tailwindlabs / tailwindcss
github.com·21h ago
Developer Tools
Databricks Blog·Jun 11·5 min read
Forward Deployed Engineering: Delivering Business Outcomes with AI
TL;DR
Customers increasingly seek solutions to specific business problems rather than just data migration. Databricks launched Forward Deployed Engineering (FDE) to embed engineers with clients, utilizing tools like Lakebase and AI Search.
✦ Why It Matters
Engineers can leverage embedded teams to accelerate AI project delivery and achieve measurable business outcomes.
Key Takeaways
- Engage engineers directly for tailored AI solutions.
- Focus on measurable business outcomes with shared OKRs.
- Leverage a global partner network for specialized skills.
- Accelerate migration and deployment timelines significantly.
Full Summary
Databricks' Forward Deployed Engineering (FDE) initiative aims to transform how businesses leverage AI by embedding engineers directly with clients to solve specific business problems. This shift from merely building data pipelines to delivering measurable business outcomes has been evident in partnerships with over 1,900 customers in the past year.
For instance, Fox Corporation collaborated with Databricks to enhance its fan engagement through AI-driven features, resulting in users spending twice as much time in their app. Similarly, JPMC successfully migrated over 5 petabytes of data and trained more than 600 users in just four months, accelerating their AI strategy.
The FDE model combines a robust data platform, engineering-led delivery, a global partner network, and direct collaboration with R&D to ensure rapid innovation and deployment. This comprehensive approach not only streamlines the transition to AI but also ensures that the solutions are tailored to the unique needs of each business.