VAST Data recently announced new enhancements to the industry-leading VAST Data Platform, making it the first and only system in the market to unify structured and unstructured data, into a single DataSpace that scales linearly to hyperscale – with unified enterprise-grade security. These new capabilities are redefining enterprise AI and analytics by combining real-time vector search, fine-grained security, and event-driven processing into a seamless, high-performance data ecosystem that powers the VAST InsightEngine, which transforms raw data into AI-ready insights through intelligent automation, enabling enterprises to build advanced AI applications, agentic workflows, and high-speed inferencing pipelines.

Organizations today face significant challenges in scaling enterprise AI deployments. AI models call for ultra-fast vectorized search and retrieval for fast access to the most up-to-date information, with AI-driven workloads requiring massive computational power and well integrated data pipelines. Enterprise AI applications involve sensitive data and mission-critical workflows, yet many AI pipelines lack enterprise-grade security, encryption, and governance controls that span all data sources.

To address these challenges, the VAST Data Platform now includes include:

• Vector Search & Retrieval: The VAST DataBase is the first and only vector databasethat supports trillion-vector scale with the ability to search large vector spaces in constant time, making it both possible and practical to index all data and make it available to agentic workflows at any scale. With AI-powered Similarity search for real-time analytics and discovery, organizations can turn real-time data into AI-driven decisions by automatically embedding vectors for search and retrieval.
• Serverless Triggers & Functions: The VAST DataEngine is the first and only solution to create real-time workflows that don’t require background ETL tools or scanning to provide generative-AI access from source data. With event-driven automation for AI workflows and real-time data enrichment, this system can embed and serve context to agentic applications instantaneously, breaking down the barriers to real-time RAG in the enterprise to allow organizations to accelerate AI and analytics with high-speed queries, serverless processing, and automated pipelines that securely ingest, process, and retrieve all enterprise data (files, objects, tables, and streams) in real-time.
• Fine-Grained Access Control & AI-Ready Security: VAST’s built-in enterprise-grade security context now offers advanced row- and column-level permissions, ensuring compliance and governance for analytics and AI workloads, while unifying permissions for raw data and vector representations.

As organizations embrace AI retrieval, and as embedding models continue to make exponential improvements in their understanding of enterprise data, only the VAST Data Platform can provide a unified, AI-ready solution that can meet the needs of extreme-scale agentic enterprises. The parallel transactional nature of VAST’s unique DASE architecture makes it possible to update vector spaces in real-time for the first time, and this shared-everything approach allows for all servers to search the entire vector space in milliseconds – enabling VAST InsightEngine to transform raw data into AI-ready insights instantly, empowering organizations to make decisions with maximum accuracy.

“Only two kinds of companies exist today: those becoming AI-driven organizations, and those approaching irrelevance,” said Jeff Denworth, Co-Founder at VAST Data. “In order to thrive in the AI era, enterprises need instant AI insights, enterprise-grade security, and limitless scalability – without worrying about managing fragmented tools or data infrastructure. The VAST InsightEngine is the only market’s first and only solution able to securely ingest, process, and retrieve all enterprise data – files, objects, tables, and streams – in real-time to make enterprise data instantly usable for accurate AI-driven decision making.”

Vertiv recently made another key addition to its industry-leading thermal management portfolio, with the introduction of the Vertiv CoolLoop Trim Cooler, in support of air and liquid cooling applications for AI (artificial intelligence) and HPC (high-performance computing). This global solution supports diverse climate conditions for hybrid-cooled or liquid-cooled data centres and AI factories.

Integrating seamlessly with high-density, liquid-cooled environments, the Vertiv CoolLoop Trim Cooler delivers operational efficiency and aligns with the industry’s evolving needs for energy-efficient and compact cooling solutions. It provides up to a 70% reduction in annual cooling energy consumption leveraging free-cooling and mechanical operation, and 40% space savings compared to traditional systems. Designed to address the challenges of modern AI factories, the system supports fluctuating supply water temperatures up to 40°C and cold plate functionality at 45°C.

Straightforward water connections provide smooth and direct system integration for the Vertiv CoolLoop Trim Cooler and the Vertiv CoolChip CDU coolant distribution units, for direct-to-chip cooling. Vertiv CoolLoop Trim Cooler can also connect directly to immersion cooling systems. This simplifies installation and operational complexity, allowing compatibility across a range of high-density cooling environments, offering time savings and cost efficiencies for customers.

“AI is dramatically changing the cooling profiles of today’s data centres, requiring innovative approaches to managing the thermal challenges inherent in 100kW+ racks,” said Sam Bainborough, vice president, thermal business EMEA at Vertiv. “Today’s announcement, and the ongoing expansion of Vertiv’s industry-leading high-density thermal management portfolio, allow us to deliver pioneering, future-ready liquid cooling and chilled water solutions to meet our customers’ AI-driven demands.”

The Vertiv CoolLoop Trim Cooler uses low-GWP refrigerant and offers a scalable cooling capacity up to almost 3 MW in the air-cooled configuration. With free cooling coils optimized for high ambient temperatures, the system is designed to operate in free cooling mode across more seasons and conditions, for reduced electrical consumption and CO₂e emissions. It is compliant with 2027 EU F-GAS regulations ban, avoiding the need for costly redesigns or infrastructure upgrades to meet this upcoming regulatory requirement.

Qlik recently announced findings from an IDC survey exploring the challenges and opportunities in adopting advanced AI technologies. The study highlights a significant gap between ambition and execution: while 89% of organizations have revamped data strategies to embrace Generative AI, only 26% have deployed solutions at scale. These results underscore the urgent need for improved data governance, scalable infrastructure, and analytics readiness to fully unlock AI’s transformative potential.

The findings, published in an IDC InfoBrief sponsored by Qlik, arrive as businesses worldwide race to embed AI into workflows, with AI projected to contribute $19.9 trillion to the global economy by 2030. Yet, readiness gaps threaten to derail progress. Organizations are shifting their focus from AI models to building the foundational data ecosystems necessary for long-term success.

Stewart Bond, Research VP for Data Integration and Intelligence at IDC, emphasized:
“Generative AI has sparked widespread excitement, but our findings reveal a significant readiness gap. Businesses must address core challenges like data accuracy and governance to ensure AI workflows deliver sustainable, scalable value.”

Without addressing these foundational issues, businesses risk falling into an “AI scramble,” where ambition outpaces the ability to execute effectively, leaving potential value unrealized.

“AI’s potential hinges on how effectively organizations manage and integrate their AI value chain,” said James Fisher, Chief Strategy Officer at Qlik. “This research highlights a sharp divide between ambition and execution. Businesses that fail to build systems for delivering trusted, actionable insights will quickly fall behind competitors moving to scalable AI-driven innovation.”

The IDC survey uncovered several critical statistics illustrating the promise and challenges of AI adoption:

• Agentic AI Adoption vs. Readiness: 80% of organizations are investing in Agentic AI workflows, yet only 12% feel confident their infrastructure can support autonomous decision-making.
• “Data as a Product” Momentum: Organizations proficient in treating data as a product are 7x more likely to deploy Generative AI solutions at scale, emphasizing the transformative potential of curated and accountable data ecosystems.
• Embedded Analytics on the Rise: 94% of organizations are embedding or planning to embed analytics into enterprise applications, yet only 23% have achieved integration into most of their enterprise applications.
• Generative AI’s Strategic Influence: 89% of organizations have revamped their data strategies in response to Generative AI, demonstrating its transformative impact.
• AI Readiness Bottleneck: Despite 73% of organizations integrating Generative AI into analytics solutions, only 29% have fully deployed these capabilities.

These findings stress the urgency for companies to bridge the gap between ambition and execution, with a clear focus on governance, infrastructure, and leveraging data as a strategic asset.

The IDC survey findings highlight an urgent need for businesses to move beyond experimentation and address the foundational gaps in AI readiness. By focusing on governance, infrastructure, and data integration, organizations can realize the full potential of AI technologies and drive long-term success.