Why object storage is the cornerstone of scalable AI: The playbook
From automating customer support to accelerating R&D, AI is transforming industries, becoming central to how they operate, compete and innovate. The numbers back it up, too: According to McKinsey, 92% of enterprises plan to increase their AI investments over the next three years.
But as organizations scale their AI ambitions, they’re running into a familiar challenge: how do you manage the explosion of unstructured data—images, documents, logs, videos—that AI systems rely on?
When managing this explosive growth of unstructured data, IBM Cloud® Object Storage plays a strategic role. Unlike traditional file or block storage, it is designed for hyperscale, flexibility and accessibility. It effortlessly handles petabytes of data, supports rich metadata tagging, and integrates with modern AI workflows, connecting seamlessly through industry-standard APIs.
IBM Cloud Object Storage isn’t just supporting AI—it’s redefining how it’s built, deployed and scaled across the enterprise.
One of the most exciting developments in enterprise AI is the rise of agentic AI: systems that can reason, plan and act independently. These agents need a persistent memory layer to store interactions, retrieve documents and track decisions over time. IBM Cloud Object Storage provides that scalable, always-on backend, enabling autonomous operations in sectors such as healthcare, logistics and customer service.
Another fast-growing architecture is retrieval-augmented generation (RAG). RAG enhances large language models by grounding their responses in real enterprise knowledge. IBM Cloud Object Storage plays a foundational role here, housing the source material—contracts, manuals, emails—that RAG systems retrieve during inference. Legal teams, cybersecurity analysts and support centers are already seeing the benefits of more accurate, context-aware AI.
As AI models become more data-hungry and multimodal, enterprises are consolidating structured and unstructured data into unified environments built on object storage. These data lakehouses support everything from model training and feature engineering to real-time analytics and compliance auditing.
The impact of IBM Cloud Object Storage in AI is already visible across sectors:
- In banking and telecom, AI chatbots are accessing customer histories from IBM Cloud Object Storage to deliver personalized, autonomous support.
- In healthcare, clinical decision systems retrieve patient records and research to help doctors make faster, more informed choices.
- In legal, AI is streamlining document review by searching case law and contracts stored in object storage.
- In cybersecurity, threat detection systems analyze logs and threat intelligence to identify and respond to incidents in real time.
- In agriculture and R&D, AI tools help with everything. From diagnosing harmful fungus on crops in the field, to accessing weather data, lab notes and patents to guide smarter decisions and accelerate innovation.
For CIOs, data managers and cloud architects, the message is clear: IBM Cloud Object Storage isn’t just supporting the growth of AI—it’s powering the next generation of intelligent, data-driven enterprise systems.
IBM Cloud Object Storage powers the architectures that provide AI-driven insight, enabling everything from real-time inference to semantic search and compliance auditing.
Querying structured and semi-structured data
Enterprises are increasingly leveraging modern query-in-place engines such as watsonx.data®, IBM Cloud Analytics Engine and other open technologies to analyze structured and semi-structured data directly within IBM Cloud Object Storage. These platforms support SQL-like queries on formats such as CSV, Parquet and JSON, allowing teams to extract insights without needing to move or transform the data first.
For example, a financial services firm might use watsonx.data to scan transaction logs stored in IBM Cloud Object Storage for fraud detection patterns. Meanwhile, a retail company could extract product performance metrics from JSON logs to power real-time dashboards. This approach is ideal for ad hoc analysis, feature extraction and lightweight ETL tasks that feed into downstream AI models.
Enabling semantic search with vector databases
For unstructured data such as documents, images and logs, traditional querying falls short. Here is where vector databases come in. By storing raw data in IBM Cloud Object Storage and indexing vector embeddings in tools like Milvus, Datastax, FAISS, Weaviate or Pinecone, enterprises can enable semantic search and retrieval-augmented generation (RAG) workflows.
Imagine a legal team that uses a RAG-based assistant to search thousands of contracts stored in IBM Cloud Object Storage. The assistant uses embeddings to understand the context of a query—such as “termination clauses in vendor agreements”—and retrieves the most relevant documents, even if the exact keywords don’t match. This RAG architecture is foundational to enhancing large language models with enterprise-specific knowledge.
By tagging objects with rich metadata such as dataset version, model type, source system or compliance tags, teams can build searchable catalogs that support fast, granular queries across billions of objects.
In regulated industries such as healthcare or finance, this support is critical. For instance, a pharmaceutical company might tag clinical trial data with version numbers and regulatory status, enabling auditors to quickly trace which datasets were used to train a specific AI model.
This supports auditability, reproducibility and governance—all essential for responsible AI.
Lakehouse architectures such as watsonx.data are transforming how enterprises manage and access data for AI. By combining the flexibility and scalability of IBM Cloud Object Storage with the performance and structure of data warehouses, lakehouses enable governed, multiengine access to AI-ready data—all in a unified environment with built-in resiliency, data lifecycle, versioning, and backup.
Whether you're training foundation models, running inference or preparing data for compliance reporting, lakehouses offer a seamless platform for working with structured, semi-structured and unstructured data. They also ensure enterprise-grade security, data lineage and cost efficiency, making them ideal for modern AI pipelines.
As AI workloads grow in complexity and scale, how you access and manage data in IBM Cloud Object Storage can make or break performance.
Here are some practical, field-tested strategies that leading enterprises use to get the most out of their AI pipelines:
- Tag your data smartly. Metadata tagging isn’t just for organization—it’s a performance booster. Tools such as watsonx.data intelligence help you tag datasets with attributes like version, source or sensitivity, making it easier to filter and retrieve exactly what your models need.
- Think beyond keywords. For unstructured data such as documents or images, integrating with vector databases (for example, Milvus, DataStax) enables semantic search. It is especially powerful in retrieval-augmented generation (RAG) and agentic AI use cases where context matters.
- Combine search strategies. Hybrid search—mixing keyword and vector-based approaches—can significantly improve both precision and recall when retrieving documents or knowledge assets.
- Keep compute and storage close. Colocating your compute and storage resources (ideally in the same availability zone) minimizes latency and boosts throughput, which is critical for training and inference workloads.
- Stick to open formats. Storing data in open, columnar formats such as Iceberg, Parquet or ORC ensures compatibility across a wide range of AI tools and engines, making your architecture more flexible and future-proof.
- Elevate AI data security practices. Data is the fuel for AI, and it’s emerging as a high-value target for attackers. IBM Cloud Object Storage provides essential data security and compliance to protect data integrity, maintain organizational trust, and avoid data compromise.
As AI becomes central to enterprises, the infrastructure behind it must be as intelligent and scalable as the models themselves. IBM Cloud Object Storage isn’t just a backend—it’s the foundation for AI innovation.
IBM Cloud Object Storage is purpose-built for this moment. With support for open formats, governance and seamless integration with watsonx®, it enables enterprises to centralize, query and protect their AI data at scale.
IBM enhances data storage for AI with low cost options including the One-Rate pricing plan—delivering predictable pricing and up to 70% savings on storage costs—making it easier to scale AI workloads without compromising performance or budget.
Whether you're building agentic AI systems, deploying RAG pipelines or modernizing your data lake, IBM Cloud Object Storage gives you the performance, durability and simplicity to move faster— and smarter— on your AI journey.
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