Techaisle Blog

As an analyst, I am trained to distinguish between strategic narrative and on-the-ground reality. I have watched Kyndryl’s journey since its spin-off with keen interest, tracking its core strategy of Alliances, Accounts, and Advanced Delivery. At its recent analyst briefing, Kyndryl provided compelling evidence that this strategy, particularly its alliance-led approach, is not just a narrative but a high-velocity revenue engine.

The company has successfully executed one of the most difficult pivots in the industry: shifting its center of gravity from a legacy infrastructure manager to an AI-first, consult-led transformation partner. The results are not trivial. Kyndryl is on a clear trajectory to grow its hyperscaler services revenue from $0.5B in FY24 to a projected $1.8B in FY26. Crucially, this shift implies a fundamental expansion in margin quality, as the company successfully breaks the linear link between revenue growth and labor intensity.

However, this success isn't just about reselling cloud services. The most profound insight from the briefing was the lynchpin for this entire pivot: the new Kyndryl Agentic AI Framework.

The Macro View: The End of Traditional Labor Arbitrage

To understand the magnitude of this pivot, we must contextualize it within the evolution of the IT services market. For two decades, the industry operated on a model of labor arbitrage—essentially engaging providers to manage legacy environments at a lower cost by shifting the work to lower-cost geographies. That model is now obsolete. The industry is undergoing a violent shift from labor-centric maintenance to IP-led modernization. "Keeping the lights on" is no longer a viable business strategy; value has migrated to "rewiring the building."

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.