This list celebrates changemakers creating meaningful impact through leadership, innovation, fresh perspectives, transformative mindsets, and lessons that resonate far beyond the workplace.
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
NATO will deploy Google Distributed Cloud, an air-gapped version of Google Cloud on-premise.
Under a multi-million dollar contract with NATO's Communication and Information Agency (NCIA), Google will deliver sovereign cloud services to NATO for edge computing and AI use cases.
GDC is a sovereign cloud in a box that’s physically disconnected from the internet but includes everything to deploy virtual machines, run workloads and use services such as Vertex AI.
Google Distributed Cloud (GDC) was a big theme at Google Public Sector's annual conference in Washington DC in October. GDC was being used in public sector deployments, defense use cases and sovereign data and cloud implementations. Typically, GDC was being used in edge locations with limited to no connectivity and hardened environments.
In a statement, GDC will support NATO Communication and Information Agency's Joint Analysis, Training and Education Centre, which is using the infrastructure to modernize and manage classified workloads.
With GDC, NATO will maintain data residency and operational controls as well as autonomy. NATO has been building an interoperable communications and information systems architecture that can bring together 12 allied nations and 36 NATO entities quickly and at scale. The goal is to enable forces to operate together on the fly.
Constellation Research's Holger Mueller said:
"Google Cloud has for a long time invested in its next-generation computing -platform - originally Anthos - now Google Distributed Cloud (GDC). The key selection criteria for a next-gen computing platform is workload portability between public clouds and on premises. The required characteristic is identicality. The tech stack in the supported deployment is identical, portable and has investment protection of code assets. With Gemini and Vertex AI running on GDC, Google sets itself apart from the other next-gen computing vendors, and is therefore very well secured for air gapped solutions required for military workloads."
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Large language models are at an interesting juncture. LLM breakthroughs have slowed and there are questions about whether they will lead to artificial general intelligence. Coupled with concerns about an AI infrastructure bubble LLMs are going to be closely watched--especially since they're the key ingredient of agentic AI.
In the end, LLMs don't have to necessarily lead to some superintelligence to have a big impact on enterprises. Enterprise AI and the AI market that fascinates venture capitalists and Wall Street investors are two different markets. There are enough AI returns for enterprises even if LLMs stagnate for the next year.
With that backdrop and this week’s headlines, it's worth pondering the key LLM questions.
Note I do not have the answers but usually know the questions to ask. Here's a look at the key LLM questions as 2025 comes to a close.
Will LLMs--and the AI agents they power--upend software as a service? This debate has been bubbling up throughout. The winner of the debate of LLMs vs. SaaS is far from settled, but it's no surprise that enterprise software vendors are scrambling to present themselves as platforms. No more cross-selling clouds. No more functional silos. Today, the masters of the cross sell are talking platforms. Salesforce, Microsoft, Workday, ServiceNow and a cast of hundreds are creating AI agents that work across their applications. Meanwhile, OpenAI and Anthropic are looking to break the SaaS margin profile to woo enterprises. The idea is simple: Relegate systems of record to plumbing where ChatGPT or Claude is the interface and workflow engine. It's early in this LLM vs SaaS debate, but the SaaS crowd has plenty of disgruntled customers looking for alternatives. The running joke is SaaS and healthcare are the two enterprise categories always guaranteed to go up. LLMs could be a SaaS replacement or just a nice negotiation tool.
Five years from now will we all say, 'those LLMs turned out to be a kick ass enterprise search'? The more I use LLMs, the more I think their greatest contribution is perusing structured and unstructured data and surfacing it easily. LLMs clearly collapse the time spent on conducting searches and doing superficial research. Yes, LLMs will make stuff up, but they're a great starting point. When combined with enterprise data and repositories that have been useless for years, LLMs are revamping the search game for companies. Suddenly, context engineering is a thing.
Is the future of UI generative? Enterprises have paid SaaS providers for years as data stores, workflow models and user interfaces (that may or may not be swell). If the UI layer collapses what exactly are you buying? Sure, SaaS providers are talking about how they could be a headless platform, but that'll likely mean lower prices. The idea that LLMs could spin up relative user interfaces on the fly have been appealing--if not a bit theoretical. However, Google's launch of Gemini 3 features a lot of interface goodies where widgets and layout themes area presented on the fly. Suddenly, a search query can provide an answer that comes in a magazine format. Answers can have code for functions like mortgage calculators. According to Google, Gemini 3 knows good design principles. Watch these UI developments closely because there's likely a big impact on enterprise software in the future.
Google published a generative UI paper to go along with the Gemini 3 launch. The company said: "Our evaluations indicate that, when ignoring generation speed, the interfaces from our generative UI implementations are strongly preferred by human raters compared to standard LLM outputs. This work represents a first step toward fully AI-generated user experiences, where users automatically get dynamic interfaces tailored to their needs, rather than having to select from an existing catalog of applications."
Will AI agents be implemented without forward deployed engineers? The most popular technology job today is the "forward deployed engineer." These folks are technical experts that can also consult and co-innovate with customers. Forward deployed engineers swoop in surface use cases, get the data models in shape and then help you implement AI agents and your digital workforce. The big idea is that forward deployed engineers can get you from pilot to production faster. A cynic would say software vendors are starting to look like consultants, which is fine since consultants are also offering software. Palantir made forward deployed engineers and its data ontology popular and now enterprise vendors are all over it. Now forward deployed engineers are swell but likely inflate the price tag for enterprises. The big question is when AI agents can do a lot of this work.
ServiceNow is investing in forward deployed engineers as well as automating as much of the implementation as possible. Amit Zavery, ServiceNow's President, Chief Product Officer & COO, said: "We have 100-plus prepackaged workflows with Agentic built in. So, you don't have to do a lot of handholding to get going. Of course, there are going to be co-innovation required. There might be something specific for our customers. That's why we're investing in FD kind of a model with forward deployed engineers who are really AI black belt who can work very closely with customers on the AI expertise required for some of those use cases."
Are LLMs a dead end in the pursuit of artificial general intelligence? Call it AGI. Call it superintelligence. The working theory is that LLMs will lead to AGI. All the cool kids think so. Here's the recipe for AGI. Advance LLMs with a ridiculous amount of GPUs, data centers, land, and power and bam here's superintelligence. That simplified recipe is behind big valuations, lots of debt and monetization schemes that may or may not work out. If interested in a contrarian view, it's worth checking out this Wall Street Journal profile of Yann LeCun, who headed Meta's Fundamental AI Research group. LeCun is arguing that world models, which are trained through visual information instead of text. The punchline of the WSJ story: "If you are a Ph.D. student in AI, you should absolutely not work on LLMs."
Hannah Mason is a marketing director at Constellation Research. She oversees brand positioning, creative direction, and content development across digital platforms, events, and campaigns. Her work spans branding and design—including graphics, sales collateral, and event presentations—along with website content management, newsletters, and Constellation Insights.
Mason also manages surveys and produces Constellation’s video collateral, including ConstellationTV. Her role ensures that the Constellation brand is communicated with clarity, creativity, and consistency across all channels.
Contact Hannah at [email protected].
...
Vice President & Principal Analyst
Constellation Research
About Liz Miller:
Liz Miller is Vice President and Principal Analyst at Constellation, focused on the org-wide team sport known as customer experience. While covering CX as an enterprise strategy, Miller spends time zeroing in on the functional demands of Marketing and Service and the evolving role of the Chief Marketing Officer, the rise of the Chief Experience Officer, the evolution of customer engagement, and the rising requirement for a new security posture that accounts for the threat to brand trust in this age of AI. With over 30 years of marketing experience, Miller offers strategic guidance on the leadership, business transformation, and technology requirements to deliver on today’s CX strategies. She has worked with global marketing organizations to transform everything from…...
Vice President and Principal Analyst
Constellation Research
Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.
Coverage Areas:
Future of Work
Tech Optimization & Innovation
Background:
Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...
ConstellationTV episode 118 covers the latest enterprise tech developments, creative advertising solutions, and strategic pivots from leading organizations. Here are the main takeaways:
SAP’s Bold Move in AI Development
During SAP TechEd Berlin, SAP made a significant announcement regarding its AI innovation. For the first time, SAP released its own large language model (LLM), known as SAP RPT-1, a groundbreaking tool specialized in understanding and processing tabular data. As highlighted in the discussion, this move underscores SAP’s unique position in the enterprise software space, being the first to ship an AI model specifically tailored to meet business users’ needs in this domain.
SAP’s renewed focus on solving practical challenges for its users was another focal point. Instead of being consumed by AI hype, the company demonstrated a clear understanding of its customers' pain points. One major issue is the S/4HANA upgrade conundrum, where legacy code and architecture present substantial hurdles. SAP’s innovative use of AI to assist with ABAP code reviews and streamline transitions to their cherished “clean core” highlights their dedication to helping businesses future-proof their systems.
This marks a significant shift for SAP, finally bridging the gap between AI innovation and users’ pressing needs—a trajectory that other enterprise software companies should observe closely.
The IBM Quantum Revolution
Moving on to quantum computing, IBM’s Quantum Developer Conference showcased some groundbreaking milestones. Among the highlights were the successful proof of error correction enabled by the Lune processor and further hardware advancements with a novel IHOP processor. These developments reveal not only the technical leaps happening in the quantum computing space but also a growing interest in equipping developers to capitalize on this technology.
With over 500 quantum developers in attendance, it’s evident that quantum computing is reaching an inflection point, where theoretical research is transforming into practical applications. For businesses, harnessing quantum computing could soon mean better performance optimization, predictive analysis on an unprecedented scale, and a competitive edge in data-heavy industries. However, businesses must prepare for the learning and skill transition this shift demands.
Advertising Innovations and Business Strategy Pivots: Oracle Analytics-Driven Geotargeting Campaigns
ConstellationTV episode 118 features an interview with Scott Searcy, a CX Supernova Award winner. Scott shared insights into his geotargeting project, facilitated by Oracle Analytics Cloud, which is a prime example of how businesses can creatively leverage data. Initially born out of a zoning change that permitted new digital assets, Scott’s team used advanced analytics tools to overlay political boundary data with latitude-longitude maps.
The project resulted in highly precise advertising campaigns that targeted swing states during political races. By identifying these opportunities, Scott’s team not only increased revenue but also demonstrated the power of combining real-world scenarios like zoning changes with enterprise-level analytics.
The implications go beyond politics. These tools have expanded to more versatile use cases, including live events, sporting events, and concert series. Leveraging geotargeting to maximize revenue while minimizing waste is a masterclass in creative, data-driven advertising. Enterprises should examine this approach to optimize their advertising efforts.
Freshworks’ Strategic Pivot
Liz Miller provides commentary on Freshworks’ recent strategic pivot—perhaps the most instructive segment of this episode for business leaders grappling with organizational complexity. Freshworks, which had previously aimed for an ambitious full-platform engagement solution, recently shifted focus toward simplifying service delivery.
Freshworks identified a core challenge many organizations face: the chaos and complexity of managing expansive platforms across diverse audiences. By re-centering its efforts on improving both employee-facing and customer-facing service delivery, Freshworks embraced simplicity as its key to growth.
Liz captured the essence of this shift with a standout quote: “Freshworks has centered on this idea that complexity is the enemy of growth.” This philosophy not only redefines their offerings but showcases a leadership team willing to pivot boldly when the market demands it.
The company’s decision reflects the need for intentional focus in today’s fast-evolving landscape. In an era where companies often chase ambitious platform solutions, Freshworks demonstrates the value of listening to the market and staying sharply attuned to user needs—something many organizations can learn from.
Central to this pivot is their Chief Marketing Officer, Mika Yamamoto, whose leadership played a crucial role in reshaping Freshworks’ vision. Mika’s ability to address the dual challenges of internal complexity and external customer satisfaction epitomizes the kind of leadership required in turbulent markets.
Practical Applications of Artificial Intelligence
Throughout the segment, a recurring theme emerges: AI’s transition from hype to application. The conversation highlights recent trends where businesses are gradually moving away from fretting about AI’s potential to “replace jobs” toward exploring its more practical use cases to address persistent challenges.
Liz Miller observes that organizations are now asking tougher questions, like: “How can AI simplify my daily operations rather than make them more complex?” AI implementations in areas such as marketing, project management, and customer service are proving far more impactful than lofty claims that AI can solve issues beyond reach.
Yet, there’s a layer of disillusionment, too. While the technology’s potential remains vast, Liz warns that businesses must set realistic expectations for its current capabilities, especially regarding the tools and solutions promised to revolutionize workflows. Understanding the present limitations of AI is just as important as understanding its possibilities.
What Businesses Can Learn from Episode 118
Enterprise Tech Success Lies in Addressing Real Problems: Both SAP and IBM serve as shining examples of how to effectively innovate within the tech space. From developing AI models rooted in functionality to pushing boundaries in quantum computing while providing accessible educational resources, these companies demonstrate that solving tangible business problems should take precedence over chasing headlines.
Creativity in Advertising is Grounded in Data: Scott Searcy’s geotargeting campaign offers lessons beyond just political advertising. Businesses must learn to combine analytics with real-world constraints to uncover untapped opportunities, whether through location-based campaigns, event planning, or revenue optimization strategies.
The Boldness to Pivot Pays Off: Freshworks’ strategic simplification serves as a powerful narrative for businesses grappling with uncertainty and internal chaos. Practicing the discipline of stepping back, re-focusing, and simplifying offerings can yield greater long-term growth—and Freshworks is proving that firsthand.
AI Hype Must Translate into Practicality: While AI remains a dominant force in enterprise solutions, its role is shifting toward practicality. Organizations must evaluate AI implementations not just for their innovation potential but for their ability to solve everyday challenges.
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Both companies have quantum computing plans first in hybrid quantum-HPC systems and then quantum. IBM's quantum efforts are well known and Cisco has been revving quantum networking efforts.
IBM and Cisco, two classical computing giants, are battling pure play quantum companies that are also investing heavily in networking systems together. For instance, IonQ has acquired multiple startups focused on quantum networking.
IBM and Cisco plan to design a network of large-scale fault tolerant quantum systems by the early 2030s.
The companies will demonstrate network quantum systems within five years.
The quantum network will serve as the base for quantum internet, communications and sensing by the late 2030s.
IBM is focused on quantum computing with Cisco delivering on networking.
According to the companies, this quantum network could run with "potentially trillions of quantum gates."
Jay Gambetta, Director of IBM Research and IBM Fellow, said the Cisco partnership works with the company's roadmap. "By working with Cisco to explore how to link multiple quantum computers like these together into a distributed network, we will pursue how to further scale quantum's computational power. And as we build the future of compute, our vision will push the frontiers of what quantum computers can do within a larger high-performance computing architecture," said Gambetta.
Vijoy Pandey, GM/SVP at Outshift by Cisco, said quantum useful scale is about networking as much as compute. "IBM is building quantum computers with aggressive roadmaps for scale-up, and we are bringing quantum networking that enables scale-out," he said.
The networked quantum computing system will aim to entangle qubits from multiple separate quantum computers located in distinct cryogenic environments.
IBM and Cisco will have to create new connections and a supporting software stack that can preserve quantum states, distribute entanglement resources and network systems.
The to-do list for this collaboration is extensive.
The companies said they will explore how to transmit qubits over longer distances with various optical technologies and transfer quantum information.
IBM will build a quantum networking unit to be the interface of the quantum processing unit. This quantum networking unit will take stationary information in the QPU and convert it to data that can be networked.
Cisco will develop a high-speed software protocol that can reconfigure network paths for quantum information.
The companies will create the hardware and open source software to act as a network bridge.
IBM will work with the Superconducting Quantum Materials and Systems Center (SQMS) to figure out how many quantum networking units could be used within quantum data centers and demonstrate the connections within the next three years.
In a diagram these networked quantum computers would look like this:
Vice President & Principal Analyst
Constellation Research
About Liz Miller:
Liz Miller is Vice President and Principal Analyst at Constellation, focused on the org-wide team sport known as customer experience. While covering CX as an enterprise strategy, Miller spends time zeroing in on the functional demands of Marketing and Service and the evolving role of the Chief Marketing Officer, the rise of the Chief Experience Officer, the evolution of customer engagement, and the rising requirement for a new security posture that accounts for the threat to brand trust in this age of AI. With over 30 years of marketing experience, Miller offers strategic guidance on the leadership, business transformation, and technology requirements to deliver on today’s CX strategies. She has worked with global marketing organizations to transform everything from…...
Adobe has entered into a definitive agreement to acquire Semrush in an all-cash transaction for $12.00 per share for a total equity value of $1.9 billion according to Adobe’s press announcement.
Semrush has a trackrecord of success in digital brand intelligence across search engine optimization (SEO), content performance and insight into paid, earned and owned media campaign performance and audits.
Q: Why is the Adobe acquisition of Semrush interesting?
Adobe’s acquisition of Semrush is interesting because it adds significant capabilities and expertise in the gathering, utilization and analysis of brand driven search, including search conducted by large language models (LLMs).
TL;DR The acquisition is the opening salvo for a new frontier where the understanding of how people search and the intelligence of how models answer sits at the very center of how marketers drive their growth strategies forward.
Welcome to the future of content creation in the age of AI summaries, where questions that mirror prompts and answers that feed answer generations haunt a whole new generation of marketers battling modern search. The formatted content above demonstrates a new normal for creation when models crave authority and clarity in content and where answers best reflect the reality of a user’s prompt. While Search Engine Optimization (SEO) pushed marketers to think in keywords and high-quality, relevant, authentic content, the new battle ground of AI-powered engine optimization has new rules and new challenges. This AI age also welcomes two new and notable entrants to the marketing lexicon: Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). While AEO is a strategy to format content to serve the answers brands expect, GEO focuses in on making brand content visible, available and referenceable by AI search tools including ChatGPT and Google Gemini.
AEO and GEO feel a bit like the wild west, reminding marketers just how long it took to perfect SEO strategies that were once managed manually with more gut and guesswork than precision automation. Today's marketing operations and digital teams are actively seeking out tools that can identify, recommend and automate these optimization efforts. Which makes the announcement of Adobe’s intention to acquire Semrush for a reported $1.9 billion so timely.
What do we know about the deal? Adobe announced their definitive agreement to acquire Semrush in an all-cash transaction set to total $1.9 billion. Many market watchers have been quick to note that the $12 per share price is almost double the closing price of $6.89 before the announcement. The 17-year-old SEO darling has seen strong performance of late, delivering a reported third-quarter revenue of $112.1 million. Beyond healthy performance, Semrush has been at the epicenter of the changing nature of content performance, being an early innovator in AI search optimization. While the press announcement boasts that Adobe works with 99% of the Fortune 100, Semrush is no shrinking violet when it comes to powering recognizable customers including Amazon, TikTok, UPS, Publicis Groupe and Wix.
Few details are expected about Adobe’s intentions with Semrush until acquisition approvals and regulatory reviews, but with Adobe’s own push into AEO and GEO with the introduction of the LLM Optimizer product earlier in the year and new innovation around AI-powered conversational journeys for consumers with Brand Concierge, there is little doubt that Semrush will be a welcome integration to Adobe's analytics, data and brand intelligence offerings. It is also safe to assume that Semrush capabilities will be central to the brand visibility intelligence marketers will need to accelerate durable outcomes powered by a more automated content supply chain.
What makes Adobe + Semrush so interesting? In the near term, Semrush will supercharge Adobe’s brand intelligence offerings, delivering actionable insights across GEO and search. Importantly Semrush’s premiere solution, Semrush One, serves as a one-stop-shop for visibility, merging the view of more traditional keyword-based SEO metrics and analytics with insight and visibility tracking across AI models from ChatGPT, Google, Perplexity and Claude. Not only does this intelligence track visibility of a brand, but Semush can also monitor markets and competitors in an effort to bring a competitive edge by identifying trends and opportunities faster. Coupled with Adobe’s already extensive analytics portfolio and newly minted Agentic AI agents trained to monitor, analyze and act automously on specific outcome drive processes, this new holistic brand intelligence capability turns early warning signs into early action systems.
In the long term, which we know is where these acquisitions get exceedingly interesting, watch for Adobe to make their intelligence capabilities become as available and ubiquitous as AI action and generation tools have become across all of Adobe’s product lines. It could answer marketing's call for the ever ellusive "easy button" that answers the questions of where and what...but also why content or experiences succeed or fall flat. This is in lock step with Adobe’s long standing AI strategy that elevates and accelerates “quality of life” AI updates that focus on optimizing a creator, an artist or a marketers time to free more wild creativity and action. Instead of toiling over manual tweaks to content fit for AI search, we can see a future where the speed at which models evolve is easily handled by the signals these visibility tools can constantly monitor and understand.
Until the details of post-deal-close operations are revealed, one can only daydream about the new intelligence doors that customers may be able to open. While early focus will be on how Adobe mainstays in the Digital Experience offerings will integrate and intertwine with the Semrush platform, the reality is that Semrush brings a robust portfolio of toolkits that address everything from SEO to visibility into localization and advertising to social media and PR bringing us back to the fundamentals of marketing's work in paid, earned and owned channels of engagement..
For Adobe this is a solid investment into AI and intelligence innovation, but it is also a bet that brand visibility data will play a critical role in the larger knowledge and data set that brands will need to continue to power enterprise AI rollouts. Historically where organizations have struggled is turning captured insights into brand placements into actionable signals that drive revenue, often ending in lengthy discussions of how to wade through the noise of marketing analytics to identify higher fidelity signal that points to opportunity. This acquisition could very well mark one of the key steps forward in that noise to signal advancement.
What is the big takeaway from this acquisition? Adobe often faces market criticism over its AI strategy...criticism that this analyst has always questioned. While many AI foundation model darlings in the market have developed their models for the sake of wild unbridled innovation and sometimes shocking application, Adobe has always remained focused on the demands of its own customers that don’t demand the capacity to create everything in AI, but instead demand the capacity to create responsibly and reliably with AI. This has sometimes held Adobe’s own AI innovation back to a more conservative approach where enterprise viability and commercial safety rule the day. But the Semrush acquisition not only bolsters Adobe’s AI vision, it answers growing problem for marketers who realize their existing content and search strategies have become less effective and are not fit for purpose in this new age of AI generated content and AI summarized search. AI search and summary generation is a new game that feels familiar, but demands new plays. This acquisition is Adobe answering that call.
Hannah Mason is a marketing director at Constellation Research. She oversees brand positioning, creative direction, and content development across digital platforms, events, and campaigns. Her work spans branding and design—including graphics, sales collateral, and event presentations—along with website content management, newsletters, and Constellation Insights.
Mason also manages surveys and produces Constellation’s video collateral, including ConstellationTV. Her role ensures that the Constellation brand is communicated with clarity, creativity, and consistency across all channels.
Contact Hannah at [email protected].
...
Vice President & Principal Analyst
Constellation Research
About Liz Miller:
Liz Miller is Vice President and Principal Analyst at Constellation, focused on the org-wide team sport known as customer experience. While covering CX as an enterprise strategy, Miller spends time zeroing in on the functional demands of Marketing and Service and the evolving role of the Chief Marketing Officer, the rise of the Chief Experience Officer, the evolution of customer engagement, and the rising requirement for a new security posture that accounts for the threat to brand trust in this age of AI. With over 30 years of marketing experience, Miller offers strategic guidance on the leadership, business transformation, and technology requirements to deliver on today’s CX strategies. She has worked with global marketing organizations to transform everything from…...
Vice President and Principal Analyst
Constellation Research
Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.
Coverage Areas:
Future of Work
Tech Optimization & Innovation
Background:
Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...
ConstellationTV episode 118 just dropped! Here's what you'll get in this episode with analyst co-hosts Liz Miller and Holger Mueller:
Tech news from SAP TechEd Berlin—automation, AI-driven upgrades, and SAP’s LLM for tabular data. Plus, highlights from the IBM Quantum Developer Conference about what’s next for quantum computing.
Interview with Supernova Award winner Scott Searcy, sharing how Oracle Analytics Cloud and innovative geo-targeting strategies transformed his digital ad placement and campaign results.
Liz gives an insider update on Freshworks’ strategic pivot to streamline service delivery, tackle complexity, and accelerate growth.
Don't miss the expert discussions and actionable insights as we cover what matters most in enterprise tech.
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Demand for Nvidia's GPUs shows no sign of slowing down as the company's data center unit delivered fiscal third quarter growth of 66%.
The company reported third quarter net income of $31.91 billion, or $1.30 a share, on revenue of $57 billion, up 66% from a year ago. Non-GAP earnings were $1.30 a share.
Wall Street was expecting non-GAAP earnings of $1.26 a share on revenue of $55.09 billion.
Nvidia CEO Jensen Huang said, "Blackwell sales are off the charts, and cloud GPUs are sold out" and that the "AI ecosystem is scaling fast — with more new foundation model makers, more AI startups, across more industries, and in more countries."
The quarter was driven by the data center unit, which delivered third quarter revenue of $51.21 billion, up 66% from a year ago. Compute revenue was $43.03 billion, up 56% from a year ago, and networking, which had revenue growth of 162% to $8.19 billion.
Gaming revenue was up 30%, professional visualization revenue was up 56% and automotive revenue was up 32%.
CFO Collette Kress said the data center unit was powered by AI agent workloads. "Blackwell Ultra is now our leading architecture across all customer categories while our prior Blackwell architecture saw continued strong demand. H20 sales were insignificant in the third quarter," said Kress. "Networking revenue was a record $8.2 billion, up 162% from a year ago from the introduction and continued growth of NVLink compute fabric for GB200 and GB300 systems."
As for the outlook, Nvidia projected fourth quarter revenue of $65 billion, give or take 2%, and gross margins of about 75%.
Constellation Research CEO R “Ray” Wang said on Fox Business that Nvidia is the “poster child” for AI and there’s not a bubble. “There's a trillion dollars of cross sell, joint investments, balance of trade, if you want to call it that. But at the end of the day, these are real profits and quality revenue. And unlike the internet age, what's happening here is, these are real profits being spent on capex,” said Wang.
Speaking on a conference call, Kress said:
"We currently have visibility to a half a trillion dollars in Blackwell and Rubin revenue from the start of this year through the end of calendar year 2026."
"We see the transition to accelerated computing and generative AI across current hyper scale workloads contributing toward roughly half of our long term opportunity. Another growth pillar is the ongoing increase in compute driven by foundation model builders."
"Enterprises broadly are leveraging AI to boost productivity, increase efficiency and reduce cost."
"We announced AI factory and infrastructure projects amounting to an aggregate of 5 million GPUs. This demand spans every market, CSPs, sovereigns, modern builders, enterprises and supercomputing centers."
Kress also addressed concerns that have been circulating on Wall Street. Regarding GPU depreciation, Kress argued that Nvidia's software stack stretches out the lifespan of GPUs due to optimization. She said:
"The long useful life of Nvidia's CUDA GPUs is a significant TCO advantage over accelerators. CUDA's compatibility and our massive installed base extend the life in our systems well beyond their original estimated useful life. For more than two decades, we have optimized the CUDA ecosystem, improving existing workloads, accelerating new ones, and increasing throughput with every software release. Most accelerators without CUDA and Nvidia's time tested and versatile architecture became obsolete within a few years. The A100 GPUs we shipped six years ago, are still running at full utilization today, powered by vastly improved software stack."
She also defended Nvidia's investments in other AI companies that are also customers. "Our strategic investments represent partnerships that grow the Nvidia CUDA AI ecosystem and enable every model to run optimally on Nvidia everywhere. We will continue to invest strategically while preserving our disciplined approach to cash flow management," she said.
Huang took on the AI bubble concerns.
"There's been a lot of talk about an AI bubble. From our vantage point, we see something very different. As a reminder, Nvidia is unlike any other accelerator in that we excel at every phase of AI, from pre training and post training to inference. We are also exceptional at science and engineering simulations, computer graphics, structured data processing to classical machine learning.
The world is going is undergoing three massive platform shifts at once. The first transition is from CPU general purpose computing to GPU accelerated computing. Secondly, AI is also reached a tipping point and is transforming existing applications while enabling entirely new ones for existing applications. The transition to agentic and physical AI will be revolutionary."
Constellation Research analyst Holger Mueller said:
"Nvidia achieved something remarkable in the quarter. Its data center revenue growth matched the profitability growth of overall Nvidia, a rare feat. It shows that Jensen Huang and team - despite all the growth - are still managing cost. The other key takeaway is that networking is now Nvidia second largest business having solidly overtaken former #1 Gaming."
Huang said on the conference call:
"Performance per watt, the efficiency of your architecture is incredibly important. And the efficiency of your architecture can't be brute force. There is no brute forcing about it. Your performance per watt translates directly absolutely directly to your revenues, which is the reason why choosing the right architecture matters so much now. The world doesn't have an excess of anything to squander. And so each generation, our economic contribution will be greater. Our value delivered will be greater but the most important thing is our energy efficiency per watt is going to be extraordinary, every single generation."
"One of the areas that is really misunderstood about the hyperscalers is that the investment on Nvidia GPUs not only improves their scale, speed and cost for -- from general purpose computing."
"Each country will fund their own infrastructure. And you have multiple countries, you have multiple industries. Most of the world's industries haven't really engaged agentic AI yet, and they're about to. All of those different industries are now getting engaged, and they're going to do their own fundraising. And so don't just look at the hyperscalers as a way to build out for the future. You got to look at the world, you got to look at all the different industries and enterprise computing is going to fund their own industry."
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Palo Alto Networks reported better-than-expected first quarter results and said the company landed total platform customers. Palo Alto Networks also said it will acquire Chronosphere, an observability platform, for $3.35 billion.
The security company reported first quarter earnings of $334 million, or 47 cents a share, on revenue of $2.5 billion. Non-GAAP earnings for the first quarter were 93 cents a share. Wall Street was expecting Palo Alto Networks to report non-GAAP earnings of 89 cents a share on revenue of $2.46 billion.
Ahead of the earnings, Palo Alto Networks and IBM said they will launch a joint offering focused on quantum-safe security. The companies will combine IBM's Quantum Safe Transformation services with the network-level, cryptographic intelligence from Palo Alto Network's platform.
CEO Nikesh Arora said the first quarter was marked by "significant platformization wins." Palo Alto has been beefing up via acquisitions with Chronosphere as well as CyberArk. Chronosphere has annual recurring revenue of $160 million as of Sept. 30.
As for the outlook, Palo Alto Networks said revenue will be between $2.57 billion to $2.59 billion, up 14% to 15%. Non-GAAP earnings will be between 93 cents a share to 95 cents a share.
For fiscal 2026, Palo Alto Networks is projecting non-GAAP earnings of $3.80 a share to $3.90 a share with revenue of $10.5 billion to $10.54 billion, up 14%.
On a conference call, Arora said:
"AI is exposing the cracks in our enterprise architectures, which do not have robust security. Patches are incomplete, platforms are missing. There is a plethora of point products across the enterprise. This gap is exactly where attackers thrive. They're testing how far they can exploit the model. They're running prompt injections, jail breaks, model manipulation. And now we're seeing the next phase, autonomous AI agents being leveraged into the attack chain."
"Our messages to customers is clear, real-time visibility and security are essential for infrastructure. This reality necessitates a paradigm shift in the industry."
"Quantum computing has seen significant innovation over the last year. We're getting more and more optimistic on the arrival of quantum and expect it to be commercialized by 2029. As is widely known, quantum computing has the ability to break current encryption across technology stacks. Enterprises have less than 5 years to get their states to quantum readiness. There is a fear that some nation-states will have quantum compute capability sooner than 2029."
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Target's comeback plan revolves around improving customer experience, plowing more money into capital expenditures and technology partnerships with the likes of OpenAI.
The retailer's third quarter results were mixed as the company beat earnings estimates, missed on revenue and narrowed its outlook. Target's third quarter same store sales were down 2.7% from a year ago.
Michael Fiddelke, Target Chief Operating Officer, will become CEO and he spent much of the company's third quarter earnings call talking about experience improvements. "We need to offer a more consistently elevated experience across our stores and digital platforms," he said. "And we need to more fully use technology to improve our speed, guest experience and efficiency throughout the business."
Target cut 1,800 jobs or 8% of its workforce in a move that was aimed at simplifying the company's structure and becoming more agile, said Fiddelke, who also sees technology as a way to improve merchandising at stores.
The turnaround plans for Target revolve around new AI-driven tools as well as an extra $1 billion in capital expenditures to improve the experience in stores. Target sees 2026 capital spending at about $5 billion.
Here's a look at Target's plan to leverage technology to improve experiences.
Merchandising. Target has equipped its teams with AI-driven consumer insights. "Our merchants now have real-time access to advanced data from what is currently trending on social media to which products and styles are resonating with consumers at Target and across the industry today, to what future trends our guests are most likely to care about, helping our team forecast needs, anticipate trends and buy both smarter and faster," said Fiddelke.
Trend tracking. Target recently launched Target Trend Brain, an internal platform that uses genAI to help teams identify and react to emerging trends and predict what's ahead.
Fiddelke added that Target is using synthetic audiences, AI-driven models to simulate consumer segments, to preview how groups will respond to campaigns and products before they launch.
Improving experiences. "Both in stores and digital, a great guest experience means a lot of things, but it starts with a warm, friendly and helpful team. In stores, we're making changes to give our team members more time to focus on what matters most, spending time helping our guests," said Fiddelke. "Through enhanced digital tools, we're reducing time devoted to backroom tasks through more efficient truck unloading and stocking. Every hour we save is being reinvested to allow more guest interaction with a focus on friendliness and service."
Target has also launched a new genAI powered gift finder on the web site and app. The goal is to use technology to ask questions to help guests narrow down choices.
Inventory. Fiddelke said Target is investing in machine learning to optimize inventory processes to improve in-stocks. In the third quarter, Target improved the on-shelf availability of its top 5,000 items by more than 150 basis points compared to a year ago.
The OpenAI partnership. Fiddelke said Target will build ChatGPT in its apps to curate conversational shopping experiences. Customers will tell the app what they're trying to do and OpenAI will make personalized recommendations. "Through this partnership, we expect to be one of the first retailers on OpenAI platforms to offer the purchase of multiple items in a single transaction, offer fresh food products on the platform and the ability to choose drive up and pickup fulfillment options in addition to the conventional shipping options offered by others," said Fiddelke.
Target's customer experience turnaround is a work in progress. The company said an investor day March 3 will lay out the broader strategy.
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Adobe said it will acquire Semrush in a deal valued at $1.9 billion as the company plans to meld search engine optimization and generative engine optimization in its marketing stack.
Adobe has been adding brand visibility tools into its marketing platform as well Adobe Experience Manager, Adobe Analytics and Adobe Brand Concierge. Enterprises are well versed in search engine optimization and need to optimized for large language models.
For marketing chiefs, maintaining visibility in LLMs is critical given the models are becoming the interface for consumers looking to make purchase decisions.
According to Adobe and Semrush, the companies will look to provide a holistic view into how brands are performing across multiple channels.
“With Semrush, we're unlocking GEO for marketers as a new growth channel alongside their SEO, driving more visibility, customer engagement and conversions across the ecosystem," said Anil Chakravarthy, president of Adobe’s Digital Experience Business.
For 2025, Semrush was projecting revenue between $443.5 million and $445.5 million, up 18% from a year ago. Semrush CEO Bill Wagner said during the company's third quarter earnings report that LLMs are additive to search engines. Semrush has pivoted to targeting enterprise customers.
"The search landscape is shape-shifting before our eyes, and we're giving marketers the power to have a full picture of their visibility, act quickly and improve their position to win," said Wagner. "AI search isn’t replacing the SEO opportunity; it’s compounding it."
