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Built to Fly: AI Agents and Digital Twins for Aviation MRO

Connect Aviation MRO data, digital twins and governed AI agents to anticipate maintenance needs, support diagnosis and improve maintenance planning.
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A digital twin is only as reliable as its maintenance data
WHY THIS “AI AGENTS & STRATEGIC INTELLIGENCE TRANSFORMING TECHNICAL OPERATIONS” CHALLENGE?
AI and digital twins cannot compensate for incomplete histories, inconsistent component identifiers or unclear data ownership. Aviation MRO organizations need an authorised data foundation that connects the relevant aircraft, component, inspection, fault and maintenance information while preserving configuration and operational context.

With that foundation in place, a digital twin can provide a contextual view of asset condition, predictive models can identify relevant patterns and governed agents can help teams retrieve evidence or prepare recommendations within a defined workflow. Qualified maintenance professionals remain accountable for engineering and airworthiness decisions.

Key benefits
Turn connected maintenance information into practical intelligence for aircraft and component workflows.
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Create connected asset context

Bring relevant aircraft, component, inspection, fault and maintenance histories into an authorised analytical view.

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Anticipate maintenance needs

Identify patterns and emerging conditions that warrant engineering review or earlier planning.

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Support diagnosis and planning

Provide evidence, comparisons and recommended next steps within defined MRO workflows.

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Scale with governance

Apply data quality, access, traceability, model monitoring and human-validation rules from pilot through production.

How NTT DATA helps

01
Prioritise MRO decisions

Assess maintenance pain points, data readiness, operational risk and value potential to select use cases with a clear owner, baseline and success measure.

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02
Create a trusted MRO data foundation

Connect the authorised aircraft, component, inspection, fault, work-order and technical information required for each use case, with shared definitions, quality controls, security and traceability.

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03
Build the digital asset context

Create the approved digital representation and data relationships needed to understand condition, configuration and maintenance history for the selected scope.

Dynamic Gantt Engine for Airport On‑Time Performance
04
Apply predictive models and governed agents

Use analytics to identify relevant patterns and agents to retrieve or organise approved evidence within defined diagnostic and planning workflows, with qualified human review.

05
Integrate, measure and improve

Embed outputs into approved MRO workflows, monitor data and model performance and measure whether the selected maintenance outcome improves against its baseline.

Building scalable, multilingual architectures
An airplane in flight
Proven impact
Measure the maintenance outcome, not only the model

AI and digital twins create value when they improve a real Aviation MRO workflow. Establish the baseline before implementation and agree the target range, measurement period, asset scope and accountable data owner for each selected use case.

Results that matter
From predictive maintenance to autonomous operational intelligence, leading airlines are already demonstrating measurable business value.
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Unplanned maintenance events

Reduction against the approved aircraft, component or fault-category baseline.

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Fault diagnosis time

Reduction in the time required to gather evidence and reach the approved diagnostic decision.

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Maintenance planning time

Reduction in the time required to prepare the selected work scope, schedule or maintenance plan.

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Aircraft or component availability

Improvement against the approved downtime, turnaround or availability baseline.

Shape the future of aviation operations with intelligent maintenance

Explore the strategies driving resilient fleet performance

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