Techaisle Blog

The technology market is awash in "Generative AI." We are saturated with demonstrations, pilots, and proofs of concept (POCs). Yet, for most organizations, the path from a compelling demo to scaled, enterprise-wide production remains elusive. The gap is fraught with challenges, not least of which are security, governance, and a clear return on investment.

In a recent analyst briefing, Google Cloud, led by Hayete Gallot, President of Customer Experience, articulated a strategy that signals a distinct and significant pivot. The narrative is moving decisively from "Generative AI" as a standalone technology to "Agentic AI" as a governed, integrated business system.

This is not a mere semantic shift. It is a fundamental reframing of the problem and the solution, moving the conversation from "what a model can do" to "what a system of agents can achieve for the business." This agent-centric strategy is built on three core pillars: a platform for governance, a framework for creating new agentic architectures, and a GTM model for partner-led scale.

The "Why": Solving for "Rampant Agents"

The generative AI market is currently a chaotic mix of boundless promise and paralyzing complexity. For enterprise customers, the landscape is a minefield. Do they risk cost escalation and vendor lock-in with proprietary, API-first models, or do they brave the "wild west" of open-source models, complex hardware requirements, and fragmented tooling? This dichotomy has created a massive vacuum in the market: the need for a trusted, stable, and open platform to bridge the gap.

Into this vacuum steps Red Hat, and its strategy, crystallized in the Red Hat AI 3.0 launch, is both audacious and familiar. Red Hat is not trying to build the next great large language model. Instead, it is making a strategic, high-stakes play to become the definitive "Linux of Enterprise AI"—the standardized, hardware-agnostic foundation that connects all the disparate pieces.

The company's legacy motto, "any application on any infrastructure in any environment", has been deliberately and intelligently recast for the new era: "any model, any hardware, any cloud". This isn't just clever marketing; it is the entire strategic blueprint, designed to address the three primary enterprise adoption-blockers: cost, complexity, and control.

The Engine: Standardizing Inference with vLLM and LLMD

Dell Technologies World 2025 (DTW) recently provided a comprehensive look into Dell's strategy and vision, with a particular focus on the transformative power of Artificial Intelligence (AI) for businesses of all sizes. Keynotes from Michael Dell and Jeff Clarke, alongside detailed briefings on Client Solutions Group (CSG) and Infrastructure Solutions Group (ISG), painted a picture of a company positioning itself as the end-to-end partner for the AI journey. While much attention often focuses on hyperscalers and large enterprises, Dell offers significant opportunities and tailored strategies for the midmarket as well as the vital channel partners who serve them.

Here are my seven key takeaways:

1. The Dell AI Factory is an End-to-End AI Framework, Not Just Hardware

Dell introduced and expanded upon the concept of the Dell AI Factory, describing it as an unmatched set of capabilities in the industry designed to help businesses get started with Generative AI and scale it. It is presented as an open, modular infrastructure with a rich ecosystem, delivering powerful GPUs, scalable storage, high-throughput networking, curated tooling, and integrated cutting-edge models, supported by deployment services. This framework covers the entire computing architecture for modern AI workloads, from PCs to data centers and the edge. Dell has helped over 3,000 businesses build their factories and launched over 200 new features since its inception a year ago. The vision is for customers to bring their own company data to the AI Factory, driving unique business outcomes.

Why this is important for Midmarket and Channel Partners: This framework provides a structured approach to AI adoption. For midmarket, it demystifies the complex landscape of AI infrastructure by offering a seemingly integrated and supported stack. They don't need to piece together disparate components or become AI experts overnight. For channel partners, the AI Factory is a complete solution portfolio to take to customers. Dell is making it easier to consume and deploy through reference architectures and packaged software. This enables partners to concentrate on delivering value and outcomes, rather than merely selling individual pieces of hardware. The concept of bringing "your own company data" to drive outcomes resonates strongly with businesses of all sizes, emphasizing that AI value is tied to their unique operations and data, which partners are often intimately familiar with.

In today's data-driven landscape, organizations grapple with myriad challenges stemming from the increasing volume, velocity, and complexity of data. These challenges encompass data governance and management, the need for predictive and prescriptive analytics, the democratization of insights, and the rapid pace of technological advancements. To address these complexities, Zoho has introduced a new AI-powered self-service BI and analytics platform.

Since 2009, Zoho has been a prominent BI and analytics platform player, offering a robust foundation for data management and preparation. Zoho's self-service analytics platform is highly versatile and capable of running on Zoho's cloud, third-party clouds, or on-premises. Furthermore, Zoho caters to the embedded market, enabling third-party applications to leverage its analytics capabilities for insights within their business tools. As of the end of 2023, Zoho serves a substantial customer base of 17,000 directly paying organizations for Zoho Analytics, with over 70,000 businesses utilizing Zoho Analytics daily, embedded as part of any other Zoho apps. While Zoho One is widely recognized as its flagship suite of applications, the widespread adoption of Zoho Analytics within Zoho One, second only to Zoho CRM, across customer and employee experience applications, finance, marketing, and low-code applications, underscores its simplicity, reliability, and value.

Zoho Analytics is a powerful tool that offers both descriptive and prescriptive capabilities, enabling businesses to extract valuable insights and make informed decisions. Its descriptive features allow users to delve into historical data, understand past business trends, and identify key drivers. On the other hand, its prescriptive capabilities, driven by machine learning and AI, provide actionable recommendations to tackle specific business challenges. This includes features like decision intelligence, which assists users in understanding the root causes of certain events and how to respond effectively. By integrating these two types of analytics, Zoho Analytics not only helps businesses comprehend their past performance but also guides them in making proactive decisions to enhance future outcomes.

Let us explore why Zoho is emerging as a market differentiator in AI-powered self-service BI and analytics platforms. Its unique blend of features and capabilities makes it a compelling choice for organizations seeking to harness the power of data-driven insights.

Data Velocity and Diversity - A Modern Challenge

In today's digital age, organizations rely on many applications to manage their operations, resulting in a deluge of data. With modern enterprises often utilizing over 100 applications, the volume and variety of data generated are staggering, encompassing both structured and unstructured formats. To address this challenge, Zoho offers a comprehensive data management hub that provides a solid foundation for organizations to handle their data effectively. Zoho's platform excels in data integration, supporting over 500 data sources and facilitating real-time stream processing. This enables businesses to extract data from cloud data warehouses, files, feeds, and other unstructured sources seamlessly. Moreover, Zoho's commitment to innovation is evident in its ongoing expansion of data connectors, with plans to add 25 more soon. The platform's ability to process data in real-time from systems like Kafka, PubNub, or Cloud PubSub empowers organizations to efficiently collect and analyze data from diverse sources, regardless of their structure or format.