June 10, 2025

AI and data-driven decision-making for leaders who want results (not just reports)

Chip LaFleur · 6 minute read

This blog is based on our recent Connectionology webinar, AI and Data-Driven Decision-Making: Smarter, Faster. We will be sharing the webinar video soon.

AI is everywhere. And if you’re leading a business, you’ve likely felt the pressure to “do something” about it. Maybe you’ve tested a few tools. Sat through a vendor demo promising operational transformation. Wondered whether you’re already behind.

Spoiler: you’re not.

But if you’re still treating AI as a vague trend instead of a practical tool for decision-making, you’re looking in the wrong direction.

We recently hosted a webinar with Connectionology Seminars to break this down in plain terms: what AI and data-driven decision-making actually look like in practice. The audience wasn’t full of early adopters chasing the next shiny object. It was lawyers and operations folks—people who want results, not hype.

And the most common question they asked: where do we even start?

Most people are just getting started with AI(and that’s okay)

Here’s something that surprised us during the webinar: even among high-performing professionals, many weren’t just unsure where to start with AI… they were still trying to make sense of the terminology.

What’s the difference between machine learning and automation? What’s structured data? How does a model “learn”? These aren’t dumb questions. They’re foundational ones. And frankly, most AI vendors do a terrible job answering them without jargon, assumptions, or spin.

We believe AI literacy isn’t optional, but it also doesn’t have to be overwhelming. That’s why we’ve built tools and resources to meet people where they are, whether they’re just beginning or ready to dig into data strategy.

One of those tools is our digital marketing glossary, a plain-language guide designed to help you decode the buzzwords and understand what actually matters. We recently expanded it to include some of the most common data and AI terms we’ve been hearing in conversation. The result is a resource that’s not academic or theoretical—it’s practical, so you can hold your own in a product demo, ask sharper questions, and lead smarter conversations inside your business.

After all, understanding the language is the first step toward making better decisions.

The case for boring AI systems

Most AI pitches lean on big, theatrical language: revolutionize, disrupt, transform. That might play well during a demo. It doesn’t help you run a business.

What actually drives progress? Clean data. Clear goals. Repeatable decisions. The boring stuff.

Here’s the part people miss: AI isn’t a standalone tool. It’s infrastructure. It shouldn’t sit on the side, generating charts or content or clever answers. It should live under the hood: quietly surfacing patterns, flagging changes, and powering decisions at scale.

Think of AI the same way you think about plumbing or wiring. If it’s designed well, you don’t have to think about it. You just get a flow of insight, action, outcomes. But if the foundation is unstable or disconnected, it doesn’t matter how advanced your tools are. Nothing moves.

That’s why flashy dashboards often fall flat. They show lead volume, conversion rates, time-to-contact. But the numbers don’t change because no one built the system to drive action. It reports. It doesn’t respond.

That’s the real shift: AI isn’t just there to summarize what happened. It should highlight what changed and help your team do something about it.

Data: The system underneath the system

Nobody gets into law or marketing because they love cleaning spreadsheets. But if you want AI to help you make better decisions, you have to understand what it needs to function. AI doesn’t invent insights. It identifies patterns in existing data. Which means the quality of your inputs will always determine the value of your outputs.

That’s where most businesses run into trouble, not because they picked the wrong tool, but because their data foundation is a mess.

Let’s say your case signings are down. You want answers, so you check your reports. But your lead data lives in five systems. One says “car crash,” another says “auto accident.” Dates and attibution data are documented differently on different platforms. And half of it hasn’t been updated in weeks.

That’s not an AI problem. That’s a data hygiene problem: inconsistent labels, missing fields, outdated records. Until that’s cleaned up, AI is just guessing. Worse, it’s guessing with false confidence. Fixing that is the first step.

The next is building a system that lets you actually use that clean data: a data warehouse. A data warehouse doesn’t just store information. It pulls it together from across your business, standardizes it, and makes it usable. It gives you a single, structured source of truth. No more digging across platforms or squinting at misaligned spreadsheets.

In short:

Data hygiene makes your information trustworthy.
Data warehousing makes it accessible and actionable.

Without both, AI is noise. With both, it becomes a powerful layer of infrastructure, one that helps your team see what changed, why it changed, and what to do next.

A real story (with a real fix)

Here’s what law firm operations look like when the systems are working.

Years ago, we worked with a personal injury firm that was seeing a puzzling decline in case signings. Web traffic was steady. Ad spend was unchanged. But there was a distinct drop in the number of new cases the firm was getting. The marketing team got nervous. Leadership was ready to start overhauling campaigns.

But when we looked at the data—all of it, in one place—a different story emerged.

The firm’s best intake rep had been reassigned to work on medical record retrieval. No one had connected the dots. But as soon as we flagged the change, they moved her back into intake, gave her a raise, and saw an immediate recovery in signings.

Back then, that insight (and more importantly, the actions that insight triggered) took time to uncover. We had to compile data and manually review much of it. Suppose our research and recommendation phase took two months. And suppose that firm lost out on just three cases each month because of their undiagnosed intake issue.

If the firm’s average attorney was $25,000.00, that’s $150,000.00 in lost revenue.

Today, that firm’s data is safe and well organized in a data warehouse. Using a platform like Clearboard, it might only take days to uncover the intake issue. That’s the kind of boring magic we’re talking about.

What AI is actually good at

So what should AI be doing for your business?

It’s simple: Anything you do repeatedly. Anything you need to do faster.

This includes:

Automated reporting: Daily lead summaries, week-over-week comparisons, spend efficiency alerts.
Early warnings: Spotting subtle declines in performance before they become problems.
Augmented decision-making: Helping your team act on trends, not just observe them.
Pattern matching: Identifying which referral sources or campaigns lead to higher-value outcomes.

But here’s the catch: it only works if AI is aligned with how your business actually runs.

In an ideal world, AI should be embedded directly into the tools and workflows your team already uses. The less friction, the better the adoption. If using AI means opening yet another platform or checking another dashboard, odds are people will ignore it.

That’s why we design around real questions like:

What changed this week?
Is it a blip or a trend?
What are we doing about it?

That said, there’s still value in a well-built standalone tool, especially when it delivers clarity without noise. Clearboard, for example, isn’t trying to be everything. It’s a streamlined dashboard built to surface the right questions and make performance easier to understand. It complements your systems. It doesn’t compete with them.

The goal isn’t to chase the most advanced AI, it’s to make your existing processes sharper, faster, and more responsive.

Keep the team in mind

Even the best systems will stall if your team is unsure how (or whether) to use them. That’s the part leaders often underestimate. Adopting AI isn’t just a technical decision. It’s a cultural one. It changes how people work, how decisions get made, and what’s expected of them day to day.

And change, even positive, exciting change, comes with friction. We’ve seen it play out:

Junior team members worry they’ll be replaced.
Senior team members wonder if their judgment is being questioned.
Everyone’s quietly unsure which tools are approved, which aren’t, and whether experimenting will get them in trouble or celebrated.

This isn’t a question of compliance. It’s a question of trust.

If you want adoption, you need to lead from the front. That means:

Being honest about your own experiments, especially when they don’t work.
Asking in team meetings, “What did you try this week?”
Making it clear that using AI to speed up work or improve accuracy isn’t cheating, it’s the goal.

And it means giving your team more than tools. It means giving them the clarity and psychological safety to explore those tools without fear of messing up.

You don’t need a perfect rollout plan. You need clear policies, guidelines, and a team that feels empowered to figure it out with you.

AI doesn’t lead. You do.

AI won’t fix bad processes. It won’t magically connect your systems. And it definitely won’t replace the judgment of someone who knows how your business works.

But with the right foundation—clean data, clear purpose, and a culture of curiosity—it can make you faster, sharper, and more confident in your decisions. So, don’t get distracted by the most advanced tools on the market. Focus on what matters: understanding your data, asking smarter questions, and making your operations just a little better every week.

Because that’s what progress actually looks like.

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