Google Gemini vs. OpenAI, DeepSeek vs. Qwen: What we're learning from model wars

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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Enterprise leaders should be forgiven for nursing a case of whiplash over the latest large language model (LLM) developments. Almost daily, there's some advance that generates headlines. Instead of chasing every little benchmark, here's a crib sheet of what we're learning from the never-ending game of LLM leapfrog.

Google is hitting its stride

With the launch of Gemini 2.5 Pro, an experimental thinking model that performs well and has been enhanced with post-training, it's clear Google DeepMind is firing well.

Let's face it, Google was caught off guard by OpenAI and has been playing catch up. It's safe to say that Google has caught up. Gemini 2.5 innovation will be built into future models from Google and rest assured those capabilities will show up at Google Cloud Next in April.

But Google's approach to models goes further than just one-offs. I've been testing Google's deep research tools on Google Advanced vs. OpenAI's ChatGPT's Deep Research. Both have slightly different twists, but compress research time nicely. Google has the mojo to delight. For instance, an instant audio podcast summarizing a report is a nice touch.

When compared with Anthropic's Claude, Gemini more than holds its own. Simply put, Google is leveraging its strengths when it comes to models.

OpenAI is betting being more human is the way

It isn't a Google Gemini launch without an OpenAI launch. OpenAI announced native image generation in ChatGPT hours after Gemini 2.5 Pro was announced. OpenAI had also launched new ChatGPT voice mode updates to give the model "a more engaging and natural tone."

Those additions fell in the bucket that wasn’t necessarily worth my time, but obscure a broader theme from OpenAI. The company is leaning into emotional intelligence and models that act more human.

The pivot is notable because OpenAI's bet is that if you create models that are more relatable--and perform well--you'll have a stickier product. Strategically, that bet makes a lot of sense. I'm an OpenAI ChatGPT subscriber and have seen nothing from rivals that would entice me to dump my $20 monthly subscription.

What remains to be seen is whether subscribing to LLMs is more like streaming where you have more than one service or it's truly zero sum. I haven't had to answer that question since Google's Gemini is baked into my Pixel 9 purchase for a few more months.

DeepSeek vs. Alibaba's Qwen is flooding the market with inexpensive and very capable models

In the US, the LLM game is a scrum between OpenAI, Anthropic, Meta's Llama, Google Gemini and a bevy of others. China is roaring back with DeepSeek vs. Alibaba's Qwen.

• DeepSeek: What CxOs and enterprises need to know
• DeepSeek's real legacy: Shifting the AI conversation to returns, value, edge
• Palantir CTO: Engineering on DeepSeek 'is exquisite'
• China vs. US AI war: Fact, fiction or missing the point?

You'd be forgiven for forgetting DeepSeek's launch this week--it was so like 2 days ago. DeepSeek released DeepSeek-V3-0324 under the MIT open source license. Alibaba released Qwen2.5-VL-32B about the same time.

DeepSeek has been mentioned on countless earnings conference calls and its potential impact on AI infrastructure spending. Nvidia CEO Jensen Huang is asked about DeepSeek nearly every few minutes. Huang's argument is that DeepSeek is additive to the industry and the need for AI infrastructure.

"This is an extraordinary moment in time. There's a profound and deep misunderstanding of DeepSeek. It's actually a profound and deeply exciting moment, and incredible the world has moved to towards reasoning. But that is even then, just the tip of the iceberg," said Huang.

While DeepSeek is China's AI front-runner now, I wouldn't count out Qwen by any stretch especially with a distribution channel like Alibaba Cloud. What China's champions really did was move the conversation toward reasoning.

This is a game of mindshare

Chasing headlines almost daily about the latest model advances is a fool's errand. There will be the latest and greatest advance almost daily.

What this drumbeat of advances really highlight is that foundational models are a game of mindshare. Let's play a game: What foundational models are we noticing less? The short answer is Anthropic's Claude, and the second less obvious answer is Grok.

Anthropic is clearly more enterprise than OpenAI and is doing interesting things that align more to corporate use cases. Via partnerships with Amazon Web Services and Google Cloud, Anthropic is well positioned. In the daily news flow, Anthropic is likely to be overlooked with announcements that Claude can now search the web.

Nevertheless, Anthropic has mindshare where it matters--corporations. Anthropic is valued at $61.5 billion and is playing a slightly different game with its economic index and focused approach.

• Anthropic outlines most popular Claude use cases
• Anthropic CEO Amodei on where LLMs are headed, enterprise use cases, scaling

There can only be a few consumer AI platforms and OpenAI is in that pole position to threaten Google.

The most overlooked model award must go to Grok. There isn't a day that goes by where Constellation Research analyst Holger Mueller isn't showing me something Grok delivered that is impressive. Grok 3 features DeepSearch, Think and Edit Image. In my tests, Grok 3 is almost as good if not better than the better-known models.

Grok 3 would probably have more mindshare if it weren't simply overlooked due to Elon Musk's other endeavors.

Enterprise whiplash an issue

CxOs can really waste a lot of time focusing on these new model advances. As these models advance at such a rapid clip, the one thing that's clear is that you'll need an abstraction layer to swap models out. Pick your platforms carefully since every vendor (Salesforce, ServiceNow, Microsoft, SAP to name just a few) want to be your go-to platform for enterprise AI.

Here's where Amazon Web Services' approach with SageMaker and Bedrock make so much sense. Google Cloud also has a lot of model choice as does Microsoft Azure, which is best known for its OpenAI partnership but has diversified nicely. Microsoft launched Researcher and Analyst, two reasoning agents built on OpenAI’s Deep Research model and Microsoft 365 Copilot.

• AWS re:Invent 2024: 7 takeaways after drinking from the firehose

You can also expect data platforms such as Snowflake and DataBricks to be big players in model choice. Add it up and there's only one mantra for enterprise CxOs: Stay focused and don't get locked into one set of models.

• Snowflake adds OpenAI models to Cortex AI via expanded Microsoft partnership, integration
• Snowflake, Anthropic ink LLM partnership, delivers strong Q3, acquires Datavolo
• Databricks launches DBRX LLM for easier enterprise customization

All of the enterprise energy should be on orchestrating these models and ultimately the AI agents they'll power.

• AI infrastructure spending is upending enterprise financial modeling
• AI? Whatever. It's all about the first party data
• Enterprise AI: Here are the trends to know right now

Commoditization is happening rapidly

The funny part of this model war is that it's unclear how they'll be monetized. Open source models--led by Meta's Llama family--are on par with the proprietary LLMs. Those models are being tailored by companies like Nvidia that'll tweak for enterprise use cases.

DeepSeek is blowing up the financial model and AWS' Nova family is likely to be good enough just like its custom silicon chips are. HuggingFace's trending models tell the tale. DeepSeek, Qwen, Nvidia's new models and Google's Gemma are dominant. But that's just today. One thing is certain: The likely price for models is free.

Enterprises will spend on agentic AI, but perhaps not yet

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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Agentic AI is dominating headlines and the enterprise technology sector, but the impact on the IT budget remains to be seen. Agentic AI is promising, but it's going to take more prototypes and proof of ROI to get the budgets rolling.

That's a takeaway from Crawford Del Prete, President of IDC, who appeared on DisrupTV. Del Prete was talking about the volatile IT budget dynamics in 2025 and said:

"The challenge I see when I talk to CIOs is when they think about AI and beyond is that they don't know where to start. This is particularly a problem with agents because when they look at agents they say, 'I'm skeptical. I know it's going to require significant human oversight. It's going to hallucinate. So, I can't just let this thing run, but I'm afraid not to run with it. I know I have to invest in it, but my budgets are under pressure.' I think we're going to see maybe a little bit of a lean back in the agent world over the course of the next couple quarters."

These comments about spending on AI agents is notable given that vendors are launching new agents and orchestration engines almost daily. Another day, another AI studio. Meanwhile, vendors are switching to hybrid models that include AI agents and genAI capabilities, but feature consumption models that may create budget volatility.

Here’s the state of agentic AI in a nutshell:

• The data wars are just starting and agentic AI may be a trigger
• AI agents bring consumption models to SaaS: Goldilocks or headache?
• Agentic AI: Three themes to watch for 2025
• Agentic AI without process optimization, orchestration will flop
• AI? Whatever. It's all about the first party data
• Enterprise AI: Here are the trends to know right now

Del Prete was upbeat about AI agents. "You've finally got a technology that can unlock the value of your unstructured data in a very automated way. But I'm seeing customers not knowing how to engage, but afraid not to engage. I think we're going to see that push-pull play out over middle of this year, until people can start getting decent prototypes moving forward," said Del Prete.

Constellation Research CEO Ray "R" Wang noted that there are a lot of vendors adding agents to every marketing sentence available. Wang said agentic AI needs to get to the point where agents become an API with a decisioning engine. Agentic AI is very basic right now, said Wang.

Del Prete noted that oversight remains an issue with AI agents across multiple use cases. "We have a lot of work to do before we can set these things loose and feel like they're as in production," he said. "They're not as easy to put into production as people think based on the media that you see associated with it."

Nevertheless, agentic AI will pull budget. "I haven't found a company yet that's willing to lean back and say, yeah, I'm not going to invest. I don't think this thing is real," said Del Prete.

Gurvinder Sahni, Chief Marketing Officer at Persistent Systems, said the company is betting on agentic AI--a space where integrators that can work across multiple systems and domains can create AI agents that actually work

Wang noted that systems integrators and services companies have done well building agents relative to software vendors because they're used to cutting across departments, functions and business processes.

Sahni said:

"We recently trained about 1,000 plus people on Salesforce Agentforce. The other big part for us is the focus on our own IP and also working with the hyperscalers and the ecosystem to build products and services with them as well."

AI infrastructure spending is upending enterprise financial modeling

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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When a technology is evolving as fast as artificial intelligence, CFOs and the finance department struggle to crowbar AI infrastructure investments into traditional depreciation models.

Typically, enterprises depreciate capital spending on IT infrastructure over multiple years. For instance, servers have a useful life of 7 years, according to the IRS. Hyperscale cloud companies tend to tweak the useful life of a server. For instance, Meta in its annual report said certain servers and network assets had a useful life of 5.5 years for fiscal 2025. Amazon now reckons the useful life of some of its servers is now 5 years down from 6 years a year ago. Alphabet depreciates over 6 years.

Here's the catch: 5 years in AI infrastructure is a lifetime. Nvidia now has an annual cadence for its GPUs and provides visibility into the roadmap through 2027. CEO Jensen Huang's bet is that Nvidia customers will continue to invest in the latest and greatest accelerated computing infrastructure because there's value and competitive advantage.

• Nvidia launches Blackwell Ultra, Dynamo. outlines roadmap through 2027
• Nvidia GTC 2025: Six lingering questions

Do companies go with shorter depreciation cycles, lease gear or just provision from the cloud even though operating expenses are already stretched? As Nvidia extends more into the enterprise and industry applications, this financial planning conundrum is going to go mainstream. It's no wonder that Nvidia has teams focused on financial solutions.

With that in mind, Nvidia held a virtual panel at GTC 2025 focused on these accounting issues. What does traditional planning look like when AI is advancing too quickly for typical technology refresh cycles?

Bill Mayo, SVP for research IT at Bristol Myers Squibb, has been investing in AI and machine learning for more than a decade, but AI advances today are moving faster than ever. "The challenge is we've had this probably first and second derivative improvement in in the pace of change that has completely broken financial models up and down the stack," said Mayo.

Richard Zoontjens, lead of the supercomputing center at Eindhoven University of Technology, said "we really need compute to compete in this era and that means financial systems have to support this fast moving world."

Zoontjens noted that the current AI cycle doesn't fit in with typical depreciation schedules and financial models. "If you buy new tools every five years, well you're not competing anymore. After two years, you lose talent and you lose innovation," he said.

The solution is that financial modeling will have to move faster. Mayo noted that Bristol Myers Squibb (BMS) has seen rapid improvement in compute, but there's still not enough to reach its biology vision. "At its core, biology is computation," said Mayo.

Mayo said that if you're using today's tech stack five years from now you're behind. Mayo said BMS hasn't figured out the financial model behind AI investments yet, but did say that its first swing was to move to a four-year depreciation cycle.

Zoontjens said his group opted for a flexible two-year renewal cycle. Flexibility is key. Zoontjens said sometimes his supercomputing center can stretch the system, but sometimes has to upgrade faster.

"The system and the lifecycle management contract that we have now provides that flexibility," said Zoontjens. "It gives us control and better agility to move and stay state of the art."

Mayo said AI investment and modeling has to revolve around the patient population and have the best insights to improve lives. That alignment helps with the costs, but BMS AI infrastructure is an operating expense due to cloud delivery. The problem is that cloud provider demand for AI infrastructure is high. Mayo said:

"We can't afford to buy a new (Nvidia) Super Pod every year and just use it for a year. I can't afford it at an OPEX rate, and frankly, neither can hyperscalers afford to buy enough to make it available fast enough that we can all consume that way."

The current situation may indicate that the havoc hitting financial models may be transitory, said Mayo, who said on-prem, co-located infrastructure as well as cloud AI services are all in the mix. "The fact of matter is, I'm buying through a time window that maybe three years from now, the financing problem might have solved itself, but TBD on that," said Mayo.

Here are a few themes on financing AI infrastructure from the IT buyers and Nvidia's Timonthy Shockley, global sales at Nvidia Financial Solutions, and Ingemar Lanevi, head of Nvidia Capital:

• Plan for data center investments that incorporate power and space savings. Nvidia systems have needed less space with each new system.
• Plan for more agile upgrade cycles to maintain capacity to compete in industries.
• There's no right answer that covers all the financial bases so there will be a mix of cloud and on-prem decisions to be made.
• Long-term depreciation will be an issue for the foreseeable future.
• Long-term cloud contracts and leases can be a challenge.
• Cross-functional teams will have to make financing decisions based on what needs to be achieved now and then where things will move later.
• Leasing models may make sense for AI infrastructure at the moment for cash flow purposes and building in upgrades.
• It's possible that a secondary market for AI infrastructure emerges for what Mayo called "gently used Super Pods." The accelerated computing market is young relative to CPUs so a secondary market may take time.
• Enterprises may look to monetize remaining residual value of AI infrastructure when it's not helpful to the buyer anymore.
• Segment investments for what needs to be cutting edge and adjust the financial model accordingly. Non-cutting edge tech investments can be depreciated over a longer period.
• Today's AI infrastructure spend is governed by financial systems, but may have to flip in the future to account for product cycles.

Mayo added the disconnect between financial planning and the AI opportunity is just a place in time.

"It's going to get solved. There's a right answer for the use case or the situation you're grappling with right now. Maybe it's a funding model, maybe it's a cash constraint. As long as we're open to try whatever, we're going to solve this problem, and then we're going to use this solution to solve all the other problems."

The data wars are just starting and agentic AI may be a trigger

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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Agentic AI is likely to have an unwelcome side effect: Data wars and lawsuits between platforms.

Process mining company Celonis filed a US antitrust complaint against SAP in the US District Court of California, San Francisco Division. The complaint alleges that SAP is restricting Celonis' access to data on its ERP platform in favor of SAP-owned rival Signavio.

Celonis and SAP have had a long relationship and the former was in the SAP Startup Focus program in 2012. Celonis alleged in its complaint that "SAP is leveraging its control over its ERP ecosystem and the impending forced migration of customers to SAP's S/4HANA cloud-based ERP solution to prevent SAP customers from sharing their own data with third-party providers, including Celonis, without paying prohibitively expensive fees.

The complaint further argues that SAP is bundling Signavio and preventing competitors like Celonis from extracting data from a customer's ERP system. Celonis alleges that SAP is making it impossible to use non-SAP process mining. SAP hasn’t responded to the lawsuit yet.

While Celonis is connecting to SAP for process intelligence, you can easily imagine that these data access skirmishes will proliferate as millions of AI agents start trying to complete tasks autonomously. Data ecosystems work--until they don't. Is orchestrating processes as an independent third party really that different from the emerging AI agent orchestration platforms?

There will be no shortage of neutral-ish agentic AI platforms. ServiceNow's latest release of its Now Platform has a bevy of tools to connect agents and orchestrate them. Boomi launched AI Studio. Kore.ai launched its AI agent platform, and eyes orchestration. Zoom evolved AI Companion with agentic AI features and plans to connect to other agents. Without data connections there won’t be cross platform orchestration.

Meanwhile, enterprise technology giants are all playing the platform game. When your data is on a platform you're locked in to some degree. For agentic AI to meet its promise, enterprises will need to connect agents on multiple platforms. It's not difficult to envision a world where SAP agents connect with Salesforce Agentforce and perhaps, ServiceNow, AWS and Google Cloud to autonomously execute a task.

Beyond the standards of AI agent communications there will be data handoffs. Some platforms, say Salesforce and Google Cloud or Databricks and SAP, will have zero copy data sharing arrangements. Other vendors may collect tolls from potential rivals not in an alliance. These tolls are going to the basis of future data wars.

• Enterprise AI: Here are the trends to know right now
• AI agents bring consumption models to SaaS: Goldilocks or headache?
• Agentic AI: Three themes to watch for 2025
• Agentic AI without process optimization, orchestration will flop

None of these data skirmishes are surprising. Enterprise vendors have typically tried to keep your data and prevent rivals from benefiting. CIOs will tell you tales about full use vs. restricted use when it comes to extracting data from one platform and sharing it with third party applications. Procurement departments can rant about expensive API usage fees and rate limits. Tight integration is also designed to keep you on one platform and make it difficult to connect third party vendors. Hyperscale cloud vendors have played the data egress fee game.

Sure, you can connect your data to third parties and even transfer data to new platforms. Just expect some technical and financial pain.

Where the enterprise technology data playbook becomes worrisome is agentic AI. The customer should have control of the enterprise data. Enterprises should be able to connect platforms and their agents to complete processes and autonomously make decisions. Vendors today like to pretend that agentic AI will only happen on their platforms.

It's worth noting that agentic AI is going to tax transactional systems with API calls. There is SaaS scale in terms of API calls and then there's agentic AI calls, which will be a whole new ballgame. There may be a need for some pricing model to address the strain.

Either way, agentic AI is going to scale the business-as-usual data charges and the big platforms are naturally inclined to put up a few barriers to smaller vendors. Simply put, Celonis' lawsuit vs. SAP isn't likely to be a process mining one off. Agentic AI will require the same type of data access at scale. The data wars between vendors is just starting and customers are going to be caught in the middle. Just remember your data belongs to you and not your vendor.

Relevant research:

• AI Trends for 2025 and Beyond
• Behind the Scenes: The Force Behind Agentforce
• There Is Only ‘One’ (1) IT Strategy in 2025 and Beyond
• Exponential Efficiency in the Age of AI

AccentCare's Heather Wilson: How customer and employee experience go together

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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Heather Wilson, Senior Vice President and Chief Communications and Marketing Officer at AccentCare, sits at the intersection of customer experience, employee experience, marketing and talent recruitment.

Why the multiple roles? For Wilson and AccentCare they all blend together. AccentCare is one of the largest post-acute healthcare providers in the US covering the range of personal care to clinical services like nursing and physical therapy as well as hospice care.

• Will CX suffer from an overreliance on AI?
• Constellation Research Ambient Experience Summit 2025: 10 CX takeaways on people, data, AI
• AX100 Spotlight: RentSpree's Michael Storey on the intersection of CX, AI and success metrics

The company's ability to tell a good human story and drive experiences also attracts talent amid a healthcare worker shortage. I caught up with Wilson at Constellation Research's Ambient Experience Summit. Here are some of the takeaways from our chat and the questions that followed.

Why customer experience (CX) and employee experience (EX) is critical to healthcare. "My role has both the CX and EX experience because, while our end client is physicians and healthcare systems, we also have to ensure a good experience for patients and families—they are a secondary referral source," she said.

The need for narratives. Wilson was brought on at AccentCare to tell the stories of clinicians and patients. She continually emphasizes storytelling in internal meetings, marketing and executive communications. "Storytelling is in my DNA. We have amazing stories in the everyday work that our clinicians do, but they weren’t being told," said Wilson. "Everything we do starts with a patient story. When executives speak, when they visit branches—we make sure they have real patient stories because it reminds us why we do this."

Transformation. Wilson said transformation in healthcare is often slow due to technology, legacy systems and older processes. Wilson modernized AccentCare's digital marketing strategy, but noted that "we live in an industry where paper brochures are a thing."

AI and analytics. Wilson said AI and analytics is being used at AccentCare for sales enablement, marketing targeting, and talent acquisition and recruitment. "We have to blend the traditional with the digital," said Wilson.

Digital transformation challenges. Wilson said barriers to healthcare digital transformation is hampered by the lack of interoperability between Electronic health records, regulations on patient data and aging patient demographics that keep non-digital engagement methods going. Full digital transformation is likely 10 years away at least, said Wilson. "Healthcare is still running on 1990s systems, and they’ve gotten away with it for a long time. But with AI, the pressure is on to finally modernize," she said.

The intersection of CX and EX. Wilson said word of mouth is critical to recruiting and patient care quality. "If you’re a clinician, you want to work at a place known for top-quality care. If you’re a doctor, you want to refer patients to a company you trust," said Wilson. "If we don’t have enough nurses, we can’t grow. It’s that simple."

Accenture Q2 solid, but federal spending on pause and customers cautious

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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Accenture said it may start seeing the effects of a US government funding pause and enterprises growing cautious amid geopolitical and economic volatility.

Speaking on Accenture's second quarter earnings conference call, CEO Julie Sweet laid out the current conditions for the consulting giant. Accenture Federal Services accounted for 8% of the company's global revenue and 16% of Americas revenue in fiscal 2024.

She said:

"As you know, the new administration has a clear goal to run the Federal government more efficiently. During this process, many new procurement actions have slowed, which is negatively impacting our sales and revenue. In addition, recently, the General Service Administration has instructed all federal agencies to review their contracts with the top 10 highest paid consulting firms contracting with the U.S. government, which includes Accenture Federal Services.

The GSA's guidance would determinate contracts that are not deemed mission critical by the federal -- by the relevant federal agencies. While we continue to believe our work for federal clients is mission critical, we anticipate ongoing uncertainty as the government's priorities evolve and these assessments unfold."

That caution has spilled over to enterprises, which have gone from bullish at the end of 2024 to uncertain. Other vendors have also noted that enterprises have turned cautious in recent weeks.

Sweet said enterprises have only recently turned cautious and haven't paused spending just yet. See: AI? Whatever. It's all about the first party data

"We are seeing an elevated level of what was already significant uncertainty in the global economic and geopolitical environment, marking a shift from our first quarter FY ‘25 earnings report in December," said Sweet. "Businesses are trying to process what this (volatility) might mean."

The forecast from Accenture lands as its results for the second quarter were solid excluding new bookings growth.

For the second quarter, Accenture reported earnings of $2.82 a share on revenue of $16.7 billion, up 5%. Both top and bottom lines beat estimates. Accenture saw $1.4 billion in generative AI bookings in the quarter.

The issue for Accenture is that new bookings for the second quarter were $20.91 billion, down 3% from a year ago. New bookings growth in the first quarter was only 1%. That slowdown in new bookings may indicate a great pause amid uncertain economic conditions.

As for the outlook, Accenture said its third quarter revenue will be between $16.9 billion and $17.5 billion compared to estimates of $17.22 billion. For fiscal 2025, Accenture projected revenue growth of 55 to 7% compared to 4% to 7% last quarter.

AI projects to save the day?

Sweet said Accenture is still seeing strong demand for AI services. Enterprises are focused on their "digital core with more AI being built-in."

"For our clients, the twin themes of achieving both cost efficiency and growth continue. The number of clients embracing Gen AI is increasing significantly and we are starting to see some tangible examples of scale in data and AI," said Sweet.

Accenture is also scaling its AI Refinery platform that focuses on business processes and agentic AI. Sweet cited customer wins in manufacturing, automotive, food and telecom via a partnership with Telstra, a leading telecom in Australia.

• Enterprise AI: Here are the trends to know right now

Sweet added that Accenture is using its AI Refinery platform and automation to cut costs with AI.

In the end, Accenture may be seeing a tale of two businesses. AI and data projects will get funding and everything else is downgraded. Sweet said the US geopolitical and economic picture is uncertain and it's unclear how customers will react, but spending in Europe could pick up. Sweet said:

"Everyone is well aware that in the last few weeks, there's been an elevated level of what was already significant uncertainty and there's a couple of big themes around that, obviously tariffs, and that's a global discussion that is not just an Americas discussion.

And there’s consumer sentiment, which is a little bit more of an Americas discussion. We're already in the heart of the discussions of clients globally who are talking about it."

Oracle launches AI Studio to deploy agents, expands Microsoft Azure pact

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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Oracle launched Oracle AI Agent Studio for Fusion Applications in a move designed to enable enterprises to customize AI agents across its platform.

The move, announced at Oracle Cloudworld in London, is the latest in a series of vendor announcements aimed at creating, deploying and orchestrating AI agents.

There is not shortage of AI agent platforms. ServiceNow's latest release of its Now Platform has a bevy of tools to connect agents and orchestrate them. Boomi launched AI Studio. Kore.ai launched its AI agent platform, and eyes orchestration. Zoom evolved AI Companion with agentic AI features and plans to connect to other agents. Salesforce obviously has Agentforce.

According to Oracle, AI Agent Studio will enable customers to create and manage their own AI agents. Oracle already has more than 50 AI agents embedded into its Fusion applications.

Oracle AI Agent Studio is available at no additional cost and includes testing, validation and security to create agents within Oracle Fusion Applications.

Holger Mueller, analyst at Constellation Research said:

"The new Oracle AI Agent Studio is an impressive next step for Oracle's AI strategy. To truly optimize the impact of AI agents, organizations need to be able to customize the way they work to fit their unique business needs. The evolution of AI across the enterprise is moving at a rapid pace and by enabling agents to be created, extended, deployed, and managed across the entire enterprise, Oracle will help its customers accelerate adoption and automation.

It's good to see Oracle delivering on a consistent architecture strategy for its AI platform and Fusion Application portfolio as AI can't be built overnight. Oracle's vertical stack depth – from the cloud infrastructure over the database makes this a compelling offering for its customers."

Oracle AI Agent Studio includes:

• Agent pre-built templates.
• Orchestration tools and agent extensibility with documents, tools, prompts and APIs.
• LLM choice via models optimized for Fusion via Llama and Cohere or third-party options.
• Native integration with Fusion tools.
• Connections to third party agents.

Oracle Database@Azure expands

Separately, Oracle said Oracle Exadata Database Service on Exascale Infrastructure on Oracle Database@Azure is generally available.

Oracle also said Oracle Base Database Service on Oracle Database@Azure will be available soon.

The company also said Oracle Database@Azure is available in the Microsoft Azure Ease US 2 region and now available in 14 regions. Oracle plans to roll out 18 regions on Azure in the next 12 months.

• Oracle's cloud, AI plans are a master class in co-opetition