Faysal Aziz
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Training

AI-Enabled Analytics & Decision Support with Microsoft Copilot

AIData

AI-Enabled Analytics & Decision Support with Microsoft Copilot

Turning operational data into faster, clearer and more defensible management decisions.

Duration: One Day / Two Days
(depending on how intensive client wants it and number of participants)

AI becomes genuinely useful to managers when it stops being treated primarily as a writing assistant and starts being used as an analytical layer over the information already moving through the business. Spreadsheets, supplier information, operational records, market data, performance reports and management dashboards contain enormous amounts of decision-making value, but extracting that value usually requires significant manual consolidation, interpretation and reporting.

This course concentrates on that problem. Rather than revisiting traditional procurement processes, participants learn how to use Microsoft's AI ecosystem to move from raw operational information to analysis, research, management reporting and decision support. The emphasis is on interrogating data, finding patterns and exceptions, comparing information from multiple sources, researching external markets and vendors, producing concise management reports, and giving AI enough context to produce results that can actually be used.

The course is deliberately grounded in the Microsoft technology environment. Microsoft 365 Copilot's Analyst agent can analyze multiple files and identify trends, statistics and outliers, while Researcher can combine workplace information with web research to generate structured, source-cited reports. Power BI Copilot can assist with creating, editing and summarizing reports, giving managers another route from structured data to decision-ready information.

Participants will also see how this capability can progress beyond individual prompting. Copilot Studio supports agents that can work with organizational knowledge, tools and connected data sources, with generative orchestration determining which resources should be used to complete a task. This creates a practical path toward AI-assisted analytical workflows rather than isolated chatbot conversations.

The instructor brings over 30 years of industry experience to the programme. The training therefore concentrates on real industry-demanded capabilities, practical management outputs and realistic enterprise constraints rather than turning the subject into an academic treatment of artificial intelligence.

Learning Outcomes

By the end of this course, participants will be able to:

  • Identify appropriate uses of AI for analytical and decision-support work.
  • Select appropriate Microsoft Copilot tools for research, data analysis, reporting and automation.
  • Prepare and structure business data so that AI can interpret it more effectively.
  • Analyze spreadsheet and operational datasets using Copilot and Analyst.
  • Identify trends, exceptions, anomalies, relationships and decision-relevant observations.
  • Use AI to consolidate information from multiple data sources.
  • Produce management summaries and decision-oriented reports from analytical results.
  • Use Researcher for structured market, vendor and external information research.
  • Apply prompt engineering techniques to analytical and reporting tasks.
  • Apply context engineering to improve accuracy, relevance and repeatability.
  • Critically validate AI-generated analysis before using it for management decisions.
  • Understand how Copilot Studio agents can be grounded with organizational data and connected to analytical workflows.
  • Recognize practical opportunities for AI agents to manage data, perform analysis and support recurring reporting.

Prerequisites

  • Working knowledge of Microsoft 365 applications.
  • Familiarity with Excel and typical business datasets.
  • Basic understanding of management reporting and data-driven decision making.
  • General familiarity with procurement, supply-chain, commercial or operational information.
  • Access to the organization's Microsoft 365 environment is recommended.
  • Microsoft 365 Copilot, Analyst, Researcher, Power BI Copilot and Copilot Studio access will depend on organizational licensing and administrator configuration.
  • No programming or AI development experience is required for the main course.

Training Outline

  1. AI as an Analytical and Decision-Support Capability
    1. Moving from AI content generation to AI-assisted analysis
    2. Operational data as AI context
    3. Structured and unstructured business information
    4. Analytical questions versus transactional questions
    5. Human judgement and AI-supported decisions
    6. Accuracy, verification and management accountability
  2. Microsoft AI and Analytics Toolchain
    1. Microsoft 365 Copilot
    2. Copilot Chat
    3. Copilot in Excel
    4. Analyst agent
    5. Researcher agent
    6. Copilot in Power BI
    7. Copilot Studio
    8. Power Automate integration
    9. Selecting the appropriate tool for the task
  3. Preparing Data for Effective AI Analysis
    1. Data structure and consistency
    2. Tables, CSV files and workbook preparation
    3. Multiple-file analysis
    4. Data quality and missing information
    5. Categories, dimensions and measures
    6. Dates, currencies and units
    7. Data normalization considerations
    8. Sensitive and restricted information
  4. Prompt Engineering for Analytical Work
    1. Defining the analytical objective
    2. Providing role and business perspective
    3. Specifying data scope
    4. Defining metrics and comparison dimensions
    5. Requesting trends, exceptions and anomalies
    6. Controlling assumptions
    7. Specifying output structure
    8. Iterative analytical prompting
    9. Prompt refinement and validation
  5. Context Engineering for Business Analysis
    1. Prompt context versus organizational context
    2. Supplying relevant files and datasets
    3. Establishing business definitions
    4. Providing decision criteria
    5. Grounding analysis in authoritative information
    6. Controlling information scope
    7. Maintaining context across analytical tasks
    8. Designing reusable analytical instructions
  6. AI-Assisted Data Analysis
    1. Copilot in Excel for structured data
    2. Analyst for multi-file analysis
    3. Data summarization
    4. Comparative analysis
    5. Trend identification
    6. Variance analysis
    7. Exception and anomaly identification
    8. Performance segmentation
    9. Relationship and driver analysis
    10. Follow-up interrogation of analytical findings
  7. Research and External Intelligence
    1. Researcher for deep information gathering
    2. Internal versus external information sources
    3. Vendor and market intelligence
    4. Supplier landscape research
    5. Capability and market comparison
    6. External risk information
    7. Supply-market developments
    8. Source citations and traceability
    9. Research validation
  8. From Analysis to Management Reporting
    1. Converting analytical findings into management information
    2. Executive summaries
    3. Decision-focused reporting
    4. KPI and variance narratives
    5. Exception reporting
    6. Risk and opportunity summaries
    7. Management briefing preparation
    8. Audience-specific reporting
    9. Evidence and source references
  9. AI-Assisted Reporting with Power BI
    1. Power BI semantic models and AI readiness
    2. Copilot-assisted data exploration
    3. Natural-language report creation
    4. Report page generation
    5. Visual selection
    6. Narrative summaries
    7. Management-level report summaries
    8. Interrogating existing reports with Copilot
    9. Validating AI-generated interpretations
  10. Applying AI to Commercial and Supply-Chain Data
    1. Spend and category information
    2. Supplier performance information
    3. Pricing and cost data
    4. Demand and consumption data
    5. Inventory and availability information
    6. Lead-time and delivery information
    7. Market and supplier intelligence
    8. Risk and exception information
    9. Cross-dataset management analysis
  11. Technical Extension: AI Agents for Data and Reporting
    1. Copilot Studio agent architecture
    2. Agent instructions and context
    3. Knowledge sources
    4. SharePoint and organizational data
    5. Structured data connections
    6. Tools and actions
    7. Generative orchestration
    8. Agent flows and Power Automate
    9. Permissions and access boundaries
    10. Testing and validation
    11. Analytical Agent Use Cases
      1. Multi-file dataset analysis
      2. Data consolidation and classification
      3. Exception and anomaly analysis
      4. Supplier and market intelligence synthesis
      5. Recurring management report generation
      6. KPI and variance summarization
      7. Data-to-report workflow automation
      8. Management information retrieval
    12. Operational Considerations
      1. Grounding and source control
      2. Data permissions
      3. Output validation
      4. Human approval points
      5. Monitoring agent results
      6. Governance and controlled deployment

Disclaimer

This training outline is intended as a structured guideline for delivery and represents the anticipated scope of the programme. The trainer may, at their professional discretion, amend, reorder, expand, reduce or substitute individual topics to accommodate participant requirements, available technology, organizational constraints, product changes or the practical progress of the course. Such adjustments may be made without prior notice where reasonably considered necessary to maintain the relevance, effectiveness and appropriate pacing of the training.

Practical, connected learning

My wider training approach brings hands-on implementation and systems thinking together, connecting technology with real operational needs.

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