Stage 3

Stage 3: Managed Workflows

A team deliberately operates an AI-enabled workflow that matters to its work. The workflow has a defined boundary and may be human-approved or perform bounded internal actions, but it is not an unowned experiment.

Challenges and limits

A strong demo can still fail on state, exceptions, permissions, integration, retries, or handoffs.
Quality checks need realistic cases and feedback from the people affected by the work.
Monitoring, maintenance, and human review carry a real cost that can erase the apparent productivity gain.
One team can create a sound workflow without yet supplying reusable organizational infrastructure.

This Stage Covers

A workflow card covering trigger, owner, users, systems, context, permissions, human review, failures, rollback, and business outcome.
Quality checks and evaluation proportional to exposure; baseline measures before investment and operational metrics after launch.
Human-in-the-loop design, escalation, bounded retries, logging, review cadence, and retirement criteria.
Practical patterns for narrow internal agents, governed context, and implementation choices that preserve control.

Signals to Move Forward

The workflow has repeatable evidence of value, known operating costs, an owner, and controls that support trust. Other teams now need the same patterns, context, or integration capability.

Next bottleneck

Turn isolated managed workflows into reusable organizational capability without centralizing everything into a slow approval process.

Field notes

Field notes for this stage are in planning.

Guides

No implementation guides target this stage yet.

Authority continuum

Authority is separate from stage maturity. It describes what an AI-enabled workflow is allowed to do; each workflow stops at the level justified by its risk, reversibility, and value.

Managed Workflows may justify Human-approved or Bounded action.

1

Advisory

AI analyzes, explains, or drafts; a person decides and performs the action.

Verification by the person using the result.

2

Human-approved

AI prepares a recommendation or proposed change; a named person reviews and authorizes consequential action.

Clear review point and accountability for the approver.

3

Bounded action

AI performs narrowly scoped internal actions with limited permissions, known constraints, and a way to detect or reverse failures.

Defined action boundary, logs, exception handling, and proportionate checks.

4

Delegated action

AI acts in an externally visible or materially consequential workflow within an approved policy and ongoing oversight.

Strong evaluation, permissions, monitoring, escalation, auditability, and review.

What Changes at This Stage

The team treats the workflow as an operational service: it has an owner, documented scope, systems context, review design, failure handling, and an outcome it is expected to improve.