AI’s fourth wave is here — are enterprises ready for what’s next?

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AI’s fourth wave is here — are enterprises ready for what’s next?

Yesterday’s emerging tech is now essential to business success — and the next wave is coming fast. To maintain competitive advantage through the next five years, which innovations must forward-thinking companies prioritize right now? At VentureBeat’s Transform 2025, Yaad Oren, global head of SAP research & innovation and Emma Brunskill, associate professor of computer science at Stanford, spoke with moderator Susan Etlinger, senior director, strategy and thought leadership, Azure AI Microsoft, about the strategies needed today, for tomorrow’s transformative technology. [embedded content] How the current landscape will shape the future The fourth generation of AI — generative AI — marks a paradigm shift in what AI brings to the table, Oren said, outlining three major places it’s bringing significant value and disruption to the enterprise. The first is the user experience and how people interact with software. The second is automation on the application layer — SAP has embedded approximately 230 AI capabilities and agents inside its applications, and plan increase this number to 400 by the end of 2025, to drive increased productivity and reduce costs. The third area is the platform — the core engine that powers each enterprise — which raises new questions about the developer experience, as well as privacy and trust. “We see a lot of disruption around UX, the application, and the platform itself that provides all the tools to deal with this new treasure trove of options AI provides to enterprises,” Oren summed up. For Brunskill, the big question is how AI can integrate with humans to drive societal value, rather than acting like a thief of human creativity and ingenuity. A recent study found that if the enterprise framed AI tools as productivity enhancing, people will use them much less frequently than if they’re framed as task enhancing. “That’s a pretty big take-home as we think about how to translate some of the extraordinary capabilities of these systems into systems that drive value for customers, for organizations and others,” Brunskill said. “We need to think about how these are framed.” Business value at the enterprise level should be top of mind, Oren added, and that means even as technology evolves, AI in the enterprise needs to go beyond technology for technology’s sake. The sexiest new technology often delivers the least value. “What you see today is a proliferation of many solutions out there that create great jumping avatars in movies that look amazing, but the value: how do you help the enterprise reduce costs? How do you help the enterprise increase productivity or revenue? How are you able to mitigate risk?” he said. “This mindset is not fully there with AI. You always need to start with a business problem. Quantify the value you would like to achieve.” Predictions for the future of AI Artificial general intelligence (AGI) is a theoretical breakthrough in which AI will match or surpass human-level versatility and problem-solving capabilities across most cognitive tasks. The future of AI, and the definition of what AGI is, will be a big topic of discussion in the next few years. Brunskill defines it the point at which AI can do any sort of cognitive task at least as well as an average human in a profession. “In terms of a lot of the white-collar jobs that just require cognitive processing, I think we’re going to make enormous strides in the next five years,” Brunskill said. “I don’t think we’re ready yet. I think we need to do a lot of creative thinking about what that will mean to industries. What is it going to do to your workforce? I’m very interested in how we think about workforce retraining and how we’re going to provide meaningful work to many people going forward. What new opportunities will we have?” The future of AI, the definition of AGI, is a big one, and we’re not as near as many folks would prefer, Oren said, but along the way we’ll see exciting new technology leaps, and six major disruption pillars: the next generation of AI beyond its current capabilities, the future of data platforms, robotics, quantum computing, next-generation Enterprise UX, and the future of cloud architecture around data privacy. “The transformer architecture in this generation is nothing compared to what’s coming,” he said. “A new type of meta-learning. AI learning to evolve and create agents by itself. Emotional AI. The future of AI, the definition of AGI, is a big one.” The future of data itself is also critical. We’re approaching the limits of real-world data — even sources like Wikipedia have already been fully absorbed by AI models. To drive the next leap in AI progress, synthetic data generation and improving data quality will be essential. Then there’s robotics which is evolving rapidly — we learned from recent innovation like DeepSeek that you can do “more with less” and install very powerful AI on the edge. Quantum will help create a paradigm shift in how we run process optimization and simulation. And the future of enterprise UX will be another disruption which will provide users new type of personalization, adaption of screens to specific context, and an immersive experience. “My kids’ generation is going to hit the workforce after 2030. What’s going to be their UX paradigm?” Oren said. “They need an emotional connection for screens. They need adaptive screens. This is totally different from what we do today.”

At VentureBeat’s Transform 2025, Yaad Oren, global head of SAP research & innovation and Emma Brunskill, associate professor of computer science at Stanford, spoke with moderator Susan Etlinger, senior director, strategy and thought leadership, Azure AI Microsoft, about the strategies needed today, for tomorrow’s transformative technology.

How the current landscape will shape the future

The fourth generation of AI — generative AI — marks a paradigm shift in what AI brings to the table, Oren said, outlining three major places it’s bringing significant value and disruption to the enterprise. The first is the user experience and how people interact with software. The second is automation on the application layer — SAP has embedded approximately 230 AI capabilities and agents inside its applications, and plan increase this number to 400 by the end of 2025, to drive increased productivity and reduce costs. The third area is the platform — the core engine that powers each enterprise — which raises new questions about the developer experience, as well as privacy and trust.

“We see a lot of disruption around UX, the application, and the platform itself that provides all the tools to deal with this new treasure trove of options AI provides to enterprises,” Oren summed up.

For Brunskill, the big question is how AI can integrate with humans to drive societal value, rather than acting like a thief of human creativity and ingenuity. A recent study found that if the enterprise framed AI tools as productivity enhancing, people will use them much less frequently than if they’re framed as task enhancing.

“That’s a pretty big take-home as we think about how to translate some of the extraordinary capabilities of these systems into systems that drive value for customers, for organizations and others,” Brunskill said. “We need to think about how these are framed.”

Business value at the enterprise level should be top of mind, Oren added, and that means even as technology evolves, AI in the enterprise needs to go beyond technology for technology’s sake. The sexiest new technology often delivers the least value.

“What you see today is a proliferation of many solutions out there that create great jumping avatars in movies that look amazing, but the value: how do you help the enterprise reduce costs? How do you help the enterprise increase productivity or revenue? How are you able to mitigate risk?” he said. “This mindset is not fully there with AI. You always need to start with a business problem. Quantify the value you would like to achieve.”

Predictions for the future of AI

Artificial general intelligence (AGI) is a theoretical breakthrough in which AI will match or surpass human-level versatility and problem-solving capabilities across most cognitive tasks. The future of AI, and the definition of what AGI is, will be a big topic of discussion in the next few years.

Brunskill defines it the point at which AI can do any sort of cognitive task at least as well as an average human in a profession.

“In terms of a lot of the white-collar jobs that just require cognitive processing, I think we’re going to make enormous strides in the next five years,” Brunskill said. “I don’t think we’re ready yet. I think we need to do a lot of creative thinking about what that will mean to industries. What is it going to do to your workforce? I’m very interested in how we think about workforce retraining and how we’re going to provide meaningful work to many people going forward. What new opportunities will we have?”

The future of AI, the definition of AGI, is a big one, and we’re not as near as many folks would prefer, Oren said, but along the way we’ll see exciting new technology leaps, and six major disruption pillars: the next generation of AI beyond its current capabilities, the future of data platforms, robotics, quantum computing, next-generation Enterprise UX, and the future of cloud architecture around data privacy.

“The transformer architecture in this generation is nothing compared to what’s coming,” he said. “A new type of meta-learning. AI learning to evolve and create agents by itself. Emotional AI. The future of AI, the definition of AGI, is a big one.”

The future of data itself is also critical. We’re approaching the limits of real-world data — even sources like Wikipedia have already been fully absorbed by AI models. To drive the next leap in AI progress, synthetic data generation and improving data quality will be essential.

Then there’s robotics which is evolving rapidly — we learned from recent innovation like DeepSeek that you can do “more with less” and install very powerful AI on the edge. Quantum will help create a paradigm shift in how we run process optimization and simulation. And the future of enterprise UX will be another disruption which will provide users new type of personalization, adaption of screens to specific context, and an immersive experience.

“My kids’ generation is going to hit the workforce after 2030. What’s going to be their UX paradigm?” Oren said. “They need an emotional connection for screens. They need adaptive screens. This is totally different from what we do today.”

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AMD: Latest news and insights

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Private data centers: 29.5% Traditional public cloud: 35.4% GPU as a service specialists: 18.5% Edge compute: 16.6% “There is little variation from training to inference, but the general pattern is workloads are concentrated a bit in traditional public cloud and then hyperscalers have significant presence in private data centers,” McGillicuddy explained. “There is emerging interest around deploying AI workloads at the corporate edge and edge compute environments as well, which allows them to have workloads residing closer to edge data in the enterprise, which helps them combat latency issues and things like that. The big key takeaway here is that the typical enterprise is going to need to make sure that its data center network is ready to support AI workloads.” AI networking challenges The popularity of AI doesn’t remove some of the business and technical concerns that the technology brings to enterprise leaders. According to the EMA survey, business concerns include security risk (39%), cost/budget (33%), rapid technology evolution (33%), and networking team skills gaps (29%). Respondents also indicated several concerns around both data center networking issues and WAN issues. Concerns related to data center networking included: Integration between AI network and legacy networks: 43% Bandwidth demand: 41% Coordinating traffic flows of synchronized AI workloads: 38% Latency: 36% WAN issues respondents shared included: Complexity of workload distribution across sites: 42% Latency between workloads and data at WAN edge: 39% Complexity of traffic prioritization: 36% Network congestion: 33% “It’s really not cheap to make your network AI ready,” McGillicuddy stated. “You might need to invest in a lot of new switches and you might need to upgrade your WAN or switch vendors. You might need to make some changes to your underlay around what kind of connectivity your AI traffic is going over.” Enterprise leaders intend to invest in infrastructure

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Such a shift, analysts say, could offer short-term benefits for enterprises, particularly in cost and access, but also introduces new operational risks. “This acquisition may potentially lower enterprise pricing through lease cost elimination and annual savings, while improving GPU access via expanded power capacity, enabling faster deployment of Nvidia chipsets and systems,” said Charlie Dai, VP and principal analyst at Forrester. “However, service reliability risks persist during this crypto-to-AI retrofitting.” This also indicates that struggling vendors such as Core Scientific and similar have a way to cash out, according to Yugal Joshi, partner at Everest Group. “However, it does not materially impact the availability of Nvidia GPUs and similar for enterprises,” Joshi added. “Consolidation does impact the pricing power of vendors.” Concerns for enterprises Rising demand for AI-ready infrastructure can raise concerns among enterprises, particularly over access to power-rich data centers and future capacity constraints. “The biggest concern that CIOs should have with this acquisition is that mature data center infrastructure with dedicated power is an acquisition target,” said Hyoun Park, CEO and chief analyst at Amalgam Insights. “This may turn out to create challenges for CIOs currently collocating data workloads or seeking to keep more of their data loads on private data centers rather than in the cloud.”

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The deployment, Kimball said, “brings Dell quality to the commodity space. Wins like this really validate what Dell has been doing in reshaping its portfolio to accommodate the needs of the market — both in the cloud and the enterprise.” Although concerns were voiced last year that Nvidia’s next-generation Blackwell data center processors had significant overheating problems when they were installed in high-capacity server racks, he said that a repeat performance is unlikely. Nvidia, said Kimball “has been very disciplined in its approach with its GPUs and not shipping silicon until it is ready. And Dell almost doubles down on this maniacal quality focus. I don’t mean to sound like I have blind faith, but I’ve watched both companies over the last several years be intentional in delivering product in volume. Especially as the competitive market starts to shape up more strongly, I expect there is an extremely high degree of confidence in quality.” CoreWeave ‘has one purpose’ He said, “like Lambda Labs, Crusoe and others, [CoreWeave] seemingly has one purpose (for now): deliver GPU capacity to the market. While I expect these cloud providers will expand in services, I think for now the type of customer employing services is on the early adopter side of AI. From an enterprise perspective, I have to think that organizations well into their AI journey are the consumers of CoreWeave.” “CoreWeave is also being utilized by a lot of the model providers and tech vendors playing in the AI space,” Kimball pointed out. “For instance, it’s public knowledge that Microsoft, OpenAI, Meta, IBM and others use CoreWeave GPUs for model training and more. It makes sense. These are the customers that truly benefit from the performance lift that we see from generation to generation.”

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And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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