The Day the Data Came Alive

If you’ve ever walked through the bowels of a ballpark at dawn—before the tours, before the crowds, before even the nachos are hot—then you know how strange it feels to be somewhere so public and yet so private. Yesterday’s Seven Hills Technology event at Great American Ballpark wasn’t just another tech panel or glossy talk about “innovation.” It was a rare look inside the Cincinnati Reds’ tech org, with badge-on access to the rooms where data isn’t just stored but actually changes what the team does next. Honestly, it’s the kind of access I wish was more common, because this is where you see how talk turns (or fails to turn) into action. Here’s what surprised me most: the friction isn’t where you think.

You might expect a stadium tour to be all show—maybe a few slides about dashboards, someone promising “AI-driven everything.” Not this time. This was a lift-the-hood session on how teams operationalize AI in workflows to make decisions, small and large: not just on the field, but in player scouting, in marketing, even in how fans experience game day. It reframed everything I thought I’d see.

Walk with me through the pipeline. It starts at the edge. Sensors tucked under stadium seats, cameras above batting cages, radar units catching snap-second details of pitcher movement. Data flows from these endpoints all through the night. By the time most folks are asleep, raw feeds from games and practices have already kicked off analysis jobs. Scrubbing, fusing, churning it into something firm enough to trust. Come morning, the team doesn’t just have “a lot of data.” They have answers that survived a gauntlet. You can see why teams with solid pipelines rely on them. It isn’t magic, and it never feels finished, because it rests on pillars for reliable AI pipelines, but the difference between an elegant flight of POCs and a pipeline that actually wakes up with the sun is night and day.

This is where the myth falls apart. It’s not dashboard theater. It’s real impact. Decisions about player health, lineup changes, even which ad runs during the seventh-inning stretch—all of it directly informed by what the AI flagged, not by someone refreshing a pretty chart.

Why Most Teams Stall: The Modernization Gap, Up Close

Walking out of that tour, the divide between tech haves and have-nots has never felt more real. The day-to-day in the Reds’ analytics rooms feels closer to a command center than the PowerPoint-heavy “AI innovation” talk I keep hearing everywhere else. But then, during the roundtable right after, you could feel the tension in everyone’s stories—so many orgs still staring at lone dashboards and liberated POCs, waiting for something to cross the chasm into day-to-day workflow. Whatever you want to call it—modernization, digital transformation, catching up—the gap isn’t shrinking. It’s widening, and we’re the ones feeling it.

What actually blocks progress? The culprits are boring, stubborn, and weirdly persistent. Integrations that can’t stand a strong wind. Data feeds that show up in batches, hours or days after you really needed them. AI insights orphaned in separate tools, never making it into the systems where people actually work. Even when someone does build a shiny new model, there’s almost never a feedback loop to track whether anyone’s decisions actually changed. The old feedback loop is missing. Did this change what someone did, or did we just check another data box?

Look, if the Reds can turn camera feeds and sensor blips into actionable decisions under game-day pressure, your team can, too. But only once you embed AI into operations so those pipelines stop being a side project and start acting like your nervous system. The thing that quietly keeps everything moving.

Principles That Move Models to Outcomes

Think about the Reds’ data-to-decision pipeline as the nerve center of their operation. It isn’t just a data warehouse with some dashboards bolted on. Imagine a constant feedback loop. Sensors in stadium seats, pitch trackers, purchase scanners at merch stands—all firing off signals that feed straight into the machine. From there, MLOps for decisioning orchestrates the processing. Scrubbing and fusing messy data, dropping it into models that dig out patterns and risks. But it’s what happens next that matters—insights get lit up right where staff and players make decisions. In scouting meetings, you’ll see recommendations surfaced at the exact moment someone is about to sign off on a prospect. In marketing, lists update with real-time fan behaviors, not last month’s averages. Personalized fan experiences don’t come off a once-a-year survey; they’re shaped by continuous input from every interaction. The pipeline stretches from the edge all the way to the heart—the moments when someone has to choose “this, not that.”

The first design principle is continuity and trust. For a pipeline to drive real decisions, data can’t drip in when it feels like it. It must arrive clean, in the right shape, and on time. Nightly, or sometimes streaming by the second. Dependability breeds trust.

If features show up late, or if last week’s data mysteriously disappears on Thursday nights, users bail. I keep stressing this. Continuity is non-negotiable. It’s the difference between a system people quietly rely on every shift, and one they check only when forced. I’ve camped out in postgame tech rooms where every hour, new files dropped into the pipeline and models reran before staff even hit their lockers. The magic isn’t high-tech; it’s the drumbeat regularity that turns raw feeds into trustworthy events, feeding player decisions at dawn and front-office moves by noon. You’d be shocked how many orgs don’t get here. They stall because, without this nightly heartbeat, everything downstream—from automated scouting to targeted marketing—feels like guesswork. This is why every serious team I know invests early in making the data show up like clockwork. When that’s true, even skeptics start using the outputs, because trust isn’t a pitch. It’s earned, one consistent refresh at a time.

But let’s be honest. Even great data dies in isolation. The next principle is to operationalize AI in workflows. That means surfacing insights exactly at the decision point—inside CRM tools, lab notebooks, scouting platforms, the POS terminals where ticket discounts are set—not banished to some “AI dashboard” you have to remember exists. Remember the earlier example? A scout shouldn’t have to alt-tab into a data portal to see a prospect’s risk profile; they should see it folded into their draft list, colored and flagged, in the same tool they use to make the pick. Macros, popups, trust-building progress indicators. Whatever directs the decision at that critical moment. This is what moves a stat from trivia to action. If you’ve ever opened twenty tabs just to get context, you know how quickly that kills momentum. The right surface, the right time—otherwise, the insight might as well not exist.

Let me break for a second—I still remember a mid-pandemic POC with a Fortune 100 where the demo absolutely popped. The room was wowed, execs nodded, and for a week the project got daily praise. Two months later, all that sizzle fizzled. The model’s outputs lived on a site product managers never checked. No re-routing to core workflow, no reminders, nothing sticky. One bad handoff, and that beautiful thing withered on the vine. Cut to the Reds—any delay between “model complete” and “actionable in system” can cost not just dollars, but the shot at winning tomorrow’s game. The cost of delay isn’t theoretical; it’s the day you miss a prospect or undersell the stadium. Everyone learns that lesson once, then glues every insight into the place it actually counts.

Last principle, and maybe the one we forget when the demo buzz wears off. Measure decisions changed. Don’t fall in love with model metrics or pretty visualizations. Track what really changed. How fast did decisions turn around, how many times did users choose the auto-recommended lineup, did ticket offers actually drive sales without extra error? This is your real scoreboard—align AI metrics with outcomes so teams trust and scale what works. You should optimize the whole pipeline for these impacts. Otherwise, it’s all slideware and theater. When you design for changed decisions, you’re not just modernizing. You’re building a nervous system that gets stronger every time you use it. That’s where the real compounding advantage lives.

Every team thinks their data is special. In my experience, only the ones who bake in continuity, trust, seamless integration, and real-world measurement see AI move the needle. Everything else just collects digital dust.

The Practical Playbook: How to Operationalize AI in Workflows From Data to Decisions That Matter

Start simple. List out the real decisions your people make each day. Not the “wouldn’t it be nice if…” hypotheticals, but the calls they actually have to make—who gets benched, which prospects to chase, what deals to run for fans, when to swap an injured starter. Inventory these high-frequency moments.

For each, write down what “successful” looks like (fewer injuries, more ticket sales, better drafts, happier fans—pick what’s real for you). Trace backward. What signals would you need, all the way from the edge (raw data) through to the model, to get an early heads-up or a smart recommendation? Find where the work happens—those actual screens, apps, or meetings where choices get made. That’s your insertion point. Don’t build for theory; map directly from decision to data to the moment action is taken, and choose the right AI approach by piloting a small slice before full workflow integration.

Let me ground this with what the Reds do, because it’s not theoretical. On their ops side, Mickey Mentzer (Director of Baseball Systems) and team run nightly jobs that churn sensor data—pitch speed, player movement, heck, even fan purchase patterns—into insights before breakfast. Real-time cues get piped straight to the folks handling scouting or adjusting the roster. The ops team doesn’t wait for someone to “pull the latest report.” The signals flow into the places (Slack threads, scouting apps, even SMS alerts for urgent injury risk) where decisions get made. That continuous handoff turns out to be everything.

Here’s how you actually thread the needle without taking everything offline. Integrate carefully. Lean on event-driven APIs or low-latency architectures to push signals where work happens, and use a feature store. It gives you one governed, central spot for both model training and putting live predictions into workflow, so you’re not stuck stitching together messy handoffs. Decouple your AI architecture to keep workflow integrations stable during model and tooling changes. If you need real-time, low-latency decisions, use event streams.

EDA’s low-latency event streams unlock real-time ML processing where the action is, making edge-to-insight architecture far less brittle. Ship small, safe slices (not a grand rewrite), and backtest them continuously. If the risk is high—roster moves, ticket pricing, major player health—wrap a human-in-the-loop so nobody gets blindsided. My admission: every place I’ve botched a launch, I skipped the guardrails and rolled out too much, too fast. Slice it smaller; it’s always worth it.

Don’t stop. Iterate each night. The point isn’t to hit “done,” but to get decisions a little faster and sharper with every cycle. Pipelines toughen up, human trust grows, and you get one step closer to outcomes that compound. Every day, every week—keep moving the needle.

So, if you’re mapping out your own data-to-decision system, start with the real decisions. Trace your signals. Find the insertion points. Ship safely, instrument ruthlessly, and never stop refining. That’s how you turn data into your actual competitive edge.

Facing Friction: Turning Objections into Advantage

Let’s be honest—almost every conversation at yesterday’s event eventually circled back to friction. Time investment, integration risk, and the big skeptical question: “How do we actually measure impact beyond dashboards?” If you’re reading this, it’s probably top of mind for you, too. Here’s the direct take. Yes, it takes real time to embed AI and data pipelines into the places where decisions actually happen. Yes, integration invites breaking changes and pushback. And yes, unless teams deliberately avoid dashboard theater, the easy outcome—another dashboard nobody checks—haunts every team. I’m not trying to hand-wave these. The only way around is to shift the definition of impact. Stop counting dashboard views or report refresh rates; start measuring the number of decisions that change because insight arrived at the right minute. That’s the only scoreboard that matters. If you’re not tracking that, you’re still stuck in the theater.

This isn’t just about baseball or sports. The same end-to-end AI pipelines that take sensor data from a ballpark and turn it into actionable insights adapt directly to other industries. Whether it’s auto-updating product recommendations for online shoppers, rerouting supply chain deliveries after a storm, or tuning marketing personalization based on live campaign results—the mechanism holds. What matters is that signals flow all the way through, from edge to model to decision point, no matter your business.

So, how do you keep this real? Instrument for outcomes, not optics. Define your KPIs like real-world adoption rates—how often did the recommended action get chosen? Track time-to-decision. Did insights arrive fast enough to be used? Pin down error-rate improvements. Are people making fewer mistakes because of the pipeline? Make these numbers visible right in the workflow tools people use—the CRM, the ticketing system, the scouting app. Forget separate dashboards no one logs into; let the reporting live in the space where behavior actually changes.

Momentum isn’t magic. Start with one high-impact decision, wire up your pipeline, prove it changes something that matters. When you see a win, stack the next one. Keep compounding. Every changed decision is the best signal that your data is finally doing its job.

There’s still one thing I puzzle over, and I’m not sure I have an answer. Even with great systems and serious buy-in, there are nights when nothing seems to move—when signals are flying and nobody’s budging, when workflows hum but culture stalls. Maybe some frictions don’t get fixed by code or dashboards. I keep wondering if that edge is more stubborn than any tech gap we see on the surface. For now, I measure what I can, watch what actually changes, and keep my eye on those moments when the data finally comes alive.