ServiceNow posts strong Q3, launches Workflow Data Fabric, expands partnerships, hires Zavery as product chief

Larry Dignan

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. ...

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ServiceNow reported better-than-expected third quarter results, launched Workflow Data Fabric, outlined partnerships with Rimini Street and Cognizant and said it will step up agentic AI efforts with Nvidia. For good measure, ServiceNow also said Google Cloud executive Amit Zavery will be the company’s new president, chief product officer and chief operating officer.

The moves come as ServiceNow delivered strong subscription growth of 22.5% in the third quarter compared to a year ago. The company inked 15 deals worth more than $5 million in annual contract value and more than 2,020 customers with more than $1 million in ACV.

For the third quarter, ServiceNow reported net income of $432 million, or $2.07 a share, on revenue of $2.8 billion. Non-GAAP earnings for the quarter were $3.72 a share. Wall Street was expecting ServiceNow to report earnings of $3.45 a share on revenue of $2.75 billion.

As for the outlook, ServiceNow projected subscription revenue of $2.875 billion to $2.88 billion.

With Now Assist AI as its fastest growing product, ServiceNow is looking to press an advantage as a platform that enables agentic AI by connecting multiple systems and the data, workflows and processes in enterprises. ServiceNow CFO Gina Mastantuono said demand for the Now Platform was “robust” and Now Assist was “already delivering fantastic results.”

ServiceNow's Xanadu release adds AI Agents, RaptorDB Pro, genAI enhancements

The hire of Zavery is also a big move. CEO Bill McDermott said the addition of Zavery gives ServiceNow a “visionary product and engineering leader with a proven history of building market defining products and scaling world class platforms.”

Zavery's most recent gig was VP, GM and Head of Platform from Google Cloud. Zavery previously was an executive at Oracle. 

In an SEC filing, ServiceNow detailed Zavery's pay package. A few nuggets worth noting:

• The base salary is $900,000. 
• Zavery's signing bonus was $3 million. 
• To replace Zavery's outstanding equity grant from Alphabet (Google Cloud's parent), ServiceNow said Zavery was eligble to receive an equity grant of $29 million. 

Zavery will take on his role as ServiceNow is already furiously building on its agentic AI plans:

• ServiceNow launched Workflow Data Fabric, a data layer that operates across enterprise systems, and a partnership with Cognizant.
• ServiceNow and Nvidia expanded a partnership to co-develop AI agents based on Nvidia NIM Blueprints.
• ServiceNow and Rimini Street outlined a partnership to put the Now Platform on top of legacy ERP systems, support them and take the savings to invest in AI innovation.

Speaking on ServiceNow's third quarter earnings call, McDermott said the company is more strategic with generative AI and agentic AI. "The C-suite is looking to us to prevent a mess with AI," said McDermott. "Leaders see the risk that every vendor's bots and agents will scatter like hornets fleeing the nest. Enterprises trust us to be the governance control tower."

McDermott said the company is now using its platform to deploy autonomous agents at scale. "We intend to be the control point that governs the deployment of agentic AI across the enterprise," he said. 

Here's a look at what was announced:

Workflow Data Fabric, Cognizant pact

ServiceNow launched Workflow Data Fabric, which is based on the Now Platform and RaptorDB Pro database. Workflow Data Fabric is available now.

The data layer features Zero Copy connections to integrate data from multiple sources to be used by AI agents. Cognizant will be the first partner for Workflow Data Fabric.

With the move, ServiceNow is adding a data integration layer to connect systems and workflows. Enterprise software vendors are increasingly adding these data layers.

Workflow Data Fabric is designed to connect, understand and process structured and unstructured streaming data across ServiceNow and third-party platforms. ServiceNow's plan is to use Workflow Data Fabric to read data and act.

Key points include:

• Workflow Data Fabric is powered by ServiceNow's Automation Engine, which includes data streaming, robotics process automation and process mining.
• The recent acquisition of Raytion is integrated with more than 500 connectors.
• ServiceNow said the launch of Workflow Data Fabric includes Zero Copy partnerships with Databricks and Snowflake and other data platforms.
• Workflow Data Fabric streamlines notifications and assigns incidents and includes ServiceNow's Knowledge Graph. The idea is that this graph can add context so AI agents can carry out tasks.

Nvidia NIM Agent Blueprints

ServiceNow and Nvidia said the companies will co-develop native AI agents based on Nvidia's NIM Agent Blueprints.

The two companies said they will develop multiple AI agent use cases for the Now Platform. These AI agents would leverage Nvidia AI Enterprise, NeMo and NIM microservices.

• Nvidia launches NIM Agent Blueprint for cybersecurity
• Accenture to use Nvidia stack for agentic AI
• Nvidia launches NIM Agent Blueprints aims for more turnkey genAI use cases

ServiceNow and Nvidia have been partners for years. Nvidia is looking to broaden its AI agent frameworks to speed up generative AI deployments.

According to the companies, the plan is to expand out-of-the-box AI agents starting with cybersecurity. ServiceNow and Nvidia said they will develop Vulnerability Analysis for Container Security AI Agent.

These turnkey AI agents will be activated through ServiceNow AI Agent Studio, which is expected to be available in 2025.

Rimini Street pact

ServiceNow said it will partner with Rimini Street to offer enterprise resource planning (ERP) support to milk legacy systems without migrations and re-platforming so enterprises can invest elsewhere.

• DSAG: SAP's innovation focus on cloud, discriminates against on-premise users
• SAP ups 2024 outlook as Q3 better than expected

Constellation Research CEO Ray Wang said the ServiceNow-Rimini Street partnership can give enterprises some breathing room since many are being prodded to upgrade to cloud-based foundations. "It is imperative that enterprises accelerate AI innovation, digital transformation and workflow automation without being slowed by the complexity and cost burden of upgrading or migrating existing enterprise software," said Wang.

The companies said they will offer a new enterprise software model that includes:

• ServiceNow's AI platform to provide a layer on top of existing ERP systems. Rimini Street will design, deploy and manage the Now Platform on top of ERP systems with a certified ServiceNow team. Savings will fund AI investments.
• Rimini Support will replace software vendor maintenance with no required upgrades or migrations for a minimum of 15 years.
• Rimini Manage that will run the day-to-day software operating and support tasks of legacy ERP systems.

For Rimini Street, the ServiceNow pact will help with the company's own transformation as it winds down Oracle Peoplesoft support to focus on VMware maintenance and other initiatives.

Anthropic's Claude 3.5 Sonnet model can use your computer

Larry Dignan

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. ...

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Anthropic upgraded Claude 3.5 Sonnet with the ability to use your computer, looking at your screen, moving cursors, clicking and typing. The company also launched Claude 3.5 Haiku.

As large language model (LLM) vendors keep upping the training ante, Anthropic continues to think through features for collaboration and now computer use.

• Anthropic launches Claude Enterprise with 500K context window, GitHub integration, enterprise security
• Anthropic adds more collaboration features to Claude for Pro, Team customers
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• Anthropic launches Claude Team plan, iOS app
• Anthropic CEO Amodei on where LLMs are headed, enterprise use cases, scaling

The company said computer use on Claude 3.5 Sonnet is available in public beta via API. Developers will be able to direct Claude to use computers and take on some of the tedious work of filling out forms and combing through files. Claude 3.5 Sonnet will be able to use any application on your computer.

Anthropic noted that computer use is still experimental and error prone, but expects rapid improvements. Asana, Canva, Cognition, DoorDash, Replit, and The Browser Company are among the companies using Claude 3.5 Sonnet and computer use. The new model is available via Anthropic, Amazon Bedrock and Google Cloud Vertex AI.

In a blog post, Anthropic outlined some of the research that went into Claude 3.5 Sonnet's computer use features. Among the takeaways:

• Claude looks at screenshots of what's visible to the user, then counts how many pixels vertically or horizontally it needs to move a cursor to click correctly.
• Anthropic spent a lot of time training Claude to count pixels. Without this skill, the model struggles to give mouse commands.
• Claude was able to turn a user's written prompt into a sequence of steps and then take action.
• The company said Claude isn't anywhere near human-level skill on the computer, but expects the gap to close.

Anthropic also outlined safety concerns. The company said:

“As with any AI capability, there’s also the potential for users to intentionally misuse Claude’s computer skills. Our teams have developed classifiers and other methods to flag and mitigate these kinds of abuses.

While computer use is not sufficiently advanced or capable of operating at a scale that would present heightened risks relative to existing capabilities, we've put in place measures to monitor when Claude is asked to engage in election-related activity, as well as systems for nudging Claude away from activities like generating and posting content on social media, registering web domains, or interacting with government websites. We will continuously evaluate and iterate these safety measures to balance Claude's capabilities with responsible use during the public beta."

SAP ups 2024 outlook as Q3 better than expected

Larry Dignan

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. ...

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SAP raised its cloud and software outlook for fiscal 2024 as the company's backlog continued to surge.

The company projected 2024 cloud and software revenue of €29.5 billion to €29.8 billion, up from the €29 billion to €29.5 billion previously projected. The company also said its free cash flow will be €3.5 billion to €4 billion.

SAP held its cloud 2024 revenue projection steady at €17.0 billion to €17.3 billion.

In the third quarter, SAP reported earnings of €1.44 billion, or €1.25 a share, on revenue of €8.47 billion, up 9% from a year ago. Cloud revenue was €4.35 billion, up 25% from a year ago. Cloud ERP revenue in the third quarter was €3.64 billion, up 34% from a year ago.

Wall Street was expecting SAP to report third quarter earnings of €1.21 a share on revenue of €8.45 billion.

• DSAG: SAP's innovation focus on cloud, discriminates against on-premise users
• SAP gives ABAP code a genAI boost, adds data lake capabilities to Datasphere
• SAP’s Joule everywhere plan: ‘We are not developing AI just for the sake of AI”

Christian Klein, CEO of SAP, said the third quarter showed strength for cloud ERP and "a significant part of our cloud deals in Q3 included AI use cases."

Speaking on SAP's earnings conference call, Klein talked up Joule and said it has the best chance to be a premier AI agent. 

"While many in the software industry talk about AI agents these days, I can assure you, Joule will be the champion of them all. So far, we have added over 500 skills to Joule and we are well on track to cover 80% of the most frequent business and analytical transactions by the end of this year. And in Q3 alone, several hundred customers licensed Joule."

Klein said that Joule's power will be the ability to perform tasks across finance, HR, sales, supply chain and other functions. "Joule will soon be able to orchestrate several AI agents to carry out complex processes end-to-end," he said. 

SAP CFO Dominik Asam said the company is seeing efficiency gains from its restructuring in 2024.

By the numbers:

• SAP's cloud backlog was up 25% in the third quarter compared to a year ago and the acquisition of WalkMe contributed 1% to that growth rate.
• Software licenses revenue in the third quarter fell 15% from a year ago.
• Restructuring expenses for the first nine months of 2024 were €2.8 billion.
• By region, SAP said it saw cloud revenue strength in Asia Pacific and Japan and EMEA. Americas growth was "robust."

Key points from SAP's earnings conference call include:

• Klein said about 30% of SAP's cloud orders included AI use cases. 
• SAP cited numerous RISE with SAP wins including grocers Schwarz Group and Sainsbury as well as Nvidia, which implemented RISE with SAP in 6 months, and Mercado Libre. 
• "Our investment in Business AI are also starting to show positive results, creating new opportunities and deepening customer engagement. Now with the added capabilities of WalkMe, we are able to further improve work flow execution and user experience," said Asam.
• Klein said that SAP's move to centralize its cloud operations is paying dividends. "We are rolling out the cloud version of HANA, much more scale, better TCO, better resiliency. And of course, we're also working with the hyperscalers. I mean, we have with RISE and on the cloud infrastructure, we have really some really strong measures we are driving to further optimize not only performance, but again, also the scalability of HANA Cloud running on the hyperscaler infrastructure," he said. 

Constellation Research's take

Constellation Research analyst Holger Mueller said:

"SAP had a good quarter, as expected. AI is the break Christian Klein and team as AI needs to live in the cloud, and that forces before skeptical CxOs to bite the bullet and move to S/4 HANA. SAP keeps struggling with the value for SAP Grow and Rise – as only 1/3 of cloud revenue comes from these initiatives, but this does not matter anymore as AI is the pull. With favorable announcements from the recent SAP TechED conferencebto help customers with the ABAP code assets as well as the announcement of an SAP DataLake, SAP is helping its existing customers more and better than before. All of this leads to a key milestone for the vendor: Cloud revenue for the first time is over 50% of SAP revenue. What is remarkable is that SAP is more profitable. Traditionally, the (now shrinking) perpetual license revenue is more profitable than cloud revenue (where SaaS vendors pay IaaS vendors). But with SAP charging more customers directly for their IaaS costs (and then paying the AWS, Google and Microsoft etc.), it is making margin from the pass through."

• SAP Q2 cloud ERP revenue up 33%, sees restructuring hitting 9,000 to 10,000 jobs
• Amazon Bedrock integrated into SAP AI Core, SAP to use AWS chips
• SAP acquires WalkMe for $1.5 billion
• Boomi aims to ease SAP Datasphere migrations

Honeywell, Google Cloud team up on industrial IoT, genAI use cases

Larry Dignan

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. ...

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Honeywell said it will integrate Google Cloud's AI into Honeywell Forge, an Internet of things platform designed for industrial use cases.

The two companies said they will create joint applications for industrial use cases in 2025 that combine Google Cloud's Gemini on Vertex AI with Honeywell's applications.

For Google Cloud, the Honeywell partnership highlights how it is leveraging AI, use cases and a focus on verticals to enter accounts often via generative AI. Thomas Kurian, CEO of Google Cloud, has focused Vertex AI on industry use cases, automation and process optimization. Kurian has also touted AI agents.

• How Google Cloud is monetizing AI
• Google Cloud Vertex AI updates focus on the practical with Context Caching, grounding services
• Google Cloud Next 2024: Google Cloud aims to be data, AI platform of choice
• Google Cloud Next: The role of genAI agents, enterprise use cases
• Google Public Sector Summit: 9 takeaways you need to know
• Google I/O 2024: Multimodal Gemini, Project Astra, AI agents and 'teammates'

Specifically, Google Cloud and Honeywell will build industrial AI agents using Google Cloud Vertex AI Search and Gemini multimodal large language models. Use cases include:

• Industrial AI agents focused on automating project design cycles and preventative maintenance.
• Cybersecurity applications that couple Google Threat Intelligence with Honeywell's Global Analysis, Research and Defense Threat Intelligence and Secure Media Exchange.
• Edge device AI that will put Google's Gemini Nano model on Honeywell edge devices across multiple industries. The two companies said they plan to offer a series of edge devices.

Honeywell has aligned its business around three big trends--automation, future of aviation and energy transfer. All those industries are ripe for AI optimization.

Microsoft launches AI agents for Dynamics 365, customization via Copilot Studio

Larry Dignan

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. ...

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Microsoft is adding 10 autonomous agents in Dynamics 365 and moving the ability to create them into public preview in Copilot Studio.

With the move, Microsoft is adding on to its Copilot stack with agentic AI agents that can complete tasks autonomously. Microsoft sees agents as the new apps for the generative AI ecosystem. Copilots are how you'll interact with the agents that will work on behalf of an individual, team or function to execute on processes.

Agentic AI has been a recurring theme of late as enterprise software vendors see them as a way to automate work and processes. The agentic AI theme reached a crescendo with Salesforce's Agentforce debut, but was picking up for months before that.

Microsoft argued that agents will easily outnumber employees and be effective on its platform because they can tap into work data in the Microsoft 365 Graph and add context via its platform and systems of record.

• Enterprises leading with AI plan next genAI, agentic AI phases
• With Salesforce push, AI Agents, agentic AI overload looms
• GenAI may be the new UI for enterprise software

Microsoft's plan for AI agents is to deploy in common processes starting with enterprise resource planning. The bet by Microsoft is that it can take its Copilot and agent infrastructure and log time sheets, close books, prep ledgers and file expense reports autonomously. Over time, Microsoft's is arguing that Copilots will effectively become the user interface of enterprise software and various silos. AI agents will automate and execute business processes and be triggered by Copilots. There will be as many agents as there are business processes.

Early adopter customers included Clifford Chance, McKinsey & Company and Pets at Home. Microsoft said that it will release pricing details as its AI agents near general availability. Software vendors have been examining new pricing models for agents given that per-seat plans don't work well.

• 13 artificial intelligence takeaways from Constellation Research’s AI Forum
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The new autonomous agents Dynamics 365 cover sales, service, finance and supply chain categories to start. Microsoft said the plan is to create more agents throughout the year. "Our goal is to drive more value for our customers across their biggest areas of pain in their processes," said Stephanie Dart, Senior Director of Product Marketing for Microsoft Dynamics 365.

Microsoft's first batch of agents include:

• Sales Qualification Agent, which will research leads, prioritize opportunities and guide outreach with personalized emails and responses.
• Supplier Communications Agent, which tracks supplier performance, detects delays and responds to free up procurement teams.

• Customer Intent and Customer Knowledge Management Agents, which will help call centers with high-volume requests and talent shortages to resolve problems autonomously.
• Sales Order Agent for Dynamics 365 Business Central will automate the order intake process from entry to confirmation.
• Financial Reconciliation Agent for Copilot for Finance aims to reduce the time spent closing the books.
• Account Reconciliation Agent for Dynamics 365 Finance is designed for accounts and controllers and automates the matching and clearing of transactions.
• Time and Expense Agent for Dynamics 365 Project Operations manages time entry, expense tracking and approval workflows autonomously.
• Case Management Agent for Dynamics 365 Customer Service automates the creation of a case, resolution, follow up and closure.
• Scheduling Operations Agent for Dynamics 365 Field Service gives dispatchers the ability to optimize schedules for technicians and accounts for changing conditions throughout the workday.

These out-of-the-box agents will be complemented by the custom ones created in Copilot Studio with guardrails, best practices and controls in place. Richard Riley, General Manager of Power Platform Marketing at Microsoft, said that Copilot Studio has the same compliance and security capabilities as the company's Power Platform, specifically Power Virtual Agents.

In addition, Microsoft's AI agents are designed with human-in-the-loop processes in mind.

Constellation Research's take

Martin Schneider, analyst at Constellation Research, said:

"The agent building tools in Copilot Studio and the 10 out-of-the-box agents are a great way for technical and non-technical users to begin exploring the use of autonomous agents inside their Dynamics environments. But I think the really interesting bit inside these announcements is the fact that Microsoft has added agentic AI to its ERP offerings.

This is a smart move for two reasons. One, the data inside ERP systems is typically more complete and of higher quality than in CRM systems (where we see the bulk of AI agents being put forth). That means the agents have more reliable and accurate insights on which to act. Second, the use cases for agentic AI inside ERP provide more immediate and measurable value. Many of the tasks agents will be performing are common, repeatable and specific - so taking them off a human’s plate drives immediate productivity. But also, by doing these tasks incredibly quickly, and at scale, larger companies can invoice and bill clients faster, close books faster, take payments, etc. This creates an immediate benevolent cycle of shortening sales and revenue collection and recognition cycles, which has the potential to increase cash flow and bottom-line metrics in significant ways."

• Microsoft outlines Copilot agents for enterprises
• Microsoft Build 2024: Microsoft Fabric opens up, adds real-time intelligence, Snowflake, Databricks connections
• Microsoft raises Dynamics 365 prices starting Oct. 1
• Microsoft Q4 strong, Azure growth 29% and a bit light

IBM rolls out Granite 3 models, makes default for Consulting Advantage

Larry Dignan

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. ...

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IBM launched Granite 3.0 8B and 2B models under the Apache 2.0 license, new models designed for CPU-based deployments and edge computing and the next-generation of Watsonx code assistant. In addition, IBM said Granit models will be the default for Consulting Advantage, an AI delivery platform used by the company's consultants.

Big Blue announced the latest Granite large language models (LLMs) at its TechXchange event. IBM said the Granite family of models is under the fully permissive Apache 2.0 license for enterprise use cases.

"IBM keeps advancing its Granite model family, alleviating the concerns that it was simply being tossed over to open source in move that CxOs have seen too often. IBM has the knowhow and data to maintain these models. The future use of Granite models in next-gen apps looks brighter than ever," said Constellation Research analyst Holger Mueller.

IBM's Granite 3.0 family includes the following:

• Granite 3.0 8B-Instruct, Granite 3.0 2B-Instruct, Granite
• 3.0 8B Base, Granite 3.0 2B Base for general purpose and language use cases.
• Granite Guardian 3.0 8B, Granite Guardian 3.0 2B focused on guardrails and safety.
• Granite 3.0 3B A800M Instruct, Granite 3.0 1B A400M Instruct,
• Granite 3.0 3B A800M Base, Granite 3.0 1B A400M Base as mixture-of-experts models.

The Granite 8B and 2B models are designed to be workhorses that deliver strong performance and cost efficiency for RAG, summarization and classification. IBM expects these models to be adopted and then fine-tuned by businesses looking to avoid the costs associated with larger models. IBM discloses the data sets used to train Granite and provides IP indemnity on watsonx.ai.

• IBM open sources Granite models, integrates watsonx.governance with AWS SageMaker
• IBM Delivers AI How It Is Meant to Be With Watsonx

IBM also released benchmarks for Granite 8B.

According to IBM, the Granite mixture-of-experts models (A800M) are designed for low-latency environments, edge use cases and CPU-based inference deployments.

As for the Granite Guardian 3.0 models, IBM said the family is designed to check user prompts and LLM responses for various risks including bias, toxicity and jailbreaking.

Going forward, IBM said it will use the Granite models and extend them with AI agent capabilities for autonomy. Granite 8B features agentic capabilities for workflows. These capabilities will be rolled out in 2025 with prebuilt agents for use cases.

On-premises AI enterprise workloads? Infrastructure, budgets starting to align

Larry Dignan

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. ...

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On-premise enterprise AI workloads are being talked about more as technology giants are betting that enterprise demand will launch in 2025 due to data privacy, competitive advantage and budgetary concerns.

The progression of these enterprise AI on-premises deployments remains to be seen, but the building blocks are now in place.

To be sure, the generative AI buildout so far has been focused on hyperscale cloud providers and companies building large language models (LLMs). These builders, many of them valued at more than a trillion dollars, are paying another trillion-dollar giant in Nvidia. That GPU reality is a nice gig if you can get it, but HPE, Dell Technologies and even the Open Compute Project (OCP) are thinking ahead toward on-prem enterprise AI.

During HPE's AI day, CEO Antonio Neri outlined the company's market segments including hyperscalers and model builders. "The hyperscaler and model builders are training large language AI models on their own infrastructure with the most complex bespoke systems. Service providers are providing the infrastructure for AI model training or fine-tuning to customers so they can place a premium on ease and time to deployment," said Neri.

Hyperscalers and model builders are a small subset of customers, but can have more than 1 million GPUs ready, added Neri. The third segment is sovereign AI clouds to support government and private AI initiatives within distinct borders. Think of these efforts as countrywide on-prem deployments.

This post first appeared in the Constellation Insight newsletter, which features bespoke content weekly and is brought to you by Hitachi Vantara.

The enterprise on-premises AI buildout is just starting, said Neri. Enterprises are moving from "experimentation to adoption and ramping quickly." HPE expects the enterprise addressable market is expected to grow at a 90% compound annual growth rate to represent a $42 billion opportunity over the next three years.

Neri said:

"Enterprises must maintain data governance, compliance, security, making private cloud an essential component of the hybrid IT mix. The enterprise customer AI needs are very different with a focus on driving business productivity and time to value. Enterprises put a premium on simplicity of the experience and ease of adoption. Very few enterprises will have their own large language AI models. A small number might build language AI models, but typically pick a large language model off the shelf that fits the needs and fine-tune these AI models using their unique data."

Neri added that these enterprise AI workloads are occurring on premises or in colocation facilities. HPE is targeting that market with an integrated private cloud system with Nvidia and now AMD.

HPE's Fidelma Russo, GM of Hybrid Cloud and CTO, said enterprises will look to buy AI systems that are essentially "an instance on-prem made up of carefully curated servers, networking and storage." She highlighted how HPE has brought LLMs on-premises for better accuracy and training on specific data.

These AI systems will have to look more like hyperconverged systems that are plug and play because enterprises won't have the bandwidth to run their own infrastructure and don't want to pay cloud providers so much. These systems are also likely to be liquid cooled.

Neil MacDonald, EVP and GM of HPE's server unit, outlined the enterprise on-prem AI challenges that go like this:

• The technology stack is alien and doesn't resemble classic enterprise cloud deployments.
• There's a learning curve on top of the infrastructure and software stack to master.
• The connection from generative AI model to enterprise data requires business context and strategy. Enterprises will also struggle to get to all of that data.

Dell Technologies recent launches of AI Factories with Nvidia and AMD highlight how enterprise vendors are looking to provide future-proof racks that can evolve with next-generation GPUs, networking and storage. These racks obviously appeal to hyperscalers and model builders, but play a bigger role by giving enterprises faith that they aren't on a never-ending upgrade cycle.

Blackstone's data center portfolio swells to $70 billion amid big AI buildout bet

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To that end, the Open Compute Project (OCP) added designs from Nvidia and various vendors to standardize AI clusters and the data centers that host them. The general idea is that these designs will cascade down to enterprises looking toward on-premises options.

George Tchaparian, CEO at OCP, said the goal of creating a standardized "multi-vendor open AI cluster supply chain" is that it "reduces the risk and costs for other market segments to follow."

Rest assured that the cloud giants will be talking about on-premises-ish deployments of their clouds. At the Google Public Sector Summit, the company spent time talking to agency leaders about being the "best on-premises cloud" for workloads that are air-gapped, separated from networks and can still run models. Oracle’s partnership with all the big cloud providers is fueled in part by being a bridge to workloads that can’t go to the public cloud.

Constellation Research analyst Holger Mueller said that he is a fan of on-premise AI deployments except with a twist--these deployments should be build on a cloud stack. He said:

"CxOs need to keep in mind that on-premises AI is pitched by vendors that have failed at providing a public cloud option - the most prominent being HPE and Dell. As there is merit for on premises - for speed, privacy and compliance - the bottleneck remains NVidia GPUs. As these are better utilized in the cloud, the cloud providers have a chance to pay more than any enterprise. And this is just compute - we have not even talked about storage / data. CxOs need to be aware of moving data and workloads every year or so - which also means extra cost, downtime and risk - something enterprises cannot afford. In short - the future of AI is in the cloud." 

The cynic in me would dismiss these on-premises AI workload mentions and think everything would go to the cloud. But there are two realities to consider that make me more upbeat about on-prem AI:

1. Infrastructure at the data center and edge will have to move closer to the data.
2. Accounting.

The first item is relatively obvious, but the accounting one is more important. On a Constellation Research client call about the third quarter AI budget survey, there was a good bit of talk about the stress enterprise operating expenses were seeing.

The art, ROI and FOMO of 2025 AI budget planning

Simply put, the last two years of generative AI pilots have taken budget from other projects that can't necessarily be put off much longer. Given the amount of compute, storage and cloud services required for generative AI science projects, enterprises are longing for the old capital expenditure approach.

If an enterprise purchases AI infrastructure it can depreciate those assets, smooth out expenses and create more predictable costs going forward.

The problem right now is that genAI is evolving so fast that a capital expenditure won't have a typical depreciation schedule. That's why these future-proof AI racks and integrated systems from HPE and Dell start to matter.

With AI building blocks being more standardized, enterprises will be able to have real operating expense vs. capital expense conversations. CFOs are arguing that on-prem AI is simply cheaper. To date, generative AI means that enterprises can't manage operating expenses well and budgets aren't sustainable. The bet here is that capital budgets are going to make more sense once the hyperscale giants standardize a bit.

Bottom line: AI workloads may wind up being even more hybrid than cloud computing.

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Blackstone's data center portfolio swells to $70 billion amid big AI buildout bet

Larry Dignan

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. ...

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Blackstone, best known as a massive asset manager with real estate, private equity and infrastructure holdings, is doubling down on the AI-fueled data center buildout and the energy that'll be needed to power those workloads.

On Blackstone's third quarter earnings conference call, the company said its data center portfolio now has $70 billion in facilities and more than $100 billion in pipeline development.

Those gaudy numbers come courtesy of the $16 billion purchase of AirTrunk, the largest data center operator in Asia Pacific.

Before the AirTrunk purchase Blackstone's data center portfolio was $55 billion with $70 billion in prospective pipeline development.

Steve Schwarzman, CEO of Blackstone, said:

“Blackstone is the largest data center provider in the world with holdings across the U.S., Europe, India, and Japan. Last month, we announced another major expansion by agreeing to acquire AirTrunk. We were uniquely positioned to execute on this investment, given our expertise in this sector, the scale of our capital, the global integration of our teams, and our connectivity to the world's largest data center customers.

Our ability to serve these customers represents a powerful illustration of how Blackstone has become a trusted solutions provider on a massive global scale to many of the largest and most valuable companies in the world."

Blackstone is doing what it does best. Follow the money. As Nvidia CEO Jensen Huang says repeatedly, more than $1 trillion will be spent on building new data centers for AI workloads. Blackstone also has invested in QTS, Coreweave and Digital Realty.

In addition, Blackstone is investing in power and utility companies that'll supply power to the data centers in its portfolio.

Blackstone's data center buildout took about three years. In the third quarter, Blackstone said its data center business "was again the single largest driver appreciation in our infrastructure real estate businesses."

Here's a look at the race to build out AI factories and energy to power them:

• DigitalOcean highlights the rise of boutique AI cloud providers
• Equinix, Digital Realty: AI workloads to pick up cloud baton amid data center boom
• The generative AI buildout, overcapacity and what history tells us
• AI infrastructure is the new innovation hotbed with smartphone-like release cadence

• Amazon invests in X-Energy Reactor, fuels small modular nuclear reactor run
• Google plans to use Kairos Power small modular reactors for data centers
• Generative AI driving interest in nuclear power for data centers
• Constellation Energy, Microsoft ink nuclear power pact for AI data center