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By Bill Murphy · Powered by Colony Spark |
THE OPERATIONS
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ISSUE #22 · AUGUST 28, 2026 |
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Hi ${first_name},
AI adoption is not the day the tool goes live. It is the day your people stop going back to their own spreadsheet.
Every section in this issue carries that theme. The Shift says nobody measures whether people actually use what was deployed. From the Floor is an operations director who built tools that worked and nobody used. The Stack is the one-page weekly record that catches it.
Bill
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We measure whether AI works. We do not measure whether anyone uses it.
The gap is not between companies that bought AI and companies that did not. It is between companies where people use the thing and companies where the tool sits there while the operation runs on the same spreadsheets it always ran on.
Every AI conversation in manufacturing is still framed as a technology question. Which model, which vendor, which use case. The 2026 data says the technology question got settled and the behavior question was never asked.
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Six signals from 2026
· 84% of manufacturers agreed executive leadership is more enthusiastic about AI than employees. In the same survey, 88% said they have the right in-house AI expertise, but only 22% said their AI investment is concentrated on upskilling their own people (RSM US, July 2026).
· Vendor surveys put manufacturing AI adoption near 88%. The federal survey puts actual business AI use at 19.8% as of May 2026. Both are accurate. They measure different things. One measures that a company has AI somewhere. The other measures whether the business used it (U.S. Census Bureau, May 2026).
· Usage splits hard by size. 37% at firms with 250+ employees, 32% at 100 to 249, under 20% at the smallest firms.
· The failure has a specific shape on the factory floor. Operators don't trust the recommendations. Supervisors revert to spreadsheets. Engineers quietly disconnect the system to keep lines running. The problem is not the algorithm. It is the absence of people in the process (Catalyst Connection, Oct 2025).
· When AI pilots underperform, manufacturers cite data quality (51%), integration (43%), security or compliance (34%), unclear ROI (30%), and adoption resistance (30%). Data quality and integration get budgets, owners, and dashboards. Adoption gets a training email.
· The measurement fix: Track engagement metrics, user confidence, decision consistency, and collaboration alongside technical KPIs like accuracy or uptime. The best models mean little if no one uses them (Catalyst Connection, Oct 2025).
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At our roundtable on August 26, Robert Wyse, owner of Industronics Service Company, described the signal he watches for: the point where employees get comfortable enough with a tool that they start advocating for it on their own, without being asked. It is an early signal, not a return. It also costs nothing to watch, and almost nobody writes it down.
That is the pattern across all of it. The only proof of adoption is that the old workaround is gone, and nobody checks. Deployment gets a project plan. Uptime gets a dashboard. Whether anyone asked for the thing gets nothing.
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For every tool you deployed this year, can you name the workaround it was supposed to replace, and can you prove that workaround is gone.
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Why adoption stays invisible.
Matt Gargas, Director of US Operations, RDT, on tools that work and get ignored
Matt Gargas runs operations at RDT, a mid-market industrial manufacturer. Speaking with the Operations Brief Roundtable on August 26 about his early experience with AI deployment, he described a failure mode nobody talks about: the tools that work but nobody uses.
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"I can't tell you how many VBA spreadsheets I built that looked good and calculated quickly but never got used. Everyone went back to their own spreadsheet."
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Matt Gargas
Director of US Operations, RDT · LinkedIn
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The tools were not broken. They worked. They looked good. They calculated fast. They were ignored. That is the difference between technically finished and operationally adopted, and it is the whole issue in two sentences. This failure mode is not new either. It predates AI by twenty years. That is exactly why the AI version was predictable.
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The takeaway
The rollout is not the finish line. A training completion report is not evidence. If the old spreadsheet is still open on somebody's second monitor ninety days in, the deployment is a line item, not a capability.
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The Adoption Ledger.
A one-page weekly record that catches what dashboards miss.
Not a dashboard. Dashboards are how the adoption question gets avoided.
Adoption has no owner in most operations. Data quality has an owner. Uptime has an owner. Whether Denise in scheduling still runs her own spreadsheet is nobody's job. The Adoption Ledger is a one-page weekly record per deployed tool, answering one question: is this tool replacing the thing it was supposed to replace.
Set up once, at deployment
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· Target task. One specific task this tool performs. If you cannot name one, that is already the finding.
· The workaround it replaces. Name it exactly. "Manual process" is not an answer. "Denise's shipping spreadsheet on the shared drive" is an answer.
· Expected users and how often. Daily, per shift, per order, per exception.
· One operating metric. Cycle time, error rate, rework, or touches per order. Pick it before go-live.
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Update weekly
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· Usage this week against the expectation.
· Workaround status: retired, shrinking, unchanged, or grown.
· Exceptions with a reason code: too slow, did not trust it, could not access it, did not fit the process, faster my way, nobody showed me, tool was wrong.
· The operating metric reading.
· Unprompted advocacy: did anyone ask for it or recommend it without being asked.
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The weekly decision
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One line per tool
Keep: usage meets expectation, workaround is shrinking or gone.
Change: usage is short and the reason codes point at a fixable cause. Fix the named cause, set a review date.
Stop: four straight weeks of unchanged workaround status with no fixable reason. Write down why, so the next vendor conversation starts from evidence.
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Three examples
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AI quoting assistant.
Target task: first-pass quote build. Workaround: the estimator's personal pricing spreadsheet. Expected on every quote under $50K. Used on 9 of 22. Workaround unchanged. Top reason code: did not trust it on nonstandard parts. Decision: change. Pull nonstandard parts out of scope and re-baseline in three weeks.
Predictive maintenance alerts on line 3.
Workaround: monthly walk-around plus operator intuition. All 11 alerts triaged within a shift. Walk-around dropped to biweekly. Unplanned downtime down. The line 2 supervisor asked to be added. Decision: keep and expand.
AI meeting notes for production standup.
Workaround: the supervisor's notebook. Used 2 of 5 days. Workaround unchanged. Reason code: faster my way. No operating metric was ever defined at deployment. Decision: stop.
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The reason codes are the whole build. "People are not using it" is not actionable. "Six exceptions this week, five coded too slow" is a specification. Log exceptions as they happen, thirty seconds each. If logging takes longer than the workaround, nobody logs it and the ledger dies. Friday, fifteen minutes, walk the page per tool and make the call. Monthly, look at reason codes across all tools. The same code showing up on four tools is a design finding, not a people problem.
Two ways to build it
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Path 1: Internal build
A shared sheet and a recurring fifteen minutes on Friday. Start with the three tools you spent the most on this year. Run it four weeks before judging anything. The discipline is the product, not the software.
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Path 2: Through your partner
Ask the ERP partner or integrator to instrument usage and report override rate, abandonment rate, and exception reasons alongside uptime. Most can. Almost none do unless asked. Put it in the next statement of work.
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A tool nobody uses is not a tool. It is a line item.
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THE OPERATIONS BRIEF
By Bill Murphy · Powered by Colony Spark
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Watching people go back to the spreadsheet, or already tracking adoption metrics? Hit reply, I read every one. Bill
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