Designing an AI Operating Model with Clear Decision Rights and Learning Loops
A practical guide to assigning AI decision rights, choosing an operating structure, and turning pilot evidence into portfolio and strategy change.
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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 testing workflow complaints, locating recurring constraints and deciding whether AI deserves a controlled trial.
Designing an Intelligent Document Processing Pipeline from Intake to Retention
A practical, technology-neutral guide to designing an IDP pipeline with traceable stages, explicit failure routes, useful review and controlled retention.
Redesigning Exceptions, Approvals and Handoffs Before Workflow Automation
A practical method for mapping real workflow behaviour, testing controls, defining handoffs and resolving exceptions before automation begins.
How to Design a Source-Grounded Generative AI Drafting Workflow
Build a controlled AI drafting workflow that keeps approved evidence, generated prose, review decisions and consequential edits clearly traceable.
How to Build a Scenario-Based Evaluation Set for a Business AI System
Build a reproducible AI evaluation set covering everyday work, hard boundaries, known failures and barred actions, with valid scoring and protected tests.
Designing a Bounded AI Assistant with Tools, Permissions and Context Limits
A practical method for setting an AI assistant’s context, tool permissions, action limits, approvals, refusals, evidence and accountable handoffs.
Redesigning Roles by Tasks When Introducing AI Assistance
A practical guide to mapping tasks, testing AI assistance, tracing hidden effort and redesigning roles only when pilot evidence is sound.
Version Prompts, Models and Workflow Logic as One Safe AI Release
Bind prompts, models, tools and workflow logic into one testable AI release, then promote it in stages and restore a complete known-good bundle.
Create an AI Inventory and Risk-Tiering Method Teams Can Maintain
Build a maintainable AI inventory, rate each use across four explainable dimensions, and route it to proportionate review as its context changes.
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