A retirement notice comes in, and someone schedules a few sessions with the person leaving. An engineer sits in, takes notes, maybe records part of a job. A document gets written. Then the expert retires, the engineer goes back to the backlog, and the next notice starts the same scramble from the beginning. Each round captures something, and none of it adds up.
A knowledge capture program is a standing process that decides whose knowledge to capture first, which jobs to capture, who owns the work, and how the result stays current after the expert has gone. It turns capture from something that happens around each retirement into something that runs every month. The method for capturing one expert's knowledge is in how to capture tribal knowledge before your experts retire, and the rest of this piece applies it across the plant.
Treating each retirement as its own capture project is a reasonable instinct. It's urgent, the person is leaving, and something is better than nothing. The trouble is what the project leaves undecided.
Who goes first? Retirement dates are the obvious queue, but they aren't the only risk. The person who matters most may be the one twenty years from retirement who is the only one on second shift who can set up a particular fixture.
Which jobs? An expert knows hundreds of jobs. Capturing all of them is impossible, and capturing the ones that come to mind first misses the ones that would hurt most to lose.
Who owns it? When capture sits on top of an engineer's real workload, it waits until the next emergency.
What happens after go-live? A captured instruction starts going stale the day the process or the product changes. Without a route for the floor to say it's wrong, the documented process drifts from the real one, and the plant is back to relying on whoever has been there longest.
Those four gaps share a cause. Knowledge loss has no end date, because people retire, move shifts, and change jobs every month, and products and processes change underneath them. A plant that captures knowledge only in response to a departure is always one departure behind.
A program closes the gaps by making four decisions once and then repeating the work on a cadence.
Rank the people whose knowledge you can least afford to lose. Retirement horizon is one input. Single-point-of-failure risk is the other, meaning the jobs only one person can do well, the setups one shift depends on, and the person everyone calls when a build goes wrong. Someone with both, close to retirement and the only one who can do the job, goes to the top.
For each person on the list, rank their jobs by three things. Risk comes first, meaning what happens to safety or quality if the job is done wrong. Frequency is next, since a job done daily by many people matters more than one done once a year. Last is how undocumented the job is today. The foreign object debris check before a housing is closed is a good example of a high-priority job. Your standard may require it, but the expert's habit of counting parts back into the tray and checking every open port is what actually catches the capped port or the dropped washer. That habit is the part worth capturing.
Give the program one owner, usually a manufacturing engineer with the authority to schedule time with the experts. Build capture sessions into the production schedule rather than squeezing them in around it. Review the queue monthly, add people and jobs as risk changes, and track a small set of measures, such as how many at-risk jobs have a released instruction, how long capture takes from recording to release, and how many corrections come back from the floor.
After release, the people doing the work should be able to flag what's wrong or missing where they see it, and the instruction's author should be able to correct it and publish a new version without starting over. This is what keeps captured knowledge accurate as the product and process change, and it's how knowledge keeps being captured from the people who stay.
Every part of the program draws on the same scarce resource, which is time with the experts. Training faces the same limit outside the plant. In the Federal Reserve's July 2026 Beige Book, a community college contact in the Cleveland district said a lack of qualified instructors for workforce training is a major challenge. Inside a plant, the qualified instructors are the experts on your priority list, and they're also needed on the line.
So the program has to get the most out of each hour an expert gives it. That hour should go to doing the job on camera and reviewing a draft. The writing, formatting, and rework belong to someone else.
Canvas Envision takes most of the work out of the stretch between recording the expert and the expert's review, and it gives the program its feedback loop after go-live.
In Envision Creator, Evie, the AI built into Canvas Envision, turns a recording of the job or an old procedure into a structured draft that an engineer refines. The authoring interface is built for engineers and manufacturing planners, so the queue doesn't depend on one specialist. Recording the experts themselves is covered in recording your experts on video.
The expert reviews the draft in a formal review in Envision Creator, or works through it as a worker will see it in Envision Operator and leaves feedback there. Either way, the feedback reaches the author. The instruction is then released through draft, review, and publish, with a version history for every revision, and Envision Portal controls which teams can open which instructions.
After release, workers signed in to Envision Operator leave feedback on the exact part or step, with a priority, and the instruction's author is notified. The author corrects the instruction and publishes the next version, and the program owner sees what's coming back from the floor. That's the feedback loop that keeps the program's output current.
Where instructions are built from engineering data, Envision Connector brings released design changes in from Product Lifecycle Management (PLM). The changes update automatically in authoring and are reviewed and published under your control, so the program keeps up with the product. CMbE, a Canvas Envision customer, generates governed work instructions from Aras process planning.
The point of a program is that it keeps working after the experts on today's list have retired. Next year's list will have different names on it, and the plant will be ready for them instead of scrambling. The new hires arriving now get instructions that carry what their predecessors knew, and that keep improving as the people who stay add what they learn.
See how Envision Creator authoring and Envision Operator feedback fit together. Request a Demo and bring the job at the top of your list.
Garth Coleman is CEO of Canvas Envision, where he is changing how manufacturers capture, share, and execute the knowledge their best people carry. He brings nearly three decades in enterprise software spanning CAD, Product Lifecycle Management (PLM), and 3D communication, including senior leadership at Dassault Systèmes.
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