According to McKinsey’s 2025 State of AI report, nearly 80% of organisations have adopted AI in at least one business function, while enterprise investment in generative AI continues to grow at record pace.
Yet despite this rapid adoption, disaster recovery strategies have struggled to keep up. Veeam’s 2025 Data Resilience Maturity Model found that only a small percentage of organisations have achieved advanced data resilience, leaving many unprepared to recover increasingly complex AI-driven environments. Traditional disaster recovery plans were designed to restore virtual machines, applications and databases not AI assets such as model checkpoints, vector databases, feature stores, training pipelines and inference services. The consequences are already becoming apparent. Organisations that lose these components don’t simply experience downtime; they risk losing months of model training, disrupting AI-powered customer experiences and delaying critical business decisions.
Most recovery plans were built long before AI workloads became business critical. They know how to restore databases and virtual machines, but they weren’t designed to recover model checkpoints, vector databases, training pipelines, or AI services that now power customer experiences and business decisions. As enterprise AI adoption accelerates, that gap is becoming increasingly difficult to ignore.
AI Is Changing What Needs to Be Protected
Traditional disaster recovery plans were built around applications and structured data.
According to McKinsey’s 2025 Global Survey on AI, more than three-quarters of organizations now use AI in at least one business function, reinforcing that AI has become an operational dependency that must be considered within business continuity and disaster recovery strategies.
AI environments are very different. A single AI workload may depend on training datasets, model checkpoints, vector databases, GPUs, APIs, and cloud services working together.
Recovering only one piece of that environment doesn’t necessarily restore the AI application. Every dependency has to come back in the right order for the system to function as expected.
As enterprise AI adoption grows, disaster recovery strategies need to evolve alongside it.
The growing reliance on AI became particularly evident in June 2025, when an outage affecting OpenAI’s ChatGPT and API services disrupted businesses that had integrated AI into customer support, software development, and internal workflows. While the incident was resolved, it highlighted how dependent organizations have become on AI-powered services and underscored the importance of planning for continuity when critical AI capabilities become unavailable. As AI moves from experimentation to core business operations, resilience planning must extend beyond traditional IT systems to include the AI ecosystem itself.
Recovery Is About More Than Backups
Backing up an AI model is only one part of an effective disaster recovery strategy. Unlike traditional applications, AI workloads rely on an entire ecosystem of interconnected components. The model itself is only as useful as the training data that shaped it, the vector databases that provide contextual information, the infrastructure that supports it, and the pipelines that continuously update and deploy it. If any of these elements are unavailable or out of sync after an outage, simply restoring the model won’t bring the AI application back to full functionality.
This is why recovery planning for AI needs to go beyond backups. Organizations should be confident that the latest version of the model can be restored, vector databases remain synchronized, inference services can resume without requiring extensive retraining, and AI workloads can return to production within acceptable recovery time objectives. Answering these questions before a disruption occurs helps ensure that AI-powered services can recover quickly, consistently, and with minimal impact on business operations.
Building AI Resilience
An AI-ready disaster recovery strategy focuses on resilience rather than simply restoration. That means regularly testing recovery procedures, protecting training data, securing storage, and ensuring AI services can recover without extended downtime.
As Microsoft notes in its Azure AI guidance, AI services depend on scalable cloud infrastructure, data availability, and operational continuity. Those same principles apply when designing disaster recovery strategies for enterprise AI.
Preparing for the Future
As AI becomes part of everyday business operations, disaster recovery can no longer focus solely on traditional workloads. Organizations need strategies that protect not only their data but also the intelligence built on top of it.
At Open Storage Solutions, we closely follow how AI is reshaping enterprise infrastructure and what those changes mean for storage and recovery. By sharing insights into emerging technologies and resilient data architectures, we help organizations prepare for the next generation of AI workloads and the recovery challenges that come with them.
An AI system is only as resilient as the infrastructure behind it. Building that resilience today will determine how confidently organizations can rely on AI tomorrow.
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