Mentor In A Pocket

by Gary Mintchell | Oct 23, 2025 | Productivity, Workforce

Articles about a worker shortage due to Boomer retirements have been a staple of trade magazine editorial ever since I became an editor in 1998. Some twenty-seven years later, those articles and news releases keep coming.

The concomitant problem is how to bring new people in. Apprenticeship programs went out the window with World War II. Businesses and manufacturers began expecting the education system to supply appropriately skilled workers. This was not going to happen despite education systems becoming increasingly industrialized. They taught basic math. Taught kids how to sit still and follow directions. Taught them to show up every day at the required location.

We need more.

It’s taken me some thought to place this new product from Derek Crager, Founder & CEO of Practical AI.

There is irony here, in that Crager touts himself as developer of an award-winning training program at Amazon—yes, the place that thinks it can replace its workers with robots. But, we will go beyond that thought for now.

I’ve not read the book, but he has also released a new book, Human First AI.

Crager says the real cost is downtime, rework, and attrition. He continues, It isn’t just a staffing problem—it’s an OEE problem. Every knowledge gap shows up in the metrics leaders actually manage: MTTR, FPY, scrap, rework, and yes, attrition. Ask any maintenance manager: the fastest way to lose a promising hire is to strand them without support on a tough job at 2 a.m. We send people to training, hand them SOPs, and hope they remember when it counts. But memory fails—especially under pressure.

His solution? Just-in-time guidance—the right step at the exact moment of need, while hands are on the task. When a technician can ask and do in the same breath, training becomes throughput. That’s the difference between teaching a concept and multiplying your best expert across every line and shift.

He called on his experience at Amazon to develop something called Pocket Mentor: A Phone Call to Your Best Expert. This is a hands-free, eyes-free mentor your team reaches by phone, anytime, on the floor or in the field. No app. No Wi-Fi. No passwords. Just tap & say, “Talk me through it” — and we will.

Here’s how it works:

• Capture once. We sit with your best people and harvest SOPs, changeovers, fault trees, “what-if” branches, and tribal tricks—the real decision trees pros use when the line’s on fire.
• Validate and govern. Content is approved by your SMEs and version-controlled with human-in-the-loop QA. Your source knowledge stays in a secure, governed box; people approve changes before they go live.
• Guide in the flow of work. A tech calls in, we ask two clarifying questions (model, symptom), then deliver step-by-step voice guidance they can follow while working—hands-free — eyes-free.
• Optional enterprise integrations. We can use your digital-twin/IoT signals today (enterprise integration) to pre-fill context—e.g., “Given Code 47 and 200 service hours, here’s the fastest fix; want me to talk you through it?”

He cites this pattern of stats.

• Up to 80% faster onboarding—because new hires can “tap & say” from day one instead of waiting for a veteran.
• ~30% reduction in downtime/rework—because the right step shows up at the right time, not after the post-mortem.
• ~53% lower early attrition—because nobody wants to feel alone on the line; support drives retention.
• 30× impact vs. traditional training—because we replace recall with real-time execution.
• 0 extra staffing to scale coaching—your best employee effectively becomes 20 or 50 virtual coaches, every shift.

Most project managers agree that you should start with a specific pilot, prove the system, then scale it out. Crager offers a few suggestions.

• Pick a chronic stopper. The two or three faults that always cause headaches (and overtime).
• Harvest the fix. Sit down with your A-team and capture the real-world fix path—model variants, hard-won “gotchas,” and the restart checklist nobody remembers at 3 a.m.
• Go live by phone. Give your night shift a number to call. Let them say, “Talk me through it.”
• Measure MTTR for 30 days. Compare to your baseline. Then expand to changeovers, start-of-shift checks, and training-intensive stations.

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Automation of Workflows Drives 20% Increase in Productivity

by Gary Mintchell | Sep 29, 2025 | Productivity, Workforce

Note: I had an allergy attack following my California trip that wiped me out for a few days last week. Now trying to catch up with a number of thoughts.

’Tis the season of surveys and reports.

Actually, that season has become about as year-around as Christmas and Halloween. Once the province of pharmaceuticals, companies commission surveys and report on the ones that support their place in the market.

Not to be entirely cynical—technology supporting the front-line worker has been burgeoning over the past few years. Perhaps this was accelerated by Covid pandemic increasing remote workers? I don’t know. 

The other interesting tidbit concerns Zebra Technologies, sponsor of this survey (companies call it research, but given my grad school courses, I balk at that description), has grown through some acquisitions from the company I knew (and sold a couple products) in the 90s.

Oh, and they had to throw AI into the mix. It’s up to you to figure out what they mean by that.

Zebra Technologies Corp. announced new research in collaboration with Oxford Economics. The study showcases how improving frontline workflows with modern technologies like AI, automation, and data improves profitability and enhances the customer experience. 

Retailers reported a 21% improvement in customer satisfaction, manufacturers cited a 19% increase in employee productivity, and T&L leaders reported a 21% increase in productivity with better workflows. In addition, the study indicates AI investments help organizations achieve real-time visibility, generate actionable insights and improve efficiency.

Companies surveyed said they were using (unspecified) AI to address loss prevention, risk detection, inventory optimization, inventory management, demand forecasting, and predictive analytics—all typical machine learning technologies that have been around for a while.

They also noted using tools that have been around for years, although greatly improved since I sold and installed them in the 90s—RFID and  machine vision.

The news release touts Zebra’s new brand narrative, “Better Every Day” affirming the company’s commitment to empower organizations with automation and AI to create new ways of working that make everyday life better.