Riyadh Air + IBM: The First AI-Native Airline Built for Operational Speed
Announced Dec 8, 2025 at IBM Think Riyadh, Riyadh Air and IBM introduced what they call the world's first AI-native airline. No legacy stack. No patchwork integrations. An operating model designed around AI, data, and orchestration from day one.
IBM Consulting coordinated 59 workstreams and more than 60 partners-including Adobe, Apple, FLYR, and Microsoft-using its AI-powered delivery platform, IBM Consulting Advantage. The airline is also adopting IBM watsonx Orchestrate to deploy agentic AI across workflows.
What "AI-Native" Looks Like in Operations
- Unified data and process fabric across finance, ops, and commercial teams.
- AI agents that propose next best actions for employees in real time.
- A chat-first digital workplace that simplifies HR and manager self-service as headcount scales.
- Crew enablement apps that connect ground, cabin, and customer data for consistent service.
- AI-enabled voice bots and agent assist to cut handling time while keeping a human touch.
- Enterprise performance management that automates planning, budgeting, forecasting, and analysis with live operational signals.
Employee Experience That Directly Improves Guest Experience
Riyadh Air is rolling out a personalized digital workplace so employees can handle HR, approvals, and knowledge in one place. That matters when you plan to double your workforce in the next 12 months.
For crews, agentic AI provides context and prompts. If a traveler is late to the gate, staff get a nudge to offer fast-track options. For customer care, voice bots and agent assist use contextual data to anticipate needs and reduce friction without losing empathy.
Operational Efficiency as the Growth Engine
Starting from a clean slate, the airline avoids incremental fixes and legacy bottlenecks. An enterprise performance management suite links financial, operational, and commercial data to support real-time decisions.
The result: tighter route profitability, faster planning cycles, and clearer reinvestment decisions. Less "where is the data," more "what action do we take now."
Implementation Notes for Operations Leaders
- Start with the operating model: map the service blueprint, define decision rights, and set latency targets for key processes (irregular ops, turn times, crew scheduling).
- Create a single event stream: flights, crew, maintenance, inventory, and customer signals synchronized for AI agents.
- Use an orchestration layer: standard APIs, policy guardrails, human-in-the-loop approvals for high-impact actions.
- Set trust controls: data governance, audit trails, content filters, and fallback procedures for every agent action.
- Measure what matters: on-time performance, crew utilization, AHT, CSAT, forecast accuracy, and route margin per ASM.
- Design for scale: modular services, partner ecosystem alignment, and change management embedded in every rollout.
Timeline and Scale
Initial flights are underway with first commercial service expected in early 2026. The strategy supports expansion to 100+ destinations by 2030, serving millions of travelers with consistent, AI-assisted operations.
Why This Matters
This isn't a new tool layered on top of old processes. It's an operating system for the airline-built with AI at the core. For operations teams, the takeaway is clear: align data, decisions, and agents around real-time actions that move KPIs.
Next Steps
- Review your current service blueprint and identify where AI agents can remove handoffs or delays.
- Pilot an orchestration layer in one high-leverage workflow (irregular ops, crew planning, or customer care) before scaling.
- If you're upskilling your team for AI-driven operations, explore practical programs by job role at Complete AI Training.
Quote That Sums It Up
"We had a clear choice-be the last airline built on legacy technology or the first built on the platforms that will define the next decade of aviation," said Adam Boukadida, Chief Financial Officer, Riyadh Air.
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