Distributed and AI-powered search for OODA loop

Transforming defence decisions with real-time intelligence access

Modern defence challenges are defined less by a lack of information and more by the difficulty of making sense of them. This is key for efficient decision-making across mission-critical defence operations. The UK Ministry of Defence (MoD) has already made data-driven operations a strategic priority. To move from strategy to action, using data and AI effectively depends on a solid data foundation and approach.

The OODA framework1 — Observe, Orient, Decide, Act — provides more than a technical model; it can serve as a guide to help defence organisations find the right path towards translating data into actionable insights. However, to enhance data-driven decision-making, defence agencies must overcome a long-standing barrier: fragmented, siloed intelligence systems.

The 6 data challenges spaces of the Defence AI Playbook

The 2024 Defence AI Playbook2 identifies six distinct ‘problem spaces’ that defence must address. Across all these spaces, the ability to rapidly access relevant information from fragmented data environments is essential. The problem spaces include:

Recognise (detecting patterns in vast sensor data) 1.

Comprehend (deriving insights from unstructured information) 1.

Predict (anticipating outcomes based on historical data) 1.

Simulate (exploring scenarios for planning) 1.

Generate (creating content from existing data patterns) 1.

Decide (automating actions to achieve goals)

By implementing a data mesh as a unified data layer and foundation for AI, defence organisations can work smarter, ensure interoperability across systems and strengthen the OODA loop to better support operational commanders and decision-makers. When combined with AI capabilities, a data mesh approach can elevate the operational advantage to a new level.

Defence AI Playbook contributor Cdre Rachel Singleton emphasises that the UK Ministry of Defence (MOD) recognises AI’s enormous potential to enhance capabilities, improve productivity, and maximise strategic advantage.4

Observe: Real-time intelligence with data mesh and distributed search

In the observe phase of the OODA loop, commanders rely on situational awareness built from vast volumes of raw data, sensor feeds, network logs, and intel reports scattered across domains. Data is frequently stored in different formats and across different systems. The result is manual correlation across siloed datasets increasing the time to insight, limited visibility, and blind spots.

The data mesh approach provides enhanced visibility and eliminates data silos, enabling mission-critical queries to be answered in near real time wherever the data resides. It ensures that information is instantly accessible and ready for unified analysis by ingesting, normalising, and indexing data from sensors and systems.

By using distributed search capabilities, defence teams can instantly query multiple systems across classification boundaries, clouds, and commands while enforcing local security controls without moving data from secure environments. This means that the information is not moved or duplicated; instead, it stays in place but becomes discoverable and usable holistically.

With a data mesh approach all relevant data across formats, including time, space, geography, compliance level, or other attributes, can be queried and analysed in a few minutes instead of hours.

Orient: Streamlined analysis via AI capabilities

In the orient phase of the OODA loop, the challenge is no longer collecting data; it’s all about analysis, interpretation, and context.

Defence organisations have a huge amount of data that is not easy to process and analyse. Manual processes are time-consuming and risky. But missions require agility and rapid time to insight.

AI capabilities are key for the orientation phase and can help:

Filter

Analyse large amounts of data

Automate reports and summaries

Perform document comparisons

Enhance search

Provide context required for critical missions

In the cybersecurity space, AI helps shift the focus from volume to clarity, helping defence teams to detect threats more effectively. It can rapidly analyse large volumes of alerts, reducing noise caused by false positives, and group alerts in potential attacks that shortens investigation time.

Defence organisations often operate with limited or no connectivity, yet still need to understand and work with their data. Retrieval augmented generation (RAG) enables secure, natural language interaction grounded in organisation-owned data and selected language models, even in disconnected, intermittent, and limited (DIL) and air-gapped environments.

By generating outputs based on trusted internal sources, RAG can enhance the orient phase and reduce the risk of hallucinations. Military leaders and their staff receive cited responses drawn directly from trusted datasets — not links or dashboards but real contextual intelligence drawn from historical reports, doctrine, and sensor inputs.

Decide: Enhanced decision-making with Elastic AI Assistant

The decide phase of the OODA loop sets the direction based on available insights. For defense leaders, this is often the most critical and time-sensitive step, as they have to decide and take action based on the information that will move their mission forward.

The Elastic AI Assistant acts as a force multiplier in this phase, turning complex, fragmented data into clear guidance and support. It can intelligently collate data from multiple sources and present decision-makers with reports, visualizations, and analysis.

With AI Assistant, defence organisations can become more agile and responsive to emerging situations without the time investment or cost of extensive technical training. This accelerates the user’s ability to be effective and aid decision-making, especially in dynamic field environments where every second matters.

Act: Enabling collaborative action across units and partners

The act phase of the OODA loop is where decisions are translated into action. It is focused on execution, carrying out the chosen course of action and ensuring it is implemented as intended. In this phase, collaboration and coordination are key.

In complex operations, collaboration depends on shared understanding. A data mesh approach can provide real-time, secure access to distributed information as well as facilitate collaboration. In defence, this is particularly important for coordinating actions between different units, partners, coalitions or departments, ensuring that everyone has access to the up-to-date information, follows the same strategy and has visibility into the same data.

With robust access controls, such as role-based access control (RBAC) and attribute-based access control (ABAC) permissions, organisations can balance openness with security. This gives the right people the right information at the right time to make informed, coordinated decisions and actions.

Enhance your mission outcomes with distributed search and AI

In environments where data problems directly become mission problems, replacing manual, costly, and time-consuming processes with secure, real-time distributed search is crucial. AI is no longer optional; it represents a significant advantage for defence decision-makers.

To support this transformation, data mesh architecture, distributed search, and RAG capabilities are available now. They can be retrofitted to current systems, enabling mission advantage today while preparing for the AI-powered future of defence.

Elastic is trusted by defence organisations worldwide, delivering enhanced search, faster insights, predictive alerts, and a scalable, secure foundation for missions. Connect with our experts today!

Explore how data mesh, AI-powered search, and insights are reshaping defence decision-making. Watch Decisions at Speed – How Defence Leaders Are Unifying Data for Real-Time Insight, part of our strategic conversation series with defence leaders.

1. The Decision Lab, "The OODA Loop."
2. Gov.UK, "The Defence AI Playbook," January 2024.
3. Gov.UK, "The Defence AI Playbook," January 2024.

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