Forward Deployed Engineering: Delivering Business Outcomes with AI

2026-06-11 · Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, quick

Summary

Databricks has launched its Forward Deployed Engineering (FDE) organization, formalizing an existing practice focused on accelerating customer business outcomes with AI. FDE aims to shift from infrastructure migration to solving specific business problems, as demonstrated by over 1,900 customer engagements in the last 12 months. For Fox Corporation, FDE engineers embedded with their team to redesign the fan experience using Lakebase, AI Search, Databricks Apps, and Model Serving, resulting in users spending approximately 2X more time in the app. Similarly, for JPMC, FDE migrated over 5 petabytes of data and 500 notebooks in four months, training 600+ users. The FDE model is differentiated by four capabilities: a robust Lakehouse data and AI platform, an engineering-led delivery model, a global partner network for scale, and direct R&D interlock for product extension and feedback.

Key takeaway

For AI Architects or Directors of ML seeking to accelerate AI adoption and achieve measurable business outcomes, consider an embedded engineering model like Databricks FDE. This approach can significantly reduce time to value by deploying elite engineering talent directly with your team, ensuring rapid movement from prototype to production. You can expect outcome-aligned pricing and direct R&D interlock, shaping the platform to your specific needs and driving deeper fan engagement or data migration at scale.

Key insights

Databricks' FDE embeds engineers to deliver AI-driven business outcomes, integrating platform capabilities with customer-specific needs.

Principles

• Focus on business outcomes, not just infrastructure.
• Embed engineering talent directly with customer teams.
• Integrate field learnings into product R&D.

Method

The FDE model involves embedding engineers, using an OKR-centric delivery, and agile service deployment to rapidly move from prototype to production, measured in weeks.

In practice

• Consolidate data onto a Lakehouse platform.
• Implement AI Search for enhanced user engagement.

Topics

• Forward Deployed Engineering
• Databricks Lakehouse
• AI Applications
• Business Outcomes
• Data Migration
• Agile Service Delivery

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, AI Architect, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.