Training programme overview
DATAS-STD-BPM-AI-001 methodology for transforming business processes: identification, mapping, documentation, AI potential assessment and prioritization of use cases. A program focused on systematic analysis and building an operational use-case portfolio.
This program teaches how to discover and map relevant business processes, offering clear steps for identification and prioritization.
It details process documentation techniques and criteria to assess AI potential, estimating expected benefits, risks and automation complexity.
Building a use-case portfolio brings these elements together to select priority initiatives; the integrated practice consolidates mapping, documentation and assessment into actionable cases.
Training · Intermediate
Courses included in this training programme
Process Mapping & Identification
Method to map processes before assessing AI opportunities: guiding principles, a 7‑level hierarchical decomposition and an APQC‑inspired value stream classification to prioritize interventions.
2 lessons · 5 activities · 0 exercises · 0 videos
Process Documentation
Standardized methodology to document a process: a 12‑section documentation sheet, identification of AI signals (sections H, I, K) and a practical workshop to produce a complete process record.
2 lessons · 3 activities · 0 exercises · 0 videos
AI Potential Assessment
Introduces a 4-criteria evaluation grid to assess processes for AI: overall potential (4 levels), applicable AI technology types (13 kinds), and estimates of expected gains and ROI. A practical tool to help prioritize automation and optimization opportunities.
1 lessons · 3 activities · 0 exercises · 0 videos
Use Case Portfolio
Explains building a use-case portfolio: creating a standardized use-case sheet and using a Value × Complexity matrix to prioritize. A pragmatic method to organize, compare and select AI initiatives by impact and feasibility.
1 lessons · 2 activities · 0 exercises · 0 videos
Integrated Practice
Provides integrated practice through a complete case study focused on a mid-size bank: process mapping, application of the evaluation grid and prioritization. Includes analysis of key success factors and risks to watch during AI deployment.
1 lessons · 4 activities · 0 exercises · 0 videos
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