Everyday Run State: Pro Edition

My husband and I have a morning ritual: we compare our sleep scores.
Before coffee, before emails - it’s a quick, daily check in:
“82.”
“Nice. I got an 89.”

But the other day, he woke up with a terrible score. The data said it took him 104 minutes to fall asleep. Two hours! Neither of us believed it - and sure enough, the culprit wasn’t insomnia, it was a loose watch strap.

The data looked conclusive, but the input was wrong.

That happens in business all the time.

When the Numbers Lie

We talk about “data-driven decisions” constantly - and now, with AI entering the mix, everyone’s chasing more dashboards, more analytics, more insight. But as my husband’s sleep score reminded me: bad data, even dressed up in charts, is still bad data.

In the latest State of Enterprise AI report from Cloudera, only 21% of enterprises said they’ve fully integrated AI into their core business. The biggest barrier? Not talent or enthusiasm - data quality.

Most companies have the data, but it’s messy: missing fields, inconsistent formatting, disconnected systems, and human workarounds that never make it into the database.

Sound familiar?

I see it all the time when I dig into ConnectWise or quoting data. A dashboard says one thing — “average project margin is 42%” — but when we trace the inputs, we find unmapped GL accounts, duplicate products, or labor hours logged under the wrong board.

The number is real, but it’s not right.

AI and the Human Gut

Here’s the tricky part: AI will happily make predictions from whatever data you give it. It doesn’t pause to ask, “Are you sure?”

That’s why the human gut still matters. You need the analyst’s curiosity - the same instinct that says, “There’s no way it took him two hours to fall asleep.”

Data without human context can mislead. Context without data can be biased. The magic happens where the two overlap - where experience meets evidence.

Reality Checks for Your Business Data

Here are a few common “sleep score” moments I see inside MSPs and Pro AV organizations — when the data looks solid, but your instincts should still ask questions:

1. Ticket closure rates look amazing… until you realize the techs are marking tickets complete before doing post-work updates.

2. Projects show 50% profit… but product costs haven’t been received, so it’s profit on paper only.

3. SLA compliance is at 98%… because a board setting excludes after-hours tickets.

4. Inventory shows zero variance… but you haven’t done a physical count in six months.

5. AI-generated reports look clean and confident… but they’re built on data that’s never been validated by the people doing the work.

The Story Behind the Metrics

Data is powerful- it helps you see patterns, spot issues, and make decisions faster. But the real advantage comes when you pair that data with what your team knows firsthand.

So before you act on a number, do the equivalent of tightening the watch strap:

• Ask where the data comes from.

• Check if it matches what people are actually experiencing.

• Use anomalies as starting points for curiosity, not conclusions.

AI might be the future of insight, but accuracy still starts with good habits - clean data, clear processes, and a healthy dose of skepticism.

The smartest organizations don’t just track data, they listen to the people who question it.