Why Your AI Initiatives Are Stuck at the Starting Line

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Data without context is just noise. Despite billions invested in data infrastructure, most Fortune 1000 companies struggle to extract meaningful value from their investments. Why? They've built complex, fragmented ecosystems focused on tools rather than outcomes.
Srujan Akula, founder of The Modern Data Company, shares how his frustration with this industry-wide problem led to a revolutionary approach. After witnessing global enterprises repeatedly fail to achieve ROI from massive data investments, he and co-founder Animesh Kumar developed an operating system for data that converges the entire management stack into a simplified, business-centric ecosystem.
What makes their approach unique is how they flip traditional data management on its head. Instead of starting with source systems and figuring out what to do later, they begin with business intent and work backward. This right-to-left paradigm dramatically improves efficiency and accelerates time-to-value. One $30 billion distribution company transformed their customer marketing capabilities in just six weeks after spending years and $70-80 million without success.
The timing couldn't be more critical as organizations rush toward AI adoption. Akula reveals that 75-80% of enterprises are "stuck at the starting line" with AI initiatives because their data lacks the context and business meaning AI requires to be effective. By positioning their operating system as the "brain" that provides this context, The Modern Data Company helps businesses move beyond experiments to embedding AI into decision-making processes.
Perhaps most refreshing is their business approach - founded on humility, empathy, accountability, and transparency, they measure success by time-to-ROI and charge based on value created rather than seats or data volume. As Akula looks toward a future where agent technologies will transform enterprise efficiency, The Modern Data Company continues expanding with federal partnerships and an upcoming SaaS offering that promises to be "an AWS for data."

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Chapters

1. Introduction to Modern Data Company (00:00:00)

2. Creating an Operating System for Data (00:03:01)

3. Distribution Company Case Study (00:05:38)

4. Convergence Over Tool Proliferation (00:08:54)

5. AI Readiness Challenges in Enterprise (00:13:44)

6. Culture and Customer-First Approach (00:17:39)

7. Future Trends and Company Roadmap (00:19:48)