You're Multiplying the Wrong Thing

This week I watched a local service business owner share an article about AI being a "force multiplier" like it was good news with no asterisks. It might be. It might also be the most dangerous framing in tech media right now. Depends entirely on what you're multiplying.

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A Force Multiplier Amplifies Everything - Including the Mess

KTVE ran a piece three days ago calling AI a force multiplier for small businesses. Experts said so. The headline is technically accurate and practically useless without one follow-up question: what force are you multiplying?

A force multiplier does exactly what the name says. It takes the force you apply and amplifies it. If you have clean dispatching, consistent job costing, and documented handoffs, yes, AI makes all of that faster and more scalable. That's the version the headlines are selling.

But if your dispatching runs on phone calls and memory, if your job costing is whatever the crew lead writes on a napkin, if the only person who knows the exceptions to your process is the guy who's been there eleven years - AI multiplies all of that too. Faster miscommunication. More inconsistency at higher volume. Errors that used to happen twice a week happening twice a day.

The launch of CallVanta this week, a consulting-led AI automation firm targeting service businesses, tells you where the market is heading. More firms are going to tell operations owners they need AI now. The sales pressure is real and it's accelerating. The pitch will be compelling.

The question you have to ask before any of that: what is the actual force my business applies right now? Not what you want it to be. What it actually is today, documented, with the exceptions and workarounds included.

If you can't answer that, you're not ready to multiply anything. You're just ready to spend money faster.

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Agentic AI Doesn't Tolerate Undefined Workflows - It Executes Them

TechRadar published something this week that every operations owner running field crews or service routes should read. The headline: "Why Agentic AI demands business process re-engineering."

This is the enterprise world finally saying out loud what I've been saying about SMBs for a while. Agentic AI doesn't just work better with clean processes. It requires them. This is a technical requirement, not a preference.

Here's why that matters at ground level. An AI agent handed an undefined workflow doesn't stop and ask for clarification. It executes. It fills in the gaps using whatever patterns it can find. In a service business where the gaps are often the most important parts, where the exception handling is where you actually differentiate, that's a serious problem.

The proof is now showing up as a new category of software. Yahoo Finance covered it four days ago: observability startups are raising money specifically to help businesses figure out what their AI agents have actually been doing. Read that again. A whole vendor category now exists because companies deployed agents without knowing what those agents would execute, and now they're paying a second layer of tooling to find out.

That's what skipping process documentation produces. Not just bad outputs. A new recurring cost to manage the fallout from the first bad decision.

If you're looking at agentic tools right now, whether it's automated scheduling, AI-assisted dispatch, or any agent that takes action on behalf of your business, the first question is not which platform. The first question is whether every step of that process is written down, including the exceptions, including the workarounds, including what happens when the regular path breaks. If it isn't, the agent will handle those moments. You just won't know how until something goes wrong.

Document first. Deploy second. That's not a preference. It's the sequence that works.

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The ROI Question Is Coming - Are You Ready to Answer It?

CIO.com ran a piece four days ago with a line that should land hard for anyone who deployed AI tools in the last twelve months without a measurement plan: "Today's AI initiatives are evolving from pilots to being embedded into daily operations. They must deliver real value back to the business in measurable terms."

The conversation has shifted. Nobody is asking whether you're using AI anymore. The question is what it returned.

For operators who defined success before they deployed, this is a good moment. You have a benchmark. You can point to something specific: tickets handled, hours recovered, job cost variance reduced, callbacks eliminated. You can answer the question.

For operators who deployed tools because the timing felt right or because a vendor made a strong case, this is a harder moment. You have activity. You might have adoption. But if you never defined what the tool was supposed to change in measurable terms, you have no baseline to compare against. You can't substantiate the ROI claim because there was no target to hit.

This is what the Assure phase is built for. Before go-live, you define what success looks like in terms the person using the tool can actually control. Not vague outcomes. Specific metrics. If the tool is handling intake calls, what's the target handle time, what's the acceptable error rate, what does a good week look like versus a bad one. Then you measure against that from day one.

The operators who did that work are going to have good answers this year. The ones who skipped it are going to be explaining why their AI spend doesn't show up anywhere on the P&L.

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The Takeaway

This week, pick one process your team runs on repetition and write down every step, including the exceptions. Not to deploy anything. Just to see what you're actually working with before someone sells you a multiplier for it.

If your business runs on field crews, service routes, or job-based work and you're feeling the pressure to move on AI without a clear read on your process gaps, that's exactly what we audit. Hit reply and tell me where the coordination breaks down. We'll figure out if you're ready to multiply.