Designing an AI Operating Model with Clear Decision Rights and Learning Loops
A practical method for assigning AI decision rights, routing evidence through review forums, and turning pilot lessons into portfolio and strategy changes.
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Independent B2B reporting on putting AI to work: picking use cases worth doing, redesigning the work around them, and running them under real governance.
How to Observe Real Work Before Proposing an AI Use Case
A practical field-research method for finding recurring workflow constraints, testing their consequences and comparing AI with simpler changes.
Designing an Intelligent Document Processing Pipeline from Intake to Retention
Design a traceable document-processing pipeline with clear stage contracts, review routes, delivery controls, and retention ownership.
Redesigning Exceptions, Approvals and Handoffs Before Workflow Automation
A practical method for mapping real work, classifying exceptions, testing approvals, defining handoffs and deciding when automation is ready.
Design a Source-Grounded Generative AI Drafting Workflow
Build a controlled AI drafting workflow that separates approved evidence from generated prose, assigns reviews, and records consequential edits.
Building a Scenario-Based Evaluation Set for a Business AI System
Build a practical scenario-based evaluation set for a bounded business AI workflow, with reproducible cases, valid grading and protected release evidence.
Design a Bounded AI Assistant with Tools, Permissions, and Context Limits
A practical guide to defining an AI assistant’s tools, information boundaries, permissions, approvals, refusals, tests, and accountable owners.
Redesigning Roles by Task When Introducing AI Assistance
A practical guide to mapping tasks, testing AI, tracing redistributed effort, preserving expertise, and changing roles only after a measured pilot.
Version Prompts, Models and Workflow Logic as One AI Release
A practical method for binding prompts, models, tools and workflow logic into one testable AI release, then promoting and restoring it safely.
Create an AI Inventory and Risk-Tiering Method Teams Can Maintain
Build a maintainable AI-use inventory, rate inherent exposure across four clear dimensions, and route each use to proportionate review as it changes.
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Ten topics covering the full life of an AI program, from first use case to retirement.