Business Process Transformation with AI

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.

Explanation of why mapping processes is necessary before any AI assessment and the guiding principle structuring the approach.

Description of the 7‑level hierarchical decomposition to analyze granularity and dependencies across activities.

APQC‑inspired value stream classification to locate critical processes and help prioritize AI use cases.

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.

Introduction to the standardized process documentation sheet with its 12 sections to capture context, activities and metrics.

Focus on sections H, I and K which surface signals relevant to automation or AI augmentation.

Practical workshop to apply the template and produce a complete, usable process documentation.

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.

Describes how a four-criteria evaluation grid operates to analyze a process’s AI potential.

Clarifies the four levels of overall potential and how they guide technology selection.

Lists thirteen AI technology types and explains how to map them to concrete needs.

Outlines an approach to estimate expected gains and ROI to inform prioritization decisions.

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.

Shows how to structure each use case with a sheet capturing need, stakeholders, data and evaluation criteria.

Presents the Value × Complexity matrix as a prioritization tool and rules for placing a use case on it.

Provides practical guidance to compare use cases and assemble a coherent portfolio.

Emphasizes making assumptions explicit to ease decision-making and trade-offs.

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.

Introduces a running case study using a mid-size bank as a concrete application context.

Describes phased application: building the process framework first, then performing a full evaluation using the grid.

Demonstrates how to progress through phases 2–4 to produce a prioritized list of actionable initiatives.

Identifies key success factors and common pitfalls to anticipate during an AI-driven transformation.