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By Bill Murphy · Powered by Colony Spark |
THE OPERATIONS
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ISSUE #19 · SEPTEMBER 11, 2026 |
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Hi ${first_name},
A correct AI answer can still be a failed workflow. The work closes only when the next operator, planner, quality lead, buyer, or warehouse leader receives the required artifact, in the required format, with the evidence, ownership, acceptance rule, and fallback needed to act. If a person still has to rebuild the work after the model responds, the workflow has produced an answer but not a delivery.
This issue moves the question from "Was the answer correct?" to "Can the next person act without rebuilding the work?" At the end, a practical field tool that turns one bounded workflow into a usable delivery contract.
Bill
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From Answer Quality to Delivery Quality.
The hidden cost is the last manual mile.
Manufacturing and distribution teams do not act on prose alone. The useful result may be a release-ready production schedule, a quality-disposition packet, a supplier-exception record, a work instruction, an audit trail, or a transaction in the required system format. If a person still has to translate an AI response into that artifact, reconstruct its evidence, find its owner, or decide what happens when it fails, the workflow has produced an answer but not a delivery.
The work of turning the response into an accepted artifact and routing it to the accountable person is the last manual mile. Interpretation, reformatting, evidence gathering, routing, approval, exception handling, and verification after the model responds. The model is one layer. The delivery is the entire system.
This is not only an editorial framing. NIST's Artificial Intelligence for Manufacturing program evaluates AI-generated engineering artifacts on completeness, clarity, traceability, operator understanding, and interoperability, and works through validation diagrams, functional flows, and audit trails linked to operator inputs, with production scheduling and process control named as pilot areas. Its roadmap for AI and machine learning in smart manufacturing puts data management, integration with heterogeneous sensing and control systems, and trustworthy, explainable, reliable operation at the center of the deployment problem. The criteria describe the delivery, not the answer.
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Three recognizable AI workflow outputs
· Production schedule. The planner receives a recommended sequence reorder, but the artifact must be import-ready, include change summary and reason for each reordered operation, attach the source constraints, show customer impact for affected orders, and validate against the ERP before release.
· Disposition record. Quality receives an AI recommendation to scrap, rework, or release with conditions, but the record must include reference photos, test data, audit trail, accountable approver, escalation trigger if rework time exceeds threshold, and archive proof that the decision was logged and verified.
· Supplier exception. Procurement receives a sourcing recommendation, but it must be routed to the category manager, include approved-vendor status and pricing tier, attach the exception justification and price comparison, and retain the last three quotes for audit.
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The operational test of AI in a real workflow is not whether the answer is correct. It is whether the next person has the artifact, evidence, destination, owner, acceptance rule, and fallback needed to act. The difference is the distance from "here is what I found" to "here is what you can do with it."
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Delivering an answer is step one. Delivering an artifact is the workflow.
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Complete the assessment and leave with a delivery specification you can copy or print and hand to the process owner, IT team, ERP partner, systems integrator, or workflow vendor.
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Agents work through approved workflows and verify the result.
Russ Ford, Honeywell Technologies, on closed-loop decision-making on the factory floor
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“Agents can perceive conditions, reason over context, execute approved workflows, interact with other agents, applications and operational systems, and verify results.”
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Ford names the finish line directly. An agent perceives the operating condition, reasons over the context it has, executes through an approved workflow, interacts with the systems and people in that workflow, and verifies what happened next. The distinction between assistance and operating delivery is whether the system can move through the workflow and confirm the result.
That last step changes everything. A fluent recommendation is not the finish line. The workflow must carry the output into an approved process and confirm what happened next. If an agent recommends a production resequence and nobody verifies whether the resequence was loaded into the ERP and did not break downstream commitments, the recommendation has no consequence.
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The takeaway
The test of whether an AI workflow is operating or advisory is whether it verifies what happened next. Verify means the workflow confirms the artifact was received, acted on, the action succeeded or failed, and the fallback executed if it did.
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The Seven-Field AI Output Delivery Contract.
Define what finished means before an AI response enters the handoff.
A one-page specification that tells the workflow what the next person must receive and how to act on it.
Many AI workflows stop at a plausible answer. The operation still needs a schedule, disposition record, supplier exception, work instruction, or system transaction that can move into the real process. The boundary between assistance and delivery is whether the workflow can move the artifact through an approved process with a clear owner, acceptance rule, and fallback.
The seven-field contract defines what finished means for one bounded AI workflow. It tells you what the next person must receive, what format makes it usable, what evidence travels with it, where it goes, who accepts or rejects it, what the pass condition is, and what happens if it fails. The tool lets you assess one workflow against these fields, see what is missing or not tested, and walk away with a specification the team can use to build or govern that workflow.
The seven fields
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· Decision or action. The exact operational move the output must enable.
· Required artifact. The deliverable the next person can use without rebuilding it.
· Required format. The template, fields, file type, or system shape that makes the artifact usable.
· Source evidence. The records, constraints, references, and change history that must travel with the result.
· Destination. The queue, record, inbox, system, or workspace where the delivery becomes actionable.
· Accountable owner. The role that accepts, rejects, acts, and resolves exceptions.
· Acceptance test and fallback. The pass condition, rejection path, safe fallback, escalation trigger, and stop condition.
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How the assessment works
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1. Choose one bounded AI workflow and name the next person or system that must act.
2. Rate each of the seven fields as Not defined, Partly defined, or Defined and tested, then add any useful notes.
3. The tool scores the handoff, flags missing critical fields, identifies the weakest field, and recommends the first repair.
4. You copy or print the completed contract and give it to the workflow owner or builder before the next pilot or automation change.
5. After a controlled test, the team updates the contract with any exception, ownership, evidence, or fallback requirement the test exposed.
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An illustrative example: production scheduling
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Before the contract:
The AI recommends a production resequence. The planner receives the recommendation and still has to rebuild the work. They extract it into a spreadsheet, check it against open orders, flag which customers get impacted, rebuild the constraint logic to make sure nothing is missed, create a change summary for the finance team, and verify it will load into the ERP without errors. The recommendation was useful, but the workflow is not closed until the planner has rebuilt the work by hand.
After the contract:
The workflow delivers an import-ready schedule. The artifact includes the reordered operations, a change summary for each affected order with reason codes, the source constraints, customer impact flagged by order, ERP-format export ready to load, and a pre-load validation showing pass or fail. If validation fails, the fallback is to retain the last approved schedule and escalate to the planning manager. The planner receives a complete, verified artifact and makes the acceptance decision. The hidden work is now visible and happens in the workflow, not in the planner's inbox.
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Note. The above is an illustrative example of how the contract clarifies the handoff. It is not a NIST case study, a completed client engagement, or proof that the recommendation is correct.
The tool runs in the browser. It does not push anything into an ERP or any other system. Its deliverable is the completed specification you can copy or print and take to the responsible team.
The value is not the score by itself. The value is the completed specification you walk away with. Use it to confirm what finished means for that workflow, hand it to the process owner or IT team, use it as the requirements handoff for a pilot or build, or attach it to a build ticket so the output is reviewed against the same contract after a controlled test.
Open the 7-field delivery contract and run it on one workflow. An Operations Brief field tool, powered by Colony Spark.
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The handoff is finished when the next person can act without rebuilding the work.
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THE OPERATIONS BRIEF
By Bill Murphy · Powered by Colony Spark
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Building an AI workflow where the next person has everything they need to act, or still translating model outputs into working artifacts? Hit reply, I read every one. Bill
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