Issue No. 09 - August 4, 2026

The AI ideas at JB Sales don't stop. That's not a complaint. Most of them are actually good.

But somewhere in the last few weeks I started noticing a gap. The question we keep asking is "what can we do with AI?" And the answer is almost always: a lot. The harder question, the one we haven't been asking consistently, is "what should we do with AI?" Those are very different conversations.

The difference between them is outcomes.

At JB Sales, we have real business goals. Things we're trying to move. Revenue targets, conversion rates, content reach, operational efficiency. When an AI idea comes in, the question isn't whether it's clever or technically interesting. The question is whether it connects to something we're actually trying to accomplish.

A lot of ideas don't survive that question. And that used to feel like saying no to progress. It doesn't anymore.

Cool AI work is genuinely exciting. I get why people chase it. There's something satisfying about building a workflow that's elegant, or automating something that used to be tedious, or finding an AI tool that does something you didn't think was possible. But cool isn't a business outcome. And if the work doesn't connect to one, it's a distraction. A well-intentioned one, maybe, but still a detour.

The shift I've been trying to make is treating outcomes as the first question, not the last one. Before we get excited about what AI could do, we ask what we're trying to achieve. Then we look at whether the AI idea moves us toward that, or just moves us.

This sounds obvious. It isn't, when you're in the middle of it. The ideas come fast and they're persuasive. Some of them are coming from smart people inside the organization who are genuinely trying to help. Saying "that's interesting, but it doesn't connect to where we're going right now" takes more discipline than it sounds like.

The other thing I've noticed: when you do have a clear outcome, the AI ideas get better. More specific. Connected to something real. The conversation stops being about features and starts being about actual problems. That's a much more useful place to be.

We haven't figured this out completely at JB Sales. We're still working through how to evaluate ideas consistently. But asking "what outcome does this serve?" before anything else has already changed the quality of the conversation.

Where this lands in the three buckets

Immediately actionable: Take your current list of AI ideas. The things your team has floated, the tools you've bookmarked, the workflows you've been meaning to try. For each one, ask: what specific business outcome does this support? If you can't answer it, it doesn't get prioritized until you can.

Start thinking about: What your actual business outcomes are right now. Not broadly ("grow the business") but specifically enough that you could evaluate an AI initiative against them and get a clear answer. Without that clarity, the outcome question won't help you.

Next phase: Building a simple way to evaluate new AI opportunities consistently as they come in. Outcome fit, time to implement, expected impact. Not a complicated framework, just enough structure so you're making the call from something more than gut instinct.

Meghan Brenner is COO at JB Sales and founder of The Operator's Notebook. The Muddy Middle publishes every Tuesday.