Use Case Intelligence RSS Search use cases Search
In-house Public Sector Australia Company newsroom

Australian Department of Defence

Real-time Analytics

Aircrew operating in complex maritime and undersea mission environments needed faster decision support from large volumes of operational data so they could identify adversaries and respond more quickly without removing human control from the aircraft crew.

Read the original story Explore all use cases

Reported impact

Business outcome

The live deployment demonstrated that the AI assistant could enhance mission effectiveness, help crews make faster and smarter decisions, and enable warfighters to locate and identify adversaries more quickly during operations.

  • Productivity Improvement

Solution

The Australian Department of Defence deployed an in-house AI assistant into the Royal Australian Air Force's P-8A Poseidon during Exercise Talisman Sabre 2025. In the published implementation, the AI system ran live inside the aircraft's systems and supported operators directly rather than making decisions autonomously, with the crew remaining in control. It ingested aircraft and undersea-related data feeds, then used advanced AI algorithms and machine learning models developed under AUKUS Pillar II to collect, analyse and classify the data in real time. The assistant returned rapid insights to help crews react in dynamic scenarios and make faster decisions. Delivery was a joint effort across Navy, Air Force, Defence Science and Technology Group, the Defence Artificial Intelligence Centre, and AUKUS partners; the source does not name storage services, governance tooling, or a separate model-serving platform beyond the aircraft integration.

Data flow

Undersea and aircraft data feeds are integrated into the AI system operating within the P-8A Poseidon's onboard systems. The system applies advanced AI algorithms and machine learning models to collect, analyse and classify the data in real time, then returns rapid insights to the aircrew as a digital assistant while operators remain in control.

Solution architecture

3 components · 2 layers
  1. Compute
    • advanced AI algorithms Process operational data during the exercise to support analysis and classification for faster decisions.
    • machine learning models Collect, analyse and classify undersea data so warfighters can locate and identify adversaries more quickly.
  2. Serving
    • AI system Acts as a digital assistant inside the aircraft's systems, integrating data feeds and surfacing rapid insights to operators.

Architecture clues

  • Built under AUKUS Pillar II through collaboration among Australia, the United Kingdom, and the United States.
  • Live AI deployment aboard a Royal Australian Air Force P-8A Poseidon during Exercise Talisman Sabre 2025.
  • The operational exercise built on earlier testing at US Navy Exercise Black Widow in March 2025.

Evidence from the source

"During the flight at Talisman Sabre 2025, the AI operated live within the aircraft’s systems."
"The AI adds value when the crew need it, and helps them make faster, smarter decisions."
"The AI system acts as a digital assistant to support operators, integrating data feeds and providing rapid insights to help aircrew respond to dynamic scenarios."
"These models are designed to collect, analyse and classify undersea data, allowing our warfighters to locate and identify adversaries more quickly than human operators could alone."
"Under AUKUS Pillar II, our forces have pooled resources to develop several advanced AI algorithms and machine learning models."
Transparency Evidence & methodology 75/100 reference value · 92% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value75/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Australian Department of Defence

    Australian Department of Defence customer story

    First-party
    https://www.defence.gov.au/news-events/news/2025-08-11/ai-takes-flight-talisman-sabre
    Source type
    Customer-published story
    Published
    Aug 11, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Aug 11, 2025 · Australian Department of Defence · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Related

Similar use cases

See all like this
In-house Oct 9, 2026 BBVA Financial Services Spain BBVA's trading desk needs to respond to institutional client requests for bond, interest-rate swap, and foreign-exchange quotes within milliseconds while balancing competitiveness, profitability, portfolio risk, and… within milliseconds Real-time AnalyticsRisk Management Atalaya · Bloomberg · Viriato NVIDIA Oct 9, 2026 California Department of Transportation (Caltrans) Public Sector United States Caltrans needed to inspect and monitor roughly 1,200 active transportation projects more frequently and safely, but manual site visits were slow, inconsistent, and risky for staff working around traffic, bridges, and… Remote operations up to 100 miles Real-time Analytics Esri ArcGIS · NVIDIA CUDA · NVIDIA Jetson Orin OpenAI Modified · Oct 7, 2026 SensoRy AI Public Sector US SensoRy AI needed to convert complex wildfire sensor readings into clear, immediate guidance for responders because detecting a fire is only useful if firefighters and officials can understand the situation quickly… answers in seconds Real-time AnalyticsRisk Management ChatGPT · ChatGPT Voice