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 durable portfolio learning.
The ModelFold Blog
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 method for studying real workflows, testing bottleneck hypotheses and choosing AI only when evidence beats simpler alternatives.
How to Design an Intelligent Document Processing Pipeline from Intake to Retention
A practical, vendor-neutral framework for designing traceable document processing from secure intake and validation to review, delivery and retention.
Redesign Exceptions, Approvals and Handoffs Before Workflow Automation
A practical method to map real workflow behaviour, test approvals, define accepted handoffs and redesign exceptions before automating.
How to Design a Source-Grounded Generative AI Drafting Workflow
Build a source-grounded AI drafting workflow that keeps evidence, prose, review decisions and consequential edits traceable through approval.
How to Build a Scenario-Based Evaluation Set for a Business AI System
A practical guide to designing reproducible AI evaluation scenarios, valid grading rules and protected release evidence for a bounded business workflow.
How to Design a Bounded AI Assistant with Tools, Permissions and Context Limits
A practical method to bound an AI assistant’s data, tools, permissions, actions, approvals, refusals, evidence and operating controls.
How to Redesign Roles by Task When Introducing AI Assistance
Map local tasks, test a fixed AI set-up, count hidden effort and redesign roles only after workload, ownership and expertise needs become visible.
Version Prompts, Models and Workflow Logic as One AI Release
A practical guide to binding prompts, models, tools and workflow logic into one testable AI release, with staged promotion and complete rollback.
Create an AI Inventory and Risk-Tiering Method Teams Can Maintain
Build a compact AI-use inventory, rate exposure across four explainable dimensions, and route every use to proportionate review as its context changes.
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Ten topics covering the full life of an AI program, from first use case to retirement.