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By Bill Murphy · Powered by Colony Spark |
THE OPERATIONS
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ISSUE #16 · JULY 3, 2026 |
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Hi there,
AI adoption in supply chain is accelerating, and confidence in the tools is climbing with it. What isn’t keeping pace is trust in AI as a decision-maker, the kind that runs without human review, without an evidence trail, without a clear escalation path. The operators winning right now aren’t the ones automating the most. They’re the ones building the right gates.
This issue lands the same week as America’s 250th. Worth remembering that the Declaration of Independence was itself a governance document. It didn’t just announce a new system. It defined who was accountable, what required review, and what couldn’t be delegated. That is exactly what the best AI deployments are doing now.
The real opportunity in operations isn’t removing the human. It’s designing exactly where the human shows up, and what they find when they get there. That’s the whole issue.
Bill
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The Confidence Gap.
Confidence in the tools is rising. Trust in them as a decision-maker is not.
Ford’s VP of hardware engineering said it in public last week: “Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product.”
They were wrong. Ford issued 152 recalls in 2025, a single-year record, and more in the first half of 2026 than any other automaker (BBC). Last month the company brought back 350 veteran engineers, the “graybeards”, to do what the AI couldn’t: apply decades of institutional knowledge to fix the training data the model never had (Bloomberg).
The story isn’t that AI failed. It’s that Ford ran it without the right foundation. No documented institutional knowledge. No gate to catch the model when it drifted. No process to surface quality slipping before it showed up in recalls. That gap is everywhere right now, and the data says it isn’t a Ford problem.
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The numbers behind the gap
· 36% of chief procurement officers say they’re very confident in their ability to redesign their function around AI (Gartner, May 2026, survey of 101 CPOs).
· 30% of supply chain organizations can point to measurable AI value in planning use cases, after all the investment (BCG Executive Perspectives).
· 47% of mid-sized businesses now use AI in operations. Almost none have built the governance structure to go with it (AllAboutAI Supply Chain Report).
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The confidence is warranted in one sense: the tools work, and adoption is real. But trust in AI as a decision-maker, making the call without human review, without an evidence trail, without a clear escalation path, has not caught up. The operators winning right now aren’t automating the most. They’re building the right gates.
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The confidence is real. The autonomy isn’t there yet. And the gap between the two is where the risk lives.
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Sources: Gartner CPO AI Confidence Survey, May 2026; Bloomberg, June 2026; BBC, June 2026; TechCrunch, June 2026; BCG Executive Perspectives, May 2026; AllAboutAI Supply Chain AI Report.
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The gains that never scale.
Fareen Mehrzai, Gartner, on why AI productivity stays stuck at the individual level
Fareen Mehrzai is a Senior Director Analyst in Gartner’s Supply Chain Practice. Presenting the CPO confidence data at the Gartner Supply Chain Symposium/Xpo in Barcelona in May, she named the reason all that investment isn’t showing up in the numbers.
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“Procurement teams are seeing productivity gains from GenAI, but without intentional redesign of roles and processes, those gains remain confined to the individual level. To improve returns on their AI investments and unlock organizational gains, CPOs must design next-generation human roles focused on guiding AI toward achieving real financial outcomes, rather than mere efficiency gains.”
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Read that again: the gains are real, but they stall at the individual. One analyst gets faster. One buyer gets faster. Nothing changes at the level of the function, because the roles and the handoffs around the AI were never redesigned to turn individual speed into organizational outcomes. Efficiency is not the same as value, and the gap between them is a design problem: who guides the AI, toward what, and who is accountable for the result.
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The challenge
Which three decisions in your operation touch customer commitments, and who, specifically, reviews the AI recommendation before it becomes a promise? If you can’t name the person for each one, that’s the design work waiting to be done.
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The Review Gate System.
Where the human shows up, and what they find when they get there.
Three decision categories, one named owner per flag, and an evidence trail that survives the audit.
The problem was never that AI can’t make recommendations. It’s that most operations teams haven’t defined what happens next. An AI flags a demand forecast anomaly. Who sees it? What do they need to act? What gets routed, and what resolves automatically? If nobody designed that, the recommendation sits in a system and nothing changes.
The pattern repeats across mid-market operations. AI gets deployed, outputs start coming, but the workflows around those outputs never got updated. The recommendation lands in a shared inbox, or a dashboard nobody checks in real time. The tool is smart. The structure around it isn’t. The operators getting the most from AI aren’t doing more automation, they’re doing better handoffs, mapping every recurring decision into one of three categories.
Three categories for every decision
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Automate, flag, or escalate
1. Automate completely. Routine, rules-based, low-consequence. Carrier selection within approved parameters. Small inventory rebalances. Order prioritization inside predefined service rules. No human in the loop every time, just a human who set the rules and reviews the pattern monthly.
2. Flag and route. Decisions that cross a threshold. A forecast that deviates more than 15% from the prior week. A supplier lead time that jumps more than two days on a key SKU. A quote that would compress margin below the line. AI flags it; a specific, named person gets it, inside a defined window, with enough context to act: prior-period data, the reason for the deviation, and the downstream impact on customer commitments.
3. Human-required. Strategic calls. Major trade-offs. Anything touching customer commitments or multi-supplier relationships. AI provides the evidence; the human decides. Both the decision and the evidence trail get logged: the recommendation, the data behind it, the timestamp, and the name of the person who made the call. Not for compliance. For traceability. Ford couldn’t trace where its training data went thin. Don’t learn it the same way.
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The companies building this aren’t buying new software. They’re doing it inside their ERP, their planning tool, or a thin workflow layer on top. The design is the work: which decisions go where, who owns each Category 2 flag, and what has to be in front of a person before they can meaningfully act on it.
Where to start
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Path 1, Internal build
Map your top 20 recurring decisions. Assign each one to Category 1, 2, or 3. For every Category 2 decision, name the owner and document the context they need to act within 24 hours. Start there. The mapping alone exposes where the process is broken, most teams discover they can’t agree on who owns what.
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Path 2, ERP or partner path
Ask your ERP partner or supply chain consultant how your current system handles exception routing today. Most platforms have the capability and it’s underused. A one-day process review focused on AI-output handoffs is usually enough to surface the top five places where recommendations land without a clear owner, and the consultants adding this layer to their post-go-live work are the ones clients keep calling back.
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Build the gate before you need it. Because when the AI is wrong, and it will be wrong, the question isn’t whether you have a review process. It’s whether you have one fast enough to catch the problem before it hits the floor.
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Build the gate before you need it. When the AI is wrong, the only question is whether you catch it before it hits the floor.
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Sources: Fulfillment IQ, April 2026; Prediction Guard, June 2026; BCG Executive Perspectives, May 2026.
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THE OPERATIONS BRIEF
By Bill Murphy · Powered by Colony Spark
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Already mapping your decisions into automate, flag, and human-required, or watching recommendations pile up with no named owner? Hit reply, I read every one. Bill
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