Tools & Platforms
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.
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.”
Tools & Platforms
AI data provider Invisible raises $100M at $2B+ valuation

Invisible Technologies Inc., a startup that provides training data for artificial intelligence projects, has raised $100 million in funding.
Bloomberg reported today that the deal values the company at more than $2 billion. Newly formed venture capital firm Vanara Capital led the round with participation from Acrew Capital, Greycroft and more than a half dozen others.
AI training datasets often include annotations that summarize the records they contain. A business document, for example, might include an annotation that explains the topic it discusses. Such explanations make it easier for the AI model being trained to understand the data, which can improve its output quality.
Invisible provides enterprises with access to experts who can produce custom training data and annotations for their AI models. Those experts also take on certain other projects. Notably, they can create data for RLHF, or reinforcement learning from human feedback, initiatives. .
RLHF is a post-training method, which means it’s used to optimize AI models that have already been trained. The process involves giving the model a set of prompts and asking human experts to rate the quality of its responses. The experts’ ratings are used to train a neural network called a reward model. This model, in turn, provides feedback to the original AI model that helps it generate more useful prompt responses.
Invisible offers a tool called Neuron that helps customers manage their training datasets. The software can combine annotated data with external information, including both structured and structured records. It also creates an ontology in the process. This is a file that explains the different types of records in a training dataset and the connections between them.
Another Invisible tool, Atomic, enables companies to collect data on how employees perform repetitive business tasks. The company says that this data makes it possible to automate manual work with AI agents. Additionally, Invisible offers a third tool called Synapse that helps developers implement automation workflows.
“Our software platform, combined with our expert marketplace, enables companies to organize, clean, label, and map their data,” said Invisible Chief Executive Officer Matthew Fitzpatrick. “This foundation enables them to build agentic workflows that drive real impact.”
Today’s funding round follows a period of rapid growth for the company. Between 2020 and 2024, Invisible’s annual revenue increased by a factor of over 48 to $134 billion. This year, the data provider doubled the size of its engineering group and refreshed its leadership team.
Invisible will use the new capital to enhance its software tools. The investment comes amid rumors that a competing provider of AI training data, Surge AI Inc., may also raise funding at a multibillion-dollar valuation
Image: Invisible
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Tools & Platforms
Anthropic Taps Higher Education Leaders for Guidance on AI

The artificial intelligence company Anthropic is working with six leaders in higher education to help guide how its AI assistant Claude will be developed for teaching, learning and research. The new Higher Education Advisory Board, announced in August, will provide regular input on educational tools and policies.
According to a news release from Anthropic, the board is tasked with ensuring that AI “strengthens rather than undermines learning and critical thinking skills” through policies and products that support academic integrity and student privacy.
As teachers adapt to AI, ed-tech leaders have called for educators to play an active role in aligning AI to educational standards.
“Teachers and educators and administrators should be in the decision-making seat at every critical decision-making point when AI is being used in education,” Isabella Zachariah, formerly a fellow at the U.S. Department of Education’s Office of Educational Technology, said at the EDUCAUSE conference in October 2024. The Office of Educational Technology has since been shuttered by the Trump administration.
To this end, advisory boards or councils involving educators have emerged in recent years among ed-tech companies and institutions seeking to ground AI deployments in classroom experiences. For example, the K-12 software company Otus formed an AI advisory board earlier this year with teachers, principals, instructional technology specialists and district administrators representing more than 20 school districts across 11 states. Similarly, software company Frontline Education launched an AI advisory council last month to allow district leaders to participate in pilots and influence product design choices.
The Anthropic board taps experts in the education, nonprofit and technology sectors, including two former university presidents and three campus technology leaders. Rick Levin, former president of Yale University and CEO of Coursera, will serve as board chair. Other members include:
- David Leebron, former president of Rice University
- James DeVaney, associate vice provost for academic innovation at the University of Michigan
- Julie Schell, assistant vice provost of academic technology at the University of Texas at Austin
- Matthew Rascoff, vice provost for digital education at Stanford University
- Yolanda Watson Spiva, president of Complete College America
The board contributed to a recent trio of AI fluency courses for colleges and universities, according to the news release. The online courses aim to give students and faculty a foundation in the function, limitations and potential uses of large language models in academic settings.
Schell said she joined the advisory board to explore how technology can address persistent challenges in learning.
“Sometimes we forget how cognitively taxing it is to really learn something deeply and meaningfully,” she said. “Throughout my career, I’ve been excited about the different ways that technology can help accentuate best practices in teaching or pedagogy. My mantra has always been pedagogy first, technology second.”
In her work at UT Austin, Schell has focused on responsible use of AI and engaged with faculty, staff, students and the general public to develop guiding principles. She said she hopes to bring the feedback from the community, as well as education science, to regular meetings. She said she participated in vetting existing Anthropic ed-tech tools, like Claude Learning mode, with this in mind.
In the weeks since the board’s announcement, the group has met once, Schell said, and expects to meet regularly in the future.
“I think it’s important to have informed people who understand teaching and learning advising responsible adoption of AI for teaching and learning,” Schell said. “It might look different than other industries.”
Tools & Platforms
Duke AI program emphasizes critical thinking for job security :: WRAL.com

Duke’s AI program is spearheaded by a professor who is not just teaching, he also built his own AI model.
Professor Jon Reifschneider says we’ve already entered a new era of teaching and learning across disciplines.
He says, “We have folks that go into healthcare after they graduate, go into finance, energy, education, etc. We want them to bring with them a set of skills and knowledge in AI, so that they can figure out: ‘How can I go solve problems in my field using AI?'”
He wants his students to become literate in AI, which is a challenge in a field he describes as a moving target.
“I think for most people, AI is kind of a mysterious black box that can do somewhat magical things, and I think that’s very risky to think that way, because you don’t develop an appreciation of when you should use it and when you shouldn’t use it,” Reifschneider told WRAL News.
Student Harshitha Rasamsetty said she is learning the strengths and shortcomings of AI.
“We always look at the biases and privacy concerns and always consider the user,” she said.
The students in Duke’s engineering master’s programs come from all backgrounds, countries, even ages. Jared Bailey paused his insurance career in Florida to get a handle on the AI being deployed company-wide.
He was already using AI tools when he wondered, “What if I could crack them open and adjust them myself and make them better?”
John Ernest studied engineering in undergrad, but sought job security in AI.
“I hear news every day that AI is replacing this job, AI is replacing that job,” he said. “I came to a conclusion that I should be a part of a person building AI, not be a part of a person getting replaced by AI.”
Reifschneider thinks warnings about AI taking jobs are overblown.
In fact, he wants his students to come away understanding that humans have a quality AI can’t replace. That’s critical thinking.
Reifschneider says AI “still relies on humans to guide it in the right direction, to give it the right prompts, to ask the right questions, to give it the right instructions.”
“If you can’t think, well, AI can’t take you very far,” Bailey said. “It’s a car with no gas.”
Reifschneider told WRAL that he thinks children as young as elementary school students should begin learning how to use AI, when it’s appropriate to do so, and how to use it safely.
WRAL News went inside Wake County schools to see how it is being used and what safeguards the district is using to protect students. Watch that story Wednesday on WRAL News.
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