1. AI Use Case Structuring & Prioritization
- Collect, clarify, and consolidate AI use cases across departments
- Evaluate use cases based on value, frequency, complexity, data readiness, and ROI
- Support leadership in prioritization decisions
- Translate vague needs into clear, actionable plans
- Maintain a realistic and structured AI roadmap
2. AI Adoption & Enablement
- Support teams in using AI tools effectively in daily operations
- Develop guidelines, templates, and best practices
- Organize internal demos, knowledge sharing, and light training sessions
- Help build a network of internal “AI Champions”
3. Knowledge Base Structuring
- Contribute to designing the internal knowledge base architecture
- Organize and standardize business documentation (processes, products, suppliers, clients, etc.)
- Identify data gaps and improve content quality
- Establish standards for documentation, validation, and updates, so AI can use them
4. AI Pilot Design & Execution
- Define objectives, scope, workflows, and KPIs for each pilot
- Coordinate between teams, stakeholders, and external partners if needed
- Monitor progress and continuously optimize pilots
- Ensure pilots remain simple, practical, and business-driven
5. Business – Data – Tools Interface
- Translate business needs into structured problem statements
- Understand constraints related to ERP, processes, and data quality
- Recommend appropriate solutions (existing tools vs. more advanced projects)
- Prevent overly complex or low-value AI initiatives
6. Impact Measurement & Continuous Improvement
- Build simple dashboards to track adoption and performance
- Measure impact, identify challenges, and uncover opportunities
- Provide regular insights and reports to leadership
- Continuously refine the AI roadmap and governance
