Tag: Google Cloud

  • Google Splits Its TPU Chip in Two. Here’s Why That Decision Matters for Enterprise Buyers.

    Google Splits Its TPU Chip in Two. Here’s Why That Decision Matters for Enterprise Buyers.

    The AI chip acronym soup of CPUs, GPUs, TPUs, etc., shows how the computing landscape continued to expand and change over the past decade. At Google Cloud Next, the company released two distinct TPUs (Tensor Processing Units) instead of one — TPU-8t, built for training, and TPU-8i, built for inference and the emerging demands of agentic workloads. The launch highlights an architectural decision that reflects how AI workloads are diverging, with real implications for how enterprise buyers should think about AI infrastructure strategy.

    What Google Actually Announced

    During a press and analyst session at Google Cloud Next, Amin Vahdat, Google’s SVP and Chief Technologist for AI Infrastructure, introduced the eighth-generation TPUs — and emphasized the plural intentionally. Vahdat said the two chips were designed from the ground up separately.

    TPU-8t is the training workhorse. Compared to last year’s Ironwood generation, it delivers roughly three times the floating-point compute per pod, twice the network bandwidth per chip, and four times the bandwidth at scale-out — all with approximately the same pod size of 9,600 chips, but with denser, faster interconnects.

    TPU-8i is the inference and agent engine. It quadruples the pod size to 1,152 chips, delivers 10x the FP8 compute, 7x larger HBM memory capacity, and offers bidirectional scale-out bandwidth. The design priority is latency, not just throughput — a meaningful distinction as enterprises move from batch processing toward real-time agentic workloads.

    Vahdat put the pace of progress plainly: “2x, 4x, 8x, 10x all in one year — the rate of progress, the rate of advancement is just stunning.”

    That’s impressive on paper. The more important question for enterprise buyers is what it means for how they plan and procure AI infrastructure.

    The Specialization Signal

    The two-chip decision acknowledges that training and inference have different physics.

    Training is throughput-bound, which means you’re moving enormous amounts of data through interconnected chips in a coordinated, largely predictable batch process. Inference, especially for the new upcoming wave of agentic systems, is latency-bound.  For this use case, chips need to respond in near-real time as agents plan, act, evaluate, and route across multiple tools and workflows.

    To address the latency problem directly, Google and DeepMind collaborated on a new network “boardfly” topology for TPU-8i, designed to reduce the number of hops between any two chips, significantly cutting chip-to-chip latency. As Vahdat described it: “Our default way of connecting them didn’t support latency. It supported bandwidth. What you really care about in the age of agents is latency — the minimum time it takes to get the data.”

    This mirrors a trend Jensen Huang surfaced at NVIDIA, where chip-to-chip connectivity is increasingly central to total system performance, not just an afterthought to compute specs. The implication: network topology is now a first-class variable in AI infrastructure design, not just chip count or memory.

    Vahdat was direct about the broader trajectory: “The age of specialization is going to continue.” His prediction for the industry — not just Google — is that workloads will continue diverging, and two chips may eventually become more. General-purpose improvements, he noted, are now yielding roughly 5% annual performance gains normalized to cost. Specialization is how you get past that ceiling.

    What This Means for Enterprise Buyers

    Enterprise buyers don’t purchase TPUs. They consume AI services through public cloud, SaaS platforms running on cloud infrastructure, and increasingly through hybrid architectures spanning on-premises and cloud. There are at least three reasons why a chip announcement matters.

    1. AI infrastructure costs are becoming a material business decision. Google is running AI inference on TPUs across Search, YouTube, Gmail, and its enterprise Gemini services. The efficiency of that infrastructure directly affects the cost structure of AI-powered services. When Google cuts inference costs through better hardware, the economics of running AI at scale improve for Google and for its cloud customers. Citadel Securities, the securities trading firm, was cited as a TPU customer that reduced costs 30% and achieved two to four times efficiency improvement on trading systems. Specialized hardware scales well beyond its original design targets.
    2. Inference is where AI delivers the most value to most enterprise buyers. For several years, we’ve been discussing the shift from large-scale frontier model training and enterprise AI fine-tuning towards inferencing. It’s finally here, and we have multiple ways to improve inference, including new TPUs designed for inference.  As Vahdat noted, using a historical parallel to web search: the heavy lifting happens in training, but the value is created in serving. “Serving is where the value is created for Gemini enterprise and search, and ads and YouTube.” Enterprise AI budgets and infrastructure roadmaps need to weigh inference infrastructure proportionally to where value is actually produced.
    3. Reliability at scale is still an unsolved problem — and it matters. Vahdat was candid about a challenge the industry rarely advertises: at the scale of tens of thousands of chips working in coordination, at least one chip will fail several times per day. If human intervention is required to detect and recover from failures, the minimum response time is 30 minutes — enough to halt progress entirely. Google’s approach delivers over 97% of good computational throughput, enabling failures to be automatically detected and remediated. Still, Google Cloud said enterprises aren’t interested in any failure. For enterprises evaluating AI infrastructure providers, reliability and observability at scale are now table-stakes questions, not nice-to-haves.

    The Agentic Infrastructure Shift Is Already Here

    A surprising forward-looking element of Vahdat’s remarks was a prediction about CPUs. As agentic systems grow, general-purpose compute will make a comeback — not to replace specialized chips, but to orchestrate them. Agents require sandboxed environments, virtual machines, code execution, and dynamic routing across inference calls. He stated that it’s CPU work.

    Enterprise infrastructure planners should take note: agentic AI isn’t just an inference problem. It’s a systems design problem that spans specialized accelerators, general-purpose compute, network topology, and increasingly, identity and governance layers sitting above the hardware. The companies Google cited as running on TPUs today — from its own consumer services to financial services firms — are already thinking holistically about infrastructure.

    The infrastructure decisions enterprises make now will determine how quickly and cost-effectively they can deploy agentic systems at scale. Building on platforms engineered for latency, reliability, and specialization is a different starting point than building on platforms that aren’t.

    Google Cloud’s eighth-generation TPUs are a signal that the advancement of AI infrastructure is far from over.  

    This article was originally published on Forbes.

  • Managing a Fleet of Thousands of Agents Is the Real Problem. Google Cloud Showed Its Answer.

    Managing a Fleet of Thousands of Agents Is the Real Problem. Google Cloud Showed Its Answer.

    Enterprises spent most of 2025 figuring out what AI agents are and which tools could build them. The conversation in 2026 is different. The question now is how to govern, monitor, and scale fleets of agents across the enterprise — without losing control of what they’re doing or why. That shift was visible at Google Cloud Next. One of the headline announcements — the Gemini Enterprise Agent Platform — is more than a product consolidation. It reflects where the enterprise AI market is actually headed: away from point tools and toward platforms that manage agents at scale, with governance and observability built in rather than bolted on afterward.Here’s what was announced, and more importantly, what it means for enterprise buyers trying to move from individual pilots to production-grade agentic systems.

    What Google Actually Announced

    Google combined both Vertex AI and Agentspace into a single unified offering called the Gemini Enterprise Agent Platform. Vertex was its managed AI development platform, while Agentspace was its enterprise-focused platform for deploying and managing agents across organizational data and applications such as Jira, Salesforce, and Google Workspace. Neither Vertex AI nor Agentspace will exist as standalone products. The new platform combines what each did separately, such as  Vertex AI’s model building and generative AI development capabilities, and Agentspace’s agent deployment, workflow automation, and enterprise application integration. The platform adds new or improved capabilities for orchestration, governance, security, and observability. That last part matters most. On the model side, enterprise buyers now have access to a model garden with more than 200 options. The model garden includes Google’s own Gemini 3.1 Pro, Gemini 3.1 Flash Image, and Lyria 3, alongside third-party models such as Anthropic’s Claude Opus, Sonnet, and Haiku. The breadth is notable. A single platform that spans first-party and third-party models gives enterprises flexibility to match the model to the task — rather than locking into one provider’s output quality for every workload.On the development side, the platform spans from low-code tooling via Agent Studio to a more capable Agent Development Kit for engineering teams. A new graph-based framework organizes agents into networks of sub-agents, allowing enterprises to define reliable logic for how agents collaborate on complex tasks. An Agent Garden provides access to a curated set of agent templates that cover use cases such as code modernization, financial analysis, economic research, and invoice processing. These templates serve as building blocks for multi-agent systems. The operational layer is where the platform makes its clearest argument for enterprise buyers. Agent Runtime delivers sub-second cold starts and supports long-running agents that maintain state for days, backed by a Memory Bank for persistent context.  This allows agents to run more complex tasks. An Agent Gateway provides unified connectivity between agents and tools across environments while enforcing a consistent security policy. Agent Sandbox provides a hardened environment for executing model-generated code and browser-based automation tasks without exposing host systems. And critically, the platform includes Agent Identity and Agent Registry. Every agent — whether built internally or sourced from a third-party partner — carries a trackable identity and operates within defined guardrails. Model Armor protections guard against prompt injection and data leakage. Testing and observability round out the picture. Agent Simulation, Agent Evaluation, and Agent Observability provide execution traces and real-time insight into agent reasoning. Agent Optimizer goes further, automatically clustering real-world failures and suggesting refined system instructions rather than requiring teams to dig through logs manually.

    Why the Consolidation Matters

    Google also announced updates to its Cloud Data Cloud offering, which improves how agents access and interpret enterprise data. Better data grounding means agents work with accurate, current, enterprise-specific information rather than relying on general model knowledge, which hallucinates at rates that make it unsuitable for business-critical decisions. The Gemini Enterprise Agent Platform competes directly with Amazon’s Bedrock AgentCore and Microsoft’s Azure AI Foundry. All three hyperscalers are converging on the same recognition: enterprise buyers don’t need more ways to build a single agent. They need platforms that manage how hundreds or thousands of agents behave, interact, and scale — without requiring a dedicated engineering team to babysit each one. That’s the real shift in 2026. The challenge is no longer proof-of-concept. It’s production life cycle management with security and governance.For enterprises that had already deployed Agentspace, the consolidation raises a practical question: what happens to what you built? Google’s framing suggests continuity rather than migration — existing Agentspace capabilities carry forward into the new platform rather than requiring a rebuild. But enterprises currently running Agentspace workflows should verify specifically how their integrations, agent configurations, and user access models map to the consolidated platform before assuming a seamless transition. Consolidations that look clean on a slide often surface friction in production environments. Ask Google directly what the migration path looks like and what, if anything, requires rework.

    What’s Hard About It

    The technology being ready and your organization being ready are two different things. A few realities worth holding onto as you evaluate this platform. Model flexibility is only valuable when you define what you need for your workloads. Two hundred models in a garden is a resource if you have a framework for selecting among them. Without a clear use-case taxonomy — which tasks require higher accuracy versus lower latency, which workloads require on-premises data handling versus cloud inference — the abundance becomes a selection problem rather than a solution. Agent Identity and Agent Registry are necessary, not sufficient. Registering agents and assigning identities is a prerequisite for governance, not the governance itself. Enterprises still need to define what each agent is authorized to do, under what conditions it escalates to a human, and how they will audit agent behavior over time. The platform provides the infrastructure for those decisions. The decisions still belong to the enterprise. Observability tooling requires someone to act on what it surfaces. Agent Optimizer, which automates the suggestion of improved instructions, is genuinely useful. But organizations still need the operational capacity to evaluate those suggestions, test changes, and maintain accountability for agent behavior in production. Automation reduces the burden; it doesn’t eliminate the need for human judgment. The partner ecosystem is announced, not fully proven. Google named a broad set of integrations and ecosystem partners. As with every hyperscaler launch, the gap between announced partnerships and certified, production-ready integrations takes time to close. Before building production workflows on partner integrations, confirm what is shipping now versus what is on the roadmap.

    What Enterprise Buyers Should Do

    If you are evaluating the Gemini Enterprise Agent Platform — or any hyperscaler agent platform — three questions will tell you more than the demo. First, ask what the platform does when an agent fails. Not how it handles errors gracefully, but what happens when an agent takes a wrong action at scale, across a fleet of similar agents running the same logic. Failure modes at scale differ from those in a pilot. Platforms that offer real-time observability and automated clustering of failure patterns are meaningfully ahead of those that don’t. Second, ask how agent identities integrate with your existing identity and access management infrastructure. If agents need to be registered and governed separately from human users, the platform adds operational overhead. If agent identities extend naturally from your current IAM framework, adoption is faster, and governance is less fragile. Third, ask how data grounding works within your specific data architecture. The value of grounding AI agents in enterprise data depends entirely on the quality and accessibility of that data. If your data is scattered across disconnected systems with inconsistent formats, the grounding layer will struggle regardless of how capable the platform is. Google’s Cloud Data improvements are in the right direction. But no platform substitutes for data readiness on the enterprise side.

    The Broader Signal

    What Google announced at Cloud Next reflects something the enterprise AI market has needed for a while. Enterprise buyers need platforms from credible vendors that treat governance, security, and observability as core features rather than afterthoughts. We spent 2024 and 2025 building agents. The organizations that will succeed in 2026 are the ones that build the management layer around those agents — the identity frameworks, the observability infrastructure, the governance policies that define what agents are allowed to do and what requires human review. The Gemini Enterprise Agent Platform is a serious attempt to deliver that layer. It is not the only attempt. Amazon and Microsoft are building toward the same destination. But the consolidation of Vertex AI and Agentspace into a unified platform with built-in governance and observability signals that Google understands where the real enterprise challenge lies. That’s the right problem to be solving. Whether this platform solves it for your specific environment depends on how well it fits your data architecture, your existing security stack, and your organizational capacity to govern agents in production. 

    Subscribe to my AI with Maribel Lopez podcast on your channel of choice at https://www.buzzsprout.com/194744.Lopez Research is a market research and strategy consulting firm specializing in enterprise AI, AI infrastructure, agentic systems, AI governance, and AI-driven customer experience. Learn more at www.lopezresearch.com.

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  • How Best Buy Uses AI to Transform Customer Experience

    How Best Buy Uses AI to Transform Customer Experience

    The electronics retailer’s methodical approach to artificial intelligence in customer experience offers lessons for companies rushing to deploy the latest tools

    Best Buy Co. faced a daunting challenge that would sound familiar to many large corporations: multiple different software applications were needed to run its contact centers, creating a maze of complexity for customer service agents and frustrating experiences for shoppers seeking help. “When I came into this space, we had 93 applications that were needed to run our contact centers,” Daniels revealed. This fragmentation created what she describes as “incredibly sticky and muddy” experiences for both agents and customers.

    Rather than simply layering AI on top of this fragmented system, the Minneapolis-based retailer took a step back. The company’s approach—starting with customer outcomes rather than flashy AI capabilities—has yielded rapid results and provides a roadmap for other businesses grappling with how to effectively deploy AI technology.

    “AI is not the goal. It is one solution and often needs to be incorporated with many others to bring an actual experience to life,” said Ashley Daniels, Best Buy’s vice president of product management, in a recent interview with Lopez Research. 

    Beyond the Hype AI Hype in Customer Experience

    Best Buy’s disciplined approach stands in stark contrast to the AI frenzy that has gripped corporate America since the debut of ChatGPT. While many companies have rushed to implement artificial intelligence tools, often with mixed results, Best Buy has focused on solving specific business problems.

    Three Pillars of Its Customer Experience AI Implementation

    Best Buy’s AI strategy focuses on three core areas, each designed to enhance different aspects of the customer journey:

    1. Customer Self-Service Empowerment

    The company launched a gen AI-powered virtual assistant that enables customers to independently handle complex tasks, such as troubleshooting product issues, rescheduling deliveries, and managing subscriptions. Best Buy didn’t just launch a simple chatbot—it’s an intelligent system that can understand context and provide meaningful solutions across web, mobile app, and phone channels.

    2. Customer Service Agent Augmentation

    Best Buy has also created AI solutions that provide advanced assistance to customer care agents. The system provides real-time conversation assessment, sentiment detection, and contextual recommendations, enabling agents to focus on building personal connections with customers. As the company notes, these tools “are designed to help reduce the mental workload for agents, allowing them to better focus on personally connecting with the Best Buy customer.”

    3. Front Line Employee Support

    Putting AI assistive tools in the hands of  front line workers can fundamentally change the customer experience. Companies like Wendy’s are experiencing the benefits of this strategy today and Best Buy story highlights a similar trend. Beyond customer-facing applications, Best Buy is developing AI assistants for front-line employees. These tools provide easier access to company resources and product guides, enabling store associates to serve customers more efficiently and confidently. 

    Additionally, Best Buy commits to transparency, always informing customers when they’re interacting with AI rather than humans. However, the company strives to make those interactions feel natural and helpful.

    The strategy appears to be working. The company deployed AI-powered conversation summarization in its contact centers in just six to eight weeks—a timeline that surprised even internal teams. But Daniels cautions that not every AI project moves that quickly. The retailer’s experience suggests that successful AI deployment requires managing expectations. While some capabilities can be implemented quickly, others take time to develop properly.

    Best Buy created a vision for its AI-powered customer service a year before the technology was fully ready to support it. “The technology is catching up to our vision,” Daniels said. “When you give the technology a chance to catch up to your vision, it makes it less painful.”

    The Role of Strategic Partnerships in Customer Experience

    Best Buy’s AI initiative relies on partnerships with Google Cloud and consulting firm Accenture—a three-way arrangement that divides responsibilities strategically. Google provides the underlying AI technology and rapid innovation. The company’s partnership with Google Cloud wasn’t just about adopting new AI capabilities—it was about fundamental architectural consolidation. Despite serving similar conversational functions, Best Buy recognized that their chatbot and IVR (Interactive Voice Response) systems were built by separate teams using different solutions. This insight led to a unified approach where AI could power consistent experiences across all customer touchpoints.

    Accenture brings implementation and regional experience from working with multiple companies. Meanwhile, Best Buy maintains control over customer experience decisions and business strategy.

    Daniels emphasizes that partnership doesn’t mean abdication of responsibility: “Just because you have partners does not mean that you get to step back and take your hands off the wheel… we’re the only people that can make decisions about the experiences we want for our customers.”

    Advice for Companies

    The retailer’s experience highlights a crucial lesson that many AI enthusiasts overlook: the underlying business infrastructure matters as much as the artificial intelligence solutions.

    Daniels compares the process to remodeling a house. If your foundation isn’t solid, your windows leak, your walls will have cracks, and you’re not going to achieve the outcomes that you want. For Best Buy, the foundation includes clean data, updated software interfaces that enable different systems to communicate, and a deep understanding of how the business operates.

    “People think that when AI shows up to save the day, we no longer need domain experts,” Daniels said. “In my opinion, it’s the exact opposite.” She likens training AI systems to managing a new teenage employee who needs guidance from experienced mentors to be effective.

    For businesses considering their own AI initiatives, Best Buy’s leaders offer two key recommendations. First, define clear outcomes before selecting technology. “Be thoughtful about what outcome it is that you want to drive,” Daniels said. Second, invest in foundational capabilities, including data quality, modernizing APIs, system integration, and employee expertise. Without these elements, even sophisticated AI tools are likely to disappoint.

    While there is considerable hype surrounding the creation of fully autonomous workloads with agentic AI, today’s AI success stories treat the technology as a tool to enhance human capabilities rather than replace them entirely. 

    Best Buy has taken this approach, and other companies, such as Cisco, have also shared how AI will assist their employees in delivering faster, better customer experiences. Best Buy’s measured approach suggests that sustainable success with AI technology comes from being thoughtful. 

  • AI in Retail: Best Buy’s Journey from 93 Apps to One Solution

    Description: In this episode from Google Cloud Next 2025, we dive deep into Best Buy's AI transformation with Ashley Daniels, VP of Product Management. Discover how one of America's largest retailers approached AI implementation strategically, moving from 93 contact center applications to a unified solution.

    Ashley shares the real story behind Best Buy's AI journey – the quick wins, unexpected challenges, and why your foundation matters more than the technology itself. From gift finder tools to revolutionizing customer care, learn practical strategies for implementing AI that actually drives business outcomes.

    Key insights covered:

    • Why treating AI as a “tool in the toolbox” leads to better results
    • The importance of starting with customer experience, not technology
    • How to build strategic partnerships for AI implementation
    • Why domain expertise becomes more critical in an AI world
    • Real timeline: Getting AI summarization live in 6-8 weeks

    Whether you're in retail, customer service, or leading digital transformation initiatives, this conversation offers actionable insights for your AI strategy.

    Hosted by Maribel Lopez, founder and principal analyst at Lopez Research who interviewed Ashley Daniels, the VP of Product Management at Best Buy.

    You can follow Ashley here  https://www.linkedin.com/in/ashley-daniels1219/ and Maribel here https://www.linkedin.com/in/maribellopez/

  • Dell AI Factory Expands with 40+ Enhancements for Enterprise AI Deployment

    Dell AI Factory Expands with 40+ Enhancements for Enterprise AI Deployment

    Dell Technologies unveiled a significant expansion of its AI Dell Factory platform at its annual Dell Technologies World conference today, announcing over 40 product enhancements designed to help enterprises deploy artificial intelligence workloads more efficiently across both on-premises environments and cloud systems.

    The Dell AI Factory is not a physical manufacturing facility but a comprehensive framework combining advanced infrastructure, validated solutions, services, and an open ecosystem to help businesses harness the full potential of artificial intelligence across diverse environments—from data centers and cloud to edge locations and AI PCs.

    The company has attracted over 3,000 AI Factory customers since launching the platform last year. In an earlier call with industry analysts, Dell shared research stating that 79% of production AI workloads are running outside of public cloud environments—a trend driven by cost, security, and data governance concerns. During the keynote, Michael Dell provided more color on the value of Dell’s AI factory concept. He said, “The Dell AI factory is up to 60% more cost effective than the public cloud, and recent studies indicate that about three-fourths of AI initiatives are meeting or exceeding expectations. That means organizations are driving ROI and productivity gains from 20% to 40% in some cases. 

    Making AI Easier to Deploy

    Organizations need the freedom to run AI workloads wherever makes the most sense for their business, without sacrificing performance or control. While IT leaders embraced the public cloud for their initial AI services, many organizations are now looking for a more nuanced approach where the company can control over their most critical AI assets while maintaining the flexibility to use cloud resources when appropriate. Over 80 percent of the companies Lopez Research interviewed said they struggled to find the budget and technical talent to deploy AI. These AI deployment challenges have only increased as more AI models and AI infrastructure services have been launched.

    Silicon Diversity and Customer Choice

    A central theme of Dell’s AI Factory message is how Dell makes AI easier to deploy while delivering choice. Dell is offering customers choice through silicon diversity in its designs, but also with ISV models. The company announced it has added Intel to its AI Factory portfolio with Intel Gaudi 3 AI accelerators and Intel Xeon processors, with a strong focus on inferencing workloads.

    Dell also announced its fourth update to the Dell AI Platform with AMD, rolling out two new PowerEdge servers—the XE9785 and the XE9785L—equipped with the latest AMD Instinct MI350 series GPUs. The Dell AI Factory with NVIDIA combines Dell’s infrastructure with NVIDIA’s AI software and GPU technologies to deliver end-to-end solutions that can reduce setup time by up to 86% compared to traditional approaches. The company also continues to strengthen its partnership with NVIDIA, announcing products leveraging NVIDIA’s Blackwell family and other updates launched at NVIDIA GTC. As of today, Dell supports choice by delivering AI solutions with all of the primary GPU and AI accelerator infrastructure providers.

    Client-Side AI Advancements

    At the edge of the AI Factory ecosystem, Dell announced enhancements to the Dell Pro Max in a mobile form factor, leveraging Qualcomm’s AI 100 discrete NPUs designed for AI engineers and data scientists who need fast inferencing capabilities. With up to 288 TOPs at 16-bit floating point precision, these devices can power up to a 70-billion parameter model, delivering 7x the inferencing speed and 4x the accuracy over a 40 TOPs NPU. Dell says the Pro Max Plus line can run a 109-billion-parameter AI model.

    The Pro Max and Plus launches follow Dell’s previous announcement of AI PCs featuring Dell Pro Max with GB 10 and GB 300 processors powered by NVIDIA’s Grace Blackwell architecture. Overall, Dell has simplified its PC portfolio but made it easier for customers to choose the right system for their workloads by providing the latest chips from AMD, Intel, Nvidia, and Qualcomm.

    On-Premise AI Deployment Gains Ecosystem Momentum

    Following the theme of choice, organizations need the flexibility to run AI workloads on-premises and in the cloud. Dell is making significant strides in enabling on-premise AI deployments with major software partners. The company announced it is the first provider to bring Cohere capabilities on-premises, combining Cohere’s generative AI models with Dell’s secure, scalable infrastructure for turnkey enterprise solutions.

    Similar partnerships with Mistral and Glean were also announced, with Dell facilitating their first on-premise deployments. Additionally, Dell is supporting Google’s Gemini on-premises with Google Distributed Cloud.

    To simplify model deployment, Dell now offers customers the ability to choose models on Hugging Face and deploy them in an automated fashion using containers and scripts. Enterprises increasingly recognize that while public cloud AI has its place, a hybrid AI infrastructure approach could deliver better economics and security for production workloads.

    The imperative for scalable yet efficient AI infrastructure at the edge is a growing need. As Michael Dell said during his Dell Technologies World keynote, “Over 75% of enterprise data will soon be created and processed at the edge, and AI will follow that data; it’s not the other way around. The future of AI will be decentralized, low latency, and hyper-efficient.”

    Dell’s ability to offer robust hybrid and fully on-premises solutions for AI is proving to be a significant advantage as companies increasingly seek on-premises support and even potentially air-gapped solutions for their most sensitive AI workloads. Key industries adopting the Dell AI Factory include finance, retail, energy, and healthcare providers.

    Scaling AI Requires a Focus on Energy Efficiency 

    Simplifying AI also requires product innovations that deliver cost-effective, energy-efficient technology. As AI workloads drive unprecedented power consumption, Dell has prioritized energy efficiency in its latest offerings. The company introduced the Dell PowerCool Enclosed Rear Door Heat Exchanger (eRDHx) with Dell Integrated Rack Controller (IRC). This new cooling solution captures nearly 100% of the heat coming from GPU-intensive workloads. This innovation lowers cooling energy requirements for a rack by 60%, allowing customers to deploy 16% more racks with the same power infrastructure.

    Dell’s new systems are rated to operate at 32 to 37 degrees Celsius, supporting significantly warmer temperatures than traditional air-cooled or water-chilled systems, further reducing power consumption for cooling. The PowerEdge XE9785L now offers Dell liquid cooling for flexible power management. Even if a company isn’t aiming for a specific sustainability goal, every organization wants to improve energy utilization. 

    Early Adopter Use Cases Highlight AI’s Opportunity

    With over 200 product enhancements to its AI Factory in just one year, Dell Technologies is positioning itself as a central player in the rapidly evolving enterprise AI infrastructure market. It offers the breadth of solutions and expertise organizations require to successfully implement production-grade AI systems in a secure and scalable fashion. However, none of this technology matters if enterprises can’t find a way to create business value by adopting it. Fortunately, examples from the first wave of enterprise early adopters highlight ways AI can deliver meaningful returns in productivity and customer experience. Let’s look at two use cases presented at Dell Tech World. 

    The Power of LLMs in Finance at JPMorgan Chase

    JPMorgan Chase took the stage to make AI real from a customer’s perspective. The financial firm uses Dell’s compute hardware, software-defined storage, client, and peripheral solutions. Larry Feinsmith, the Managing Director and Head of Global Tech Strategy, Innovation & Partnerships at JPMorgan Chase, said, “We have a hybrid, multi-cloud, multi-provider strategy. Our private cloud is an incredibly strategic asset for us. We still have many applications and data on-premises for resiliency, latency, and a variety of other benefits.” 

    Feinsmith also spoke of the company’s Large Language Model (LLM) strategy. He said, “Our strategy is to use a constellation of models, both foundational and open, which requires a tremendous amount of compute in our data centers, in the public cloud, and, of course, at the edge. The one constant thing, whether you’re training models, fine-tuning models, or finding a great use case that has large-scale inferencing, is that they all will drive compute. We think Dell is incredibly well positioned to help JPMorgan Chase and other companies in their AI journey.”

    Feinsmith noted that using AI isn’t new for JPMorgan Chase. For over a decade, JPMorgan Chase has leveraged various types of AI, such as machine learning models for fraud detection, personalization, and marketing operations. The company uses what Feinsmith called its LLM suite, which over 200,000 people at JPMorgan Chase use today. The generative AI application is used for QA summarization and content generation using JPMorgan Chase’s data in a highly secure way. Next, it has used the LLM suite architecture to build applications for its financial advisors, contact center agents, and any employee interacting with its clients. Its third use case highlighted changes in the software development area. JPMorgan Chase rolled out code generation AI capabilities to over 40,000 engineers. It has achieved as much as 20% productivity in the code creation and expects to leverage AI throughout the software development life cycle. Going forward, the financial firm expects to use AI agents and reasoning models to execute complex business processes end-to-end.

    Seemantini Godbole, EVP and Chief Digital and Information Officer at Lowe's, shared insights on designing the strategy for AI

    How AI Makes It Easier For Employees to Serve Customers at Lowe’s

    Lowe’s Home Improvement Stores provided another example of how companies are leveraging Dell Technology and AI to transform the customer and employee experience. Seemantini Godbole, EVP and Chief Digital and Information Officer at Lowe’s, shared insights on designing the strategy for AI when she said, “How should we deploy AI? We wanted to do impactful and meaningful things. We did not want to die a death of 1000 pilots, and we organized our efforts across how we sell, how we shop, and how we work. How we sell was for our associates. How we shop is for our customers, and how we work is for our headquarters employees. For whatever reason, most companies have begun with their workforce in the headquarters. We said, No, we are going to put AI in the hands of 300,000 associates.” For example, she described a generative AI companion app for store associates. “Every store associate now has on his or her zebra device a ChatGPT-like experience for home improvement.”, said Godbole. Lowe’s is also deploying computer vision algorithms at the edge to understand issues such as whether a customer in a particular aisle is waiting for help. The system will then send notifications to the associates in that department. Customers can also ask various home improvement questions, such as what paint finish to use in a bathroom, at Lowes.com/AI.

    Designing A World Where AI Delivers Human Opportunity

    Michael Dell said, “We are entering the age of ubiquitous intelligence, where AI becomes as essential as electricity, with AI, you can distill years of experience into instant insights, speeding up decisions and uncovering patterns in massive data. But it’s not here to replace humans. AI is a collaborator that frees your teams to do what they do best, to innovate, to imagine, and to solve the world’s toughest problems.” 

    While there are many AI deployment challenges ahead, the customer examples shared at Dell Technologies World provide a glimpse into a world where AI benefits both customers and employees. The challenge now is to do this sustainably and ethically at scale.  

  • Google Cloud’s Vertex And Models Advance Enterprise AI Agent Adoption

    Google Cloud’s Vertex And Models Advance Enterprise AI Agent Adoption

    New Reasoning Models and AI Agent Capabilities Promise To Transform Business Applications

    Enterprises need reliable platforms that combine powerful models with practical deployment capabilities. Google Cloud’s latest enhancements to Vertex AI and the Gemini model family offer businesses a comprehensive solution for building, deploying, and managing AI applications with unprecedented speed and efficiency. Vertex AI is Google Cloud’s platform to orchestrate the three pillars of production AI: models, data, and AI agents.

    Google Cloud has significantly enhanced its Vertex AI platform with new capabilities centered around reasoning models and agent ecosystems, improving the ability for enterprises to build and deploy artificial intelligence applications. The Vertex AI platform now supports over 200 models besides Google’s. The cloud provider’s latest Gemini 2.5 models represent a fundamental shift from simple response generation to what Google calls “reasoning models” – AI systems that demonstrate transparent step-by-step thinking before producing outputs. Reasoning models can work through complex analyses across multiple information sources and make nuanced decisions based on enterprise data and

    Google offers two complementary models targeting different business needs. Gemini 2.5 Pro, designed for complex problem-solving with a one-million token context window, enables sophisticated analysis of extensive documents and codebases. Meanwhile, Gemini 2.5 Flash offers optimized performance for high-volume, cost-sensitive applications where efficiency at scale is paramount.

    Organizations have faced insurmountable barriers to developing trust in AI outputs without understanding how AI arrives at conclusions. The first step in this process was listing the sources AI used in responses. Still, reasoning models enhance this by demonstrating their thought process, marking a critical advancement for enterprises requiring explainable AI for compliance and governance requirements.

    The availability of a combination of solutions that offer cost, performance, and transparency is a step in the right direction for supporting the wide range of enterprise AI requirements. Early adopters report compelling results. Moody’s claims Google’s Solution provided over 95% accuracy and an 80% reduction in processing time for complex financial document analysis. Box has implemented AI extract agents for unstructured data processing across procurement and reporting workflows, demonstrating practical applications in information management. But it takes more than AI models to build robust strategies.

    Bolstering AI Agent Capabilities With New Tools

    The number one agentic AI concern enterprise buyers have expressed to Lopez Research is fear that agents will make and implement the wrong decision. Many organizations shared concern that AI orchestration solutions are half-baked, and there’s fear that agents won’t operate properly because the data and work streams required to complete a task span multiple applications and services. To solve this, companies are looking for robust AI orchestration to coordinate and manage various AI systems, models, or components to work together seamlessly in solving complex tasks. Finally, it’s not as easy as you click a button and deploy an army of agents. Companies need tools that help them more easily build and deploy custom and out-of-the-box agents faster.

    New Solutions Aim to Overcome Enterprise AI Deployment Concerns

    To address these concerns, Google announced a wave of new multiagent ecosystem capabilities in its Vertex AI that allow multiple AI systems to work together to accomplish complex tasks. The company introduced several components to enable this approach, including the Agent Development Kit (ADK), the Agent2Agent protocol, Agent Engine, and updates to Agentspace.

    Minimizing the Data Collaboration Problem with the Agent2Agent Protocol

    Most vendors claim they can provide fully autonomous AI agents. Still, most buyers prefer to deploy these agents semi-autonomously to reduce concerns about process failures or inaccuracies. To address the enterprise buyer issue of data access and execution across various applications, Google introduced the Agent2Agent protocol, an open standard for enabling communication between agents built on different frameworks and vendors. Google launched the protocol with the support of over 50 industry partners, including Salesforce, ServiceNow, and UiPath. The Agent2Agent initiative addresses one of the most significant barriers to enterprise AI adoption: painful integration challenges to create interoperability across disparate systems.

    Making it Easier for Developers of All Skill Levels to Build AI

    Meanwhile, the Agent Development Kit (ADK), agent engine, and other advances in the Vertex AI platform help bootstrap the development of agents. Agent Development Kit, an open-source framework, allows developers to build sophisticated agents with approximately 100 lines of code –dramatically reducing development complexity. It also offers pre-built samples through Agent Garden to further accelerate development. ADK offers compatibility with over 200 models from providers like Anthropic, Meta, and Mistral AI.

    The companion Agent Engine provides a fully managed runtime for deployment, eliminating the traditional challenges such as rebuilding the agent to move from prototype to production. Agent engine also provides evaluation tools to measure and improve agent quality.

    Security and data integration capabilities round out the platform, with configurable content filters, identity controls, and Google Cloud’s Virtual Private Cloud (VPC) service controls providing multi-layered protection. Equally valuable is the platform’s ability to connect agents to enterprise data through various methods, including standard protocols and direct API integration.

    Improving Access to AI Agents

    Once a company can design, manage, and secure agents, the biggest obstacle to success is getting agents ubiquitously adopted within the enterprise. Agentspace aims to help employees find, publish, and consume agents. Agentspace, launched in December 2024, allows employees (and agents) to find information from across their organization, synthesize and understand it with Gemini’s multimodal intelligence, and act on it with AI agents. Enterprises can discover and adopt agents quickly and easily with Agent Gallery and create agents with Google’s no-code Agent Designer. Firms can also deploy Google-built agents, such as its new Deep Research and Idea Generation agents, to help employees generate and validate business ideas and synthesize dense information.

    At the conference, Google announced that Agentspace is integrated with Chrome Enterprise, letting employees leverage Agentspace’s unified search capabilities from the Chrome search box. Bringing Agentspace directly into Chrome will help employees easily and securely find information, including data and resources, right within their existing workflows.

    Perhaps what was most surprising was to learn that actual businesses are deploying agents today. Client quotes during the keynote and on Google Cloud’s website demonstrated that business impact is already evident across diverse industries. For example, Revionics has implemented a multiagent system for optimizing retail pricing, while Renault Group developed agents to strategically place EV charging infrastructure using geographical analysis. Gordon Food Service is using Agentspace to change how it accesses enterprise knowledge with searches grounded in its data across Google Workspace and other sources like ServiceNow. These early examples demonstrate the potential for complex automation of previously human-intensive analytical workflows.

    The Key takeaway: AI Agents Will Happen

    The strategy provides elements for sophisticated developers, novice designers, and employees who must find and use agents to improve their workflow. The availability of models, connectors, and out-of-the-box agents will help eliminate painful trade-offs between model capability, enterprise integration, and production readiness. The result isn’t merely faster development but significantly more reliable agents prepared for mission-critical enterprise workflows.

    As reasoning models and multi-agent systems evolve from experimental concepts to production realities, organizations should evaluate not only the capabilities of individual models but also the broader infrastructure required for responsible enterprise deployment. The key consideration for executives evaluating AI investments isn’t individual technical capabilities but rather the breadth of the portfolio and ecosystem to accelerate time-to-value while maintaining governance requirements. Google’s latest enhancements to Vertex AI and AI agent tooling suggest a maturing approach focused on practical enterprise adoption rather than merely advancing technical benchmarks.

  • Google Cloud’s Ironwood TPU Forges Better Enterprise AI

    Artificial intelligence infrastructure has emerged as the critical battleground for cloud computing dominance. At this year’s Google Cloud Next conference, the company demonstrated its intensified commitment to AI infrastructure, unveiling strategic investments, such as the Ironwood Tensor Processing Units (TPUs), designed to transform enterprise AI deployment across industries.


    “We’re investing in the full stack of AI innovation,” stated Sundar Pichai, CEO of Google and Alphabet, who outlined plans to allocate $75 billion in capital expenditure toward this vision. This substantial commitment reflects the scale of investment required to maintain competitive positioning in the rapidly evolving AI infrastructure market. Innovating in AI requires courage and deep pockets.

    Google Cloud articulated a full stack strategy focused on developing AI-optimized infrastructure spanning three integrated layers: purpose-built hardware, foundation models, and tooling for building and orchestrating multi-agent systems. During the keynote presentation, Google Cloud introduced the Ironwood TPU its seventh-generation Tensor Processing Units (TPUs), representing a significant advancement in AI computational architecture.

    Optimizing Infrastructure for AI With TPUs

    Cloud Computing infrastructure started as a method of replacing and optimizing on-premise data centers. Today, cloud computing providers are adding specific infrastructure to support new computing requirements introduced with supporting AI. TPUs are specialized processors developed by Google specifically to accelerate AI and machine learning workloads—with particular optimization for deep learning operations. TPUs deliver superior performance-per-dollar compared to general-purpose GPUs or CPUs across numerous machine learning use cases, resulting in reduced infrastructure costs or increased computational capability within existing budget constraints.

    Ironwood TPUs represent a cornerstone component of Google Cloud’s AI Hypercomputer architecture, which integrates optimized hardware and software components for high-demand AI workloads. The AI Hypercomputer platform constitutes a supercomputing system that combines performance-optimized silicon, open software frameworks, machine learning libraries, and flexible consumption models designed to enhance efficiency throughout the AI lifecycle—from training and tuning to inference and serving.

    According to Google’s technical specifications, these specialized AI processors deliver computational performance that’s 3,600 times more powerful and 29 times more energy efficient than the original TPUs launched in 2013. Ironwood also demonstrates a 4-5x performance improvement across multiple operational functions compared to the previous version 6 Trillium TPU architecture.

    Ironwood implements advanced liquid cooling systems and proprietary high-bandwidth Inter-Chip Interconnect (ICI) technology to create scalable computational units called “pods” that integrate up to 9,216 chips. At maximum pod configuration, Ironwood delivers 24 times the computational capacity of El Capitan, currently ranked as the world’s largest supercomputer.


    To maximize this infrastructure’s utility, Google Cloud has developed Pathways, a machine learning runtime created by Google DeepMind that enables efficient distributed computing across multiple TPU chips. Pathways on Google Cloud simplifies scaling beyond individual Ironwood Pods, allowing for the orchestration of hundreds of thousands of Ironwood chips for next-generation AI computational requirements. Google uses Pathways internally to train advanced models such as Gemini and now extends these same distributed computation capabilities to Google Cloud customers.

    Marrying Business Impact With Economics

    While the industry has witnessed a proliferation of smaller, specialized AI models, significant AI chip innovation remains essential to deliver the performance requirements for supporting advanced reasoning and multimodal models.

    According to Amin Vahdat, VP/GM of ML, Systems & Cloud AI at Google Cloud, “Ironwood is designed to gracefully manage the complex computation and communication demands of ‘thinking models,’ which encompass Large Language Models (LLMs), Mixture of Experts (MoEs) and advanced reasoning tasks.” This architecture addresses the market requirement for modular, scalable systems that deliver improved performance and accuracy while optimizing both cost efficiency and energy utilization.


    For enterprises implementing large-scale AI initiatives, Google’s hardware advancements translate to quantifiable benefits across three dimensions:

    1. Economic Efficiency. Google’s specialized hardware substantially increases computational density per dollar, reducing the total cost of ownership for AI infrastructure. Organizations can deploy increasingly sophisticated AI models without corresponding linear increases in computing expenditures.
    2. Sustainability Metrics. As AI model complexity increases (across systems like Gemini, ChatGPT, and advanced image generators), the underlying computational infrastructure generates significantly more heat and power consumption. Liquid cooling technology, implemented in Ironwood, delivers substantially higher thermal efficiency compared to conventional air cooling, enabling chips to operate at higher frequencies without thermal throttling. This innovation addresses power consumption—a critical consideration for both cloud providers and enterprise buyers with sustainability commitments. The enhanced performance-per-watt metrics of these TPUs help organizations address environmental impact concerns while scaling their AI capabilities.
    3. Time-to-Market Acceleration. The exponential increase in processing capacity means that AI model training and inference workflows—previously requiring weeks or months of computation—can now be completed in days or hours. This compression of development timelines enables organizations to iterate more rapidly and operationalize AI solutions with significantly reduced deployment cycles.

    Why TPUs Matters to Enterprise Buyers

    Organizations are over the phase of interesting AI proof of concept trials that never make it to production-grade systems. 2025 is the year that organizations expect to deploy use cases with quantifiable business value while laying the foundation for what’s next. Google Cloud’s enhanced AI infrastructure enables practical enterprise applications today while supporting previously constrained by computational economics or performance limitations. Consider the impact of AI today and tomorrow in:

    • Financial Services Analytics. During the Google Cloud Next keynote, Deutsche Bank shared how it uses technology from Google Cloud to power an AI-powered research agent named DB Lumina for faster data analysis. Many banking and investment firms are investigating how to use enhanced AI infrastructure to process market data streams, detect complex pattern anomalies in real-time, and enable more responsive trading strategies and comprehensive risk management frameworks.
    • Customer Experience Transformation. Retail and service organizations can implement sophisticated recommendation engines and multimodal conversational AI systems that process customer interactions with minimal latency while incorporating rich contextual understanding. For example, Verizon uses Google Cloud’s Customer Engagement Suite to enhance its customer service for over 115 million connections with AI-powered tools, like the Personal Research Assistant which accurately answers 95% of questions, helping agents provide faster, more accurate, and personalized support. The next era focuses on cracking the code for personalization, advanced marketing assets, and empathetic conversational AI.
    • Computational Medicine. Today, healthcare organizations use AI to improve patient experiences with data gathering and summarization features. For example, Seattle Children’s Hospital used Google Cloud’s generative AI to create Pathway Assistant, an AI-powered agent that improves clinicians’ access to complex information and the latest evidence-based best practices needed to treat patients. As we advance, healthcare institutions can leverage advances in AI infrastructure to accelerate the analysis of complex imaging datasets, genomic sequences, and patient records, potentially enhancing diagnostic accuracy and treatment protocol optimization.

    Get Comfortable With Change

    As competition intensifies among cloud infrastructure providers, Google’s substantial investment in AI represents a strategic assessment that enterprise computing will increasingly prioritize AI-driven workloads—and that organizations will select platforms offering the optimal combination of performance, cost efficiency, and energy sustainability.


    The only constant in the AI market will be change. Business leaders must be comfortable with continuously adapting strategies to leverage AI advancements. For CIOs and technology leaders developing their AI implementation roadmaps, Google Cloud’s hardware innovations, such as the Ironwood TPU, present technical and economic justifications to reevaluate their infrastructure strategy as AI becomes increasingly central to operational excellence and competitive differentiation.