Delivery operating model01
OpenAI Deployment Company
Who connects intelligence to actual work?
Move from model access to sustained operational use while reducing repeated implementation work.
CASE STUDIES / FDE IN PRACTICE
From business problems to engineering decisions
Nine public cases and ten field exercises. Explore discovery, implementation, consequential decisions and the artifacts you should deliver.
01Discover
02Design
03Decide
04Verify
Choose a case close to your work. Read the context, examine the decisions, then produce reproducible coursework. Public context links to original sources; FDE exercises use synthetic scenarios.
Expert workflow case02
AIG × Anthropic
Less material handling. More expert judgment.
Reduce evidence preparation time so experts can judge risk from complete, traceable facts.
Engineering investigation case03
Hardware root-cause analysis
From searching logs to resolving failures
Give engineers reproducible failure hypotheses sooner, not longer log summaries.
Data-platform productization case04
Airbus × Palantir / Skywise
Reuse the business connections, not just a dashboard
Turn fragmented records into business objects teams can use to coordinate around an aircraft or component.
Specialist document-production case05
Novo Nordisk / NovoScribe
Fast generation is not the whole delivery cycle
Reduce retrieval, copying and rewriting while keeping authors and reviewers on one evidence chain.
Knowledge assistant and evaluation case06
Morgan Stanley × OpenAI
Trust grows from repeatable quality evidence
Turn expert quality expectations into repeatable release checks, supporting both usefulness and trust.
Customer service and action-loop case07
Klarna × OpenAI
Volume is not the whole service story
Resolve common requests faster and hand complex ones to people with the context they need.
Resource coordination and operations case08
Tampa General Hospital × Palantir
Connect decisions to the operational rhythm
Keep resources, priorities and ownership aligned to current conditions, reducing information waits and repeated coordination.
Outcome-service business-model case09
Sensors Data / RaaS
Define the result before pricing it
Connect technical capability, operational action and verifiable benefit, with explicit control and shared responsibility.
FIELD EXERCISE10
Growth research
Turn specifications into evidenced buying reasons
Create distinctive positioning that product and marketing can jointly verify.
FIELD EXERCISE11
Sales prospecting
Send sales into a meeting with a judgment
Reduce unfocused preparation and prioritize opportunities worth validating.
FIELD EXERCISE12
Paid media
Reconcile reports before recommending budget
Avoid budget decisions based on inconsistent reports and connect analysis to a controlled experiment.
FIELD EXERCISE13
Business review
Turn a sales decline into the next test
Move the review from arguing over charts to selecting the next useful business test.
FIELD EXERCISE14
Knowledge service
Answer from the right page for the right model
Reduce wrong answers and repeated handoff by providing device-appropriate next steps.
FIELD EXERCISE15
Customer experience
Turn a complaint into actionable issue units
Reduce missed issues and connect customer feedback to product, fulfillment and service improvements.
FIELD EXERCISE16
Knowledge operations
Turn corrections into reliable future answers
Embed expert corrections into the system and reduce repeated error and rework.
FIELD EXERCISE17
Store operations
Hand intent to someone who can act
Reduce lost intent and duplicate outreach; connect conversations to traceable store service.
FIELD EXERCISE18
Decision research
Multiple perspectives still need verification
Help decision-makers identify missing information and evidence that could change the choice.
FIELD EXERCISE19
Enterprise skills
Turn expert practice into an inspectable method
Help teams reuse expert methods consistently and reduce opaque variation and handoff cost.
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