Use this section to ground the prep in public examples of deployed AI and data systems.
These pages are not confidential interview leaks. They are cited case studies that show what FDE-shaped work looks like when it reaches real users and real organizations. Read them for what each one is: Airbus is the closest to the embedded forward-deployed model, Morgan Stanley is a vendor-customer AI deployment, and John Deere is a production-AI case built by a company's own acquired team — each page opens by saying exactly what it is and what it is not.
Cases
- Morgan Stanley x OpenAI - internal knowledge access for financial advisors.
- John Deere & Blue River - See & Spray - computer vision and precision agriculture in the field.
- Airbus x Palantir - operational data integration at industrial scale.
How To Use This Section
Read one case after you have covered the relevant technical or customer section. Ask:
- Who depended on the system?
- What failure modes mattered?
- What made adoption hard?
- What evidence would prove it worked?
Best Next Step
Start with Morgan Stanley x OpenAI if you want the clearest AI knowledge-work example.
Next: Morgan Stanley × OpenAI — the clearest end-to-end case.
How to read a deployment case
Do not read these cases as architecture recipes. Read them for the constraint that organized the work: advisor trust under a compliance bar at Morgan Stanley, real-time physical-world performance at John Deere, and multi-party data governance at Airbus. The architecture follows the constraint, not the other way around.
Each case separates documented public facts from interpretation. Not every company calls the work forward deployed and public sources rarely expose team structure or internal failures. The value is learning to reconstruct the customer outcome, technical bottleneck, evaluation method, adoption gate, and transferable pattern without inventing missing detail.
Questions to carry into an interview
- What customer outcome made the deployment worth doing, and who owned that measure?
- Which constraint dominated the design, and what tempting approach did it rule out?
- How was correctness measured before and after launch?
- What made users trust and adopt the system, and what would have caused rejection?
- What became reusable product capability rather than permanent customer-specific work?