The top five RTInsights articles of 2025 collectively highlight a major shift. Organizations are moving away from monolithic architectures and experimental pilots toward integrated, scalable, and intelligence-ready ecosystems.
Across the technology landscape in 2025, a common thread emerged: enterprises were rapidly rethinking where and how data is processed, managed, and applied as AI becomes foundational to business operations.
Whether examining the rise of neoclouds purpose-built for advanced AI workloads, the evolving promises and pitfalls of Industry 4.0, the renewed importance of structured data in the era of AI agents, the strategic dominance of edge computing, or the growing appeal of PostgreSQL for AI-driven applications, the top five RTInsights articles of 2025 collectively highlight a major shift. Organizations are moving away from monolithic architectures and experimental pilots toward integrated, scalable, and intelligence-ready ecosystems.
Taken together, these articles show that the future of digital transformation lies in new technologies and in the ability to operationalize data efficiently and responsibly across distributed environments.
With that as an introduction, here are the top five RTInsights articles of the year.
What Are Neoclouds and Why Does AI Need Them?
This article explains the concept of neoclouds, a new generation of cloud providers built specifically for AI workloads rather than general-purpose IT. Neoclouds focus on delivering high-performance GPU-based computing optimized for machine learning and large-scale model training or inference. Unlike traditional hyperscalers (such as AWS, Azure, and GCP), neocloud operators emphasize rapid GPU availability, flexible pricing, and bare-metal or dedicated access. These objectives and goals help avoid the supply bottlenecks, steep costs, and unpredictable lead times often associated with high-end GPUs.
The article argues that as AI becomes more pervasive across industries in 2025, infrastructure providers need to evolve, and neoclouds meet that need. They enable startups, researchers, and enterprises to access AI-grade compute power without massive upfront capital investment or long procurement delays. Neoclouds also help lower the barrier to entry for AI/ML development and support scalability for sustained workloads, potentially democratizing access to advanced AI capabilities.
Why Has Industry 4.0 Fallen Short? Addressing the Gaps in Industrial Transformation
This piece reflects on how the ambitious vision of Industry 4.0 (i.e., smart, interconnected factories powered by IoT, AI, cloud, and real-time analytics) has often failed to live up to expectations. While the promise involved predictive maintenance, optimized supply chains, improved quality, and increased agility, many companies have found themselves stuck. Their projects frequently remain as isolated pilots rather than scaling across operations. Common challenges include fragmented implementations, data overload without actionable insight, high costs, legacy system incompatibilities, and difficulties justifying ROI.
Additionally, the article cites deeper organizational and cultural barriers. They include cybersecurity concerns, a workforce lacking digital skills, a lack of standardization, and vendor lock-in from proprietary solutions. The article concludes that to realize Industry 4.0’s potential, rather than treating new technologies as point solutions, companies need integrated strategies that combine unified data platforms, modern analytics (including AI), edge computing, robust security, and investments in people and processes.
7 Reasons PostgreSQL is a Great Choice for AI Projects
Here, the author makes a strong case for using PostgreSQL (Postgres) as the backbone database for AI and machine learning projects. The article argues that Postgres combines flexibility, scalability, and maturity. That makes it a solid foundation for AI workloads in 2025 without the drawbacks of narrow or proprietary “AI-only” databases.
Key advantages listed include: built-in or easily added support for vector search (via extensions like pgvector), which is crucial for similarity-based search or embeddings-based AI; rich indexing options (B-tree, hash, GiST, etc.) for efficient queries; native support for JSON/JSONB and NoSQL-style storage for semi-structured data; parallel query execution for performance; and strong scalability through replication, sharding, and distributed architectures.
Postgres also provides robust access controls, encryption, and auditing to enhance data security and compliance. Finally, as a mature open-source system with an active community and broad ecosystem, Postgres enables flexibility and long-term maintainability.
Overall, the article presents Postgres as a pragmatic, cost-effective, and versatile choice that can support both traditional structured data needs and modern AI workflows, while avoiding the complexity and fragmentation that can arise from using separate specialized databases for AI tasks.
Edge Computing Set to Dominate Data Processing by 2030
This article explores how edge computing is likely to overtake traditional centralized data centers as the primary locus of data processing by the early 2030s. According to projections cited, about 74% of the world’s data will be processed outside of classical data centers by that time, driven largely by the growing demand for low-latency, AI-powered, and geographically localized applications.
The rise of edge computing is closely tied to the proliferation of AI, particularly generative AI, which favor localized processing for speed, responsiveness, and bandwidth efficiency. The article notes that total spending on edge computing is expected to grow rapidly, creating new opportunities for telecom operators, hyperscalers, and enterprises alike. Telecom carriers, in particular, are highlighted as positioned to benefit by embedding edge solutions into their networks (e.g., through open radio access network architectures).
As a result, edge computing is framed as a strategic shift in how data is handled: decentralizing compute power, reducing latency, enabling real-time decision-making, and opening new business models for AI, IoT, and real-time analytics applications.
With AI Agents on the Scene, Structured Data is Back in Vogue
In this article, the authors argue that the resurgence of structured data within enterprises in 2025 is tied to the growing prevalence of AI agents and their need for reliable, well-organized input. As AI agents become more embedded in business workflows, structured data, which is easier to query, analyze, integrate, and validate, offers advantages over unstructured formats that might require more preprocessing, are less consistent, or harder to manage for compliance and reliability.
The article suggests that structured data enables better governance, consistency, traceability, and integration with business systems. These are all critical when AI agents act autonomously or make decisions. As firms increasingly lean into AI-driven automation, analytics, and decision support, structured data becomes a foundation, providing clarity and structure for model input, downstream processing, auditing, and compliance.
In effect, rather than being sidelined by the rise of large unstructured data (text, images, etc.), structured data is reclaiming importance because it supports AI agents in ways unstructured data often cannot: speed, reliability, traceability, and easier integration with existing data ecosystems.
A Final Word on 2025
While each article approaches a different facet of modern enterprise digital efforts, they converge on a shared insight: AI is reshaping infrastructure, processes, and priorities across industries.
Neoclouds and the rise of edge computing reflect a decentralization of compute power to meet the performance demands of generative AI and real-time analytics.
Industry 4.0’s struggles underscore that technology alone cannot deliver transformation without cohesive strategies, integrated data, and organizational alignment.
PostgreSQL’s strengths for AI projects, along with the resurgence of structured data, reveal that reliable, well-governed data foundations are essential as AI moves into production and agents take on autonomous tasks.
Ultimately, the central message is that success in the AI era requires combining the right infrastructure with the right data architecture, and doing so in a way that enables scalability, agility, and trust.
