AI Won't Save You But Data Intelligence Will

Ari Kaplan—Global Head of Evangelism at Databricks and a pioneer in sports analytics—explains why businesses fixated on AI often overlook the real advantage: making better decisions with their own data. He shares lessons from his work building analytics teams for Major League Baseball, advising McLaren’s F1 strategy, and helping companies apply AI where it actually works—without falling into hype-driven traps.

1. Baseball Changed Its Rules Because of Data—Most Businesses Still Ignore It**
     Defensive shifts in MLB went from 3% to over 50% of plays, forcing the league to ban the strategy entirely**. The Cubs didn’t get better players—they just used better data**. This kind of shift is possible in every industry, but most companies still aren’t taking full advantage of their own data.  

2. AI Models Are a Commodity—Your Data Pipeline Isn’t  **
     Every company has access to the same foundation models, yet many believe AI will give them a unique edge. The real differentiator is how well companies organize and extract insights from their own data**. Instead of chasing the latest GPT model, the real investment should be in data intelligence and infrastructure.  

3. Most AI Teams Waste Time Chasing Models Instead of Fixing Data Retrieval**  
     Businesses obsess over model upgrades, but Kaplan has seen companies get bigger performance gains from improving OCR, chunking strategies, and retrieval pipelines**. AI success isn’t about having the best model—it’s about feeding models structured, high-signal data.  

4. 2025 Won’t Be the Year of AI Agents—It’ll Be the Year We Watch Them Fail
  Everyone is hyping up agentic AI systems, but most companies will struggle with failure modes, debugging, and business logic.** Expect more hard lessons about why agents break** than success stories. The real winners will be those who focus on reliability over raw automation.  

5. Data Intelligence Will Automate Decision-Making—But You Still Need Humans to Stop Bad Calls**  
     Kaplan worked on sports analytics systems that recommended lineup changes, but coaches still had to decide when to override the data**. The same is true for AI in business—automation helps, but blindly trusting models leads to bad decisions**. Keeping humans in the loop is essential for catching edge cases and strategic missteps.

You can read the full transcript here.

00:00 The Impact of Data on Baseball Strategy

00:45 Introduction to Ari Kaplan and His Work

01:10 Exploring AI vs. Data Intelligence

01:33 Sponsorship and Podcast Information

01:50 Ari Kaplan's Role at Databricks

02:56 The Evolution of Data and AI at Databricks

03:41 The Importance of Data Intelligence

06:37 Challenges and Metrics in AI and Data Science

15:15 Automation in Data Science and Machine Learning

20:24 The Role of Human Expertise in AI

30:34 The Rise of Data Intelligence

32:48 Human Rights Work and Gen AI

33:55 A Journey Through Sports Analytics

37:15 The Evolution of Baseball Analytics

40:10 Travis Kelsey and the Power of Details

44:43 Advice for Data Science Leaders

49:17 The Future of Data Intelligence

51:18 Tools and Processes for Data Accessibility

54:29 The Role of Human Judgment in Automation

58:57 Conclusion and Final Thoughts