How Forward Deployed Engineers (FDEs) Drive Enterprise AI Transformation
What does it take to move generative AI from a simple chatbot to a powerful, scaled enterprise workflow? In this episode of Executive Insights, Michael Krantz (Editor-in-Chief at Box) sits down with Box AI Architects Alex and Gilbert to discuss the evolving role of Forward Deployed Engineers (FDEs). Discover how organizations are breaking the "chat box paradigm" to integrate large language models (LLMs) into deterministic business processes. Alex and Gilbert share real-world success stories—including how they helped a customer increase their medical chart data extraction accuracy from 70% to over 90% (and targeting 99%+) through rigorous prompt engineering and process re-engineering. Learn why enterprise context, robust evaluation frameworks (e-vals), and empowering line-of-business staff are the ultimate differentiators for successful AI adoption. Key topics covered: The transition from step-based automation to autonomous thinking agents. Why LLMs require deep enterprise context to produce specific business outcomes. How to break down complex workflows into atomic, manageable components. The critical role of human-in-the-loop validation for authenticity and quality. Practical advice for planning and architecting your organization's AI program.

AI-First Playbook: Inside Box CEO Aaron Levie’s strategy for turning AI hype into real ROI | Box

The 3 Barriers Blocking Real Results in Enterprise AI

Google DeepMind Distinguished Eng (L9): How To Land a Job at a Frontier Lab | Vlad Feinberg

Stanford CS153 Frontier Systems | Scale, AGI, and the Future of Everything

Andrej Karpathy: From Vibe Coding to Agentic Engineering w/ Stephanie Zhan

Closing the gap between AI pilots and real enterprise impact with Slalom

Inside Anthropic, the $965 Billion AI Juggernaut | The Circuit

Why (Senior) Engineers Struggle to Build AI Agents — Philipp Schmid, Google DeepMind

How GPT, Claude, and Gemini are actually trained and served – Reiner Pope

From Idea to $650M Exit: Lessons in Building AI Startups

How to Design an AI Native Engineering Organization

Das Claude Mythos/Fable CHAOS erklärt

Turing Award Winner: Disagreeing with Google, Postgres, Future Problems | Mike Stonebraker

Building AI Agents that actually work (Full Course)

Demis Hassabis: Agents, AGI & The Next Big Scientific Breakthrough

Want to Run AI Agents Locally? Here is The Bare Minimum Setup/Build

The Real Reason Behind the Giga-IPO Boom

Designing Data-intensive Applications with Martin Kleppmann

How AI agents & Claude skills work (Clearly Explained)

