Mark Dangelo Talks About AI
In this Rick’s Room episode, host Rick Grant sits down with Mark Dangelo—author of five books, Case Western Reserve University faculty member, long-time consultant, and frequent contributor to MBA NewsLink, Reuters, and the Data Foundation—to discuss the realities, challenges, and opportunities of artificial intelligence (AI) in business.
Dangelo explains why less than 10% of AI projects make it out of pilot stages: leaders tend to focus on a single component instead of approaching AI as a parallel, multi-faceted challenge that requires changes in data architecture, governance, and corporate culture. He emphasizes:
- AI is not “plug-and-play.” Sustainable AI adoption requires simultaneous changes to strategy, data infrastructure, processes, and people.
- Data comes first. Clean, consistent, and interconnected data is the foundation; systems should be built after the data is aligned.
- The problem is often the fuel, not the engine. Many AI failures stem from poor-quality or inconsistent data rather than flawed algorithms.
- Corporate silos remain a barrier. While AI can break them down, success depends on governance and data standards, not just technology.
- Smaller companies can gain an edge. With low-code and no-code AI tools now available, agile organizations can adopt and adapt faster than larger competitors.
- The future is modular. AI adoption should follow a quick-hit, building-block approach—deploying targeted solutions in 30–90 day cycles instead of multi-year, high-cost IT overhauls.
Dangelo sees a future where smaller, well-prepared firms can outmaneuver larger players by assembling the right AI “Legos”—mixing proprietary and open-source tools—to rapidly create competitive advantages.
Show Highlights:
Rick: Welcome back to Rick’s Room. This show is built to recreate that end-of-conference moment—when you finally sit down with someone you’ve been wanting to talk to all day. Today, that person is Mark D’Angelo—author of five books, professor at Case Western Reserve University, consultant to major corporations, and regular contributor to MBA NewsLink, Reuters, and the Data Foundation. Mark, thanks for joining me.
Mark: My pleasure—thanks for having me.
Rick: If we were actually in those comfy chairs right now, what would you want to talk about?
Mark: The big topic right now is artificial intelligence. AI means many things to different people, but most corporate AI projects—less than 10%—never make it past pilots. They don’t scale because leaders focus on one thing at a time, but AI demands a parallel approach: strategy, data architecture, legacy systems, processes, and people must be addressed together.
Rick: You saw this coming years ago—your 2018 book even included a conversation with an AI. Now it’s common. What’s next?
Mark: Back then, I wrote about humans bonding with tech and needing to tackle multiple challenges at once. Today, AI can take on mundane tasks or synthesize complex information, but success requires thinking holistically. Education also has to change—we can’t train students to work in silos when AI requires a mosaic approach.
Rick: And data quality is still the foundation, right?
Mark: Absolutely. AI failures are often about bad “fuel,” not bad engines. Consistent, interconnected data—enabled by data meshes, fabrics, and advanced architectures—is essential. Otherwise, you get hallucinations and wrong results.
Rick: We’ve been promised enterprise integration before, but silos persist. Will AI finally make it happen?
Mark: It can, but only if we start with data, not systems. Data standards in industries like banking and healthcare help, but we now deal with zetabytes of data doubling every two years. The tech—like retrieval augmented generation and advanced GPUs—exists, but without consistent data, the results will be flawed.
Rick: That sounds complex—maybe too much for smaller firms?
Mark: Not necessarily. Low-code/no-code AI tools have leveled the playing field. What used to take months of coding can now be done with drag-and-drop building blocks. Smaller companies can adapt faster and even outcompete bigger ones if they take a modular approach.
Rick: So if there’s one key to getting AI right?
Mark: Two, actually:
- Focus on data first, systems second.
- Change governance—this isn’t just an IT project. Build in short, focused sprints (30–90 days) that stack together like Legos.
Rick: That’s encouraging. Smaller firms could move faster and rewrite the competitive landscape.
Mark: Exactly. With the right mix of open-source and proprietary tools, they can leapfrog bigger players—quickly and cost-effectively.
Rick: As always, Mark, you’ve left me wanting to hear more. We’ll have you back soon.
Mark: Looking forward to it.
