Emirates partners with OpenAI to apply AI across airline operations
Emirates is rolling out an enterprise-wide AI program with OpenAI to improve operations and customer experience. The plan includes deploying ChatGPT Enterprise, building AI skills across teams, and standing up an AI Centre of Excellence to move fast on practical use cases.
This isn't about labs and slide decks. It's about faster decisions in the ops control center, better crew and maintenance planning, and tighter turnarounds that reduce delays.
What's in the collaboration
- Company-wide deployment of ChatGPT Enterprise with enterprise controls
- AI literacy programs for staff and an internal champion network
- An AI Centre of Excellence to guide research, experimentation, and rollout
- Leadership sessions to scope use cases, fund pilots, and clear roadblocks
- Early access to new research and the chance to collaborate on sector initiatives
- Sandbox environments for rapid prototyping and safe testing
Ali Serdar Yakut, EVP of IT at Emirates, said the goal is to apply AI to complex commercial and operational problems, improving how the airline serves customers and runs the business. Rod Solaimani of OpenAI noted that the partnership will help embed intelligence across day-to-day work and re-think parts of the travel experience.
Priority use cases for operations
- Ops control center co-pilot: faster disruption response with scenario plans, gate/crew swaps, and customer comms drafts
- Crew planning: roster optimization, smarter pairing, leave bidding assistance, and schedule change impact checks
- Maintenance and engineering: predictive task scheduling, e-log parsing, parts availability suggestions, and workcard assistance
- Turnaround and ground ops: real-time exception triage, delay code guidance, and auto-generated shift handovers
- Safety and compliance: event report triage, trend detection, and policy Q&A with source references
- Contact center and airport service: agent assist, intent detection, and consistent policy answers in multiple languages
- Procurement and contracting: contract review, clause comparison, and vendor query automation
- Revenue and network: demand signals summarization, competitor move scanning, and what-if planning support
How rollout will work
- Governance: AI Centre of Excellence sets standards, reviews use cases, and tracks outcomes
- Delivery engine: small cross-functional pods run 4-6 week sprints with clear success criteria
- Data access: secure connections to knowledge bases and ops systems, with role-based permissions
- Evaluation: human-in-the-loop, red-teaming, and feedback prompts to improve responses over time
- Change enablement: champions in each station/department, quick reference guides, and micro-trainings
What to measure
- Ops metrics: on-time performance, misconnects, delay minutes per departure, turn-time variance
- Service metrics: average handle time, first-contact resolution, CSAT/NPS, complaint rate
- Productivity: cases handled per agent, tasks per planner, engineering task cycle time
- Quality and risk: policy adherence, audit findings, data leakage incidents
- Financial impact: re-accommodation costs, fuel and crew disruption costs, overtime
Playbook for ops leaders
- Nominate 2-3 champions per function (OCC, crew, M&E, airport, service)
- List the top 10 pain points by cost and customer impact; pick two high-confidence pilots
- Define guardrails: data access, approval paths, and what must stay human-reviewed
- Stand up a sandbox with real but limited datasets; ship a demo every Friday
- Set KPIs per pilot (e.g., -10% delay minutes on specific routes) and decide go/no-go by week 6
- Document wins and failures; templatize prompts, SOPs, and rollout checklists
Why this matters for operations
The biggest gains come from shaving minutes off routine work and reducing variance during irregular operations. AI assistants that summarize, suggest next steps, and draft comms free teams to make better calls faster.
The tech stack matters, but the real edge will come from disciplined pilots, tight metrics, and frontline adoption. Keep the loop short: test, measure, iterate.
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