresh intervals
- Configuring the AI prediction threshold for anomaly detection
- Setting confidence intervals and forecast reliability tolerances
- Enabling ML models for revenue, expense, and working capital projections
- Activating automatic seasonality detection in time-series models
- Configuring holiday adjustment rules in forecasting algorithms
- Setting up financial forecast alerts and deviation triggers
- Managing AI model lifecycle: Training, evaluation, and deployment
- Understanding model drift detection and retraining schedules
Module 4: Data Preparation for AI-Enhanced Forecasting - Assessing data quality for predictive modelling
- Identifying missing, outlier, and inconsistent financial records
- Techniques for handling zero-balance periods in time series
- Smoothing irregular data using aggregation and transformation
- Standardising currency, units, and account codes across entities
- Aligning historical records with current chart of account structures
- Using data cleansing rules in the Data Management Framework
- Validating imported data against predefined financial thresholds
- Defining relevant forecast drivers: Volume, price, headcount, etc.
- Mapping non-financial inputs to financial outcomes
- Creating derived metrics for predictive input (e.g. AR days, burn rate)
- Time alignment of external market indicators with internal data
- Leveraging budget and actuals data as training inputs
- Handling ledger adjustments and reversals in training sets
- Setting data sampling frequency: Daily, weekly, monthly
Module 5: Building Predictive Forecast Models - Navigating the Forecasting workspace in Dynamics 365
- Creating a new AI-driven forecast project
- Selecting appropriate forecast variables: Revenue, expenses, cash
- Choosing between additive and multiplicative seasonality models
- Specifying forecast horizon: Short-term, mid-term, long-term
- Defining model input time ranges and backtesting windows
- Incorporating known future events into forecast assumptions
- Modelling the impact of contracts, renewals, and sales pipelines
- Configuring predictive models for intercompany transactions
- Applying moving average and exponential smoothing techniques
- Using ARIMA and ETS models within Dynamics 365 tools
- Enabling automatic model selection for optimal accuracy
- Setting constraints for forecast outputs (minimum, maximum, growth caps)
- Incorporating inflation or exchange rate trends as external factors
- Building scenario-specific models for M&A or divestiture planning
Module 6: Validating and Interpreting AI Forecast Outputs - Accessing and reviewing the AI forecast generation log
- Interpreting model accuracy metrics: MAPE, RMSE, MAE
- Understanding prediction confidence bands and uncertainty ranges
- Visualising forecast outputs using built-in charting tools
- Comparing AI forecasts against historical performance
- Backtesting models against known outcomes for validation
- Using holdout datasets to test model generalisability
- Identifying overfitting and underfitting in financial models
- Analysing model residuals for pattern detection
- Validating predictive ability across different business units
- Assessing forecast stability under changing market conditions
- Detecting anomalies and outliers in predicted series
- Reviewing model variable importance and feature weights
- Validating forecast logic with finance stakeholders
- Documenting model assumptions and limitations for audit use
Module 7: Scenario Planning and What-If Analysis - Creating custom forecast scenarios: Optimistic, pessimistic, base
- Duplicating and modifying existing models for sensitivity testing
- Adjusting growth rates, cost structures, and volume assumptions
- Assessing the impact of delayed payments or early collections
- Modelling the effect of new hires or restructuring plans
- Simulating market downturns or supply chain disruptions
- Testing pricing strategy changes on revenue streams
- Evaluating the financial impact of capital expenditures
- Running scenario comparisons using dashboard overlays
- Exporting scenario outputs for executive presentations
- Defining trigger points for scenario activation
- Automating scenario generation based on KPI deviations
- Integrating risk probabilities into scenario weighting
- Using scenario planners for board-level decision support
- Documenting assumptions for regulatory and audit purposes
Module 8: Cash Flow Forecasting with AI - Setting up AI-powered cash flow forecasting in Dynamics 365
- Selecting relevant data sources: Bank feeds, AP, AR, payroll
- Configuring dynamic cash position projections
- Forecasting operating, investing, and financing cash flows
- Predicting short-term liquidity gaps and surpluses
- Incorporating payment term variability and supplier patterns
- Modelling customer payment behaviour and collection risks
- Automating forecasting of recurring and non-recurring items
- Integrating bank reconciliation data for accuracy improvement
- Forecasting cash needs for dividend payments and debt servicing
- Aligning cash forecasts with treasury and investment plans
- Monitoring forecast-to-actual variance in cash positions
- Detecting cash flow anomalies before they become crises
- Creating rolling 13-week cash flow models with AI support
- Exporting cash flow forecasts to liquidity dashboards
Module 9: Revenue Forecasting Using Predictive Analytics - Configuring AI models for revenue forecasting by product line
- Linking sales pipeline data from Dynamics 365 Sales to forecasts
- Weighting opportunities by probability and stage maturity
- Incorporating seasonality into subscription and licence revenue
- Modelling contract renewals and expansion revenue
- Forecasting revenue for project-based and time-and-materials work
- Adjusting forecasts for currency fluctuations and pricing changes
- Handling multi-year contracts with variable deliverables
- Incorporating customer churn and retention rates into predictions
- Using historical win rates to refine forecast accuracy
- Validating forecasts against CRM conversion benchmarks
- Forecasting revenue for new markets and product launches
- Aligning revenue forecasts with general ledger recognition rules
- Creating regional revenue breakdowns with local drivers
- Documenting forecast rationale for revenue assurance audits
Module 10: Expense and Cost Forecasting Optimisation - Building AI models for operating expense forecasting
- Separating fixed, variable, and semi-variable cost behaviours
- Forecasting payroll costs with headcount and benefit inputs
- Predicting utility and overhead escalation patterns
- Modelling one-time and project-based expenses
- Forecasting procurement and vendor spend trends
- Incorporating purchase order data into expense models
- Predicting travel and entertainment spend based on historical usage
- Using budget vs actual data to refine forecast assumptions
- Forecasting depreciation and amortisation schedules
- Modelling R&D and capitalised expense pipelines
- Adjusting forecasts for inflation or commodity price shifts
- Creating zero-based forecasting iterations using AI support
- Monitoring forecast accuracy across cost centres
- Linking cost forecasts to performance dashboards
Module 11: Integrating Forecast Data into Financial Statements - Mapping forecast outputs to income statement line items
- Translating revenue and expense forecasts into P&L projections
- Generating AI-supported balance sheet estimates
- Forecasting working capital components: AR, inventory, AP
- Projecting debt and equity movements based on forecasts
- Creating rolling financial statements using updated forecasts
- Automating financial report generation from forecast models
- Linking forecasts to budgeting and planning cycles
- Aligning forecast data with GAAP and IFRS reporting standards
- Setting audit trails for forecast-influenced financial entries
- Using forecast scenarios to stress test financial health
- Exporting forecast-based reports for external disclosure
- Reconciling forecast assumptions with management commentary
- Presenting forecasted statements to audit committees
- Versioning forecasted financials for regulatory submissions
Module 12: Dashboard Visualisation and Executive Reporting - Designing executive dashboards using Power BI integration
- Creating visual KPI summaries from forecast models
- Displaying forecast accuracy trends over time
- Highlighting forecast deviations and alert thresholds
- Using heat maps to visualise regional performance risks
- Building interactive drill-down reports for detailed analysis
- Exporting dashboards to PDF or PowerPoint for board meetings
- Setting up automated dashboard refresh cycles
- Configuring role-based dashboard views for stakeholders
- Incorporating commentary and assumptions into visuals
- Using forecast overlays for strategic planning sessions
- Embedding dashboard widgets into intranet portals
- Sharing secure links with remote executives and advisors
- Creating printable one-page forecast snapshots
- Documenting dashboard logic for compliance purposes
Module 13: AI Forecast Governance and Audit Readiness - Establishing ownership and accountability for AI forecasts
- Creating a forecasting governance charter
- Documenting model inputs, assumptions, and configurations
- Implementing version control for forecast models
- Setting up model approval workflows for changes
- Conducting peer reviews of forecast logic and outputs
- Archiving historical forecast versions for audit trails
- Ensuring compliance with SOX, GDPR, and financial regulations
- Generating model validation reports for internal audit
- Preparing for external auditor inquiries about AI use
- Managing access controls for forecast data and tools
- Logging user actions within the forecasting environment
- Establishing escalation paths for forecast discrepancies
- Training finance teams on responsible forecasting practices
- Auditing model performance against business outcomes
Module 14: Change Management and Stakeholder Adoption - Communicating the value of AI forecasting to non-technical leaders
- Managing resistance from teams used to manual processes
- Running pilot projects to demonstrate forecast accuracy gains
- Training controllers and analysts on new forecasting methods
- Creating user guides and quick-reference job aids
- Setting up feedback loops for continuous improvement
- Defining key adoption metrics and tracking progress
- Involving IT teams in integration and support planning
- Establishing a centre of excellence for financial forecasting
- Scheduling regular forecast review cadences
- Onboarding new team members using standardised playbooks
- Measuring user proficiency and confidence post-implementation
- Recognising and rewarding successful forecast accuracy
- Scaling best practices across divisions and geographies
- Creating a roadmap for ongoing forecasting maturity
Module 15: Advanced Integration with External Systems - Connecting Dynamics 365 to ERP and CRM systems
- Integrating with Excel for ad hoc analysis and reporting
- Using Power Automate to streamline data flows
- Importing macroeconomic indicators from external sources
- Linking to bank APIs for real-time transaction feeds
- Syncing with HR systems for headcount forecasting
- Connecting to project management tools for cost tracking
- Using Azure Logic Apps for complex data pipelines
- Setting up data synchronisation schedules and monitors
- Handling data transformation during integration
- Validating data consistency across systems
- Managing error handling and reconciliation processes
- Ensuring compliance in cross-system data sharing
- Documenting integration architecture for IT review
- Using integration for real-time forecast updates
Module 16: Certification Readiness and Real-World Project - Reviewing all core concepts for certification exam preparation
- Navigating the final assessment structure and format
- Practising scenario-based questions with detailed feedback
- Building a complete AI-driven forecast for a sample organisation
- Selecting appropriate models based on business context
- Configuring the forecasting environment from scratch
- Importing and cleansing sample financial datasets
- Generating multi-scenario forecasts with commentary
- Creating executive dashboards and visual outputs
- Documenting all assumptions, configurations, and results
- Submitting the project for review against rubric criteria
- Receiving structured feedback on strengths and improvements
- Preparing for real-world implementation in your organisation
- Developing a 30-day rollout plan for your team
- Earning your Certificate of Completion issued by The Art of Service
- Navigating the Forecasting workspace in Dynamics 365
- Creating a new AI-driven forecast project
- Selecting appropriate forecast variables: Revenue, expenses, cash
- Choosing between additive and multiplicative seasonality models
- Specifying forecast horizon: Short-term, mid-term, long-term
- Defining model input time ranges and backtesting windows
- Incorporating known future events into forecast assumptions
- Modelling the impact of contracts, renewals, and sales pipelines
- Configuring predictive models for intercompany transactions
- Applying moving average and exponential smoothing techniques
- Using ARIMA and ETS models within Dynamics 365 tools
- Enabling automatic model selection for optimal accuracy
- Setting constraints for forecast outputs (minimum, maximum, growth caps)
- Incorporating inflation or exchange rate trends as external factors
- Building scenario-specific models for M&A or divestiture planning
Module 6: Validating and Interpreting AI Forecast Outputs - Accessing and reviewing the AI forecast generation log
- Interpreting model accuracy metrics: MAPE, RMSE, MAE
- Understanding prediction confidence bands and uncertainty ranges
- Visualising forecast outputs using built-in charting tools
- Comparing AI forecasts against historical performance
- Backtesting models against known outcomes for validation
- Using holdout datasets to test model generalisability
- Identifying overfitting and underfitting in financial models
- Analysing model residuals for pattern detection
- Validating predictive ability across different business units
- Assessing forecast stability under changing market conditions
- Detecting anomalies and outliers in predicted series
- Reviewing model variable importance and feature weights
- Validating forecast logic with finance stakeholders
- Documenting model assumptions and limitations for audit use
Module 7: Scenario Planning and What-If Analysis - Creating custom forecast scenarios: Optimistic, pessimistic, base
- Duplicating and modifying existing models for sensitivity testing
- Adjusting growth rates, cost structures, and volume assumptions
- Assessing the impact of delayed payments or early collections
- Modelling the effect of new hires or restructuring plans
- Simulating market downturns or supply chain disruptions
- Testing pricing strategy changes on revenue streams
- Evaluating the financial impact of capital expenditures
- Running scenario comparisons using dashboard overlays
- Exporting scenario outputs for executive presentations
- Defining trigger points for scenario activation
- Automating scenario generation based on KPI deviations
- Integrating risk probabilities into scenario weighting
- Using scenario planners for board-level decision support
- Documenting assumptions for regulatory and audit purposes
Module 8: Cash Flow Forecasting with AI - Setting up AI-powered cash flow forecasting in Dynamics 365
- Selecting relevant data sources: Bank feeds, AP, AR, payroll
- Configuring dynamic cash position projections
- Forecasting operating, investing, and financing cash flows
- Predicting short-term liquidity gaps and surpluses
- Incorporating payment term variability and supplier patterns
- Modelling customer payment behaviour and collection risks
- Automating forecasting of recurring and non-recurring items
- Integrating bank reconciliation data for accuracy improvement
- Forecasting cash needs for dividend payments and debt servicing
- Aligning cash forecasts with treasury and investment plans
- Monitoring forecast-to-actual variance in cash positions
- Detecting cash flow anomalies before they become crises
- Creating rolling 13-week cash flow models with AI support
- Exporting cash flow forecasts to liquidity dashboards
Module 9: Revenue Forecasting Using Predictive Analytics - Configuring AI models for revenue forecasting by product line
- Linking sales pipeline data from Dynamics 365 Sales to forecasts
- Weighting opportunities by probability and stage maturity
- Incorporating seasonality into subscription and licence revenue
- Modelling contract renewals and expansion revenue
- Forecasting revenue for project-based and time-and-materials work
- Adjusting forecasts for currency fluctuations and pricing changes
- Handling multi-year contracts with variable deliverables
- Incorporating customer churn and retention rates into predictions
- Using historical win rates to refine forecast accuracy
- Validating forecasts against CRM conversion benchmarks
- Forecasting revenue for new markets and product launches
- Aligning revenue forecasts with general ledger recognition rules
- Creating regional revenue breakdowns with local drivers
- Documenting forecast rationale for revenue assurance audits
Module 10: Expense and Cost Forecasting Optimisation - Building AI models for operating expense forecasting
- Separating fixed, variable, and semi-variable cost behaviours
- Forecasting payroll costs with headcount and benefit inputs
- Predicting utility and overhead escalation patterns
- Modelling one-time and project-based expenses
- Forecasting procurement and vendor spend trends
- Incorporating purchase order data into expense models
- Predicting travel and entertainment spend based on historical usage
- Using budget vs actual data to refine forecast assumptions
- Forecasting depreciation and amortisation schedules
- Modelling R&D and capitalised expense pipelines
- Adjusting forecasts for inflation or commodity price shifts
- Creating zero-based forecasting iterations using AI support
- Monitoring forecast accuracy across cost centres
- Linking cost forecasts to performance dashboards
Module 11: Integrating Forecast Data into Financial Statements - Mapping forecast outputs to income statement line items
- Translating revenue and expense forecasts into P&L projections
- Generating AI-supported balance sheet estimates
- Forecasting working capital components: AR, inventory, AP
- Projecting debt and equity movements based on forecasts
- Creating rolling financial statements using updated forecasts
- Automating financial report generation from forecast models
- Linking forecasts to budgeting and planning cycles
- Aligning forecast data with GAAP and IFRS reporting standards
- Setting audit trails for forecast-influenced financial entries
- Using forecast scenarios to stress test financial health
- Exporting forecast-based reports for external disclosure
- Reconciling forecast assumptions with management commentary
- Presenting forecasted statements to audit committees
- Versioning forecasted financials for regulatory submissions
Module 12: Dashboard Visualisation and Executive Reporting - Designing executive dashboards using Power BI integration
- Creating visual KPI summaries from forecast models
- Displaying forecast accuracy trends over time
- Highlighting forecast deviations and alert thresholds
- Using heat maps to visualise regional performance risks
- Building interactive drill-down reports for detailed analysis
- Exporting dashboards to PDF or PowerPoint for board meetings
- Setting up automated dashboard refresh cycles
- Configuring role-based dashboard views for stakeholders
- Incorporating commentary and assumptions into visuals
- Using forecast overlays for strategic planning sessions
- Embedding dashboard widgets into intranet portals
- Sharing secure links with remote executives and advisors
- Creating printable one-page forecast snapshots
- Documenting dashboard logic for compliance purposes
Module 13: AI Forecast Governance and Audit Readiness - Establishing ownership and accountability for AI forecasts
- Creating a forecasting governance charter
- Documenting model inputs, assumptions, and configurations
- Implementing version control for forecast models
- Setting up model approval workflows for changes
- Conducting peer reviews of forecast logic and outputs
- Archiving historical forecast versions for audit trails
- Ensuring compliance with SOX, GDPR, and financial regulations
- Generating model validation reports for internal audit
- Preparing for external auditor inquiries about AI use
- Managing access controls for forecast data and tools
- Logging user actions within the forecasting environment
- Establishing escalation paths for forecast discrepancies
- Training finance teams on responsible forecasting practices
- Auditing model performance against business outcomes
Module 14: Change Management and Stakeholder Adoption - Communicating the value of AI forecasting to non-technical leaders
- Managing resistance from teams used to manual processes
- Running pilot projects to demonstrate forecast accuracy gains
- Training controllers and analysts on new forecasting methods
- Creating user guides and quick-reference job aids
- Setting up feedback loops for continuous improvement
- Defining key adoption metrics and tracking progress
- Involving IT teams in integration and support planning
- Establishing a centre of excellence for financial forecasting
- Scheduling regular forecast review cadences
- Onboarding new team members using standardised playbooks
- Measuring user proficiency and confidence post-implementation
- Recognising and rewarding successful forecast accuracy
- Scaling best practices across divisions and geographies
- Creating a roadmap for ongoing forecasting maturity
Module 15: Advanced Integration with External Systems - Connecting Dynamics 365 to ERP and CRM systems
- Integrating with Excel for ad hoc analysis and reporting
- Using Power Automate to streamline data flows
- Importing macroeconomic indicators from external sources
- Linking to bank APIs for real-time transaction feeds
- Syncing with HR systems for headcount forecasting
- Connecting to project management tools for cost tracking
- Using Azure Logic Apps for complex data pipelines
- Setting up data synchronisation schedules and monitors
- Handling data transformation during integration
- Validating data consistency across systems
- Managing error handling and reconciliation processes
- Ensuring compliance in cross-system data sharing
- Documenting integration architecture for IT review
- Using integration for real-time forecast updates
Module 16: Certification Readiness and Real-World Project - Reviewing all core concepts for certification exam preparation
- Navigating the final assessment structure and format
- Practising scenario-based questions with detailed feedback
- Building a complete AI-driven forecast for a sample organisation
- Selecting appropriate models based on business context
- Configuring the forecasting environment from scratch
- Importing and cleansing sample financial datasets
- Generating multi-scenario forecasts with commentary
- Creating executive dashboards and visual outputs
- Documenting all assumptions, configurations, and results
- Submitting the project for review against rubric criteria
- Receiving structured feedback on strengths and improvements
- Preparing for real-world implementation in your organisation
- Developing a 30-day rollout plan for your team
- Earning your Certificate of Completion issued by The Art of Service
- Creating custom forecast scenarios: Optimistic, pessimistic, base
- Duplicating and modifying existing models for sensitivity testing
- Adjusting growth rates, cost structures, and volume assumptions
- Assessing the impact of delayed payments or early collections
- Modelling the effect of new hires or restructuring plans
- Simulating market downturns or supply chain disruptions
- Testing pricing strategy changes on revenue streams
- Evaluating the financial impact of capital expenditures
- Running scenario comparisons using dashboard overlays
- Exporting scenario outputs for executive presentations
- Defining trigger points for scenario activation
- Automating scenario generation based on KPI deviations
- Integrating risk probabilities into scenario weighting
- Using scenario planners for board-level decision support
- Documenting assumptions for regulatory and audit purposes
Module 8: Cash Flow Forecasting with AI - Setting up AI-powered cash flow forecasting in Dynamics 365
- Selecting relevant data sources: Bank feeds, AP, AR, payroll
- Configuring dynamic cash position projections
- Forecasting operating, investing, and financing cash flows
- Predicting short-term liquidity gaps and surpluses
- Incorporating payment term variability and supplier patterns
- Modelling customer payment behaviour and collection risks
- Automating forecasting of recurring and non-recurring items
- Integrating bank reconciliation data for accuracy improvement
- Forecasting cash needs for dividend payments and debt servicing
- Aligning cash forecasts with treasury and investment plans
- Monitoring forecast-to-actual variance in cash positions
- Detecting cash flow anomalies before they become crises
- Creating rolling 13-week cash flow models with AI support
- Exporting cash flow forecasts to liquidity dashboards
Module 9: Revenue Forecasting Using Predictive Analytics - Configuring AI models for revenue forecasting by product line
- Linking sales pipeline data from Dynamics 365 Sales to forecasts
- Weighting opportunities by probability and stage maturity
- Incorporating seasonality into subscription and licence revenue
- Modelling contract renewals and expansion revenue
- Forecasting revenue for project-based and time-and-materials work
- Adjusting forecasts for currency fluctuations and pricing changes
- Handling multi-year contracts with variable deliverables
- Incorporating customer churn and retention rates into predictions
- Using historical win rates to refine forecast accuracy
- Validating forecasts against CRM conversion benchmarks
- Forecasting revenue for new markets and product launches
- Aligning revenue forecasts with general ledger recognition rules
- Creating regional revenue breakdowns with local drivers
- Documenting forecast rationale for revenue assurance audits
Module 10: Expense and Cost Forecasting Optimisation - Building AI models for operating expense forecasting
- Separating fixed, variable, and semi-variable cost behaviours
- Forecasting payroll costs with headcount and benefit inputs
- Predicting utility and overhead escalation patterns
- Modelling one-time and project-based expenses
- Forecasting procurement and vendor spend trends
- Incorporating purchase order data into expense models
- Predicting travel and entertainment spend based on historical usage
- Using budget vs actual data to refine forecast assumptions
- Forecasting depreciation and amortisation schedules
- Modelling R&D and capitalised expense pipelines
- Adjusting forecasts for inflation or commodity price shifts
- Creating zero-based forecasting iterations using AI support
- Monitoring forecast accuracy across cost centres
- Linking cost forecasts to performance dashboards
Module 11: Integrating Forecast Data into Financial Statements - Mapping forecast outputs to income statement line items
- Translating revenue and expense forecasts into P&L projections
- Generating AI-supported balance sheet estimates
- Forecasting working capital components: AR, inventory, AP
- Projecting debt and equity movements based on forecasts
- Creating rolling financial statements using updated forecasts
- Automating financial report generation from forecast models
- Linking forecasts to budgeting and planning cycles
- Aligning forecast data with GAAP and IFRS reporting standards
- Setting audit trails for forecast-influenced financial entries
- Using forecast scenarios to stress test financial health
- Exporting forecast-based reports for external disclosure
- Reconciling forecast assumptions with management commentary
- Presenting forecasted statements to audit committees
- Versioning forecasted financials for regulatory submissions
Module 12: Dashboard Visualisation and Executive Reporting - Designing executive dashboards using Power BI integration
- Creating visual KPI summaries from forecast models
- Displaying forecast accuracy trends over time
- Highlighting forecast deviations and alert thresholds
- Using heat maps to visualise regional performance risks
- Building interactive drill-down reports for detailed analysis
- Exporting dashboards to PDF or PowerPoint for board meetings
- Setting up automated dashboard refresh cycles
- Configuring role-based dashboard views for stakeholders
- Incorporating commentary and assumptions into visuals
- Using forecast overlays for strategic planning sessions
- Embedding dashboard widgets into intranet portals
- Sharing secure links with remote executives and advisors
- Creating printable one-page forecast snapshots
- Documenting dashboard logic for compliance purposes
Module 13: AI Forecast Governance and Audit Readiness - Establishing ownership and accountability for AI forecasts
- Creating a forecasting governance charter
- Documenting model inputs, assumptions, and configurations
- Implementing version control for forecast models
- Setting up model approval workflows for changes
- Conducting peer reviews of forecast logic and outputs
- Archiving historical forecast versions for audit trails
- Ensuring compliance with SOX, GDPR, and financial regulations
- Generating model validation reports for internal audit
- Preparing for external auditor inquiries about AI use
- Managing access controls for forecast data and tools
- Logging user actions within the forecasting environment
- Establishing escalation paths for forecast discrepancies
- Training finance teams on responsible forecasting practices
- Auditing model performance against business outcomes
Module 14: Change Management and Stakeholder Adoption - Communicating the value of AI forecasting to non-technical leaders
- Managing resistance from teams used to manual processes
- Running pilot projects to demonstrate forecast accuracy gains
- Training controllers and analysts on new forecasting methods
- Creating user guides and quick-reference job aids
- Setting up feedback loops for continuous improvement
- Defining key adoption metrics and tracking progress
- Involving IT teams in integration and support planning
- Establishing a centre of excellence for financial forecasting
- Scheduling regular forecast review cadences
- Onboarding new team members using standardised playbooks
- Measuring user proficiency and confidence post-implementation
- Recognising and rewarding successful forecast accuracy
- Scaling best practices across divisions and geographies
- Creating a roadmap for ongoing forecasting maturity
Module 15: Advanced Integration with External Systems - Connecting Dynamics 365 to ERP and CRM systems
- Integrating with Excel for ad hoc analysis and reporting
- Using Power Automate to streamline data flows
- Importing macroeconomic indicators from external sources
- Linking to bank APIs for real-time transaction feeds
- Syncing with HR systems for headcount forecasting
- Connecting to project management tools for cost tracking
- Using Azure Logic Apps for complex data pipelines
- Setting up data synchronisation schedules and monitors
- Handling data transformation during integration
- Validating data consistency across systems
- Managing error handling and reconciliation processes
- Ensuring compliance in cross-system data sharing
- Documenting integration architecture for IT review
- Using integration for real-time forecast updates
Module 16: Certification Readiness and Real-World Project - Reviewing all core concepts for certification exam preparation
- Navigating the final assessment structure and format
- Practising scenario-based questions with detailed feedback
- Building a complete AI-driven forecast for a sample organisation
- Selecting appropriate models based on business context
- Configuring the forecasting environment from scratch
- Importing and cleansing sample financial datasets
- Generating multi-scenario forecasts with commentary
- Creating executive dashboards and visual outputs
- Documenting all assumptions, configurations, and results
- Submitting the project for review against rubric criteria
- Receiving structured feedback on strengths and improvements
- Preparing for real-world implementation in your organisation
- Developing a 30-day rollout plan for your team
- Earning your Certificate of Completion issued by The Art of Service
- Configuring AI models for revenue forecasting by product line
- Linking sales pipeline data from Dynamics 365 Sales to forecasts
- Weighting opportunities by probability and stage maturity
- Incorporating seasonality into subscription and licence revenue
- Modelling contract renewals and expansion revenue
- Forecasting revenue for project-based and time-and-materials work
- Adjusting forecasts for currency fluctuations and pricing changes
- Handling multi-year contracts with variable deliverables
- Incorporating customer churn and retention rates into predictions
- Using historical win rates to refine forecast accuracy
- Validating forecasts against CRM conversion benchmarks
- Forecasting revenue for new markets and product launches
- Aligning revenue forecasts with general ledger recognition rules
- Creating regional revenue breakdowns with local drivers
- Documenting forecast rationale for revenue assurance audits
Module 10: Expense and Cost Forecasting Optimisation - Building AI models for operating expense forecasting
- Separating fixed, variable, and semi-variable cost behaviours
- Forecasting payroll costs with headcount and benefit inputs
- Predicting utility and overhead escalation patterns
- Modelling one-time and project-based expenses
- Forecasting procurement and vendor spend trends
- Incorporating purchase order data into expense models
- Predicting travel and entertainment spend based on historical usage
- Using budget vs actual data to refine forecast assumptions
- Forecasting depreciation and amortisation schedules
- Modelling R&D and capitalised expense pipelines
- Adjusting forecasts for inflation or commodity price shifts
- Creating zero-based forecasting iterations using AI support
- Monitoring forecast accuracy across cost centres
- Linking cost forecasts to performance dashboards
Module 11: Integrating Forecast Data into Financial Statements - Mapping forecast outputs to income statement line items
- Translating revenue and expense forecasts into P&L projections
- Generating AI-supported balance sheet estimates
- Forecasting working capital components: AR, inventory, AP
- Projecting debt and equity movements based on forecasts
- Creating rolling financial statements using updated forecasts
- Automating financial report generation from forecast models
- Linking forecasts to budgeting and planning cycles
- Aligning forecast data with GAAP and IFRS reporting standards
- Setting audit trails for forecast-influenced financial entries
- Using forecast scenarios to stress test financial health
- Exporting forecast-based reports for external disclosure
- Reconciling forecast assumptions with management commentary
- Presenting forecasted statements to audit committees
- Versioning forecasted financials for regulatory submissions
Module 12: Dashboard Visualisation and Executive Reporting - Designing executive dashboards using Power BI integration
- Creating visual KPI summaries from forecast models
- Displaying forecast accuracy trends over time
- Highlighting forecast deviations and alert thresholds
- Using heat maps to visualise regional performance risks
- Building interactive drill-down reports for detailed analysis
- Exporting dashboards to PDF or PowerPoint for board meetings
- Setting up automated dashboard refresh cycles
- Configuring role-based dashboard views for stakeholders
- Incorporating commentary and assumptions into visuals
- Using forecast overlays for strategic planning sessions
- Embedding dashboard widgets into intranet portals
- Sharing secure links with remote executives and advisors
- Creating printable one-page forecast snapshots
- Documenting dashboard logic for compliance purposes
Module 13: AI Forecast Governance and Audit Readiness - Establishing ownership and accountability for AI forecasts
- Creating a forecasting governance charter
- Documenting model inputs, assumptions, and configurations
- Implementing version control for forecast models
- Setting up model approval workflows for changes
- Conducting peer reviews of forecast logic and outputs
- Archiving historical forecast versions for audit trails
- Ensuring compliance with SOX, GDPR, and financial regulations
- Generating model validation reports for internal audit
- Preparing for external auditor inquiries about AI use
- Managing access controls for forecast data and tools
- Logging user actions within the forecasting environment
- Establishing escalation paths for forecast discrepancies
- Training finance teams on responsible forecasting practices
- Auditing model performance against business outcomes
Module 14: Change Management and Stakeholder Adoption - Communicating the value of AI forecasting to non-technical leaders
- Managing resistance from teams used to manual processes
- Running pilot projects to demonstrate forecast accuracy gains
- Training controllers and analysts on new forecasting methods
- Creating user guides and quick-reference job aids
- Setting up feedback loops for continuous improvement
- Defining key adoption metrics and tracking progress
- Involving IT teams in integration and support planning
- Establishing a centre of excellence for financial forecasting
- Scheduling regular forecast review cadences
- Onboarding new team members using standardised playbooks
- Measuring user proficiency and confidence post-implementation
- Recognising and rewarding successful forecast accuracy
- Scaling best practices across divisions and geographies
- Creating a roadmap for ongoing forecasting maturity
Module 15: Advanced Integration with External Systems - Connecting Dynamics 365 to ERP and CRM systems
- Integrating with Excel for ad hoc analysis and reporting
- Using Power Automate to streamline data flows
- Importing macroeconomic indicators from external sources
- Linking to bank APIs for real-time transaction feeds
- Syncing with HR systems for headcount forecasting
- Connecting to project management tools for cost tracking
- Using Azure Logic Apps for complex data pipelines
- Setting up data synchronisation schedules and monitors
- Handling data transformation during integration
- Validating data consistency across systems
- Managing error handling and reconciliation processes
- Ensuring compliance in cross-system data sharing
- Documenting integration architecture for IT review
- Using integration for real-time forecast updates
Module 16: Certification Readiness and Real-World Project - Reviewing all core concepts for certification exam preparation
- Navigating the final assessment structure and format
- Practising scenario-based questions with detailed feedback
- Building a complete AI-driven forecast for a sample organisation
- Selecting appropriate models based on business context
- Configuring the forecasting environment from scratch
- Importing and cleansing sample financial datasets
- Generating multi-scenario forecasts with commentary
- Creating executive dashboards and visual outputs
- Documenting all assumptions, configurations, and results
- Submitting the project for review against rubric criteria
- Receiving structured feedback on strengths and improvements
- Preparing for real-world implementation in your organisation
- Developing a 30-day rollout plan for your team
- Earning your Certificate of Completion issued by The Art of Service
- Mapping forecast outputs to income statement line items
- Translating revenue and expense forecasts into P&L projections
- Generating AI-supported balance sheet estimates
- Forecasting working capital components: AR, inventory, AP
- Projecting debt and equity movements based on forecasts
- Creating rolling financial statements using updated forecasts
- Automating financial report generation from forecast models
- Linking forecasts to budgeting and planning cycles
- Aligning forecast data with GAAP and IFRS reporting standards
- Setting audit trails for forecast-influenced financial entries
- Using forecast scenarios to stress test financial health
- Exporting forecast-based reports for external disclosure
- Reconciling forecast assumptions with management commentary
- Presenting forecasted statements to audit committees
- Versioning forecasted financials for regulatory submissions
Module 12: Dashboard Visualisation and Executive Reporting - Designing executive dashboards using Power BI integration
- Creating visual KPI summaries from forecast models
- Displaying forecast accuracy trends over time
- Highlighting forecast deviations and alert thresholds
- Using heat maps to visualise regional performance risks
- Building interactive drill-down reports for detailed analysis
- Exporting dashboards to PDF or PowerPoint for board meetings
- Setting up automated dashboard refresh cycles
- Configuring role-based dashboard views for stakeholders
- Incorporating commentary and assumptions into visuals
- Using forecast overlays for strategic planning sessions
- Embedding dashboard widgets into intranet portals
- Sharing secure links with remote executives and advisors
- Creating printable one-page forecast snapshots
- Documenting dashboard logic for compliance purposes
Module 13: AI Forecast Governance and Audit Readiness - Establishing ownership and accountability for AI forecasts
- Creating a forecasting governance charter
- Documenting model inputs, assumptions, and configurations
- Implementing version control for forecast models
- Setting up model approval workflows for changes
- Conducting peer reviews of forecast logic and outputs
- Archiving historical forecast versions for audit trails
- Ensuring compliance with SOX, GDPR, and financial regulations
- Generating model validation reports for internal audit
- Preparing for external auditor inquiries about AI use
- Managing access controls for forecast data and tools
- Logging user actions within the forecasting environment
- Establishing escalation paths for forecast discrepancies
- Training finance teams on responsible forecasting practices
- Auditing model performance against business outcomes
Module 14: Change Management and Stakeholder Adoption - Communicating the value of AI forecasting to non-technical leaders
- Managing resistance from teams used to manual processes
- Running pilot projects to demonstrate forecast accuracy gains
- Training controllers and analysts on new forecasting methods
- Creating user guides and quick-reference job aids
- Setting up feedback loops for continuous improvement
- Defining key adoption metrics and tracking progress
- Involving IT teams in integration and support planning
- Establishing a centre of excellence for financial forecasting
- Scheduling regular forecast review cadences
- Onboarding new team members using standardised playbooks
- Measuring user proficiency and confidence post-implementation
- Recognising and rewarding successful forecast accuracy
- Scaling best practices across divisions and geographies
- Creating a roadmap for ongoing forecasting maturity
Module 15: Advanced Integration with External Systems - Connecting Dynamics 365 to ERP and CRM systems
- Integrating with Excel for ad hoc analysis and reporting
- Using Power Automate to streamline data flows
- Importing macroeconomic indicators from external sources
- Linking to bank APIs for real-time transaction feeds
- Syncing with HR systems for headcount forecasting
- Connecting to project management tools for cost tracking
- Using Azure Logic Apps for complex data pipelines
- Setting up data synchronisation schedules and monitors
- Handling data transformation during integration
- Validating data consistency across systems
- Managing error handling and reconciliation processes
- Ensuring compliance in cross-system data sharing
- Documenting integration architecture for IT review
- Using integration for real-time forecast updates
Module 16: Certification Readiness and Real-World Project - Reviewing all core concepts for certification exam preparation
- Navigating the final assessment structure and format
- Practising scenario-based questions with detailed feedback
- Building a complete AI-driven forecast for a sample organisation
- Selecting appropriate models based on business context
- Configuring the forecasting environment from scratch
- Importing and cleansing sample financial datasets
- Generating multi-scenario forecasts with commentary
- Creating executive dashboards and visual outputs
- Documenting all assumptions, configurations, and results
- Submitting the project for review against rubric criteria
- Receiving structured feedback on strengths and improvements
- Preparing for real-world implementation in your organisation
- Developing a 30-day rollout plan for your team
- Earning your Certificate of Completion issued by The Art of Service
- Establishing ownership and accountability for AI forecasts
- Creating a forecasting governance charter
- Documenting model inputs, assumptions, and configurations
- Implementing version control for forecast models
- Setting up model approval workflows for changes
- Conducting peer reviews of forecast logic and outputs
- Archiving historical forecast versions for audit trails
- Ensuring compliance with SOX, GDPR, and financial regulations
- Generating model validation reports for internal audit
- Preparing for external auditor inquiries about AI use
- Managing access controls for forecast data and tools
- Logging user actions within the forecasting environment
- Establishing escalation paths for forecast discrepancies
- Training finance teams on responsible forecasting practices
- Auditing model performance against business outcomes
Module 14: Change Management and Stakeholder Adoption - Communicating the value of AI forecasting to non-technical leaders
- Managing resistance from teams used to manual processes
- Running pilot projects to demonstrate forecast accuracy gains
- Training controllers and analysts on new forecasting methods
- Creating user guides and quick-reference job aids
- Setting up feedback loops for continuous improvement
- Defining key adoption metrics and tracking progress
- Involving IT teams in integration and support planning
- Establishing a centre of excellence for financial forecasting
- Scheduling regular forecast review cadences
- Onboarding new team members using standardised playbooks
- Measuring user proficiency and confidence post-implementation
- Recognising and rewarding successful forecast accuracy
- Scaling best practices across divisions and geographies
- Creating a roadmap for ongoing forecasting maturity
Module 15: Advanced Integration with External Systems - Connecting Dynamics 365 to ERP and CRM systems
- Integrating with Excel for ad hoc analysis and reporting
- Using Power Automate to streamline data flows
- Importing macroeconomic indicators from external sources
- Linking to bank APIs for real-time transaction feeds
- Syncing with HR systems for headcount forecasting
- Connecting to project management tools for cost tracking
- Using Azure Logic Apps for complex data pipelines
- Setting up data synchronisation schedules and monitors
- Handling data transformation during integration
- Validating data consistency across systems
- Managing error handling and reconciliation processes
- Ensuring compliance in cross-system data sharing
- Documenting integration architecture for IT review
- Using integration for real-time forecast updates
Module 16: Certification Readiness and Real-World Project - Reviewing all core concepts for certification exam preparation
- Navigating the final assessment structure and format
- Practising scenario-based questions with detailed feedback
- Building a complete AI-driven forecast for a sample organisation
- Selecting appropriate models based on business context
- Configuring the forecasting environment from scratch
- Importing and cleansing sample financial datasets
- Generating multi-scenario forecasts with commentary
- Creating executive dashboards and visual outputs
- Documenting all assumptions, configurations, and results
- Submitting the project for review against rubric criteria
- Receiving structured feedback on strengths and improvements
- Preparing for real-world implementation in your organisation
- Developing a 30-day rollout plan for your team
- Earning your Certificate of Completion issued by The Art of Service
- Connecting Dynamics 365 to ERP and CRM systems
- Integrating with Excel for ad hoc analysis and reporting
- Using Power Automate to streamline data flows
- Importing macroeconomic indicators from external sources
- Linking to bank APIs for real-time transaction feeds
- Syncing with HR systems for headcount forecasting
- Connecting to project management tools for cost tracking
- Using Azure Logic Apps for complex data pipelines
- Setting up data synchronisation schedules and monitors
- Handling data transformation during integration
- Validating data consistency across systems
- Managing error handling and reconciliation processes
- Ensuring compliance in cross-system data sharing
- Documenting integration architecture for IT review
- Using integration for real-time forecast updates