Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

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.

• Google launches Gemini 3, Google Antigravity, generative UI features
• Anthropic, Microsoft Azure, Nvidia ink $30 billion compute pact
• Microsoft launches Agent 365, a parade of AI agents at Ignite 2025
• Anthropic: Claude eyes real world impact, industrial use cases

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.

• Among CxOs, SaaS platform fatigue setting in
• The big AI, SaaS, transformation themes to watch in 2025’s home stretch
• LLM giants need to build apps, ecosystems to go with the models
• Pondering the future of enterprise software

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.

• A look at the intersection of AI and customer experience
• Agentic AI: Is it really just about UX disruption for now?
• Target cuts deal with OpenAI as it plans customer experience overhaul
• Intuit, OpenAI forge integration pact: Why it could be a $100 million win win

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.

• CCE 2025: AI agents: Dreams, reality and what's next
• Enterprise AI: It's all about the proprietary data
• AI agents, automation, process mining starting to converge

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."

• AGI may be far away, but 'jagged AI' will still take jobs
• The rise of good enough LLMs

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

IBM and Cisco said they will build a connected network of quantum computers that will look to scale to hundreds of thousands of qubits.

The companies plan to demonstrate multiple networked quantum computers within five years.

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 launches IBM Quantum Nighthawk processor
• IBM, AMD eye hybrid quantum computing, supercomputer architectures
• Cisco launches quantum lab, unveils quantum networking chip

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.

See: Quantum computing pure plays duel with giants, rivals

Key points:

• 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:

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

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."

• Anthropic, Microsoft Azure, Nvidia ink $30 billion compute pact
• Dell brings more automation to Nvidia AI factory deployments
• Nvidia's GTC Washington DC news barrage lands amid US vs China AI backdrop
• Nvidia DGX Spark now available for $3,999, but real impact will be AI at the edge
• Nvidia to invest $100 billion in OpenAI, which will deploy Nvidia next-gen AI infrastructure
• Nvidia invests $5 billion in Intel, aims to codevelop for data centers, PCs

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."

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

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.

• Palo Alto Networks buys CyberArk in $25 billion bet on identity

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."

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

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 gets new CEO, continuity in tech, AI strategy

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.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

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.

The deal values Semrush at $12 a share.

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.

• Adobe expand GenStudio, launches custom Firefly models, Firefly Foundry
• Adobe reports strong Q3, raises outlook
• Adobe launches LLM Optimizer, GenStudio and Firefly updates

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."

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Workday said it will acquire Pipedream, an integration platform for AI agents that features more than 3,000 pre-built connectors. The acquisition complements the purchases of Sana and Flowise as Workday builds out its AI agent infrastructure.

The purchase, outlined at Workday Rising EMEA, lands along with a set of Workday announcements. For instance, Workday launched Workday GO, which provides a HR, payroll and financials suite for midsized businesses.

Workday's purchase of Pipedream is part of the company's plan to build an AI agent ecosystem and become a platform that can orchestrate and manage its own AI agents as well as third party agents.

• Workday launches Workday Data Cloud, Workday Build, more AI agents
• Workday spends $1.1 billion on Sana, aims to be front door to work
• Workday acquires Flowise to speed up building AI agents, automated workflows

Pipedream has more than 3,000 pre-built connectors to enterprise apps such as Asana, HubSpot and Atlassian applications. Pipedream has more than 5,000 customers. Workday's plan is to combine Pipedream, Sana and Flowise to create an end-to-end AI agent platform. "To deliver on the vision of creating the future of work front end built on Sana, Workday needs connectors and more agents. That is what Pipedream delivers, and it is good to see Workday working on making its radically different vision than traditional HCM a little more tangible," said Constellation Research analyst Holger Mueller. 

Terms of the deal weren't disclosed.

At Workday Rising EMEA, the company also announced the following:

• Workday GO, an offering designed to target midsized enterprises. Workday GO aims to simplify and combine HR and financial applications in one unified package. Workday also rolled out Workday GO Global Payroll, a partner network and a deployment AI assistant to go along with the launch.
• Workday launched a sovereign cloud for EU that designed to keep the company's HR and finance applications in country along with customer data.
• A partnership with Google Cloud BigQuery to provide zero-copy access into Workday Data Cloud.