Course Objective & Scope
Many organizations begin their AI journey with scattered wish lists or technology-first experiments, only to see initiatives stall in pilot purgatory because they lack clear business value or data readiness. This 1-2 day hands-on workshop is designed to systematically map departmental bottlenecks, translate them into structured AI use case candidates, and align leadership around an objective prioritization framework.
Participants work directly on real processes from their own business units, scoring each candidate across business impact, ROI potential, data availability, integration complexity, and governance risk. By the end of the workshop, the team leaves with a calibrated priority matrix, detailed opportunity canvases for the top 3-5 use cases, and a concrete 90-day execution roadmap.
Who Should Attend?
- Department heads, directors, and process owners leading AI adoption initiatives
- Product managers, business analysts, and digital transformation or PMO leaders
- Data, AI, and IT architecture leads partnering with business units on roadmaps
- Corporate innovation, strategy, and operational excellence professionals
Key Takeaways
- Map departmental pain points to core AI archetypes such as extraction, synthesis, copilot assistance, prediction, and agentic automation
- Filter out non-AI problems that are better solved with standard deterministic rules or process cleanup
- Score use case candidates systematically across business value, financial
ROI, data readiness, technical feasibility, and risk - Draft complete AI opportunity canvases defining target users, data sources, baseline metrics, and target
KPIs - Build a balanced 90-day AI portfolio roadmap combining immediate quick wins with foundational capabilities
Curriculum
From Business Bottlenecks to AI Opportunities
- Identifying operational friction: cycle time delays, manual rework, knowledge silos, and decision latency
- Core AI archetypes: extraction, synthesis,
RAG, predictive analytics, and agentic workflows - When not to use AI: drawing the line between deterministic automation and generative AI
Departmental Use Case Discovery Lab
- Cross-functional mapping of end-to-end workflows and pain points
- Generating candidate pools across customer operations, finance, HR, supply chain, and commercial teams
- Structuring raw ideas into standardized use case definition cards
Feasibility, Data Readiness, and Risk Assessment
- Evaluating data availability, quality, privacy constraints, and system integration dependencies
- Assessing hallucination tolerance, regulatory exposure, and human-in-the-loop checkpoints
- Decision criteria for Build vs. Buy vs. extending existing enterprise Copilot/SaaS tools
ROI Modeling and the Prioritization Matrix
- Quantifying value through time saved, throughput gains, error reduction, and revenue lift
- Scoring and calibrating candidates on the Value vs. Feasibility matrix
- Separating high-impact quick wins from long-term strategic bets and low-value distractions
Opportunity Canvases and the 90-Day Roadmap
- Authoring detailed opportunity canvases for the top 3-5 prioritized use cases with baseline and target KPIs
- Sequencing PoC, pilot, and production milestones into a 30-60-90 day execution plan
- Peer review, executive pitch dry-run, and final alignment on ownership and next steps
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