Building What’s Next with AI, Apps, and a Smarter Data Foundation

The conversation around AI has shifted. We’re no longer asking if it can transform how businesses operate—we’re asking how fast we can make it happen responsibly, at scale, and with lasting value.

What we’re seeing now—both in the market and reinforced with our attendance at Databricks Data and AI Summit 2025—isn’t just technical progress. It’s a signal that the platforms, patterns, and governance models needed to truly operationalize AI are finally falling into place—and are much more simplified.

From HIPAA-compliant applications built directly on your lakehouse to tailored intelligence agents powered by your data, Databricks is turning infrastructure into outcomes. And for enterprises, that means the time to act is now.

The lakehouse is no longer just for analytics

With LakeBase and Databricks Apps now generally available, the lakehouse has evolved from a data warehouse alternative into a true platform for application development. LakeBase brings OLTP-style capabilities natively to the lakehouse, while Databricks Apps provides a secure, compliant foundation for deploying internal tools—with integration into popular dev environments like Replit.

For industries with stringent data needs—like healthcare and financial services—HIPAA compliance unlocks new use cases: responsive dashboards, field tools, lightweight workflows—all running directly on governed data.

West Monroe’s take: These tools offer an entirely new approach to application delivery. We’re helping clients build smarter, domain-specific apps that are faster to deploy, easier to govern, and deeply integrated with their existing data strategy.

AI that understands your business

Custom AI is only as valuable as its context. That’s why innovations like Agent Bricks and AI Functions in SQL are so meaningful—they lower the barrier to intelligent automation and let organizations apply GenAI exactly where it matters, and with the Lakehouse supply structured and unstructured data for the optimal context.

Agent Bricks lets teams create auto-optimized, task-driven agents based on their own data semantics. Meanwhile, AI Functions in SQL makes it possible to embed multimodal AI (text, image, audio) directly into data workflows—no Python required.

Add MLflow 3.0 to the mix—with prompt tracking, versioning, and full observability—and you’ve got the tools to govern the full LLM lifecycle with confidence. It’s a topic that Doug MacWilliams dug into at DAIS during his session, “Disruptive Forces: LLMs and the New Age of Data Engineering,” where we explored how data engineering is evolving with generative AI to offer new approaches that are highly productive

West Monroe’s take: We’re seeing the strongest momentum where organizations focus on tailored intelligence. The future isn’t about general-purpose AI—it’s about agents and insights tuned to your workflows, models monitored for drift, and decisions made with full visibility.

The infrastructure is ready, and so is the business case

From Serverless GPU Compute that spins up powerful AI environments in seconds to Spark Declarative Pipelines and LakeFlow Designer, Databricks has doubled down on making data ingestion, transformation, and scaling easier across technical skill levels.

Just as important, new tools like Unity Catalog Metrics and Databricks One support enterprise adoption by building trust in both the data and the interface. With improved semantic layers and attribute-based access control (A/BAC), governed self-service is finally within reach.

Meanwhile, MLOps and Data Observability are becoming boardroom conversations. Leaders want answers to model drift, lineage tracking, and quality issues before they affect customer experiences or regulatory compliance.

What real transformation looks like

Every industry is moving—from experimentation to embedded intelligence:

• Banking: AI-driven personal investment and loan growth
• Insurance: Hyper-personalized coverage models
• Healthcare: Clinical research and patient engagement (ask us about OMOP in a box)
• Life Sciences: Next-best-action for providers
• Manufacturing: Predictive supply chain analytics
• Utilities: Grid modernization and customer insights

At West Monroe, we’re already helping clients harness this new platform era, from building intelligent agents and GenAI apps to aligning governance strategies and delivering real-time insight. As a Databricks partner, we bring deep platform expertise and a front-row seat to what’s next.

We’ve spent the past two years developing GenAI accelerators and approaches that help clients move from exploration to execution. The debut of LakeBridge—Databricks’ GenAI-augmented evolution of BladeBridge—validates the direction we’ve already taken with Hopper, our proprietary solution for migrating and modernizing legacy code at scale.

From OLTP to GenAI, Databricks is breaking down silos and accelerating the convergence of data engineering, analytics, and machine learning. These announcements aren’t just technical milestones—they’re a signal to every organization building toward a digital future.

We know what it takes to connect the dots between technical capability and business value. Because it’s not about adopting AI. It’s about activating it—intelligently, securely, and with speed.

Authors: Cam Cross, Carrie Knowles, and Doug McWilliams