How Corteva Is Accelerating Data Onboarding with Databricks Lakeflow

Enterprise AI doesn’t fail because organizations can’t ingest data. It fails because onboarding new data sources is still slow, inconsistent, and expensive. At DAIS 2026, Mehul Bhuva from Corteva Agriscience showed how Databricks Lakeflow is helping change that.

Enterprise AI doesn’t fail because organizations can’t ingest data. It fails because onboarding new data sources is still slow, inconsistent, and expensive. Every new source requires custom engineering. Schemas differ. Governance requirements vary. And each new integration that takes weeks is a delay to the AI initiative that depends on it.

At DAIS 2026, Mehul K. Bhuva from Corteva Agriscience took to the stage to show what a different approach looks like. His session on accelerating data source onboarding with Databricks Lakeflow was one of the more practically grounded presentations of the summit — a real enterprise, a real challenge, and a real framework for solving it.

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, automation-first approach, Mehul has built context-driven frameworks that operate at enterprise scale and his DAIS 2026 session is a direct reflection of that philosophy applied to one of the most persistent challenges in enterprise data: getting new sources onto the platform quickly and reliably.

THE CHALLENGE

Why data source onboarding is still a bottleneck

For large enterprises like Corteva — operating across global markets with diverse data sources, multiple business units, and strict governance requirements — data source onboarding is not a solved problem. Each new data source that needs to enter the platform represents an engineering effort: designing the pipeline, handling the schema, applying the right governance controls, and integrating with the downstream systems that depend on the data.

When that process is slow or inconsistent, it creates a compounding problem for enterprise AI. AI models that need new data sources wait. Genie spaces that should cover new domains cannot be extended. The platform’s capability grows faster than the business’s ability to onboard the data that would put it to work.

THE SESSION

What Corteva built with Databricks Lakeflow

Mehul’s session demonstrated how Corteva is using Databricks Lakeflow to standardize and accelerate data source onboarding across the enterprise. Rather than treating each new data source as a bespoke engineering project, Corteva has built a framework that applies consistent patterns: standardized ingestion pipelines, governed data flows, and a context-driven approach that ensures new data enters the platform in a form that downstream teams can reliably use.

Why Lakeflow matters for this problem

Databricks Lakeflow provides the orchestration and pipeline infrastructure that makes standardized onboarding feasible at enterprise scale. Rather than maintaining a collection of individually engineered pipelines, Lakeflow enables teams to define reusable ingestion patterns that can be applied across data sources consistently — with built-in governance, lineage, and observability. For an enterprise like Corteva, operating across diverse data sources and business domains, that standardization is the difference between onboarding that scales and onboarding that stays a bottleneck.

The broader principle Mehul demonstrated

What made the session compelling beyond the Corteva specifics was the broader principle it illustrated. Faster, more consistent onboarding is not just an engineering win — it is a business accelerator. Every data source that reaches the platform faster, with consistent governance applied from the start, is a source that AI initiatives can depend on sooner. The onboarding framework becomes the foundation that scalable AI is built on.

WHAT RESONATED WITH US

Onboarding is the first step. Understanding is what follows.

What resonated most with us in Mehul’s session was a distinction that often gets overlooked in data engineering conversations: the difference between getting data into the platform and making that data consistently understood across the enterprise.

Mehul’s framework does the hard work of getting data onto the Databricks Lakehouse consistently. What builds on top of that — the shared business context that allows AI systems to understand what the data means, how entities relate, and what rules apply — is where Kobai focuses. The two are complementary. Standardized onboarding creates the right conditions for establishing shared business context. Shared business context is what makes that onboarded data genuinely useful for AI at scale.

KOBAI’S VIEW

A blueprint for modern data platforms

At Kobai, we are proud to spotlight leaders like Mehul who are driving meaningful progress on the practical challenges of enterprise data. His work at Corteva is not just an implementation success — it is a demonstration of how modern enterprises can rethink data onboarding, apply consistent governance from the start, and build the foundation that scalable AI requires.

Kobai extends the Databricks Lakehouse with the Business Context Layer that allows enterprise AI to understand how your business actually works — built directly within Databricks under Unity Catalog governance, consuming the data that Lakeflow and the Lakehouse make available.