The world of AI data management is evolving quickly as IT and business leaders begin to realize the single truth that cuts across all industries: the right data strategy is the dealmaker for AI success. Here are the topics defining enterprise data infrastructure and data management right now.
Defining Enterprise Data Infrastructure and Data Management
1. Line of business use of AI goes beyond chatbots and niche uses is creating new, multifaceted unstructured data requirements
To date, enterprise AI has focused on using mass commercial tools like Claude and ChatGPT to automate rote productivity tasks and generate content or code. As agentic AI makes its way into the mainstream, the opportunity is expanding to complex industry-specific use cases such as improving patient diagnoses and treatment plans across life-threatening conditions, aiding advanced exploration techniques in the energy industry, and supplementing design work in an architectural firm. These kinds of projects require new processes:
- Industry context including vertical metadata needs to be surfaced and made readily available for both AI and analytics. This is especially crucial for unstructured data, which lacks this information in the filesystem metadata.
- The curation of authoritative, domain-specific data and controlled data workflows to identify relevant data and eliminate noise and to avoid the leak of sensitive and IP data while ensuring highly accurate, safe outcomes.
- IT and data teams will be working together to prepare and classify the entire unstructured data estate to begin fueling these initiatives with accuracy.
- Also important is a new framework for execution: which data types and from what departments demand priority for classification and AI monitoring?
2. Enterprise IT teams will need new partners for AI data management
AI models require unstructured data to deliver the results that CXOs want and information workers need, yet this data is large and expensive to move, distributed across many applications and storage, and is largely dark to AI, according to IDC. Addressing the unstructured data quality and access problem for expanding use cases means that IT teams running storage and infrastructure will need to form partnerships across the business. These partnerships include security and compliance departments that can help with sensitive data classification and mitigation, determining which data types are most at risk, and what to do with them when discovered. Then there are the data engineers and analysts who need access to unstructured data from analytics tools such as Snowflake and Databricks. Storage engineers will need to gain a broader understanding of how these analytics tools work and their data requirements. Thirdly, line of business heads who can help with projects such as data classification. What datasets are the highest priority, and what are the keywords that they want to be able to search on, what should be excluded, and what should be made available in BI tools and AI models?
3. The rise of FinOps for AI cost optimization
Some 40% of companies are spending more than $10M a year on AI, according to a recent study. Most of this spending goes toward inferencing, and these massive, hard-to-predict bills are creating a demand for AI-specific cost measurement, or AI FinOps. Several factors go into reducing AI costs, including improving GPU utilization and deploying a hybrid AI infrastructure optimized for different data workloads. Data management is an underestimated factor. It will require granular analysis of growing volumes of unstructured data across hybrid storage silos to understand high-value versus low-value data, which can be moved to lower-cost storage. Better analytics will inform the burgeoning practice of metadata enrichment to classify data. With more streamlined data assets that are identifiable through search, organizations can reduce the cost of storage and reduce AI processing costs by sending only the right data for the project at hand into AI tools. Copies can be automatically deleted from cloud storage after the AI processing has finished.
4. AI data preparation myths will get tested
With the increasing awareness that most of the unstructured data that AI relies upon is not ready for use, AI data preparation techniques are coming out of the woodwork. So what really works? Common myths include: your storage vendor’s AI features will handle data preparation, when that is only true for data stored on that vendor’s platform. Most enterprises have multi-vendor storage which means enterprise IT needs a vendor-agnostic data intelligence layer to unify and reconcile disparate data assets. ETL and ELT based approaches for unstructured data are another trap, because while they are common for structured and semi-structured data, the method of copying data into pipelines before enriching it drives up costs and timelines when it comes to petabyte size file data stores. Performance as the primary storage requirement for AI Is another myth being questioned by prominent analysts. Understanding data, including cleaning out redundant, obsolete and trivial (ROT) data is critical for AI.
5. Storage vendors are introducing AI data services but they won’t fit every use case.
Large data storage providers are announcing data discovery, classification and governance for AI as core capabilities now included within their storage platforms. This creates a real decision point for IT leaders: spend nothing extra and use what is already in the storage console or bring in an independent platform. If an IT environment is using more than one file storage platform (including cloud) you will have truncated visibility and workflows that can introduce higher risk and costs for AI. Data services and service level agreements (SLAs) that transcend all storage platforms from on-prem to cloud will save time, money and effort while delivering greater transparency, accountability and a single version of the truth.
6. Vector databases have peaked for AI indexing
Vector database capabilities are now embedded in platforms from Snowflake, major storage vendors, and data lakehouse providers. AI models themselves are increasingly handling vectorization internally. The vector database as a discrete, high-value standalone infrastructure layer is losing its value. Whether an enterprise uses Pinecone, Weaviate, a lakehouse vector store, or model-native embedding, the data preparation layer that sits before all of them is what determines quality. Classification, metadata enrichment, sensitive data detection, and data selection at scale are the durable requirements.
7. Hybrid AI mobility: what it means for unstructured data management and orchestration
A major shift is the recognition that AI workloads will not live in a single location but distributed across public cloud, on-premises environments and edge locations. Hybrid AI infrastructure is forecast to grow at 31% annually through 2032, according to Fortune Business Insights. This model will ensure the best price for performance ratio for different workloads and projects while also accommodating industry-specific security and compliance requirements. IT leaders are discovering that their AI success depends less on the model they choose and more on whether they can efficiently find, govern, move, and manage data across hybrid environments without undue complexity and costs.
8. Multi-agent orchestration is becoming a data infrastructure problem
Orchestrating multiple AI agents across enterprise systems is fast becoming a core IT responsibility. Without a unified data layer, each agent queries its own siloed view, producing incomplete or even inaccurate outputs. Governance is another risk: when an autonomous agent chain takes action, accountability is diffuse and audit trails are fragmented. The underlying data problem compounds everything. AI agents are only as reliable as the context they retrieve, and most (63%) of organizations either do not have, or are unsure whether they have, the right data management practices for AI, according to Gartner. Managing data across agentic AI will require a unified metadata system that spans multiple storage and cloud architectures and delivers unified context to data no matter where it lives. This requires going beyond storage-centric architectures to an independent data management plane.
9. AI will create an explosion of metadata and noise unless managed correctly
So far, unstructured data, which is over 80% of an enterprise’s footprint, has gone largely unclassified and unanalyzed because of the complexity involved. Some call it dark data. With AI, this can change as AI can be used to inspect unstructured file contents and help with classification. But be careful what you unleash. Blindly running AI on all enterprise unstructured data will create an explosion of metadata that will potentially impair data quality since a lot of unstructured data is noise for any given use case. A systematic way to enrich the right unstructured data with the right context at the right time will become incredibly vital to the success of AI and to the security of enterprise data.
10. Unstructured data ingestion to lakehouses will lose the overhead
AI and lakehouse adoption in the enterprise have exposed a fundamental infrastructure mismatch. The data that analytics and AI systems need most, such as files, documents, media and research archives, is also the hardest to deliver because of its size and siloed nature. Current ingestion approaches require copying raw data in bulk to lakehouse platforms, which is costly, slow, and often takes weeks to months at the petabyte scale. Plus, ETL tools were not designed for the size, scale and complexity of enterprise unstructured datasets. The next wave of AI and analytics readiness will not be about generating more data. It will be about data classification, metadata management and making the right unstructured data accessible to lakehouse environments without the overhead of moving it.
Across every trend shaping enterprise AI today, a single theme emerges: The organizations that will see success with AI are not necessarily those with the most advanced models or the most compute. They are the ones that can find, classify, govern, and deliver the right data at the right time across increasingly complex hybrid environments. They will also understand unstructured data management techniques to keep AI costs in budget. The organizations that will see success with AI are not necessarily those with the most advanced models or the most compute.
