Mastering AI-Driven Power BI Automation for Enterprise Decision Making
You're not behind. But you're not ahead either. And in today’s data-driven enterprises, standing still is falling behind. Every day you delay mastering AI-powered automation in Power BI, you risk being overlooked for high-impact projects, promotions, or the next strategic initiative that defines careers. Data teams are under pressure to deliver faster insights, automate repetitive reporting, and uncover predictive intelligence - all without increasing headcount. If you’ve ever felt bogged down by manual dashboards, reactive analytics, or unclear ROI from your Power BI investments, this is your turning point. Mastering AI-Driven Power BI Automation for Enterprise Decision Making is not another technical tutorial. It’s a battle-tested blueprint for professionals who want to transform from report builders into decision architects. This program gives you the exact frameworks, toolkits, and strategic methodology to go from idea to board-ready AI-automated Power BI solution in under 30 days. One senior analyst at a Fortune 500 insurance provider used these exact methods to cut monthly financial reporting time from 22 hours to 45 minutes, embedding predictive churn models directly into executive dashboards. She was fast-tracked for leadership training and led her first cross-functional team within 90 days. The tools are available. The data exists. What’s missing is a proven system that connects AI capabilities with real business outcomes. This course is that system. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced. Immediate Access. Zero Time Conflicts.
This course is designed for working professionals who need flexibility without sacrificing results. You gain on-demand access to the full program the moment you enroll. Study at your own pace, on your own schedule, with no mandatory live sessions, deadlines, or time zone constraints. Most learners complete the core curriculum in 28 to 35 hours, spread over 3 to 5 weeks depending on workload. Many report building their first fully automated AI-integrated Power BI dashboard within 10 days of starting. Lifetime Access & Future-Proof Learning
You’re not buying a one-time course. You’re investing in lifetime access to an evolving curriculum. As Microsoft updates Power BI and AI services, we update the content - at no additional cost. Your access never expires. Revisit modules, re-download templates, re-engage with refreshed workflows whenever you need. All materials are mobile-friendly and accessible 24/7 from any device. Whether you're refining a DAX expression on your phone during transit or reviewing M code on your tablet at home, your learning moves with you. Direct Instructor Guidance & Support
You’re not navigating this alone. Enrolled learners receive direct guidance through structured feedback loops, clarifications on advanced automation logic, and access to a private support channel managed by our Power BI engineering mentors - all with a 24-hour response window for critical implementation questions. Certification That Commands Attention
Upon completion, you’ll earn a Certificate of Completion issued by The Art of Service - a globally recognised credential trusted by enterprises, auditors, and hiring managers across 117 countries. This is not a participation trophy. It's verification that you can design, deploy, and govern AI-driven automation within enterprise Power BI environments. No Hidden Fees. No Surprises.
Pricing is straightforward. One upfront investment covers everything. No recurring charges. No premium tiers. No upsells. You get full access to all modules, resources, templates, and updates immediately. - Visa
- Mastercard
- PayPal
100% Satisfaction Guaranteed: Your Risk, Eliminated
We remove the risk entirely. If you complete the first two modules and feel this course isn’t delivering exceptional value, contact support for a full refund - no questions, no friction. This promise ensures confidence before, during, and after your enrollment. Will This Work for Me?
Yes - even if you’re new to AI, still building confidence in Power Query, or working within strict IT governance policies. This course was built for diverse enterprise realities. You'll find role-specific pathways for: - Power BI developers needing to scale their output
- Business analysts transitioning into predictive analytics
- BI managers aiming to reduce report latency
- Data stewards ensuring governance in AI automation
- Technical consultants delivering faster client outcomes
This works even if: your organisation hasn't adopted AI broadly, you’re not a data scientist, your datasets are messy, or you've failed with Power Automate or Azure ML integrations before. After enrollment, you’ll receive a confirmation email. Your detailed access instructions and learning portal login will follow separately once your course materials are fully prepared. Your journey begins the moment you open the first module.
Module 1: Foundations of AI-Enhanced Power BI in the Enterprise - Understanding the shift from reactive reporting to proactive decision intelligence
- Mapping Power BI automation to enterprise objectives: ROI, agility, compliance
- Defining AI in the context of Power BI: what’s possible, what’s practical
- Overview of Microsoft’s AI ecosystem: Copilot, Cognitive Services, Azure ML
- Identifying high-impact automation opportunities within your organisation
- Assessing current Power BI maturity: where your team stands
- Establishing success metrics for automation projects
- Navigating governance, security, and data ownership constraints
- Building stakeholder alignment: securing buy-in from IT, compliance, and leadership
- Creating your personal automation roadmap with 30-60-90 day milestones
Module 2: Power BI Architecture for Scalable Automation - Designing data models that support AI-driven insights
- Best practices for star schema optimisation in automated environments
- Configuring incremental refresh for large-scale datasets
- Setting up composite models for real-time and historical data blending
- Partitioning strategies to enhance performance and reduce load times
- Leveraging Power BI datasets as semantic layers for AI integration
- Implementing row-level security in automated report flows
- Version control for Power BI files using ALM and DevOps pipelines
- Documenting model relationships for auditability and transferability
- Architecting for multi-environment deployment: Dev, Test, Prod
Module 3: Advanced Data Transformation with Power Query - Automating data cleansing using custom transformation functions
- Creating reusable query templates for standardised inputs
- Dynamic column selection and renaming using parameters
- Error handling in Power Query: managing broken sources gracefully
- Calling REST APIs with authentication headers and batch logic
- Scheduling and monitoring dataflow refreshes via service settings
- Using List.Generate and List.Accumulate for complex logic
- Optimising query folding for SQL and Snowflake sources
- Parameterising connections for environment switching
- Building self-healing data pipelines with fallback logic
Module 4: Integrating AI Services into Power BI Workflows - Connecting Power BI to Azure Cognitive Services (Text Analytics, Vision, Language)
- Calling Azure Machine Learning endpoints from Power Query
- Using the Power BI Copilot feature for insight generation
- Embedding pre-trained models for sentiment, entity, and key phrase extraction
- Configuring custom AI models via Bring Your Own Container (BYOC)
- Building No-Code AI workflows using Power Automate and AI Builder
- Scoring customer data using predictive models embedded in reports
- Automating anomaly detection across time-series metrics
- Implementing language translation pipelines for global dashboards
- Validating AI outputs against ground-truth datasets
Module 5: Automating Report Generation and Distribution - Creating template-based reports using Power BI’s report themes
- Using paginated reports for regulatory and financial outputs
- Scheduling batch report exports to PDF, PowerPoint, Excel
- Automating distribution via Power Automate and email triggers
- Generating compliant report packages with metadata and audit trails
- Dynamic recipient routing based on organisational hierarchies
- Setting conditional delivery rules: threshold-based alerts
- Archiving reports to SharePoint or OneDrive in structured folders
- Tracking delivery status and failure recovery workflows
- Integrating with Microsoft Teams for proactive notifications
Module 6: Dynamic Dashboard Personalisation and User Adaptation - Building role-tailored dashboards using RLS and user context
- Creating adaptive layouts based on user device and screen size
- Implementing bookmark-driven navigation flows for guided analysis
- Automating dashboard personalisation using user preference data
- Tracking user interactions for behaviour-based dashboard optimisation
- Using What-If parameters to enable scenario planning
- Embedding natural language Q&A with custom synonyms
- Configuring drill-through filters with dynamic context
- Adding dynamic titles and KPI labels using DAX
- Designing tooltips that surface AI-derived insights
Module 7: DAX and Measures for Intelligent Automation - Writing dynamic measures that adapt to filter context
- Using CALCULATE and FILTER with complex business logic
- Time intelligence patterns for YoY, QoQ, rolling averages
- Creating forecast measures using linear regression in DAX
- Implementing dynamic variance analysis across business units
- Building reusable measure templates with standard naming
- Leveraging variables to improve performance and readability
- Optimising measure groups for query performance
- Using SWITCH TRUE patterns for multi-condition logic
- Testing and validating DAX logic with sample datasets
Module 8: Power Automate Integration for End-to-End Workflows - Connecting Power BI alerts to Power Automate flows
- Triggering actions based on data thresholds and anomalies
- Updating SharePoint lists or SQL tables from Power BI events
- Creating approval workflows for dashboard changes
- Syncing Power BI comments with internal ticketing systems
- Automating workspace provisioning for new projects
- Scheduling metadata audits using flow-driven refresh checks
- Sending proactive insights to Slack or Teams channels
- Generating AI-powered summaries from report data
- Logging automation activity for compliance and troubleshooting
Module 9: Data Governance and Compliance in Automated Environments - Implementing data classification and sensitivity labelling
- Configuring Microsoft Purview integration for Power BI
- Tracking data lineage from source to insight
- Enforcing usage compliance with Power BI audit logs
- Managing capacity workloads to prevent resource overuse
- Setting up data loss prevention (DLP) policies
- Auditing AI model usage and decision transparency
- Documenting model assumptions and limitations
- Creating an AI ethics checklist for automation projects
- Preparing for regulatory audits with standard operating procedures
Module 10: Building Predictive Analytics Pipelines - Designing churn prediction models using historical data
- Creating customer lifetime value (CLV) projections
- Forecasting demand using time-series decomposition
- Implementing clustering for customer segmentation
- Integrating predictive scores into operational dashboards
- Evaluating model accuracy using confusion matrices
- Automating retraining cycles based on data drift
- Setting up monitoring for model performance decay
- Linking predictions to action plans in Power Apps
- Validating predictive outputs against actual business outcomes
Module 11: Real-Time Data Streaming and Monitoring - Connecting Power BI to Azure Event Hubs for real-time ingestion
- Configuring streaming datasets for live dashboards
- Creating real-time KPIs for operational control rooms
- Setting dynamic thresholds for alerting on live data
- Integrating IoT device data into enterprise visualisations
- Optimising visual refresh rates for performance
- Reducing latency in streaming data pipelines
- Handling disconnections and data gaps gracefully
- Adding time-window filters for live trend analysis
- Exporting streaming snapshots for post-event review
Module 12: Custom Visual Development for Automation - Importing and configuring certified custom visuals
- Built-in visuals: smart narratives, decomposition trees, key influencers
- Developing parameterised custom visuals using Power BI Dev Tools
- Embedding AI-generated explanations into visual tooltips
- Creating reusable visual templates for standard reports
- Ensuring accessibility and screen reader compatibility
- Testing visuals across devices and resolutions
- Managing visual certification and approval workflows
- Deploying private visuals via organisational apps
- Tracking visual usage and adoption across workspaces
Module 13: Automation Orchestration with Azure Logic Apps - Scaling beyond Power Automate with enterprise-grade workflows
- Calling Power BI REST APIs using Logic Apps HTTP actions
- Scheduling complex ETL sequences with dependency chains
- Orchestrating multi-step data prep and model refresh cycles
- Integrating on-premises data gateways into cloud workflows
- Handling retry policies and error escalation paths
- Monitoring workflow execution with Azure Monitor
- Reducing manual intervention in monthly close processes
- Generating executive summaries automatically at period end
- Linking financial data to board reporting calendars
Module 14: Performance Optimisation and Load Testing - Analysing report load times using Performance Analyzer
- Identifying inefficient DAX expressions and visual queries
- Minimising data model size through column pruning
- Choosing appropriate visual types for large datasets
- Testing dashboard performance under concurrent user load
- Implementing caching strategies for frequently used reports
- Monitoring memory and CPU usage in Power BI Premium
- Setting up alerts for performance degradation
- Using query diagnostics to trace backend execution
- Writing efficient M code that minimises redundant steps
Module 15: Change Management and Organisational Adoption - Developing a communication plan for new automation rollouts
- Creating user training kits for AI-enhanced dashboards
- Running pilot programs with high-visibility stakeholders
- Gathering feedback and iterating on dashboard design
- Measuring adoption through usage analytics and telemetry
- Debriefing post-launch: what worked, what didn’t
- Establishing centres of excellence for Power BI governance
- Certifying internal Power BI champions and super users
- Aligning automation with enterprise digital transformation goals
- Scaling success across departments and geographies
Module 16: Building Your AI-Driven Dashboard Portfolio - Defining portfolio standards: consistency, branding, usability
- Selecting high-impact projects for inclusion
- Documenting business impact and ROI per dashboard
- Adding annotations to explain AI logic and automation triggers
- Incorporating before-and-after comparisons
- Creating executive summaries for non-technical reviewers
- Hosting your portfolio in a secure, accessible workspace
- Linking portfolio items to your Certificate of Completion
- Using the portfolio in performance reviews and job interviews
- Continuously updating with new automation examples
Module 17: Final Implementation Project: From Concept to Board-Ready - Selecting a real-world business problem for automation
- Defining success criteria and stakeholder expectations
- Designing the end-to-end data and AI pipeline
- Building the Power BI model with automated refresh logic
- Integrating at least one AI service (e.g., sentiment, forecasting)
- Configuring dynamic distribution and alerting
- Implementing governance and audit controls
- Writing a compelling executive summary and use case brief
- Presenting your solution in a board-ready format
- Receiving structured feedback from course mentors
Module 18: Certification, Career Advancement & Next Steps - Final assessment: demonstrating mastery of AI automation principles
- Uploading your capstone project for evaluation
- Receiving your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn, CVs, and professional profiles
- Accessing career advancement resources and job templates
- Joining the private alumni network of certified practitioners
- Receiving invitations to exclusive enterprise workshops
- Staying updated with monthly automation insights from our team
- Contributing to the community knowledge base
- Planning your next certification or specialisation pathway
- Understanding the shift from reactive reporting to proactive decision intelligence
- Mapping Power BI automation to enterprise objectives: ROI, agility, compliance
- Defining AI in the context of Power BI: what’s possible, what’s practical
- Overview of Microsoft’s AI ecosystem: Copilot, Cognitive Services, Azure ML
- Identifying high-impact automation opportunities within your organisation
- Assessing current Power BI maturity: where your team stands
- Establishing success metrics for automation projects
- Navigating governance, security, and data ownership constraints
- Building stakeholder alignment: securing buy-in from IT, compliance, and leadership
- Creating your personal automation roadmap with 30-60-90 day milestones
Module 2: Power BI Architecture for Scalable Automation - Designing data models that support AI-driven insights
- Best practices for star schema optimisation in automated environments
- Configuring incremental refresh for large-scale datasets
- Setting up composite models for real-time and historical data blending
- Partitioning strategies to enhance performance and reduce load times
- Leveraging Power BI datasets as semantic layers for AI integration
- Implementing row-level security in automated report flows
- Version control for Power BI files using ALM and DevOps pipelines
- Documenting model relationships for auditability and transferability
- Architecting for multi-environment deployment: Dev, Test, Prod
Module 3: Advanced Data Transformation with Power Query - Automating data cleansing using custom transformation functions
- Creating reusable query templates for standardised inputs
- Dynamic column selection and renaming using parameters
- Error handling in Power Query: managing broken sources gracefully
- Calling REST APIs with authentication headers and batch logic
- Scheduling and monitoring dataflow refreshes via service settings
- Using List.Generate and List.Accumulate for complex logic
- Optimising query folding for SQL and Snowflake sources
- Parameterising connections for environment switching
- Building self-healing data pipelines with fallback logic
Module 4: Integrating AI Services into Power BI Workflows - Connecting Power BI to Azure Cognitive Services (Text Analytics, Vision, Language)
- Calling Azure Machine Learning endpoints from Power Query
- Using the Power BI Copilot feature for insight generation
- Embedding pre-trained models for sentiment, entity, and key phrase extraction
- Configuring custom AI models via Bring Your Own Container (BYOC)
- Building No-Code AI workflows using Power Automate and AI Builder
- Scoring customer data using predictive models embedded in reports
- Automating anomaly detection across time-series metrics
- Implementing language translation pipelines for global dashboards
- Validating AI outputs against ground-truth datasets
Module 5: Automating Report Generation and Distribution - Creating template-based reports using Power BI’s report themes
- Using paginated reports for regulatory and financial outputs
- Scheduling batch report exports to PDF, PowerPoint, Excel
- Automating distribution via Power Automate and email triggers
- Generating compliant report packages with metadata and audit trails
- Dynamic recipient routing based on organisational hierarchies
- Setting conditional delivery rules: threshold-based alerts
- Archiving reports to SharePoint or OneDrive in structured folders
- Tracking delivery status and failure recovery workflows
- Integrating with Microsoft Teams for proactive notifications
Module 6: Dynamic Dashboard Personalisation and User Adaptation - Building role-tailored dashboards using RLS and user context
- Creating adaptive layouts based on user device and screen size
- Implementing bookmark-driven navigation flows for guided analysis
- Automating dashboard personalisation using user preference data
- Tracking user interactions for behaviour-based dashboard optimisation
- Using What-If parameters to enable scenario planning
- Embedding natural language Q&A with custom synonyms
- Configuring drill-through filters with dynamic context
- Adding dynamic titles and KPI labels using DAX
- Designing tooltips that surface AI-derived insights
Module 7: DAX and Measures for Intelligent Automation - Writing dynamic measures that adapt to filter context
- Using CALCULATE and FILTER with complex business logic
- Time intelligence patterns for YoY, QoQ, rolling averages
- Creating forecast measures using linear regression in DAX
- Implementing dynamic variance analysis across business units
- Building reusable measure templates with standard naming
- Leveraging variables to improve performance and readability
- Optimising measure groups for query performance
- Using SWITCH TRUE patterns for multi-condition logic
- Testing and validating DAX logic with sample datasets
Module 8: Power Automate Integration for End-to-End Workflows - Connecting Power BI alerts to Power Automate flows
- Triggering actions based on data thresholds and anomalies
- Updating SharePoint lists or SQL tables from Power BI events
- Creating approval workflows for dashboard changes
- Syncing Power BI comments with internal ticketing systems
- Automating workspace provisioning for new projects
- Scheduling metadata audits using flow-driven refresh checks
- Sending proactive insights to Slack or Teams channels
- Generating AI-powered summaries from report data
- Logging automation activity for compliance and troubleshooting
Module 9: Data Governance and Compliance in Automated Environments - Implementing data classification and sensitivity labelling
- Configuring Microsoft Purview integration for Power BI
- Tracking data lineage from source to insight
- Enforcing usage compliance with Power BI audit logs
- Managing capacity workloads to prevent resource overuse
- Setting up data loss prevention (DLP) policies
- Auditing AI model usage and decision transparency
- Documenting model assumptions and limitations
- Creating an AI ethics checklist for automation projects
- Preparing for regulatory audits with standard operating procedures
Module 10: Building Predictive Analytics Pipelines - Designing churn prediction models using historical data
- Creating customer lifetime value (CLV) projections
- Forecasting demand using time-series decomposition
- Implementing clustering for customer segmentation
- Integrating predictive scores into operational dashboards
- Evaluating model accuracy using confusion matrices
- Automating retraining cycles based on data drift
- Setting up monitoring for model performance decay
- Linking predictions to action plans in Power Apps
- Validating predictive outputs against actual business outcomes
Module 11: Real-Time Data Streaming and Monitoring - Connecting Power BI to Azure Event Hubs for real-time ingestion
- Configuring streaming datasets for live dashboards
- Creating real-time KPIs for operational control rooms
- Setting dynamic thresholds for alerting on live data
- Integrating IoT device data into enterprise visualisations
- Optimising visual refresh rates for performance
- Reducing latency in streaming data pipelines
- Handling disconnections and data gaps gracefully
- Adding time-window filters for live trend analysis
- Exporting streaming snapshots for post-event review
Module 12: Custom Visual Development for Automation - Importing and configuring certified custom visuals
- Built-in visuals: smart narratives, decomposition trees, key influencers
- Developing parameterised custom visuals using Power BI Dev Tools
- Embedding AI-generated explanations into visual tooltips
- Creating reusable visual templates for standard reports
- Ensuring accessibility and screen reader compatibility
- Testing visuals across devices and resolutions
- Managing visual certification and approval workflows
- Deploying private visuals via organisational apps
- Tracking visual usage and adoption across workspaces
Module 13: Automation Orchestration with Azure Logic Apps - Scaling beyond Power Automate with enterprise-grade workflows
- Calling Power BI REST APIs using Logic Apps HTTP actions
- Scheduling complex ETL sequences with dependency chains
- Orchestrating multi-step data prep and model refresh cycles
- Integrating on-premises data gateways into cloud workflows
- Handling retry policies and error escalation paths
- Monitoring workflow execution with Azure Monitor
- Reducing manual intervention in monthly close processes
- Generating executive summaries automatically at period end
- Linking financial data to board reporting calendars
Module 14: Performance Optimisation and Load Testing - Analysing report load times using Performance Analyzer
- Identifying inefficient DAX expressions and visual queries
- Minimising data model size through column pruning
- Choosing appropriate visual types for large datasets
- Testing dashboard performance under concurrent user load
- Implementing caching strategies for frequently used reports
- Monitoring memory and CPU usage in Power BI Premium
- Setting up alerts for performance degradation
- Using query diagnostics to trace backend execution
- Writing efficient M code that minimises redundant steps
Module 15: Change Management and Organisational Adoption - Developing a communication plan for new automation rollouts
- Creating user training kits for AI-enhanced dashboards
- Running pilot programs with high-visibility stakeholders
- Gathering feedback and iterating on dashboard design
- Measuring adoption through usage analytics and telemetry
- Debriefing post-launch: what worked, what didn’t
- Establishing centres of excellence for Power BI governance
- Certifying internal Power BI champions and super users
- Aligning automation with enterprise digital transformation goals
- Scaling success across departments and geographies
Module 16: Building Your AI-Driven Dashboard Portfolio - Defining portfolio standards: consistency, branding, usability
- Selecting high-impact projects for inclusion
- Documenting business impact and ROI per dashboard
- Adding annotations to explain AI logic and automation triggers
- Incorporating before-and-after comparisons
- Creating executive summaries for non-technical reviewers
- Hosting your portfolio in a secure, accessible workspace
- Linking portfolio items to your Certificate of Completion
- Using the portfolio in performance reviews and job interviews
- Continuously updating with new automation examples
Module 17: Final Implementation Project: From Concept to Board-Ready - Selecting a real-world business problem for automation
- Defining success criteria and stakeholder expectations
- Designing the end-to-end data and AI pipeline
- Building the Power BI model with automated refresh logic
- Integrating at least one AI service (e.g., sentiment, forecasting)
- Configuring dynamic distribution and alerting
- Implementing governance and audit controls
- Writing a compelling executive summary and use case brief
- Presenting your solution in a board-ready format
- Receiving structured feedback from course mentors
Module 18: Certification, Career Advancement & Next Steps - Final assessment: demonstrating mastery of AI automation principles
- Uploading your capstone project for evaluation
- Receiving your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn, CVs, and professional profiles
- Accessing career advancement resources and job templates
- Joining the private alumni network of certified practitioners
- Receiving invitations to exclusive enterprise workshops
- Staying updated with monthly automation insights from our team
- Contributing to the community knowledge base
- Planning your next certification or specialisation pathway
- Automating data cleansing using custom transformation functions
- Creating reusable query templates for standardised inputs
- Dynamic column selection and renaming using parameters
- Error handling in Power Query: managing broken sources gracefully
- Calling REST APIs with authentication headers and batch logic
- Scheduling and monitoring dataflow refreshes via service settings
- Using List.Generate and List.Accumulate for complex logic
- Optimising query folding for SQL and Snowflake sources
- Parameterising connections for environment switching
- Building self-healing data pipelines with fallback logic
Module 4: Integrating AI Services into Power BI Workflows - Connecting Power BI to Azure Cognitive Services (Text Analytics, Vision, Language)
- Calling Azure Machine Learning endpoints from Power Query
- Using the Power BI Copilot feature for insight generation
- Embedding pre-trained models for sentiment, entity, and key phrase extraction
- Configuring custom AI models via Bring Your Own Container (BYOC)
- Building No-Code AI workflows using Power Automate and AI Builder
- Scoring customer data using predictive models embedded in reports
- Automating anomaly detection across time-series metrics
- Implementing language translation pipelines for global dashboards
- Validating AI outputs against ground-truth datasets
Module 5: Automating Report Generation and Distribution - Creating template-based reports using Power BI’s report themes
- Using paginated reports for regulatory and financial outputs
- Scheduling batch report exports to PDF, PowerPoint, Excel
- Automating distribution via Power Automate and email triggers
- Generating compliant report packages with metadata and audit trails
- Dynamic recipient routing based on organisational hierarchies
- Setting conditional delivery rules: threshold-based alerts
- Archiving reports to SharePoint or OneDrive in structured folders
- Tracking delivery status and failure recovery workflows
- Integrating with Microsoft Teams for proactive notifications
Module 6: Dynamic Dashboard Personalisation and User Adaptation - Building role-tailored dashboards using RLS and user context
- Creating adaptive layouts based on user device and screen size
- Implementing bookmark-driven navigation flows for guided analysis
- Automating dashboard personalisation using user preference data
- Tracking user interactions for behaviour-based dashboard optimisation
- Using What-If parameters to enable scenario planning
- Embedding natural language Q&A with custom synonyms
- Configuring drill-through filters with dynamic context
- Adding dynamic titles and KPI labels using DAX
- Designing tooltips that surface AI-derived insights
Module 7: DAX and Measures for Intelligent Automation - Writing dynamic measures that adapt to filter context
- Using CALCULATE and FILTER with complex business logic
- Time intelligence patterns for YoY, QoQ, rolling averages
- Creating forecast measures using linear regression in DAX
- Implementing dynamic variance analysis across business units
- Building reusable measure templates with standard naming
- Leveraging variables to improve performance and readability
- Optimising measure groups for query performance
- Using SWITCH TRUE patterns for multi-condition logic
- Testing and validating DAX logic with sample datasets
Module 8: Power Automate Integration for End-to-End Workflows - Connecting Power BI alerts to Power Automate flows
- Triggering actions based on data thresholds and anomalies
- Updating SharePoint lists or SQL tables from Power BI events
- Creating approval workflows for dashboard changes
- Syncing Power BI comments with internal ticketing systems
- Automating workspace provisioning for new projects
- Scheduling metadata audits using flow-driven refresh checks
- Sending proactive insights to Slack or Teams channels
- Generating AI-powered summaries from report data
- Logging automation activity for compliance and troubleshooting
Module 9: Data Governance and Compliance in Automated Environments - Implementing data classification and sensitivity labelling
- Configuring Microsoft Purview integration for Power BI
- Tracking data lineage from source to insight
- Enforcing usage compliance with Power BI audit logs
- Managing capacity workloads to prevent resource overuse
- Setting up data loss prevention (DLP) policies
- Auditing AI model usage and decision transparency
- Documenting model assumptions and limitations
- Creating an AI ethics checklist for automation projects
- Preparing for regulatory audits with standard operating procedures
Module 10: Building Predictive Analytics Pipelines - Designing churn prediction models using historical data
- Creating customer lifetime value (CLV) projections
- Forecasting demand using time-series decomposition
- Implementing clustering for customer segmentation
- Integrating predictive scores into operational dashboards
- Evaluating model accuracy using confusion matrices
- Automating retraining cycles based on data drift
- Setting up monitoring for model performance decay
- Linking predictions to action plans in Power Apps
- Validating predictive outputs against actual business outcomes
Module 11: Real-Time Data Streaming and Monitoring - Connecting Power BI to Azure Event Hubs for real-time ingestion
- Configuring streaming datasets for live dashboards
- Creating real-time KPIs for operational control rooms
- Setting dynamic thresholds for alerting on live data
- Integrating IoT device data into enterprise visualisations
- Optimising visual refresh rates for performance
- Reducing latency in streaming data pipelines
- Handling disconnections and data gaps gracefully
- Adding time-window filters for live trend analysis
- Exporting streaming snapshots for post-event review
Module 12: Custom Visual Development for Automation - Importing and configuring certified custom visuals
- Built-in visuals: smart narratives, decomposition trees, key influencers
- Developing parameterised custom visuals using Power BI Dev Tools
- Embedding AI-generated explanations into visual tooltips
- Creating reusable visual templates for standard reports
- Ensuring accessibility and screen reader compatibility
- Testing visuals across devices and resolutions
- Managing visual certification and approval workflows
- Deploying private visuals via organisational apps
- Tracking visual usage and adoption across workspaces
Module 13: Automation Orchestration with Azure Logic Apps - Scaling beyond Power Automate with enterprise-grade workflows
- Calling Power BI REST APIs using Logic Apps HTTP actions
- Scheduling complex ETL sequences with dependency chains
- Orchestrating multi-step data prep and model refresh cycles
- Integrating on-premises data gateways into cloud workflows
- Handling retry policies and error escalation paths
- Monitoring workflow execution with Azure Monitor
- Reducing manual intervention in monthly close processes
- Generating executive summaries automatically at period end
- Linking financial data to board reporting calendars
Module 14: Performance Optimisation and Load Testing - Analysing report load times using Performance Analyzer
- Identifying inefficient DAX expressions and visual queries
- Minimising data model size through column pruning
- Choosing appropriate visual types for large datasets
- Testing dashboard performance under concurrent user load
- Implementing caching strategies for frequently used reports
- Monitoring memory and CPU usage in Power BI Premium
- Setting up alerts for performance degradation
- Using query diagnostics to trace backend execution
- Writing efficient M code that minimises redundant steps
Module 15: Change Management and Organisational Adoption - Developing a communication plan for new automation rollouts
- Creating user training kits for AI-enhanced dashboards
- Running pilot programs with high-visibility stakeholders
- Gathering feedback and iterating on dashboard design
- Measuring adoption through usage analytics and telemetry
- Debriefing post-launch: what worked, what didn’t
- Establishing centres of excellence for Power BI governance
- Certifying internal Power BI champions and super users
- Aligning automation with enterprise digital transformation goals
- Scaling success across departments and geographies
Module 16: Building Your AI-Driven Dashboard Portfolio - Defining portfolio standards: consistency, branding, usability
- Selecting high-impact projects for inclusion
- Documenting business impact and ROI per dashboard
- Adding annotations to explain AI logic and automation triggers
- Incorporating before-and-after comparisons
- Creating executive summaries for non-technical reviewers
- Hosting your portfolio in a secure, accessible workspace
- Linking portfolio items to your Certificate of Completion
- Using the portfolio in performance reviews and job interviews
- Continuously updating with new automation examples
Module 17: Final Implementation Project: From Concept to Board-Ready - Selecting a real-world business problem for automation
- Defining success criteria and stakeholder expectations
- Designing the end-to-end data and AI pipeline
- Building the Power BI model with automated refresh logic
- Integrating at least one AI service (e.g., sentiment, forecasting)
- Configuring dynamic distribution and alerting
- Implementing governance and audit controls
- Writing a compelling executive summary and use case brief
- Presenting your solution in a board-ready format
- Receiving structured feedback from course mentors
Module 18: Certification, Career Advancement & Next Steps - Final assessment: demonstrating mastery of AI automation principles
- Uploading your capstone project for evaluation
- Receiving your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn, CVs, and professional profiles
- Accessing career advancement resources and job templates
- Joining the private alumni network of certified practitioners
- Receiving invitations to exclusive enterprise workshops
- Staying updated with monthly automation insights from our team
- Contributing to the community knowledge base
- Planning your next certification or specialisation pathway
- Creating template-based reports using Power BI’s report themes
- Using paginated reports for regulatory and financial outputs
- Scheduling batch report exports to PDF, PowerPoint, Excel
- Automating distribution via Power Automate and email triggers
- Generating compliant report packages with metadata and audit trails
- Dynamic recipient routing based on organisational hierarchies
- Setting conditional delivery rules: threshold-based alerts
- Archiving reports to SharePoint or OneDrive in structured folders
- Tracking delivery status and failure recovery workflows
- Integrating with Microsoft Teams for proactive notifications
Module 6: Dynamic Dashboard Personalisation and User Adaptation - Building role-tailored dashboards using RLS and user context
- Creating adaptive layouts based on user device and screen size
- Implementing bookmark-driven navigation flows for guided analysis
- Automating dashboard personalisation using user preference data
- Tracking user interactions for behaviour-based dashboard optimisation
- Using What-If parameters to enable scenario planning
- Embedding natural language Q&A with custom synonyms
- Configuring drill-through filters with dynamic context
- Adding dynamic titles and KPI labels using DAX
- Designing tooltips that surface AI-derived insights
Module 7: DAX and Measures for Intelligent Automation - Writing dynamic measures that adapt to filter context
- Using CALCULATE and FILTER with complex business logic
- Time intelligence patterns for YoY, QoQ, rolling averages
- Creating forecast measures using linear regression in DAX
- Implementing dynamic variance analysis across business units
- Building reusable measure templates with standard naming
- Leveraging variables to improve performance and readability
- Optimising measure groups for query performance
- Using SWITCH TRUE patterns for multi-condition logic
- Testing and validating DAX logic with sample datasets
Module 8: Power Automate Integration for End-to-End Workflows - Connecting Power BI alerts to Power Automate flows
- Triggering actions based on data thresholds and anomalies
- Updating SharePoint lists or SQL tables from Power BI events
- Creating approval workflows for dashboard changes
- Syncing Power BI comments with internal ticketing systems
- Automating workspace provisioning for new projects
- Scheduling metadata audits using flow-driven refresh checks
- Sending proactive insights to Slack or Teams channels
- Generating AI-powered summaries from report data
- Logging automation activity for compliance and troubleshooting
Module 9: Data Governance and Compliance in Automated Environments - Implementing data classification and sensitivity labelling
- Configuring Microsoft Purview integration for Power BI
- Tracking data lineage from source to insight
- Enforcing usage compliance with Power BI audit logs
- Managing capacity workloads to prevent resource overuse
- Setting up data loss prevention (DLP) policies
- Auditing AI model usage and decision transparency
- Documenting model assumptions and limitations
- Creating an AI ethics checklist for automation projects
- Preparing for regulatory audits with standard operating procedures
Module 10: Building Predictive Analytics Pipelines - Designing churn prediction models using historical data
- Creating customer lifetime value (CLV) projections
- Forecasting demand using time-series decomposition
- Implementing clustering for customer segmentation
- Integrating predictive scores into operational dashboards
- Evaluating model accuracy using confusion matrices
- Automating retraining cycles based on data drift
- Setting up monitoring for model performance decay
- Linking predictions to action plans in Power Apps
- Validating predictive outputs against actual business outcomes
Module 11: Real-Time Data Streaming and Monitoring - Connecting Power BI to Azure Event Hubs for real-time ingestion
- Configuring streaming datasets for live dashboards
- Creating real-time KPIs for operational control rooms
- Setting dynamic thresholds for alerting on live data
- Integrating IoT device data into enterprise visualisations
- Optimising visual refresh rates for performance
- Reducing latency in streaming data pipelines
- Handling disconnections and data gaps gracefully
- Adding time-window filters for live trend analysis
- Exporting streaming snapshots for post-event review
Module 12: Custom Visual Development for Automation - Importing and configuring certified custom visuals
- Built-in visuals: smart narratives, decomposition trees, key influencers
- Developing parameterised custom visuals using Power BI Dev Tools
- Embedding AI-generated explanations into visual tooltips
- Creating reusable visual templates for standard reports
- Ensuring accessibility and screen reader compatibility
- Testing visuals across devices and resolutions
- Managing visual certification and approval workflows
- Deploying private visuals via organisational apps
- Tracking visual usage and adoption across workspaces
Module 13: Automation Orchestration with Azure Logic Apps - Scaling beyond Power Automate with enterprise-grade workflows
- Calling Power BI REST APIs using Logic Apps HTTP actions
- Scheduling complex ETL sequences with dependency chains
- Orchestrating multi-step data prep and model refresh cycles
- Integrating on-premises data gateways into cloud workflows
- Handling retry policies and error escalation paths
- Monitoring workflow execution with Azure Monitor
- Reducing manual intervention in monthly close processes
- Generating executive summaries automatically at period end
- Linking financial data to board reporting calendars
Module 14: Performance Optimisation and Load Testing - Analysing report load times using Performance Analyzer
- Identifying inefficient DAX expressions and visual queries
- Minimising data model size through column pruning
- Choosing appropriate visual types for large datasets
- Testing dashboard performance under concurrent user load
- Implementing caching strategies for frequently used reports
- Monitoring memory and CPU usage in Power BI Premium
- Setting up alerts for performance degradation
- Using query diagnostics to trace backend execution
- Writing efficient M code that minimises redundant steps
Module 15: Change Management and Organisational Adoption - Developing a communication plan for new automation rollouts
- Creating user training kits for AI-enhanced dashboards
- Running pilot programs with high-visibility stakeholders
- Gathering feedback and iterating on dashboard design
- Measuring adoption through usage analytics and telemetry
- Debriefing post-launch: what worked, what didn’t
- Establishing centres of excellence for Power BI governance
- Certifying internal Power BI champions and super users
- Aligning automation with enterprise digital transformation goals
- Scaling success across departments and geographies
Module 16: Building Your AI-Driven Dashboard Portfolio - Defining portfolio standards: consistency, branding, usability
- Selecting high-impact projects for inclusion
- Documenting business impact and ROI per dashboard
- Adding annotations to explain AI logic and automation triggers
- Incorporating before-and-after comparisons
- Creating executive summaries for non-technical reviewers
- Hosting your portfolio in a secure, accessible workspace
- Linking portfolio items to your Certificate of Completion
- Using the portfolio in performance reviews and job interviews
- Continuously updating with new automation examples
Module 17: Final Implementation Project: From Concept to Board-Ready - Selecting a real-world business problem for automation
- Defining success criteria and stakeholder expectations
- Designing the end-to-end data and AI pipeline
- Building the Power BI model with automated refresh logic
- Integrating at least one AI service (e.g., sentiment, forecasting)
- Configuring dynamic distribution and alerting
- Implementing governance and audit controls
- Writing a compelling executive summary and use case brief
- Presenting your solution in a board-ready format
- Receiving structured feedback from course mentors
Module 18: Certification, Career Advancement & Next Steps - Final assessment: demonstrating mastery of AI automation principles
- Uploading your capstone project for evaluation
- Receiving your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn, CVs, and professional profiles
- Accessing career advancement resources and job templates
- Joining the private alumni network of certified practitioners
- Receiving invitations to exclusive enterprise workshops
- Staying updated with monthly automation insights from our team
- Contributing to the community knowledge base
- Planning your next certification or specialisation pathway
- Writing dynamic measures that adapt to filter context
- Using CALCULATE and FILTER with complex business logic
- Time intelligence patterns for YoY, QoQ, rolling averages
- Creating forecast measures using linear regression in DAX
- Implementing dynamic variance analysis across business units
- Building reusable measure templates with standard naming
- Leveraging variables to improve performance and readability
- Optimising measure groups for query performance
- Using SWITCH TRUE patterns for multi-condition logic
- Testing and validating DAX logic with sample datasets
Module 8: Power Automate Integration for End-to-End Workflows - Connecting Power BI alerts to Power Automate flows
- Triggering actions based on data thresholds and anomalies
- Updating SharePoint lists or SQL tables from Power BI events
- Creating approval workflows for dashboard changes
- Syncing Power BI comments with internal ticketing systems
- Automating workspace provisioning for new projects
- Scheduling metadata audits using flow-driven refresh checks
- Sending proactive insights to Slack or Teams channels
- Generating AI-powered summaries from report data
- Logging automation activity for compliance and troubleshooting
Module 9: Data Governance and Compliance in Automated Environments - Implementing data classification and sensitivity labelling
- Configuring Microsoft Purview integration for Power BI
- Tracking data lineage from source to insight
- Enforcing usage compliance with Power BI audit logs
- Managing capacity workloads to prevent resource overuse
- Setting up data loss prevention (DLP) policies
- Auditing AI model usage and decision transparency
- Documenting model assumptions and limitations
- Creating an AI ethics checklist for automation projects
- Preparing for regulatory audits with standard operating procedures
Module 10: Building Predictive Analytics Pipelines - Designing churn prediction models using historical data
- Creating customer lifetime value (CLV) projections
- Forecasting demand using time-series decomposition
- Implementing clustering for customer segmentation
- Integrating predictive scores into operational dashboards
- Evaluating model accuracy using confusion matrices
- Automating retraining cycles based on data drift
- Setting up monitoring for model performance decay
- Linking predictions to action plans in Power Apps
- Validating predictive outputs against actual business outcomes
Module 11: Real-Time Data Streaming and Monitoring - Connecting Power BI to Azure Event Hubs for real-time ingestion
- Configuring streaming datasets for live dashboards
- Creating real-time KPIs for operational control rooms
- Setting dynamic thresholds for alerting on live data
- Integrating IoT device data into enterprise visualisations
- Optimising visual refresh rates for performance
- Reducing latency in streaming data pipelines
- Handling disconnections and data gaps gracefully
- Adding time-window filters for live trend analysis
- Exporting streaming snapshots for post-event review
Module 12: Custom Visual Development for Automation - Importing and configuring certified custom visuals
- Built-in visuals: smart narratives, decomposition trees, key influencers
- Developing parameterised custom visuals using Power BI Dev Tools
- Embedding AI-generated explanations into visual tooltips
- Creating reusable visual templates for standard reports
- Ensuring accessibility and screen reader compatibility
- Testing visuals across devices and resolutions
- Managing visual certification and approval workflows
- Deploying private visuals via organisational apps
- Tracking visual usage and adoption across workspaces
Module 13: Automation Orchestration with Azure Logic Apps - Scaling beyond Power Automate with enterprise-grade workflows
- Calling Power BI REST APIs using Logic Apps HTTP actions
- Scheduling complex ETL sequences with dependency chains
- Orchestrating multi-step data prep and model refresh cycles
- Integrating on-premises data gateways into cloud workflows
- Handling retry policies and error escalation paths
- Monitoring workflow execution with Azure Monitor
- Reducing manual intervention in monthly close processes
- Generating executive summaries automatically at period end
- Linking financial data to board reporting calendars
Module 14: Performance Optimisation and Load Testing - Analysing report load times using Performance Analyzer
- Identifying inefficient DAX expressions and visual queries
- Minimising data model size through column pruning
- Choosing appropriate visual types for large datasets
- Testing dashboard performance under concurrent user load
- Implementing caching strategies for frequently used reports
- Monitoring memory and CPU usage in Power BI Premium
- Setting up alerts for performance degradation
- Using query diagnostics to trace backend execution
- Writing efficient M code that minimises redundant steps
Module 15: Change Management and Organisational Adoption - Developing a communication plan for new automation rollouts
- Creating user training kits for AI-enhanced dashboards
- Running pilot programs with high-visibility stakeholders
- Gathering feedback and iterating on dashboard design
- Measuring adoption through usage analytics and telemetry
- Debriefing post-launch: what worked, what didn’t
- Establishing centres of excellence for Power BI governance
- Certifying internal Power BI champions and super users
- Aligning automation with enterprise digital transformation goals
- Scaling success across departments and geographies
Module 16: Building Your AI-Driven Dashboard Portfolio - Defining portfolio standards: consistency, branding, usability
- Selecting high-impact projects for inclusion
- Documenting business impact and ROI per dashboard
- Adding annotations to explain AI logic and automation triggers
- Incorporating before-and-after comparisons
- Creating executive summaries for non-technical reviewers
- Hosting your portfolio in a secure, accessible workspace
- Linking portfolio items to your Certificate of Completion
- Using the portfolio in performance reviews and job interviews
- Continuously updating with new automation examples
Module 17: Final Implementation Project: From Concept to Board-Ready - Selecting a real-world business problem for automation
- Defining success criteria and stakeholder expectations
- Designing the end-to-end data and AI pipeline
- Building the Power BI model with automated refresh logic
- Integrating at least one AI service (e.g., sentiment, forecasting)
- Configuring dynamic distribution and alerting
- Implementing governance and audit controls
- Writing a compelling executive summary and use case brief
- Presenting your solution in a board-ready format
- Receiving structured feedback from course mentors
Module 18: Certification, Career Advancement & Next Steps - Final assessment: demonstrating mastery of AI automation principles
- Uploading your capstone project for evaluation
- Receiving your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn, CVs, and professional profiles
- Accessing career advancement resources and job templates
- Joining the private alumni network of certified practitioners
- Receiving invitations to exclusive enterprise workshops
- Staying updated with monthly automation insights from our team
- Contributing to the community knowledge base
- Planning your next certification or specialisation pathway
- Implementing data classification and sensitivity labelling
- Configuring Microsoft Purview integration for Power BI
- Tracking data lineage from source to insight
- Enforcing usage compliance with Power BI audit logs
- Managing capacity workloads to prevent resource overuse
- Setting up data loss prevention (DLP) policies
- Auditing AI model usage and decision transparency
- Documenting model assumptions and limitations
- Creating an AI ethics checklist for automation projects
- Preparing for regulatory audits with standard operating procedures
Module 10: Building Predictive Analytics Pipelines - Designing churn prediction models using historical data
- Creating customer lifetime value (CLV) projections
- Forecasting demand using time-series decomposition
- Implementing clustering for customer segmentation
- Integrating predictive scores into operational dashboards
- Evaluating model accuracy using confusion matrices
- Automating retraining cycles based on data drift
- Setting up monitoring for model performance decay
- Linking predictions to action plans in Power Apps
- Validating predictive outputs against actual business outcomes
Module 11: Real-Time Data Streaming and Monitoring - Connecting Power BI to Azure Event Hubs for real-time ingestion
- Configuring streaming datasets for live dashboards
- Creating real-time KPIs for operational control rooms
- Setting dynamic thresholds for alerting on live data
- Integrating IoT device data into enterprise visualisations
- Optimising visual refresh rates for performance
- Reducing latency in streaming data pipelines
- Handling disconnections and data gaps gracefully
- Adding time-window filters for live trend analysis
- Exporting streaming snapshots for post-event review
Module 12: Custom Visual Development for Automation - Importing and configuring certified custom visuals
- Built-in visuals: smart narratives, decomposition trees, key influencers
- Developing parameterised custom visuals using Power BI Dev Tools
- Embedding AI-generated explanations into visual tooltips
- Creating reusable visual templates for standard reports
- Ensuring accessibility and screen reader compatibility
- Testing visuals across devices and resolutions
- Managing visual certification and approval workflows
- Deploying private visuals via organisational apps
- Tracking visual usage and adoption across workspaces
Module 13: Automation Orchestration with Azure Logic Apps - Scaling beyond Power Automate with enterprise-grade workflows
- Calling Power BI REST APIs using Logic Apps HTTP actions
- Scheduling complex ETL sequences with dependency chains
- Orchestrating multi-step data prep and model refresh cycles
- Integrating on-premises data gateways into cloud workflows
- Handling retry policies and error escalation paths
- Monitoring workflow execution with Azure Monitor
- Reducing manual intervention in monthly close processes
- Generating executive summaries automatically at period end
- Linking financial data to board reporting calendars
Module 14: Performance Optimisation and Load Testing - Analysing report load times using Performance Analyzer
- Identifying inefficient DAX expressions and visual queries
- Minimising data model size through column pruning
- Choosing appropriate visual types for large datasets
- Testing dashboard performance under concurrent user load
- Implementing caching strategies for frequently used reports
- Monitoring memory and CPU usage in Power BI Premium
- Setting up alerts for performance degradation
- Using query diagnostics to trace backend execution
- Writing efficient M code that minimises redundant steps
Module 15: Change Management and Organisational Adoption - Developing a communication plan for new automation rollouts
- Creating user training kits for AI-enhanced dashboards
- Running pilot programs with high-visibility stakeholders
- Gathering feedback and iterating on dashboard design
- Measuring adoption through usage analytics and telemetry
- Debriefing post-launch: what worked, what didn’t
- Establishing centres of excellence for Power BI governance
- Certifying internal Power BI champions and super users
- Aligning automation with enterprise digital transformation goals
- Scaling success across departments and geographies
Module 16: Building Your AI-Driven Dashboard Portfolio - Defining portfolio standards: consistency, branding, usability
- Selecting high-impact projects for inclusion
- Documenting business impact and ROI per dashboard
- Adding annotations to explain AI logic and automation triggers
- Incorporating before-and-after comparisons
- Creating executive summaries for non-technical reviewers
- Hosting your portfolio in a secure, accessible workspace
- Linking portfolio items to your Certificate of Completion
- Using the portfolio in performance reviews and job interviews
- Continuously updating with new automation examples
Module 17: Final Implementation Project: From Concept to Board-Ready - Selecting a real-world business problem for automation
- Defining success criteria and stakeholder expectations
- Designing the end-to-end data and AI pipeline
- Building the Power BI model with automated refresh logic
- Integrating at least one AI service (e.g., sentiment, forecasting)
- Configuring dynamic distribution and alerting
- Implementing governance and audit controls
- Writing a compelling executive summary and use case brief
- Presenting your solution in a board-ready format
- Receiving structured feedback from course mentors
Module 18: Certification, Career Advancement & Next Steps - Final assessment: demonstrating mastery of AI automation principles
- Uploading your capstone project for evaluation
- Receiving your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn, CVs, and professional profiles
- Accessing career advancement resources and job templates
- Joining the private alumni network of certified practitioners
- Receiving invitations to exclusive enterprise workshops
- Staying updated with monthly automation insights from our team
- Contributing to the community knowledge base
- Planning your next certification or specialisation pathway
- Connecting Power BI to Azure Event Hubs for real-time ingestion
- Configuring streaming datasets for live dashboards
- Creating real-time KPIs for operational control rooms
- Setting dynamic thresholds for alerting on live data
- Integrating IoT device data into enterprise visualisations
- Optimising visual refresh rates for performance
- Reducing latency in streaming data pipelines
- Handling disconnections and data gaps gracefully
- Adding time-window filters for live trend analysis
- Exporting streaming snapshots for post-event review
Module 12: Custom Visual Development for Automation - Importing and configuring certified custom visuals
- Built-in visuals: smart narratives, decomposition trees, key influencers
- Developing parameterised custom visuals using Power BI Dev Tools
- Embedding AI-generated explanations into visual tooltips
- Creating reusable visual templates for standard reports
- Ensuring accessibility and screen reader compatibility
- Testing visuals across devices and resolutions
- Managing visual certification and approval workflows
- Deploying private visuals via organisational apps
- Tracking visual usage and adoption across workspaces
Module 13: Automation Orchestration with Azure Logic Apps - Scaling beyond Power Automate with enterprise-grade workflows
- Calling Power BI REST APIs using Logic Apps HTTP actions
- Scheduling complex ETL sequences with dependency chains
- Orchestrating multi-step data prep and model refresh cycles
- Integrating on-premises data gateways into cloud workflows
- Handling retry policies and error escalation paths
- Monitoring workflow execution with Azure Monitor
- Reducing manual intervention in monthly close processes
- Generating executive summaries automatically at period end
- Linking financial data to board reporting calendars
Module 14: Performance Optimisation and Load Testing - Analysing report load times using Performance Analyzer
- Identifying inefficient DAX expressions and visual queries
- Minimising data model size through column pruning
- Choosing appropriate visual types for large datasets
- Testing dashboard performance under concurrent user load
- Implementing caching strategies for frequently used reports
- Monitoring memory and CPU usage in Power BI Premium
- Setting up alerts for performance degradation
- Using query diagnostics to trace backend execution
- Writing efficient M code that minimises redundant steps
Module 15: Change Management and Organisational Adoption - Developing a communication plan for new automation rollouts
- Creating user training kits for AI-enhanced dashboards
- Running pilot programs with high-visibility stakeholders
- Gathering feedback and iterating on dashboard design
- Measuring adoption through usage analytics and telemetry
- Debriefing post-launch: what worked, what didn’t
- Establishing centres of excellence for Power BI governance
- Certifying internal Power BI champions and super users
- Aligning automation with enterprise digital transformation goals
- Scaling success across departments and geographies
Module 16: Building Your AI-Driven Dashboard Portfolio - Defining portfolio standards: consistency, branding, usability
- Selecting high-impact projects for inclusion
- Documenting business impact and ROI per dashboard
- Adding annotations to explain AI logic and automation triggers
- Incorporating before-and-after comparisons
- Creating executive summaries for non-technical reviewers
- Hosting your portfolio in a secure, accessible workspace
- Linking portfolio items to your Certificate of Completion
- Using the portfolio in performance reviews and job interviews
- Continuously updating with new automation examples
Module 17: Final Implementation Project: From Concept to Board-Ready - Selecting a real-world business problem for automation
- Defining success criteria and stakeholder expectations
- Designing the end-to-end data and AI pipeline
- Building the Power BI model with automated refresh logic
- Integrating at least one AI service (e.g., sentiment, forecasting)
- Configuring dynamic distribution and alerting
- Implementing governance and audit controls
- Writing a compelling executive summary and use case brief
- Presenting your solution in a board-ready format
- Receiving structured feedback from course mentors
Module 18: Certification, Career Advancement & Next Steps - Final assessment: demonstrating mastery of AI automation principles
- Uploading your capstone project for evaluation
- Receiving your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn, CVs, and professional profiles
- Accessing career advancement resources and job templates
- Joining the private alumni network of certified practitioners
- Receiving invitations to exclusive enterprise workshops
- Staying updated with monthly automation insights from our team
- Contributing to the community knowledge base
- Planning your next certification or specialisation pathway
- Scaling beyond Power Automate with enterprise-grade workflows
- Calling Power BI REST APIs using Logic Apps HTTP actions
- Scheduling complex ETL sequences with dependency chains
- Orchestrating multi-step data prep and model refresh cycles
- Integrating on-premises data gateways into cloud workflows
- Handling retry policies and error escalation paths
- Monitoring workflow execution with Azure Monitor
- Reducing manual intervention in monthly close processes
- Generating executive summaries automatically at period end
- Linking financial data to board reporting calendars
Module 14: Performance Optimisation and Load Testing - Analysing report load times using Performance Analyzer
- Identifying inefficient DAX expressions and visual queries
- Minimising data model size through column pruning
- Choosing appropriate visual types for large datasets
- Testing dashboard performance under concurrent user load
- Implementing caching strategies for frequently used reports
- Monitoring memory and CPU usage in Power BI Premium
- Setting up alerts for performance degradation
- Using query diagnostics to trace backend execution
- Writing efficient M code that minimises redundant steps
Module 15: Change Management and Organisational Adoption - Developing a communication plan for new automation rollouts
- Creating user training kits for AI-enhanced dashboards
- Running pilot programs with high-visibility stakeholders
- Gathering feedback and iterating on dashboard design
- Measuring adoption through usage analytics and telemetry
- Debriefing post-launch: what worked, what didn’t
- Establishing centres of excellence for Power BI governance
- Certifying internal Power BI champions and super users
- Aligning automation with enterprise digital transformation goals
- Scaling success across departments and geographies
Module 16: Building Your AI-Driven Dashboard Portfolio - Defining portfolio standards: consistency, branding, usability
- Selecting high-impact projects for inclusion
- Documenting business impact and ROI per dashboard
- Adding annotations to explain AI logic and automation triggers
- Incorporating before-and-after comparisons
- Creating executive summaries for non-technical reviewers
- Hosting your portfolio in a secure, accessible workspace
- Linking portfolio items to your Certificate of Completion
- Using the portfolio in performance reviews and job interviews
- Continuously updating with new automation examples
Module 17: Final Implementation Project: From Concept to Board-Ready - Selecting a real-world business problem for automation
- Defining success criteria and stakeholder expectations
- Designing the end-to-end data and AI pipeline
- Building the Power BI model with automated refresh logic
- Integrating at least one AI service (e.g., sentiment, forecasting)
- Configuring dynamic distribution and alerting
- Implementing governance and audit controls
- Writing a compelling executive summary and use case brief
- Presenting your solution in a board-ready format
- Receiving structured feedback from course mentors
Module 18: Certification, Career Advancement & Next Steps - Final assessment: demonstrating mastery of AI automation principles
- Uploading your capstone project for evaluation
- Receiving your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn, CVs, and professional profiles
- Accessing career advancement resources and job templates
- Joining the private alumni network of certified practitioners
- Receiving invitations to exclusive enterprise workshops
- Staying updated with monthly automation insights from our team
- Contributing to the community knowledge base
- Planning your next certification or specialisation pathway
- Developing a communication plan for new automation rollouts
- Creating user training kits for AI-enhanced dashboards
- Running pilot programs with high-visibility stakeholders
- Gathering feedback and iterating on dashboard design
- Measuring adoption through usage analytics and telemetry
- Debriefing post-launch: what worked, what didn’t
- Establishing centres of excellence for Power BI governance
- Certifying internal Power BI champions and super users
- Aligning automation with enterprise digital transformation goals
- Scaling success across departments and geographies
Module 16: Building Your AI-Driven Dashboard Portfolio - Defining portfolio standards: consistency, branding, usability
- Selecting high-impact projects for inclusion
- Documenting business impact and ROI per dashboard
- Adding annotations to explain AI logic and automation triggers
- Incorporating before-and-after comparisons
- Creating executive summaries for non-technical reviewers
- Hosting your portfolio in a secure, accessible workspace
- Linking portfolio items to your Certificate of Completion
- Using the portfolio in performance reviews and job interviews
- Continuously updating with new automation examples
Module 17: Final Implementation Project: From Concept to Board-Ready - Selecting a real-world business problem for automation
- Defining success criteria and stakeholder expectations
- Designing the end-to-end data and AI pipeline
- Building the Power BI model with automated refresh logic
- Integrating at least one AI service (e.g., sentiment, forecasting)
- Configuring dynamic distribution and alerting
- Implementing governance and audit controls
- Writing a compelling executive summary and use case brief
- Presenting your solution in a board-ready format
- Receiving structured feedback from course mentors
Module 18: Certification, Career Advancement & Next Steps - Final assessment: demonstrating mastery of AI automation principles
- Uploading your capstone project for evaluation
- Receiving your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn, CVs, and professional profiles
- Accessing career advancement resources and job templates
- Joining the private alumni network of certified practitioners
- Receiving invitations to exclusive enterprise workshops
- Staying updated with monthly automation insights from our team
- Contributing to the community knowledge base
- Planning your next certification or specialisation pathway
- Selecting a real-world business problem for automation
- Defining success criteria and stakeholder expectations
- Designing the end-to-end data and AI pipeline
- Building the Power BI model with automated refresh logic
- Integrating at least one AI service (e.g., sentiment, forecasting)
- Configuring dynamic distribution and alerting
- Implementing governance and audit controls
- Writing a compelling executive summary and use case brief
- Presenting your solution in a board-ready format
- Receiving structured feedback from course mentors