4 minute read
Six Sanders in the Shop: Why One AI Tool Won’t Do It All
You wouldn’t use one sander for the whole job.
Walk into any woodshop and you’ll find a belt sander for stock removal, a random orbit for the field, a detail sander for corners, and a block for the final pass. Nobody stands there insisting one tool should do all four. The job decides the tool. That’s just how the work goes.
Somehow that common sense evaporates the moment “AI” enters the conversation. Shops go looking for the one platform that does everything, or, worse, end up with six people quietly running six different tools. Chat, agents, workflow orchestration, RPA: these are different grits for different jobs. Knowing which is which is the whole game.
The short version
- One AI tool won’t do it all. Each type is built for a different kind of work. Reach for the wrong one and you get rework, not a shortcut.
- The job picks the tool, not the other way around. Start from the work in front of you, then choose the grit that fits it.
- None of it works on a messy foundation. A detail sander on rotten wood still gives you rotten wood. Clean data first.
Match the grit to the job
Here’s how the four map to the tools you’re actually being sold. Reach for the wrong one and you either burn through the material or spend forever on a job the right tool would’ve finished in minutes.
RPA is the belt sander
Brute-force stock removal: high-volume, rules-based work with no judgment required. Fast and powerful on the right material, ruinous on the wrong one.
Workflow orchestration is the random orbit
The tool for the broad field, where systems hand off to each other and the work needs to move smoothly across a wide surface.
Agents are the detail sander
Into the corners a rigid workflow can’t reach: the tasks that need to adapt to what they find instead of following a fixed script.
Chat is the block
The final human pass. Judgment applied by hand, where it matters: the finish work no machine should be trusted to sign off on alone.
Before any of it works, the boring stuff has to be right
Here’s the part nobody wants to hear: none of these tools save you a minute if your PSA data is a mess. Board structure, ticket types, resolution notes, that unglamorous foundation decides how far automation can actually take you.
The shops winning with AI didn’t start with the flashy tool. They started by cleaning up what the tool would run on. A detail sander on rotten wood still gives you rotten wood.
Start where the data already points
Somewhere in your PSA is a stack of closed tickets with no real labor behind them, resolved on autopilot, no human time logged. That pile is your automation candidate list, and most shops have never once looked at it.
Pull your closed tickets from last month with zero logged labor and sort by ticket type. The type that shows up most is your first automation. No guessing. The data just told you where to start.
The bigger picture
All of this comes out of a way of thinking we call AIMSP, an operating model for running and measuring an MSP by the work the entire operation gets done, automation and people combined, not just the hours engineers personally log.
Wherever you are on that path, it starts the same way: knowing which grit fits which job in your shop. If you’re not sure where yours lands, that’s worth a conversation.
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