Data As Lifeblood: Making AI in BI Work for Your Organization

In this episode, Triad hosts Kristin Rydland and Jessica Lidh welcome Craig Colangelo, Senior Solutions Architect at PM Square, to discuss the convergence of analytics and artificial intelligence. Craig argues that we are living in an extraordinary moment for technology—comparable to the Industrial Revolution—where AI is compressing a century’s worth of progress into just a decade. He explains that the traditional distinction between business intelligence and business analytics is fading, replaced by a new reality where tooling matters less than strategy and best practices. With AI-augmented workflows, natural language chat interfaces, and agentic features baked into modern analytics platforms, organizations can now put data in the hands of the 70% of employees who have traditionally lacked access. Craig shares how PM Square helps clients navigate this shift through strategic ideation sessions, roadmapping, and hands-on conversion work. He also offers a powerful, relatable example: using an AI chatbot called “Buddy” to automate customer service and registration for his youth soccer franchise, Soccer Buddies. His core message is that if a small business can leverage these tools, so can state and local governments—and the time to start is now, before the bus runs you over.

The Center of Gravity Is Shifting in Analytics:

“The center of gravity is shifting from, like, building dashboards off of a data warehouse more into making data available to all the different ways that consumers and bots consume it throughout the organization… to me, like, the narrow boxes that were BI or BA, in the old days have kind of evolved into something brand new, that’s probably more enriching, not only for organizations, but probably personally, too, for practitioners in the space.”

Tooling Matters Less. Best Practices Matter More:

“To me, I think that tooling matters less these days. I think some of the tenants, the best practices kind of matter more. And in my mind, that would be taking advantage of Agentic AI… using the chatbot features within them to have natural language conversations with your data… That opens it up to the 70% of most organizations that don’t have access to proper analytics tools… empower your organization to make decisions based on data, rather than just gut.”

Clients Are Moving from Tactical to Strategic:

“What surprised me most is probably how willing and ready our clients were to move from the tactical to the strategic… people definitely see the value of caring more and spending more time on the strategy side of things… more high-level strategic work, really roadmapping, a lot of conversion, a lot of retooling, upskilling… as these tools matter less and less, and as they become more and more available, they’re kind of like commodity services now.”

The Future of Analytics Is Conversational:

“I think I saw something from maybe Gartner that by the early 2030s, over 50% of consumption of analytics, so your data consumers, they’re going to be using some sort of a chat interface. It’s not going to be the UI that comes with the tool… whether it’s agentic features, where you’re using an MCP to call that from outside of the system, or whether you’re creating chatbots that interact with it.”

AI Is Compressing a Century into a Decade:

“I see AI as kind of being something that condenses a lot of the work into freeing up more time for you to do more meaningful things. The way [Dario Amodei] describes AI is that what would normally take humanity a century to produce is going to be produced in the next 10 years… spot on. That’s exactly what it’s doing.”

Data Is the Lifeblood. Treat It That Way:

“Data is still the lifeblood of all of these systems, whether it’s AI or operational systems. So at the end of the day, your data is a valuable commodity, so treat it as such… when you start to quantify the impact of data and really put it in terms that your organization can understand, it helps everybody to really be able to lean into spending money on what is truly the lifeblood of not just AI, but the organization, and that’s data.” to implement, gives you time to react… allows you a window of time to get it done right.”