ABSTRACT: Generative AI is driving explosive data growth, pushing enterprises beyond legacy storage, requiring smarter storage for AI. Modern data platforms unite secure storage, zero-trust principles, and multi-cloud integration for uninterrupted AI operations. Dell’s PowerStore illustrates this shift with immutable snapshots, advanced replication, and intelligent data reduction to tame rising costs and complexity. Seamless developer tools and QLC-based drives power continuous innovation while cutting energy use. These next-gen platforms help organizations unlock AI’s full potential and thrive in the data-driven era by merging security, scalability, and simplicity.
Death, Taxes, and Data Growth
That twist on the old adage underscores the unstoppable force of data proliferation. According to the conversations and research, over 80% of critical organizational data resides on-premises, yet enterprises also leverage multiple public clouds. This dichotomy intensifies complexity, making it harder to manage and protect data consistently across environments.
Even more challenging is budget allocation for AI. Research from Enterprise Tech Research (ETR) points out that around 20% of organizations, see below from the October 2024 Drill Down Generative AI study, haven’t progressed beyond the proof-of-concept (POC) stage for generative AI due to budget constraints. Forty-four percent are reallocating budgets—often taken from infrastructure. Hence, “more with less” becomes a rallying cry: how do you grow AI projects while reducing storage expenditures?
Answer: build a data platform, not just a storage system. Storage capacity alone won’t solve AI challenges; enterprises need end-to-end platforms that integrate high-performance storage, data protection, intelligence, and automation.
Data-Centric Protection in a Zero-Trust World
The New Face of Cyber Threats
As AI grows more sophisticated, so do the threats. From ransomware to insider tampering and data attacks have increased in frequency and complexity. The stakes have never been higher, especially when the data is fueling AI models for mission-critical applications.
Beyond Snapshots: Holistic Resilience
In a conversation with Dell Technologies, we discussed Dell’s heritage in data protection and how PowerStore integrates security features from the ground up. Capabilities such as immutable snapshots, advanced replication, and built-in cyber-resilience measures can help organizations recover swiftly from attacks or corruption. But even more impactful is the ability to back up to and restore from the cloud using PowerProtect directly, without third-party servers.
Zero-Trust Architecture
Zero-trust means validating every user and every request, inside or outside the network. As Dell has done, implementing zero-trust as a core design principle helps reduce the risk of insider threats and lateral movement by attackers. This architecture adds another layer of assurance that your AI data and the storage systems remain uncompromised.
Data Flexibility: Navigating Multi-Cloud and Edge
Data Where You Need It, When You Need It
AI thrives on large, diverse data sets. But enterprise data now spans on-premises, edge locations, and public clouds. A modern data platform must deliver fluid data mobility and consistent management experiences across these locations.
Multi-Cloud Integration
PowerStore’s integration with multi-cloud environments via replication and backup to public clouds illustrates the flexibility needed for AI. Automated policy-based data movement ensures data is in the right location for training models or running inferencing jobs. Reducing friction for data scientists—whether they need local HPC clusters or elastic cloud resources—speeds AI development cycles.
Developer and Platform Engineering Focus
Part of “data access” also means offering frictionless integrations for developers, DevOps, and platform engineering teams. Features like Kubernetes CSI drivers, Terraform providers, and Ansible playbooks help unify storage management with the broader application lifecycle. Through solutions like ServiceNow integration, self—service provisioning models empower developers to spin up resources quickly, ensuring data availability for AI workflows without bogging down IT teams.
Scale: Handling Petabytes with Ease
Exponential Growth Demands
Massive scale is a hallmark of AI. Training generative AI models often require petabytes of data. Businesses increasingly combine historical data with real-time streams for continuous learning and improvement. A data platform must seamlessly expand capacity and compute resources while maintaining performance and availability.
Data Reduction and Storage Efficiency
Dell’s PowerStore offers a 5:1 data reduction guarantee, which many users surpass in real-world scenarios—some hitting as high as 9:1. Data reduction techniques, such as deduplication and compression, save on power, cooling, and floor space, all while offsetting the ballooning data volumes needed for AI.
Always On: No Downtime Allowed
AI applications increasingly serve external customers around the clock. Downtime or slow performance can mean lost revenue or flawed analytics. With zero-downtime software upgrades and “dynamic node affinity,” modern storage architectures can rebalance resources automatically, ensuring high performance even when physical components fail or require maintenance.
The Sustainability Angle
Power and Cooling Constraints
Training AI models on GPU-accelerated nodes is power-intensive. Data centers, particularly at the edge, face real limitations on how much additional power they can draw. An efficient storage platform that can handle more data in a smaller footprint frees up power and cooling capacity for AI compute.
QLC Innovations
PowerStore’s QLC-based drives, such as the 3200Q model, exemplify how storage vendors leverage and innovate to reduce power draw while increasing density. These smaller form factors deliver more gigabytes-per-watt, directly addressing sustainability goals. As more regions pass stricter environmental regulations, the capacity to do more in less space becomes a competitive differentiator.
Easing the Operational Burden Through AI and Automation
AI for Storage and Storage for AI
While AI workloads demand robust storage infrastructure, AI-driven insights can simplify storage management. Features like intelligent tiering, predictive analytics for drive failures, and automated workload balancing fall under “AIOps.” By applying machine learning to operational data, these platforms can proactively detect anomalies, optimize performance, and reduce manual intervention. This is not new but has been enhanced by GenAI techniques paired with traditional AI / ML.
Unified Policies and Simpler Management
Policy-based architectures allow administrators to apply consistent data protection, replication, or retention rules across the environment with a few clicks or lines of code. This drastically lowers the risk of human error and speeds up provisioning and updates, which is vital in the fast-moving world of DevOps and AI-enabled applications.
Our ANGLE
AI is no longer a nice-to-have; it’s the driving force behind digital transformation initiatives across industries. To unlock AI’s potential, organizations must evolve from purely storage-focused models to fully integrated data platforms that unify security, multi-cloud access, scalability, and sustainability.
The growing intersection of AI requirements and next-generation storage design will continue to evolve and enable more efficiency, scale, and security. With features like advanced data reduction, zero-downtime software updates, built-in zero-trust security, and deep automation, modern solutions, in solutions such as Dell’s PowerStore, point the way to a future where data platforms are the linchpin of AI-driven innovation.
Ultimately, enterprises must recognize that data must be organized, protected, and instantly accessible to fuel AI. And AI, in turn, will fuel the next wave of intelligent applications and agents. The winners will be those who prepare their infrastructure not just to store data but to harness and deliver it as a secure, flexible, and infinitely scalable resource for the AI age.
This need for an evolved platform was highlighted in a recent event discussing Dell Technologies’ latest “Smart Storage for Tomorrow’s Opportunities,” also sponsored by Intel. Click here for further exploration of why storage must transform into a full-scale data platform, mainly focusing on three foundational pillars: security, flexibility, and scale.
