2 min read

Workflow Ontology Series #10: The Workflow Ontology Audit

A workflow ontology audit tests whether the system reflects operational reality. By examining object types, actual states, ownership, queues, blockers, approvals, and handoffs, teams can find where labels drift from work and target redesign where delay hurts.
3 min read

Work Design for the AI Era Series #1: AI Adoption Is a Work Design Problem

Asking which tasks to automate is a decent starting point for AI adoption, but it is ultimately too narrow to address work design.
3 min read

Work Design for the AI Era Series #2: AI Grafting: The Trap of Automating the Existing Process

Adding AI to one step while preserving every queue, approval, handoff, and review is automation without transformation. It accelerates labor inside a flawed process, often increasing review demand, exceptions, and ambiguity while leaving the operating model untouched.
3 min read

Work Design for the AI Era Series #4: The Four Work Modes: Assist, Review, Exception, Automate

Instead of a single AI strategy, workflows require specific decisions about when to assist, review, manage exceptions, or fully automate.
3 min read

Work Design for the AI Era Series #3: The Unit of Work Has Changed

AI changes the unit of work from a person performing a task to a loop of human judgment, machine assistance, context, review, exceptions, decisions, and learning. Designing that loop determines whether faster output produces better outcomes.
2 min read

Work Design for the AI Era Series #5: Hidden Work Is Still Work

Visible time savings can conceal the context assembly, prompting, validation, cleanup, exception handling, and learning transfer that AI creates. Counting and designing this hidden labor prevents teams from mistaking displaced effort for a genuinely improved workflow.
3 min read

Work Design for the AI Era Series #6: Review Capacity Becomes the Bottleneck

As AI lowers production costs, scarce review capacity can become the new constraint. Risk-based review paths, clear reviewers, and precise quality checks keep growing draft volume from overwhelming expert attention and erasing gains elsewhere in the workflow.
3 min read

Work Design for the AI Era Series #8: Accountability Cannot Be Automated Away

AI may generate, edit, or send work, but responsibility must remain attached to outcomes. A clear accountability contract names the outcome owner, quality bar, review scope, risk boundaries, and evidence needed to reconstruct decisions when something goes wrong.

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