Curriculum
- 6 Sections
- 49 Lessons
- 1 Day
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- Foundations of GenAI-Assisted Business Dashboards8
- 1.1Dashboard evolution: Static – Interactive – GenAI-augmented
- 1.2Key Concepts: semantic layer, data models, GPT connectors, AI visuals, prompt-to-dashboard, auto-insights, LLM-ops, natural language querying (NLQ)
- 1.3Difference between traditional BI vs GenAI-augmented BI
- 1.4Situational Awareness: Why dashboards fail- cognitive overload, wrong metrics, bad layouts, no business storyline
- 1.5How GenAI accelerates: data prep, KPI explanation, anomaly detection, narrative generation
- 1.6Real Use Cases: Finance- automated variance narratives, Sales: AI-generated pipeline summaries,
- 1.7Real Use Cases: Supply Chain: predictive inventory dashboards, HR: workforce analytics with natural-language Q&A
- 1.8Exercise: Identify 5 broken dashboards in your organization – what GenAI can fix
- Designing High-Impact KPIs & Insight Architecture8
- 2.1KPI frameworks: SMART, OKR alignment, lead/lag indicators
- 2.2Insight hierarchy: descriptive – diagnostic – predictive – prescriptive
- 2.3KPI storytelling: metric – movement – meaning – implication
- 2.4Situational Awareness: How misaligned KPIs create misleading dashboards
- 2.5Common pitfalls: vanity metrics, duplicated KPIs, inconsistent denominators
- 2.6AI-driven KPI suggestions based on business function
- 2.7Real Use Cases: Cost-to-serve dashboards, Productivity & efficiency scorecards, ESG & operational sustainability KPIs
- 2.8Exercise: Build a KPI-to-Storyboard Map for one functional problem (finance/supply chain/sales/etc.)
- GenAI for Data Preparation, Modelling & Auto-Insights9
- 3.1Data prep automation: AI data cleansing, data classification, schema matching, outlier detection
- 3.2Automated insight engines: trend detection, seasonality, what-changed analysis
- 3.3AI-assisted modelling: relationship detection, measure creation, semantic reconciliation Situational Awareness
- 3.4When to trust GenAI insights vs when to override manually
- 3.5How AI identifies patterns humans miss
- 3.6Risks: hallucinations, incorrect joins, unclear logic
- 3.7Use Cases: Finance month-end consolidation summarised by AI, Customer churn prediction embedded into dashboards
- 3.8Use Cases: Procurement price movement clustering
- 3.9Exercise: Clean a sample dataset using GenAI – generate insights – validate relevance
- Interactive Dashboards for Business Functions8
- 4.1Drill-through, cross-filtering, bookmarks, story points
- 4.2AI visuals: key influencers, decomposition tree, anomaly indicators
- 4.3Natural-language visual exploration (Show me sales variance by region last quarter)
- 4.4Situational Awareness: Which dashboard styles fit which business functions
- 4.5Choosing the right visualization: time-series, geospatial, scatter, funnel, pareto
- 4.6Accessibility, UX, and executive design rules
- 4.7Real Use Cases: Real-time operations dashboards (manufacturing, logistics), Predictive cashflow dashboards, HR hiring funnel with AI-detected bottlenecks
- 4.8Exercise: Redesign an existing dashboard layout using a GenAI-assisted design canvas
- Data Storytelling & Executive Narratives with GenAI8
- 5.1Business storytelling structure: context – insight – implication – action
- 5.2LLM-generated business narratives & slide summaries
- 5.3Humanising dashboards: personas, scenarios, decision journeys
- 5.4Situational Awareness: When dashboards confuse executives, Good vs bad narrative examples
- 5.5How GenAI improves clarity, simplicity & persuasion
- 5.6Real Use Cases: QBR/MPR decks auto-generated from dashboards
- 5.7GenAI narrative for revenue, cost, supply, talent metrics, Automated decision briefs for CXOs
- 5.8Exercise: Convert a dashboard into a 2-minute business story aided by GenAI tools
- Advanced Features – GenAI Agents, Automation & Governance8
- 6.1AI agents for reporting workflows
- 6.2Automated alerting mechanisms
- 6.3Governance guidelines: data lineage, model transparency, metric reconciliation
- 6.4Situational Awareness: Avoiding errors from AI summaries, Guardrails for data security, compliance, confidentiality
- 6.5Golden KPI governance frameworks
- 6.6Real Use Cases: Alerts for sudden dips/spikes, Automated project status dashboards
- 6.7Controlled self-service BI for non-technical users
- 6.8Exercise: Review an AI-generated narrative – identify risks, inconsistencies, missing context