13 Enterprise AI is moving from isolated experimentation into production workflows. But the shape of that shift looks different in every industry. A hospital using AI to support diagnostic imaging has a different risk profile than a bank using AI for fraud detection. A manufacturer analyzing sensor data from factory equipment has different latency and data movement requirements than a public sector agency building AI into citizen services. The use cases vary, but the infrastructure lesson is consistent: AI success depends on the data layer. As AI moves from experimentation into production, organizations are discovering that scaling AI is fundamentally a data infrastructure challenge. Models, GPUs, and applications matter, but production AI ultimately depends on how effectively data is stored, moved, governed, protected, and made available across the full AI lifecycle. That is one of the clearest takeaways from independent research from Freeform Dynamics, based on input from 504 senior IT and data professionals at medium and large enterprises active with private AI. The study, Storage Infrastructure for Enterprise AI: Lessons from Seasoned Adopters on Building Scalable Sovereign Environments, focused on storage infrastructure requirements for private AI implementations running in datacenters or hosted environments where organizations control the infrastructure stack.The findings show that private AI is becoming a production infrastructure decision, not just a model strategy. Across the survey, 81% of respondents said private AI based on infrastructure they control is critical to success, including 43% who said this fully applies and 38% who said it somewhat applies. That does not mean the public cloud disappears from enterprise AI. Hybrid models remain common, and cloud services will continue to play an important role. But as organizations scale AI into business-critical environments, they are becoming more deliberate about where data and workloads should live. For many, the priority is control. Control over data location, model access, governance, performance, resilience, cost, and compliance. Those requirements become especially important in regulated and data-intensive sectors, where AI cannot be separated from the infrastructure that feeds, protects, and governs it. Looking at the findings by industry makes the point clearer. Each sector is adopting AI through the lens of its own workflows, risk profile, and data types. But across sectors, the same infrastructure questions keep reappearing. Where does the data live? How is it protected? How quickly can it be accessed? How does it move across the pipeline? And how does it remain useful over time? Enterprise AI is not one workload Much of the public conversation around AI still centers on generative AI and large language models. Inside enterprises, the picture is broader. The Freeform Dynamics research found that organizations are active across a wide range of AI genres, including traditional machine learning, RAG-enhanced foundation LLMs, fine-tuned or customized models, computer vision, time-series and IoT analytics, recommendation and personalization technologies, and edge or distributed AI. That diversity matters. In the survey, 68% of respondents were active with at least three different AI genres, while 29% were active with at least five. Each of these workloads places different demands on infrastructure: Training may require high-throughput access to massive datasets. Inference may depend on low-latency access to models, vector stores, and reference data. Computer vision requires efficient handling of large image repositories. Time-series and IoT analytics depend on continuous streams of operational data. RAG-enhanced LLMs need trusted access to enterprise content and knowledge sources. That’s where industry context matters. Enterprises are not adopting AI in the abstract. They are applying it to specific workflows, data types, and business risks. Healthcare and life sciences: Governed AI for sensitive, high-stakes data Healthcare and life sciences organizations are moving forward with AI, but their adoption pattern is more measured than in some other sectors. The survey shows strong use of established AI approaches in healthcare and life sciences: The report notes that healthcare and life sciences organizations show notably lower LLM adoption, likely reflecting concerns about output variability, hallucinations and regulatory constraints. At the same time, computer vision is already well-established in diagnostic imaging. For healthcare and life sciences leaders, the takeaway is not that the sector is behind. It is that AI must be deployed in ways that can be validated, governed and trusted. These organizations manage some of the most sensitive and data-rich environments in the enterprise world, from patient records and clinical notes to imaging data, research datasets, and genomic information. As AI expands across diagnostics, operations, research, and patient engagement, infrastructure has to support more than raw performance. It must also support access control, lifecycle management, data integrity, retention, recovery, and auditability. In healthcare and life sciences, trusted AI starts with trusted data infrastructure. Financial services: Advanced AI at data-driven scale Financial services organizations have spent decades building around data, analytics, and automation. That history shows up clearly in the survey. Among financial services respondents, the survey data showed: This points to a mature and balanced AI strategy. Financial institutions are adopting newer LLM-based approaches for knowledge management and decision support while continuing to rely on machine learning for long-established use cases such as fraud detection, risk modeling, algorithmic trading, customer segmentation, marketing, and retention. The infrastructure stakes are high. Financial services organizations need to move quickly, but not loosely. AI systems often depend on large volumes of transaction data, market data, customer data, and reference data. Latency can affect decision-making. Data quality can affect risk. Security, resilience, auditability, and regulatory compliance are always in the frame. For financial institutions, private AI is not only about adopting advanced models. It is about operationalizing them inside data environments that can support real-time insight without compromising control. Manufacturing: Operational AI from edge to core Manufacturing’s AI story is highly operational. AI is increasingly tied to physical systems, production environments, plant-floor data, and supply chain execution. The survey shows broad AI adoption across manufacturing respondents: These findings reflect the wide range of AI use cases already emerging across manufacturing. The report notes that manufacturers have long used computer vision with trained models for automated quality control, while time-series analytics and machine learning have become integral to production management, supply chain optimization, and predictive maintenance. Manufacturing also brings a distinctive infrastructure challenge: AI has to work across edge and core environments. Data may come from sensors, machines, video feeds, inspection systems, logistics platforms, and enterprise applications. Some workloads require fast processing close to equipment or production lines. Others need centralized analysis across plants, suppliers, and regions. That creates a demanding storage and data management profile. Manufacturing AI needs infrastructure that can handle distributed data, mixed workloads, metadata at scale, video and sensor data growth, and resilience across operational environments. For manufacturers, AI is not only about better analytics. It is becoming part of how operations run. Public sector: Sovereignty, control and continuity Public sector AI is shaped by a different set of pressures. Use cases may include citizen services, records management, transportation, public safety, benefits administration, research, or mission-oriented analytics. Many of these areas involve sensitive data, long retention requirements, and high expectations for continuity. For public sector organizations, the case for private AI often starts with governance and trust. Teams need clarity over where data resides, who can access it, how it is protected, and how services will recover if something goes wrong. The survey’s broader findings support that view. In the report, private AI is defined as a model where AI workloads run on infrastructure the organization controls, such as its own datacenter, a co-location facility, or a bare-metal hosting environment. The report also notes that sovereignty, compliance, and data proximity requirements are driving greater use of private deployments. That does not make public sector AI less ambitious. It makes the infrastructure requirements more explicit. AI systems in these environments have to be governed, resilient, and explainable enough to support public trust. The shared lesson: Production AI depends on the full data pipeline The vertical differences are important. They explain why AI infrastructure cannot be designed around a generic view of the enterprise. But the shared lesson is just as important: production AI depends on the full data pipeline. That pipeline includes data preparation, ingestion, training, tuning, inference, model storage, vector stores, reference data, protection, recovery, and lifecycle management. Each stage can place different demands on storage. The survey found that 86% of respondents recognize that different AI pipeline stages have distinct storage needs. Storage performance is also emerging as a major concern. In the research: 57% of respondents said they are highly focused on preventing storage performance from becoming a bottleneck as AI activity and data volumes grow. That is higher than the percentages focused on compute/GPU availability and network bandwidth. This is a usefult corrective to the GPU-centered AI narrative. Compute is critical, but production AI also depends on how efficiently organizations can feed models with data, retrieve data at runtime, protect pipelines, recover from incidents, and govern information across its lifecycle. Object storage is increasingly part of that foundation. Across the survey: 91% of organizations report meaningful use of object storage to support AI applications and pipelines, with 44% using it extensively and 47% using it quite a bit. That aligns with the realities of enterprise AI. AI workloads often involve massive volumes of unstructured data — images, documents, logs, sensor data, and business records — that must remain accessible across tools, teams, and pipeline stages. For every industry, the storage layer is where performance, resilience, sovereignty, and lifecycle control come together. The experience advantage The survey also shows that experience changes how organizations think about AI infrastructure. Seasoned adopters tend to define AI storage needs earlier in the project lifecycle. They look across the full AI-related data pipeline rather than treating each stage as a separate infrastructure problem. They also show a stronger preference for flexible storage platforms over individual point solutions, with the goal of preserving longer-term relevance and return on investment. That lesson matters as AI programs mature. Many infrastructure problems do not surface during early pilots. They appear when AI projects move into production, when more teams depend on the outputs, when data volumes grow, when compliance requirements become more complex, or when several AI workloads begin competing for the same infrastructure. Healthcare and life sciences organizations need trusted data pipelines for sensitive and regulated data. Financial services firms need speed, scale, resilience, and auditability. Manufacturers need edge-to-core architectures that can support real-time operational insight. Public sector organizations need sovereignty, continuity, and strong governance. The use cases differ, but the direction is the same. AI is becoming a data infrastructure strategy. Across industries, the organizations seeing the greatest success with private AI are not treating infrastructure as an afterthought. They are designing for the full data pipeline, from ingestion and storage to governance, protection, inference, and long-term reuse. As AI becomes increasingly embedded in business operations, the ability to manage data across that lifecycle will become a defining factor in how successfully organizations scale AI. AI strategy is becoming data infrastructure strategy As enterprise AI scales, the winners will not be defined only by who adopts the newest model first. They will be defined by who can put AI to work safely, reliably, and repeatedly across real business environments. That requires infrastructure designed around data: where it lives, how it moves, how it’s secured, how it performs, how it’s governed, and how it’s recovered. Private AI is gaining momentum because many organizations need that level of control. The industry lens makes the case even clearer. Every sector has its own AI adoption pattern, but the infrastructure requirements are converging. Across healthcare, financial services, manufacturing, and the public sector, AI success increasingly depends on the ability to manage data across the full lifecycle, from ingestion and storage to governance, protection, inference, recovery, and long-term reuse. Download the Freeform Dynamics AI Survey Report, Storage Infrastructure for Enterprise AI: Lessons from Seasoned Adopters on Building Scalable Sovereign Environments, to learn what 500+ enterprises running private AI have already learned about scaling AI while maintaining control, resilience, and governance. Related content: Production AI is a data pipeline problem: Insights from 504 enterprises running private AI
