Insights from our Field CTO Raghu Vatte in recent panel discussion with Kelly Parker (Fivetran), Christopher Bartlett (Nationwide), Eric Ferguson (Wilson Bank & Trust), Frank Kartmann (Deutsche Bank)
When enterprise leaders from different industries gather to discuss AI and data transformation, the conversation quickly moves beyond buzzwords to real-world friction points. Our Field CTO, Raghu Vatte, recently joined a panel alongside experts from major banks, hospitals, and data infrastructure companies to discuss what’s really happening in enterprise AI adoption. Several critical insights emerged that every data leader should consider.
Here’s what we learned about the gap between AI ambition and execution, and what successful organizations are doing differently.
1. The “Moat Mindset”: Your Data Is Your Competitive Edge
The Reality: Every organization has come to realize that their real competitive advantage lies in the data they own, not the tools they use.
” Everyone has come to realize that their real moat is the data they own.” – says Raghu Vatte, AI Squared
The Insight: Organizations are shifting from technology-first to data-first thinking. The question is no longer “What’s the latest AI tool?” but “How do we unlock unique insights from our proprietary data that competitors can’t replicate?”
What This Means for You: Start treating your data as a strategic asset that needs curation, governance, and optimization specifically for AI consumption, not just storage and reporting.
2. The Model Validation Trap: When “Better” Models Hurt ROI
The Challenge: One of the most overlooked obstacles to AI adoption is the constant pressure to upgrade models, which can destroy return on investment.
Chris Bartlett from Nationwide Children’s Hospital shared a critical pain point: ” If you force me to revalidate every six months or a year, you might be cutting into my profits, in which case it’s no longer a useful project.”
The Solution: Raghu explained that this tension is exactly why AI Squared exists – to help enterprises benefit from continuous improvements in AI models without disrupting their validated workflows. He further explains that companies want the reliability of long-lived models, but also need access to innovations that deliver better results. AI Squared’s approach is to balance these competing needs, ensuring innovation doesn’t come at the cost of enterprise stability.
What This Means for You: When evaluating AI solutions, prioritize platforms that can handle model updates behind the scenes without requiring complete revalidation of your business processes.
3. The Three Pillars of AI Adoption Success
The Framework: Based on the panel discussion, three critical hurdles must be addressed for successful data-to-insights transformation:
- Data Governance: Establishing proper data quality, security, and compliance frameworks
- Continuous Data Flow: Ensuring fresh, relevant data reaches AI systems for real-time insights
- Insight Surfacing: Making insights accessible and actionable for non-technical users
As Raghu noted: “Your insights are only as good as the data and the data quality that you provide… and the last one is about how do you surface those insights.”
What This Means for You: Don’t start with the AI model, start with these three foundations. Most AI projects fail not because of algorithms, but because of weak data infrastructure or poor user experience.
4. The Executive-Reality Gap: Bridging AI Perception vs. Practice
The Problem: There’s a significant disconnect between what executives think is happening with AI in their organizations and what’s actually occurring at ground level.
Billy Parker from Fivetran referenced a CIO Magazine survey that perfectly captured this divide: “The executives are like, we are awesome. We’re doing it. We have AI mastered. We’re using it in all different parts of our business, and the rest of us are like, no way, dude. We’re not using it. We’re skeptical.”
The Solution: Focus on bridging this gap through better communication and realistic expectations. Success comes from aligning executive vision with operational reality.
What This Means for You: Create regular feedback loops between leadership and implementation teams. Set realistic milestones and celebrate incremental progress rather than promising transformation overnight.
5. The Future is “Data-First, Tools-Second”
The Trend: The panel revealed a significant shift in thinking about data consumption and tool selection.
Raghu highlighted an emerging pattern: “People are not thinking about replacing people by AI yet, at least. But they’re definitely thinking about replacing tools… I don’t really need an Excel to create a pivot chart for me so that some other AI tool can look at it and then give me the insight. And if I can ask the data, wherever my data is, hey, give me that insight… Then why do I need these two tools in the middle?”
The Evolution: Organizations are moving toward direct data-to-insight workflows, eliminating intermediate tools that add complexity without adding value.
What This Means for You: When planning your data architecture, think about end-to-end insight generation rather than tool-by-tool procurement. The future belongs to platforms that can go directly from raw data to business insights.
The Final Takeaway
Unlocking the true value of data isn’t about chasing shiny tools or massive transformations. It’s about:
- Treating data as a product with owners, governance, and quality metrics.
- Leveraging real-time insights that deliver tangible ROI today.
- Bridging the gap between innovation and enterprise stability so trust is never compromised.
The leaders on this panel agreed: the organizations that succeed will be those who can balance speed, governance, and usability – transforming their data into a moat that drives long-term advantage.
