The UK Ministry of Defence (MOD) has selected Red Hat to architect a unified AI and hybrid cloud backbone across its entire estate. Announced today, the agreement is designed to break down data silos and accelerate the deployment of AI models from the data centre to the tactical edge.

For CIOs, it’s part of a broader move away from fragmented and project-specific AI pilots toward a more platform engineering approach. By standardising on Red Hat’s infrastructure, the MOD aims to decouple its AI capabilities from underlying hardware, allowing algorithms to be developed once and deployed anywhere—whether on-premise, in the cloud, or on disconnected field devices.

Red Hat industrialises the AI lifecycle for the MOD

The agreement focuses on the Defence Digital Foundry, the MOD’s central software delivery hub. The Foundry will now provide a consistent MLOps environment to all service branches, including the Royal Navy, British Army, and Royal Air Force.

At the core of this initiative is Red Hat AI, a suite that includes Red Hat OpenShift AI. This platform addresses a familiar bottleneck in enterprise AI: the “inference gap” between data science teams and operational infrastructure.

The new agreement will allow MOD developers to collaborate on a single platform, choosing the most appropriate AI models and hardware accelerators for their specific mission requirements without being locked into a single vendor’s ecosystem.

This standardisation is vital for “enabling AI at scale,” according to Red Hat. By unifying disparate efforts, the MOD intends to reduce the duplication that often plagues large government IT programs. The platform supports optimised inference, ensuring that AI models can run efficiently on restricted hardware footprints often found in military environments.

Mivy James, CTO at the UK MOD, said: “Easing access to Red Hat platforms becomes all the more important for the UK Ministry of Defence in the era of AI, where rapid adoption, replicating good practice, and the ability to scale are critical to strategic advantage.”

Bridging legacy and autonomous systems

A major hurdle for defence modernisation is the coexistence of legacy virtualised workloads with modern, containerised AI applications. The agreement includes Red Hat OpenShift Virtualization, which provides a “well-lit migration path” for existing systems. This allows the MOD to manage traditional virtual machines alongside new neural networks on the same control plane to reduce operational complexity and cost.

The MOD deal also incorporates Red Hat Ansible Automation Platform to drive enterprise-wide AI automation. In an AI context, automation is the enforcement mechanism for governance. It ensures that as models are retrained and redeployed, the underlying configuration management, security orchestration, and service provisioning remain compliant with rigorous defence standards.

Security and ecosystem alignment

Deploying AI in defence naturally requires a “consistent security footprint” that can withstand sophisticated cyber threats.

The Red Hat platform enables DevSecOps practices, integrating security gates directly into the software supply chain. This is particularly relevant for maintaining a trusted software pedigree when integrating code from approved third-party providers, who can now align their deliverables with the MOD’s standardised Red Hat environment.

Joanna Hodgson, Regional Manager for the UK and Ireland at Red Hat, commented: “Red Hat offers flexibility and scalability to deploy any application or any AI model on their choice of hardware – whether on premise, in any cloud, or at the edge – helping the UK Ministry of Defence to harness the latest technologies, including AI.”

The deployment shows that AI maturity is moving beyond the model itself to the infrastructure that supports it. Success in high-stakes environments like defence depends less on individual algorithm performance and more on the ability to reliably deliver, update, and govern those models at scale.

See also: Chinese hyperscalers and industry-specific agentic AI

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Agentic AI offers insurance leaders a path to scalable efficiency as the sector confronts a tough digital transformation.

Insurers hold deep data reserves and employ a workforce skilled in analytic decision-making. Despite these advantages, the industry has largely failed to advance beyond pilot programmes. Research suggests only seven percent of insurers have scaled these initiatives effectively across their organisations.

The barrier is rarely a lack of interest. Instead, legacy infrastructure and fragmented data architectures often stop integration before it starts. Financial pressure compounds the technical debt. The sector has absorbed losses exceeding $100 billion annually for six consecutive years. High-frequency property losses are now a structural issue that standard operational tweaks cannot fix.

Automating complex insurance workflows with agentic AI

Intelligent agents provide a way to bypass these bottlenecks. Unlike passive analytical tools, these systems support autonomous tasks and help make decisions under human supervision. Embedding these agents into workflows allows companies to navigate legacy constraints and talent shortages.

Workforce augmentation is a primary application. Sedgwick, in collaboration with Microsoft, deployed the Sidekick Agent to assist claims professionals. The system improved claims processing efficiency by more than 30 percent through real-time guidance.

Operational gains extend to customer support. Standard chatbots usually answer a query or transfer the user to a queue. An agentic solution manages the process from end-to-end. This can include capturing the first notice of loss, requesting missing documentation, updating policy and billing systems, and proactively notifying customers of next steps.

This “resolve, not route” approach has produced results in live environments. One major insurer implemented over 80 models in its claims domain. The rollout cut complex-case liability assessment time by 23 days and improved routing accuracy by 30 percent. Customer complaints fell by 65 percent during the same period.

Such promising metrics indicate that agentic AI can compress cycle times and control loss-adjustment expenses for the insurance industry, all while maintaining necessary oversight.

Navigating internal friction

Adoption requires navigating internal resistance. Siloed teams and unclear priorities often slow deployment speed. A shortage of talent in specialised roles, such as actuarial analysis and underwriting, also limits how effectively companies use their data. Agentic AI can target these areas to augment roles that are hard to fill.

Success relies on aligning technology with specific business goals. Establishing an ‘AI Center of Excellence’ provides the governance and technical expertise needed to stop fragmented adoption. Teams should start with the high-volume and repeatable tasks to refine models through feedback loops.

Industry accelerators can also speed up the process. Many platforms are now available with prebuilt frameworks that can support the full lifecycle of agent deployment. This approach reduces implementation time and aids compliance efforts.

Of course, technology matters less than organisational readiness. About 70 percent of scaling challenges are organisational rather than technical. Insurers must build a culture of accountability to see returns on these tools.

Agentic AI is a necessity for insurance leaders trying to survive in a market defined by financial pressure and legacy complexity. Addressing structural challenges improves efficiency and resilience. Executives who invest in scalable frameworks will position themselves to lead the next era of innovation.

See also: Chinese hyperscalers and industry-specific agentic AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

Chinese hyperscalers have defined a distinct trajectory for agentic AI, combining language models with frameworks and infrastructure tailored for autonomous operation in commercial contexts. Alibaba, Tencent and Huawei aim to embed these systems into enterprise pipelines and consumer ecosystems, offering tools that can operate with a degree of autonomy.

These offerings are accessible in the West markets but have not yet achieved the same level of enterprise penetration on mainland European and US soil. To find more common uses of Chinese-flavoured agentic AI, we need to look to the Middle and Far East, South America, and Africa, where Chinese influence is stronger.

(Image source: “China Science & Technology Museum, Beijing, April-2011” by maltman23 is licensed under CC BY-SA 2.0.)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.