Everyone keeps asking whether AI kills Power BI or makes it stronger. Rui Romano flips that entire question on its head. As the Microsoft PM behind PBIP, TMDL, and all the file format work that rebuilt Power BI's foundation, he explains how the platform accidentally became one of the most AI-ready systems in analytics - and it wasn't by accident, not really. His team was solving problems for real developers who were tired of unsupported workarounds and offshore relay races. They weren't training agents. But the work they did means AI now feels native instead of duct-taped on.

What we learned was that the semantic model is still the highest ground in this whole space. While other tools let AI stumble through raw tables and pray the math holds up, a proper model gives AI the one thing it absolutely cannot fake: context. Relationships. Business logic that works at every level of granularity without falling apart. Rui breaks down why that matters now more than ever, why all the hardening work his team did keeps your models from exploding when an agent gets ambitious, and why the future of BI isn't about cranking out another hundred pixel-perfect dashboards. It's about fast iteration, lower friction, and answers you can trust at scale. Dashboards still matter - but only the ones people use.

This conversation goes deep on architecture, not hype. Rui talks about what's changing right now, what still needs work, and why natural language will eventually beat drag-and-drop for a lot of what we do today. If you've been wondering whether to invest in real semantic modeling or just let AI figure it out from scratch every single time, this episode makes the case for why foundations always win. Always.

Listen in and get ahead of the shift. And if the episode lands for you, leave us a review to help other folks find the show.

This episode starts with a familiar scene. A role opens, the applications pour in, and suddenly you're staring at a mountain of resumes that deserve real attention but arrive faster than anyone can process. The mix had everything… experienced candidates, newcomers trying to break in, and a growing stack of AI-generated submissions that looked sharp until you asked a second question.

That's where Haystack came in. Instead of using AI as a blunt filter, Rob and the team treated it like a collaborator. Teach it what matters. Teach it what P3 looks for in a teammate. Teach it how to separate real signal from polished noise. What came back wasn't a robot recruiter. It was clarity.

And Haystack is only half the story. As the conversation unfolds, Rob and Justin zoom out into the broader pattern they're seeing across all the small, useful agents taking shape inside P3. The stuff that isn't blind hype. The stuff that quietly fixes overloaded parts of the business and makes the human decisions easier to get right.

Because that's the through-line here. When AI handles the overflow, people get to spend their time on the work that actually requires judgment.

Queue it up and hear what happens when AI stops pretending to be magic and starts doing real work. And if you've got a corner of the business that's begging for that kind of clarity, we can help you find the tiny build that changes everything.

Rob finally cracked his years long standoff with the podcast lair cat, and the fix was hilariously simple. That small victory ends up setting the tone for the whole episode, because everything that follows has the same energy: real problems that only make sense once you shrink the solution down.

As Rob talks through the cat truce, Justin brings in a different kind of chaos. A customer service bot that sounded fully in command yet never actually did the thing it said it did. Pair that with a hiring queue full of AI written applications, and the whole picture starts to come into focus.

Once you see the pattern, you can't unsee it. The wins only show up when the AI job gets small. The fantasy football tool works the moment AI stops trying to scrape the entire internet and instead only writes the human part. The hiring filter works when AI is there to catch repetitive patterns, not run the whole show. Even the experiments coming out of Danielson Labs click only because the AI calls are tiny and the real work sits in regular code.

Everything points in the same direction. Let AI handle the one thing only AI can do, then let normal tech take it from there.

Everyone's talking about AI like it's plug-and-play. Spoiler: it's not. In this episode, Rob digs into why Big Tech's billions in AI R&D haven't yet turned into matching revenue — and what that means for the rest of us. The truth? The real business wins don't come from off-the-shelf models; they come from smart customization. Rob breaks down the "magic Lego brick" approach that separates hype from practical reality, showing how everyday tools like Power BI and Power Automate can connect to AI in surprisingly simple (and powerful) ways. He also revisits Bill Krolicki's "Vendor Bot" example to prove that you don't need to be a researcher or a billionaire to make AI deliver real results. If you've ever opened ChatGPT, asked it to "optimize operations," and gotten nowhere — this one's for you.

When the bubble pops, what's left standing?

Everyone's calling AI a bubble — and yeah, there's some wild stuff happening behind the scenes. Billions changing hands between the same few tech giants. Investments that look more like circular loans than innovation.

But what if the real bubble isn't AI at all?

Rob breaks down what's really inflating, what's not, and why your business shouldn't lose sleep over it. The headlines may sound like the sky's falling, but the tools driving real results aren't going anywhere.

Some bubbles burst. Others just float higher. Listen now to hear Rob's take on which kind this is and why the real value of AI doesn't care what the stock chart says.

Everyone's talking about AI. Almost no one's getting specific. And that's the problem.

Rob and Justin break down the three categories that matter: chat agents you talk to, embedded assistants baked into your workflows, and headless systems running quietly in the background doing the grunt work nobody wants to do.

This isn't theory. This is how AI is finally working for real companies.

They walk through Vendor Bot, Scheduler Bot, and Budget Bot — small, focused tools that do one thing exceptionally well. Because here's what keeps happening: the "one bot to rule them all" projects collapse under their own weight every single time.

But stack a few narrow, reliable bots together? That's when things get interesting.

If you've been wondering why AI works for some teams and stalls for others, this episode has your answer. Spoiler: it's not about going bigger. It's about getting ruthlessly specific.

The "we're all in on AI" era is over. The "we know where it fits" era just began.

If you like the show, be sure to leave us a review or share it on your platform of choice.

AI without the committee: Most manufacturers are still holding meetings about AI. Bill Krolicki just built it. As CFO of Interpak, he didn't wait for a strategy deck or a vendor pilot—he wired Power BI and Power Automate into a self-running operation. His bots read supplier emails, catch late shipments before they blow up production, and update the ERP while everyone else is still asking who owns the spreadsheet.

Rob Collie and Justin Mannhardt sit down with Bill to talk about what happens when finance stops waiting and starts building. From "Vendor Bot" to the soon-to-launch "Budget Bot," it's a front-row look at how AI turns from theory to throughput when a data person is actually in charge—no consultants, no six-month roadmaps, just results.

If you've had your fill of AI hype and want to see what it looks like when someone actually ships something, this is your episode. And if you enjoyed it, leave us a review on your favorite podcast platform—it helps other no-BS practitioners find us.

Last week we got a facelift—new name, new look, same deep data dives. This week? We prove the rebrand wasn't just cosmetic.

Rob kicks things off with a time machine moment: his first gig at Microsoft in the '90s, building the Windows Installer. The running joke back then? "Installing yesterday's apps tomorrow." Cut to 2025, and that exact same code shows up while he's configuring an AI tool for data modeling. Build something right, and it really sticks around.

And that's the bridge, AI context management isn't some brave new world. It's the same discipline that made Power BI models and Copilot integrations actually useful. You don't need to burn it all down and start over. You just need to get specific enough to matter.

If you've suffered through bloated "AI strategy" decks or watched a model confidently hallucinate through your business logic, this episode's for you. The fix isn't fancier AI—it's giving it structure, purpose, and the right context to work with. That's how you turn a show pony into a workhorse.

Bottom line: AI isn't a revolution. It's a new faucet. And the people who know how to connect it—and what to feed it—are already leading the next wave of transformation.

Everyone's talking like AI is coming for your job. Rob's here to tell you it's coming for your skill set. If you already think in tables, models, and relationships, congratulations—you're built for what's next. This episode is part pep talk, part reality check, and all proof that data people aren't getting replaced. We're getting promoted.

Rob takes you inside Microsoft's campus and out into the real world, where big firms are burning millions on AI theater while mid-market teams quietly pull ahead. The twist? You don't need to change the AI to win with it. You just need to feed it the right data. That means the same instincts that made you good at Power BI now make you dangerous—in the best way.

This one's for the data geners, the ones who never flinched at a gnarly spreadsheet and always saw potential where others saw pain. AI isn't a threat. It's your next playground. Listen in and meet the only cavalry that's actually showing up: you.

Check out the first episode of Raw Data with Rob Collie and get ready to lead the AI charge.

Nobody loves requirements docs. They're the corporate equivalent of writing a novel just so someone can skim the back cover. The real question is whether you can ditch all that and go straight from "here's what I need" to a working Power BI model. In this episode, Rob and Justin push AI into that role and see what breaks, what builds, and what actually saves you time.

Turns out, the magic isn't in making AI look impressive on a demo slide. It's in whether it can wire up tables, relationships, and measures fast enough that your team can skip the plumbing and jump right to the good part: asking "does this answer the question?" instead of "why won't this table join?" That's the test, and it's the only one that matters.

From tools that feel like friendly appliances to those that lean full hacker-mode, Rob and Justin run the gauntlet. They even crack open Copilot's inner workings to see how answers really get formed. It's a gritty look at whether AI can finally cut the "first-hour tax" every project pays and give leaders a faster path to value.