Freshworks delivers strong Q3, ups outlook, targets business teams

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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Freshworks reported better-than-expected third quarter earnings and upped its outlook for the fourth quarter as the company is expanding wallet share. The company is also aiming to land more business users.

The company reported a third quarter net loss of $7.5 million, or 2 cents a share, on revenue of $215.1 million, up 15% from a year ago. Non-GAAP earnings were 16 cents a share.

Wall Street was expecting Freshworks to report non-GAAP third quarter earnings of 13 cents a share on revenue of $208.8 million.

Key figures include:

• Freshworks had 24,377 customers contributing more than $5,000 in annual recurring revenue.
• Freddy AI doubled annual recurring revenue from a year ago to more than $20 million.
• ARR for Freshservice beyond the IT department is growing as Freshservice for business teams has doubled year over year.

Freshworks will launch a standalone version of FreshService for Business Teams, which won't require the broader platform. The standalone enterprise service management product, aimed at legal, HR, finance and facilities, currently has an annual run rate of $35 million, double from a year ago.

• Constellation ShortList™ Incident Management
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As for the outlook, Freshworks upped its outlook. The company projected non-GAAP fourth quarter earnings of 10 cents a share to 12 cents a share on revenue of $217 million to $220 million. For 2025, Freshworks is projecting non-GAAP earnings of 62 cents a share to 64 cents a share on revenue of $833.1 million to $836.1 million.

In the long run, Freshworks is gunning to be a rule of 40 company with revenue growth in the mid-teens consistently.

Freshworks recently held its investor day where it noted that upmarket demand in the mid-market and enterprise has been growing revenue share. Nevertheless, Freshworks faces tough competition in employee experience as well as customer experience.

Here's the employee experience landscape.

And here's the customer experience landscape.

We caught up with Freshworks CEO Dennis Woodside to talk shop. Here are the key points.

Competitive landscape. Woodside said "we're competing in a 20,000 person company like Seagate. They don't have a large set of resources to throw at an ITSM platform. They want faster time to value." In ITSM, Freshworks' primary competition is ServiceNow in larger accounts and Atlassian in developer led companies.

AI strategy. Woodside said next week at Freshworks Refresh the company will launch four pre-built AI agents for industries. The company already has AI agents for customer support, a Copilot for agent productivity and AI insights for management.

CX. Our CX business has not made the up-market shift as aggressively as our IT business. It will be over time, but right now, it's still more of an SMB-centered business," said Woodside. The CX business is growing at 7% to 8% while ITSM is growing at 20% to 23% clip. Half of the top accounts buy ITSM and CX.

Customer sentiment. Woodside said: "CIOs are just trying to figure out how they can possibly support all of these new AI point solutions, and they're going back to what they already have, looking for AI embedded in their existing solutions." CIOs are also looking for alternatives to large vendors as well as ways to consolidate AI tools within existing systems of record.

Quantinuum launches Helios quantum computer, touts fidelity, enterprise customers

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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Quantinuum launched its new Helios quantum computer, a high-performance general purpose commercial system with 98 fully connected qubits and fidelity north of 99.9%.

The launch is aimed squarely at enterprises looking to deploy quantum computing for certain use cases. Indeed, Amgen, BlueQubit, BMW Group, JPMorgan Chase and SoftBank are initial customers pursuing biologics, fuel cell catalysts, financial analytics and organic materials.

Quantinuum said it has also signed a strategic partnership with Singapore’s National Quantum Office (NQO) and National Quantum Computing Hub (NQCH). The deal provides access to Helios as well as a R&D center in Singapore.

Helios includes a first-of-its-kind real-time control engine with a software stack that gives developers the ability to program similar to the way they program classical computers. Helios also includes Guppy, which is a Python-based programming language for hybrid quantum and classical compute.

Quantinuum said Helios is available through Quantinuum's cloud service as well as on-premise. Dr. Rajeeb Hazra, President and CEO of Quantinuum, said "for the first time enterprises can access a highly accurate general purpose quantum computer to drive real world impact, transforming how industries innovate – from drug discovery to finance to advanced materials."

• Quantinuum valued at $10 billion after $600 million venture round
• Quantinuum fleshes out quantum software stack with open source Guppy, Selene
• Quantinuum, partners create true verifiable randomness, eye quantum computing for cybersecurity
• Quantinuum launches generative AI quantum framework, sees quantum computing as synthetic data generator
• 2025 is the year of quantum computing

• Research: Constellation ShortList™ Quantum Computing Platforms | Quantum Computing Software Platforms | Quantum Full Stack Player

According to the company, Helios has the ability to enhance generative AI with quantum generated data. Those use cases could include data analysis, material design and quantum chemistry. Quantinuum said it expanded its partnership with Nvidia to integrate Nvidia GB200 AI accelerators with Helios via NVQLink. In addition, Quantinuum will switch to Nvidia accelerated computing for Helios and future systems, using Quantinuum Guppy alongside the Nvidia's CUDA-Q platform to perform real-time error correction critical to its roadmap.

Quantinuum also said it is launching two new programs to develop an ecosystem for quantum computing. Q-Net is a user group that will spur collaboration with customers and a startup partner program to develop third-party applications on Helios.

Constellation Research received a briefing on Helios from Dr. David Hayes, Director of Computational Design and Theory at Quantinuum. Here are the key points:

• Unprecedented Quantum Performance: "We really do believe Helios has the highest fidelity machine in the world at this scale. It’s almost 100 cubits. We got close. It's 98 and that first number there is the two qubit gate fidelity, 99.92%,” said Hayes.
• Breakthrough in Quantum Error Correction: Hayes said Helios reached an efficient error correction ratio. "We get to 48 [logical qubits], but even that, I think, will be surprising to people out there. We didn't quite get to 94 in this case, but we didn't quite get to a two to one encoding ratio for error correction,” he said.
• Practical Scientific Applications: Hayes said Helios successfully modeled a high-temperature superconductor to demonstrate that quantum computers are moving beyond theoretical demonstrations to real scientific research.
• Quantum Programming Environment. Helios includes Guppy. "Guppy was designed from the go-to make fault-tolerant programming really, really user friendly,” said Hayes. "It's Python based to make it easy to use, but it's a lot more performant than Python."
• Future Development and AI Integration. Hayes said Quantinuum is exploring the intersections between quantum computing and AI. "We're using AI in the lab to create new quantum circuits, more efficient quantum circuits, and we can have AI kind of fill in the gaps," said Hayes.

Google Cloud Gemini models go GA on Databricks

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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Google said its Gemini 2.5 Pro and Gemini 2.5 Flash now run natively in Databricks and can be run using SQL, Python and Databricks tools.

According to Google, Gemini models will run natively via an integration between the Databricks Intelligence Platform and Google Cloud's Vertex AI. The general idea is to run Gemini models where data resides. The companies announced a partnership in June.

Key points:

• Teams can apply Gemini models to their data with Batch Inference.
• Developers can build AI agents with Agent Bricks and connect them to private data.
• Use real-time APIs for high-intelligence models.
• Access Gemini models with compliance, governance and observability.

Google noted that Gemini models on Databricks via SQL or Python is designed to simplify the process for applying large language models (LLMs) to enterprise data. Use cases include automating tasks like contract analysis, parsing PDFs, summarizing transcripts and classifying images.

The Gemini offerings are generally available.

AMD's data center, PC units shine in Q3

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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AMD reported better-than-expected third quarter results as its data center unit delivered revenue growth of 22% and its PC sales grew 46% from a year ago.

The chipmaker reported third quarter earnings of $1.24 billion, or 75 cents a share, on revenue of $9.246 billion, up 36% from a year ago. Non-GAAP earnings in the quarter were $1.20 a share.

Wall Street was expecting AMD to report non-GAAP earnings of $1.17 a share on revenue of $8.75 billion.

AMD CEO Lisa Su said the quarter was fueled by "broad based demand for our high-performance EPYC and Ryzen processors and Instinct AI accelerators."

By the numbers:

• Data center sales in the third quarter were $4.3 billion, up 22% from a year ago, with strong demand for 5th Gen AMD EPYC processors and AMD Instinct MI350 Series GPUs. Data center operating income was $1.07 billion.
• Client and gaming revenue was $4 billion in the third quarter, which was up 73% from a year ago. Client revenue was $2.8 billion, up 46% and gaming revenue was $1.3 billion, up 181% from a year ago. AMD said sales of Ryzen processors and Radeon gaming GPUs were strong. Operating income was $867 million.
• Embedded revenue was $857 million, down 8% from a year ago, with operating income of $283 million.

During the quarter, AMD inked deals with multiple hyperscalers as well as OpenAI.

As for the outlook, AMD said fourth quarter revenue will be about $9.6 billion, give or take $300 million, or revenue growth of 25% compared to a year ago. The outlook doesn't include revenue from AMD Instinct MI308 shipments to China.

The company will hold an investor day next week with more details on AMD's straetgy. AMD's Su said the following on the earnings call. 

• "It's a pretty unique time for AI right now. There's just so much compute demand across all of the workloads. With OpenAI, we are planning multiple quarters out, ensuring that the power is available, and that the supply chain is available. The key point is the first gigawatt we will start deploying in the second half of '26 and you know that work is well underway," said Su.
• Interest is strong for AMD's Helios designs and MI450 AI accelerators. "I think the interest in Helios has just expanded over the last number of weeks, certainly with some of the announcements that we've made with OpenAI and OCI," said Su.
• "Given what we see today, we see a very good demand environment into 2026," said Su.
• "AMD commercial PC momentum accelerated in the quarter with rising PC sell through up more than 30% year over year, as enterprise adoption grew sharply, driven by large wins with Fortune 500 companies across healthcare, financial services, manufacturing, automotive and pharmaceuticals."

Perplexity, Amazon AI agent spat just the start

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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Perplexity said it "received an aggressive legal threat from Amazon" demanding it prohibits its Comet browser users from using AI assistants on Amazon. Get used to similar kerfuffle.

Agentic AI is going to uproot a lot of well-established models. Commerce will likely be one of the larger categories disrupted. In Perplexity's blog, the company accused Amazon of being a bully, but the reality is we're in uncharted territory. Is an AI assistant the same as a human shopper (probably not)? Should an AI agent be valued like a human relationship (probably not)? Will AI agents mean an overall decrease in impulse buys? And can AI agents step in the middle of the customer relationship in commerce (probably)?

All of these questions will have to be answered along with business arrangements on the back end.

You can read Perplexity's somewhat overwrought post yourself, but the big picture is this:

• Perplexity says AI agents represent the user like a human assistant would.
• "Your AI assistant must be indistinguishable from you,"
• Agentic AI could empower users but could also be a tool to shape commerce traffic (ads too for that matter).
• The real beef is over who will have the power.

During Shopify's third quarter earnings call, President Harley Finkelstein riffed on AI agents and commerce.

"Put simply, AI is able to fundamentally change how we shop, moving from search to conversation, helping all consumers purchase more efficiently. And that's why we built the Commerce for Agents tools that we introduced on our last call, Catalog, Universal Cart and Checkout Kit. These tools make it easier for agents to shop across merchant stores on a buyer's behalf.

But here's the thing. Agentic commerce is so much more than just the last click. Think about it in 3 layers: product discovery, purchasing experience and the post-purchase journey. Now if you're only looking at the payment or checkout layer, you're missing the bigger picture of what we're building: a seamless and intuitive shopping experience end to end."

Finkelstein noted that Shopify is well positioned because it has the data behind the commerce transaction. It has structured data across billions of products and can surface relevant items in second. As shopping becomes more conversational, it's more personalized. Shopify has teamed up with OpenAI's ChatGPT as well as Perplexity.

Finkelstein said:

"Once a shopper finds what they want, Universal Cart and Checkout Kit make add to cart and checkout seamless inside the conversation. ChatGPT, along with Microsoft Copilot have already partnered with us here to make in-chat shopping flows possible.

And finally, post purchase. We're investing in tools that help agents keep customers engaged and informed, order status, return, support, reorder prompts, so the experience stays smooth and merchants build durable relationships with their customers. Of course, different permutations will emerge as agentic commerce evolves, and we are preparing our merchants to be well positioned for whatever path wins."

Commerce is a huge category and the battles are just beginning. What remains to be seen is who owns the keys to the data as well as the funnel.

SAP rolls out developer tools for Joule, ecosystem connections

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 advanced its plans for developer tools, SAP Build, the Joule roadmap and connecting to a broader ecosystem including a partnership with Snowflake.

At SAP TechEd 2025 in Berlin, the company outlined a series of AI-driven tools in SAP Build as well as a set of Joule Agents designed to enable developers to move faster.

Muhammad Alam, a member of the Executive Board at SP, said the innovations at SAP TechEd create a "unique flywheel of applications, data and AI put developers in the driver’s seat."

Specifically, SAP outlined the following:

• Developers who use agentic AI platforms such as Cursor, Claude Code, Cline and Windsurf can now use SAP frameworks via SAP Build local Model Context Protocol (MCP) servers.
• Visual Studio Code users will be able to use SAP Build via extensions.
• The extension will be available later on the Open VSX Registry.
• Joule Studio will get new tools to customize SAP ready-to-use agents as well as build new ones grounded in SAP business data.
• SAP rolled out new Joule AI assistants to coordinate multiple agents across workflows, departments and applications for finance, supply chain and HR.
• The company partnered with Snowflake on a new SAP Snowflake extension for SAP Business Data Cloud. The partnership gives joint customers the ability to move data bidirectionally.
• SAP HANA Cloud knowledge graph ending can now automatically generate knowledge graphs.
• SAP launched its first enterprise relational foundation model, which is focused on business outcomes. SAP-RPT-1, short for the first-generation Relational Pre-trained Transformer, can make predictions for common business scenarios including delivery delays and payment risk.

• SAP Q3 mixed, outlook for 2025 at low end

Constellation Research analyst Holger Mueller said the SAP TechEd lineup was compelling. He said:

"SAP delivered a compelling keynote, showing how the SAP product teams work together for customer success on AI, application development and more - which was not always the case and good to see. With innovations on the Joule, SAP's agentic framework, advances on SAP Business Data Cloud, language driven and vibe code based development, ABAP models coming to on premises customers, SAP has shown significant innovation dynamic for its customer base. The question now is will it help SAP customers to tackle the task at hand - the upgrade to S/4HANA Cloud."

Here are the key points from Alam at SAP TechEd. 

App–Data–AI Flywheel & ROI

• SAP’s thesis: Real ROI comes from the seamless integration of applications, data, and AI, rather than isolated AI experiments.
• SAP’s business suite spans finance, supply chain, HR, CRM, and more, giving it an advantage in creating a “global maximum” of value versus “local optimizations” seen in siloed systems.
• Embedded AI in unified applications enables automation and insight across end-to-end business processes.

Assistants, Agents, and Human Productivity

• SAP envisions AI as an assistive layer first—to make people smarter, faster, and more efficient—before evolving toward autonomous execution once trust is built.
• Example: demand-planning and supply-planning agents collaborating autonomously across functions.
• The “assistant” concept begins with human-in-the-loop confidence building, leading later to autonomous operations when maturity allows.

AI Hype vs. Real ROI

• AI hype has inflated expectations. Many companies now realize value only comes from simplifying and structuring the equation—embedding AI in core workflows, not generating random apps.
• 95% of firms once reported no ROI (MIT study), but a newer study found 74% now see positive ROI—mainly those that adapt workflows and culture rather than just bolt on AI.
• ROI perception varies: executives tend to report success, managers often don’t see it yet.

SAP’s Approach to AI Value Creation

• SAP focuses on incremental AI adoption—enhancing existing roles and processes rather than replacing them.
• Companies should start by making core roles (like AR clerks or billing agents) more efficient, then progress toward autonomous execution.
• AI maturity follows a staged “value journey”: assistive → semi-autonomous → autonomous → deep research.

Developers, Product Managers & the AI Workforce

• SAP employs ~40,000 developers but sees enough backlog for 200,000 developers’ worth of work—AI helps scale output, not replace people.
• AI agents are multiplying productivity (7x–12x) in some teams by automating code generation, testing, and design.
• SAP Build and Joule Studio Agent Builder will let developers and customers create AI agents in low-code/no-code environments.
• Roles like product manager, QA, UX, designer will evolve but not disappear—AI will change team ratios and workflows.

Data Ecosystem & Partnerships

• Business Data Cloud (BDC) launched in 2025 integrates with Snowflake and Databricks, allowing zero-copy data sharing.
• SAP aims for open, governed, interoperable data management, letting customers combine SAP and non-SAP data seamlessly.

Organizational Design & Industry Collaboration

• SAP is experimenting with “Dev Games of the Future” to rethink team structures and collaboration models for AI-augmented development.
• Emphasis on evolving from people upward, not top-down reorganization.
• SAP collaborates with large customers and institutions (e.g., Linux Foundation) on defining future role structures and standards.

ROI Beyond Headcount Reduction

• SAP stresses AI ROI doesn’t equate to layoffs—it’s about growth, productivity, and redeployment of talent.
• Customers view AI as a tool to expand capacity, not shrink the workforce.

Accountability & Measurement for AI

• ROI and accountability come from embedding AI in structured business processes where performance can be measured (e.g., sourcing contracts closed, savings achieved).
• SAP’s new Agent Topology Index maps and tracks agents (SAP and non-SAP) to measure outcomes and performance signals.

Quantum Computing Outlook

• SAP is experimenting with quantum computing but sees it as post-2030 for commercial rollout.
• Quantum will serve different use cases than AI, likely focusing on performance and optimization problems.

Snowflake takes Snowflake Intelligence GA, launches developer tools, integrates with SAP BDC

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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Snowflake launched Snowflake Intelligence to general availability, outlined a set of new developer tools and forged a pact with SAP so Snowflake AI Data Cloud and SAP Business Data Cloud are interoperable.

The company said Snowflake Intelligence, outlined at Snowflake Summit earlier this year, is now generally available. Snowflake said that Snowflake Horizon Catalog and Snowflake Openflow, also announced at Snowflake Summit, are also generally available.

Snowflake Intelligence is designed to create agents that can operate via natural language, leverage structured and unstructured data and accelerate AI and machine learning pipelines. Customers embedded in Snowflake Intelligence include Cisco, Fanatics, Toyota Motor Europe and Wolfspeed.

• Snowflake launches Agent GPA, aims to grade your AI agents
• Snowflake, Palantir forge data integration partnership
• Snowflake launches Cortex AI for Financial Services, MCP Server
• Snowflake, Salesforce, BlackRock lead effort to standardize semantic data

According to Snowflake, prebuilt agents can be accessed through the Snowflake Intelligence interface or using Cortex Agents API. Cortex Agents orchestrate unstructured and structured data with LLMs including OpenAI GPT and Anthropic Claude.

Snowflake also outlined a suite of new development tools that feature a collaboration environment, open source integrations and data quality capabilities.

The developer tools include:

• Cortex Code in private preview. Cortex Code is a revamped AI assistant in the Snowflake interface and helps customers understand usage, optimizations and fine tuning.
• Enhancements to Snowflake Cortex AISQL, which is generally available. Developers can build AI pipelines in Snowflake Dynamic Tables.
• Snowflake Workspaces for collaboration and direct Git Integration and VS Code Integration.

Under the SAP-Snowflake partnership, the companies said Snowflake's data and AI platform will be available as an extension for SAP Business Data Cloud. SAP launched Business Data Cloud via a partnership with Databricks.

SAP and Snowflake will enable the following:

• Zero copy sharing between the two platforms.
• Data and AI teams can work within a unified governance framework and harmonize SAP and non-SAP data.
• Simplify AI governance, ground AI in enterprise knowledge bases and build tailored agents.
• Leverage semantically rich data.
• SAP Business Data Cloud can leverage Snowflake's AI, analytics, data engineering, marketplace and collaboration tools.

In addition to SAP Snowflake solution extension for SAP Business Data Cloud, the companies said the partnership includes SAP Business Data Cloud Connect for Snowflake, which enables bidirectional, zero copy data sharing. SAP Snowflake will be available in the first quarter with SAP Business Data Cloud Connect landing in the first half of 2026.

Snowflake launches Agent GPA, aims to grade your AI agents

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. ...

Read more

Snowflake is looking to give your AI agents a GPA. While the company is grading the accuracy of AI agents, it's really evaluating goals, plans and actions (GPA) in an open source framework that reaches near human levels of error detection rates and localization accuracy.

The framework, called Agent GPA, was outlined at its Build conference. For enterprises deploying agentic AI, Snowflake's efforts are worth a look.

In a blog post, Snowflake's AI Research team said evaluating AI agents comes down to trust. Snowflake said:

"An agent’s answer may appear successful, but the path it took to get there may not be. Was the goal achieved efficiently? Did the plan make sense? Were the right tools used? Did the agent follow through? Without visibility into these steps, teams risk deploying agents that look reliable but create hidden costs in production. Inaccuracies can waste compute, inflate latency and lead to the wrong business decisions, all of which erode trust at scale."

Snowflake argued that current evaluation frameworks fall short because they focus on the final answer, not the process behind the answers. Here's a look at the Agent GPA framework. Agent GPA, outlined in a paper, is available in Truelens.

Snowflake's Agent GPA was the headliner among a set of items released by the company's research team.

Other items include:

• Text-to-SQL V1.5, a specialized model that fuels Snowflake Intelligence, Snowflake's enterprise agent, by tackling the slowness, cost, and dialect issues of general LLMs. The specialized model makes text-to-SQL queries up to 3 times faster while maintaining accuracy.
• New optimizations will be introduced for Cortex AISQL, a tool that integrates AI directly into SQL queries, enabling teams to analyze all data types and build flexible AI pipelines using familiar SQL syntax.
• The Cortex AISQL enhancements improve AI operator efficiency and cost, featuring 2-8x more performant execution plans, 2-6x faster inference (at 90-95% accuracy), and a 15-70x reduction in execution costs and time through techniques like cost-aware optimization, adaptive model cascading, and query enhancements.