Designing an AI Operating Model with Clear Decision Rights and Learning Loops
Build an AI operating model that assigns decision rights, balances central control with domain ownership, and turns pilot evidence into strategy.
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-research method for testing workflow complaints against real cases before choosing an AI experiment or a simpler operational change.
How to Design an Intelligent Document Processing Pipeline from Intake to Retention
A practical, technology-neutral framework for controlling document intake, extraction, review, delivery and retention through explicit stage contracts.
Redesigning Exceptions, Approvals and Handoffs Before Workflow Automation
A practical method for mapping real workflows, testing controls, defining handoffs and resolving ambiguity before automation begins.
How to Design a Source-Grounded Generative AI Drafting Workflow
Build a controlled AI drafting workflow that separates approved evidence from prose, assigns review decisions and records meaningful changes.
Building a Scenario-Based Evaluation Set for a Business AI System
Build a reproducible scenario-based evaluation set covering routine work, difficult boundaries, prohibited actions and protected release evidence.
How to Design a Bounded AI Assistant with Tools, Permissions and Context Limits
A practical method for bounding AI assistants through capability-specific context, permissions, approvals, refusals, evidence and release tests.
Redesigning Roles by Tasks When Introducing AI Assistance
A practical method for mapping tasks, testing AI assistance, tracing redistributed effort and redesigning roles only when ownership and workload are clear.
Version Prompts, Models and Workflow Logic as One AI Release
Bind prompts, models, tools and workflow logic into one testable AI release, then promote and restore the same known configuration safely.
How to Build an AI Inventory and Risk-Tiering Method Teams Can Maintain
Build a maintainable AI inventory, rate exposure across four practical dimensions and route each use to proportionate review as its context changes.
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Ten topics covering the full life of an AI programme, from first use case to retirement.