Accelerating Data Onboarding at Corteva with Databricks Lakeflow

Corteva Agriscience’s Mehul Bhuva took to the DAIS 2026 stage with a practical blueprint for how modern enterprises can make data usable, discoverable, and AI-ready at scale. Kobai is proud to spotlight his contribution.

At Kobai, we take pride in spotlighting leaders who are redefining what is possible in data and AI. At this year’s Data + AI Summit 2026, we are excited to recognize Mehul K. Bhuva, who represented Corteva Agriscience on a global stage and delivered a compelling perspective on the future of scalable data and AI platforms.

THE SPEAKER

Mehul K. Bhuva, Corteva Agriscience

With over two decades of experience, Mehul is a recognized Data & AI Platform Engineer, researcher, and thought leader in modern data architectures. His work consistently bridges the gap between enterprise data engineering and applied AI — helping organizations evolve from fragmented systems to intelligent, context-aware platforms.

Known for his practical yet forward-looking approach, Mehul has been at the forefront of building context-driven, automation-first frameworks that enable real-world impact at scale.

THE SESSION

Accelerating Corteva’s Data Source Onboarding with Lakeflow

At DAIS 2026, Mehul presented how Corteva is transforming its data onboarding strategy using Databricks Lakeflow. By introducing a standardized, context-driven framework, he demonstrated how enterprises can significantly reduce onboarding time, improve governance, and scale data ingestion across diverse sources.

His approach reflects a broader industry shift toward automation-first architectures that are essential for modern, AI-ready data platforms — a shift that is accelerating as organizations move from data collection to data activation.

WHY THIS MATTERS

From data collection to data activation

As organizations scale, the challenge is no longer just collecting data — it is making data usable, discoverable, and meaningful. Getting data into a governed platform is solved. The next question is whether the people and AI systems that depend on that data can actually understand what it means and how it relates to the decisions they need to make.

Mehul’s work at Corteva addresses exactly this challenge. A context-driven onboarding framework does more than accelerate ingestion — it establishes the shared understanding of what incoming data means in the context of the enterprise that relies on it. That understanding is what allows AI quality to improve consistently as more data is brought into the platform.

By connecting datasets, entities, and relationships with shared business meaning, organizations enable both humans and AI systems to reason over data more reliably — unlocking smarter analytics, more accurate AI models, and faster decisions. These are the foundations for any organization looking to lead in the AI era.

KOBAI’S PERSPECTIVE

Aligned on the vision: from data management to enterprise understanding

At Kobai, we are deeply aligned with the direction Mehul is pointing. Our mission goes beyond helping organizations manage data, we focus on helping them create, govern, and operationalize the shared business context that makes data genuinely useful for AI and decisions.

Kobai extends the Databricks Lakehouse with the semantic intelligence layer that connects data to meaning: defining what enterprise entities are, how they relate to each other, and what rules govern those relationships in a form that Genie, agents, analytics, and applications can all consume consistently. Graph structures are built directly within Databricks under Unity Catalog governance, so shared context is available to every AI consumer on the platform without introducing a separate system to operate.

We are proud to be part of a community where leaders like Mehul are driving meaningful change. His work at Corteva is not just an implementation success, it is a practical demonstration of how modern enterprises can rethink data onboarding, build for AI readiness from the ground up, and establish the context foundation that intelligent architectures require.