When the ABB FIA Formula E World Championship launched its first race through Beijing’s Olympic Park in 2014, the idea of all-electric motorsport still bordered on experimental. Batteries couldn’t yet last a full race, and drivers had to switch cars mid-competition. Just over a decade later, Formula E has evolved into a global entertainment brand broadcast in 150 countries, driving both technological innovation and cultural change in sport.  

“Gen4, that’s to come next year,” says Dan Cherowbrier, Formula E’s chief technology and information officer. “You will see a really quite impressive car that starts us to question whether EV is there. It’s actually faster—it’s actually more than traditional [internal combustion engines] ICE.” 

That acceleration isn’t just happening on the track. Formula E’s digital transformation, powered by its partnership with Infosys, is redefining what it means to be a fan. “It’s a movement to make motor sport accessible and exciting for the new generation,” says principal technologist at Infosys, Rohit Agnihotri. 

From real-time leaderboards and predictive tools to personalized storylines that adapt to what individual fans care most about—whether it’s a driver rivalry or battery performance—Formula E and Infosys are using AI-powered platforms to create fan experiences as dynamic as the races themselves. “Technology is not just about meeting expectations; it’s elevating the entire fan experience and making the sport more inclusive,” says Agnihotri.  

AI is also transforming how the organization itself operates. “Historically, we would be going around the company, banging on everyone’s doors and dragging them towards technology, making them use systems, making them move things to the cloud,” Cherowbrier notes. “What AI has done is it’s turned that around on its head, and we now have people turning up, banging on our door because they want to use this tool, they want to use that tool.” 

As audiences diversify and expectations evolve, Formula E is also a case study in sustainable innovation. Machine learning tools now help determine the most carbon-optimal way to ship batteries across continents, while remote broadcast production has sharply reduced travel emissions and democratized the company’s workforce. These advances show how digital intelligence can expand reach without deepening carbon footprints. 

For Cherowbrier, this convergence of sport, sustainability, and technology is just the beginning. With its data-driven approach to performance, experience, and impact, Formula E is offering a glimpse into how entertainment, innovation, and environmental responsibility can move forward in tandem. 

“Our goal is clear,” says Agnihotri. “Help Formula E be the most digital and sustainable motor sport in the world. The future is electric, and with AI, it’s more engaging than ever.” 

This episode of Business Lab is produced in partnership with Infosys. 

Full Transcript:  

Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab, and into the marketplace.  

The ABB FIA Formula E World Championship, the world’s first all-electric racing series, made its debut in the grounds of the Olympic Park in Beijing in 2014. A little more than 10 years later, it’s a global entertainment brand with 10 teams, 20 drivers, and broadcasts in 150 countries. Technology is central to how Formula E is navigating that scale and to how it’s delivering more powerful personalized experiences.  

Two words for you: elevated fandom.  

My guests today are Rohit Agnihotri, principal technologist at Infosys, and Dan Cherowbrier, CTIO of Formula E.  

This episode is produced in partnership with Infosys.  

Welcome, Rohit and Dan. 

Dan Cherowbrier: Hi. Thanks for having us. 

Megan: Dan, as I mentioned there, the first season of the ABB FIA Formula E World Championship launched in 2014. Can you talk us through how the first all-electric motor sport has evolved in the last decade? How has it changed in terms of its scale, the markets it operates in, and also, its audiences, of course? 

Dan: When Formula E launched back in 2014, there were hardly any domestic EVs on the road. And probably if you’re from London, the ones you remember are the hybrid Priuses; that was what we knew of really. And at the time, they were unable to get a battery big enough for a car to do a full race. So the first generation of car, the first couple of seasons, the driver had to do a pit stop midway through the race, get out of one car, and get in another car, and then carry on, which sounds almost farcical now, but it’s what you had to do then to drive innovation, is to do that in order to go to the next stage. 

Then in Gen2, that came up four years later, they had a battery big enough to start full races and start to actually make it a really good sport. Gen3, they’re going for some real speeds and making it happen. Gen4, that’s to come next year, you’ll see acceleration in line with Formula One. I’ve been fortunate enough to see some of the testing. You will see a really quite impressive car that starts us to question whether EV is there. It’s actually faster, it’s actually more than traditional ICE. 

That’s the tech of the car. But then, if you also look at the sport and how people have come to it and the fans and the demographic of the fans, a lot has changed in the last 11 years. We were out to enter season 12. In the last 11 years, we’ve had a complete democratization of how people access content and what people want from content. And as a new generation of fan coming through. This new generation of fan is younger. They’re more gender diverse. We have much closer to 50-50 representation in our fan base. And they want things personalized, and they’re very demanding about how they want it and the experience they expect. No longer are you just able to give them one race and everybody watches the same thing. We need to make things for them. You see that sort of change that’s come through in the last 11 years. 

Megan: It’s a huge amount of change in just over a decade, isn’t it? To navigate. And I wonder, Rohit, what was the strategic plan for Infosys when associating with Formula E? What did Infosys see in partnering with such a young sport? 

Rohit: Yeah. That’s a great question, Megan. When we looked at Formula E, we didn’t just see a racing championship. We saw the future. A sport, that’s electric, sustainable, and digital first. That’s exactly where Infosys wants to be, at the intersection of technology, innovation, and purpose. Our plan has three big goals. First, grow the fan base. Formula E wants to reach 500 million fans by 2030. That is not just a number. It’s a movement to make motor sport accessible and exciting for the new generation. To make that happen, we are building an AI-powered platform that gives personalized content to the fans, so that every fan feels connected and valued. Imagine a fan in Tokyo getting race insights tailored for their favorite driver, while another in London gets a sustainability story that matters to him. That’s the level of personalization we are aiming for. 

Second, bringing technology innovation. We have already launched the Stats Centre, which turns race data into interactive stories. And soon, Race Centre will take this to the next level with real time leaderboards to the race or tracks, overtakes, attack mode timelines, and even AI generated live commentary. Fans will not just watch, they will interact, predict podium finishes, and share their views globally. And third, supports sustainability. Formula E is already net-zero, but now their goal is to cut carbon by 45% by 2030. We’ll be enabling that through AI-driven sustainability, data management, tracking every watt of energy, every logistics decision. and modeling scenarios to make racing even greener. Partnering with a young sport gives us a chance to shape its digital future and show how technology can make racing exciting and responsible. For us, Formula E is not just a sport, it’s a statement about where the world is headed. 

Megan: Fantastic. 500 million fans, that’s a huge number, isn’t it? And with more scale often comes a kind of greater expectation. Dan, I know you touched on this a little in your first question, but what is it that your fans now really want from their interactions? Can you talk a bit more about what experiences they’re looking for? And also, how complex that really is to deliver that as well? 

Dan: I think a really telling thing about the modern day fan is I probably can’t tell you what they want from their experiences, because it’s individual and it’s unique for each of them. 

Megan: Of course. 

Dan: And it’s changing and it’s changing so fast. What somebody wants this month is going to be different from what they want in a couple of months’ time. And we’re having to learn to adapt to that. My CTO title, we often put focus on the technology in the middle of it. That’s what the T is. Actually, if you think about it, it’s continual transformation officer. You are constantly trying to change what you deliver and how you deliver it. Because if fans come through, they find new experiences, they find that in other sports. Sometimes not in sports, they find it outside, and then they’re coming in, and they expect that from you. So how can we make them more part of the sport, more personalized experience, get to know the athletes and the personalities and the characters within it? We’re a very technology centric sport. A lot of motor sport is, but really, people want to see people, right? And even when it’s technology, they want to see people interacting with technology, and it’s how do you get that out to show people. 

Megan: Yeah, it’s no mean feat. Rohit, you’ve worked with brands on delivering these sort of fan experiences across different sports. Is motor sports perhaps more complicated than others, given that fans watch racing for different reasons than just a win? They could be focused on team dynamics, a particular driver, the way the engine is built, and so on and so forth. How does motor sports compare and how important is it therefore, that Formula E has embraced technology to manage expectations? 

Rohit: Yeah, that’s an interesting point. Motor sports are definitely more complex than other sports. Fans don’t just care about who wins, they care about how some follow team strategies, others love driver rivalries, and many are fascinated by the car technology. Formula E adds another layer, sustainability and electric innovation. This makes personalization really important. Fans want more than results. They want stories and insights. Formula E understood this early and embraced technology. 

Think about the data behind a single race, lap times, energy usage, battery performance, attack mode activation, pit strategies, it’s a lot of data. If you just show the raw numbers, it’s overwhelming. But with Infosys Topaz, we turn that into simple and engaging stories. Fans can see how a driver fought back from 10th place to finish on the podium, or how a team managed energy better to gain an edge. And for new fans, we are adding explainer videos and interactive tools in the Race Center, so that they can learn about their sport easily. This is important because Formula E is still young, and many fans are discovering it for the first time. Technology is not just about meeting expectations; it’s elevating the entire fan experience and making the sport more inclusive. 

Megan: There’s an awful lot going on there. What are some of the other ways that Formula E has already put generative AI and other emerging technologies to use? Dan, when we’ve spoken about the demand for more personalized experiences, for example. 

Dan: I see the implementation of AI for us in three areas. We have AI within the sport. That’s in our DNA of the sport. Now, each team is using that, but how can we use that as a championship as well? How do we make it a competitive landscape? Now, we have AI that is in the fan-facing product. That’s what we’re working heavily on Infosys with, but we also have it in our broadcast product. As an example, you might have heard of a super slow-mo camera. A super slow-mo camera is basically, by taking three cameras and having them in exactly the same place so that you get three times the frame rate, and then you can do a slow-motion shot from that. And they used to be really expensive. Quite bulky cameras to put in. We are now using AI to take a traditional camera and interpolate between two frames to make it into a super slow image, and you wouldn’t really know the difference. Now, the joy of that, it means every camera can now be a super slow-mo camera. 

Megan: Wow. 

Dan: In other ways, we use it a little bit in our graphics products, and we iterate and we use it for things like showing driver audio. When the driver is speaking to his engineer or her engineer in the garage, we show that text now on screen. We do that using AI. We use AI to pick out the difference between the driver and another driver and the team engineer or the team principal and show that in a really good way. 

And we wouldn’t be able to do that. We’re not big enough to have a team of 24 people on stenographers typing. We have to use AI to be able to do that. That’s what’s really helped us grow. And then the last one is, how we use it in our business. Because ultimately, as we’ve got the fans, we’ve got the sport, but we also are running a business and we have to pick up these racetracks and move them around the world, and we have all these staff who have to get places. We have insurance who has to do all that kind of stuff, and we use it heavily in that area, particularly when it comes to what has a carbon impact for us. 

So things like our freight and our travel. And we are using the AI tools to tell us, a battery for instance, should we fly it? Should we send it by sea freight? Should we send it by row freight? Or should we just have lots of them? And that sort of depends. Now, a battery, if it was heavy, you’d think you probably wouldn’t fly it. But actually, because of the materials in it, because of the source materials that make it, we’re better off flying it. We’ve used AI to work through all those different machinations of things that would be too difficult to do at speed for a person. 

Megan: Well, sounds like there’s some fascinating things going on. I mean, of course, for a global brand, there is also the challenge of working in different markets. You mentioned moving everything around the world there. Each market with its own legal frameworks around data privacy, AI. How has technology also helped you navigate all of that, Dan? 

Dan: The other really interesting thing about AI is… I’ve worked in technology leadership roles for some time now. And historically, we would be going around the company, banging on everyone’s doors and dragging them towards technology, making them use systems, making them move things to the cloud and things like that. What AI has done is it’s turned that around on its head, and we now have people turning up, banging on our door because they want to use this tool, they want to use that tool. And we’re trying to accommodate all of that and it’s a great pleasure to see people that are so keen. AI is driving the tech adoption in general, which really helps the business. 

Megan: Dan, as the world’s first all-electric motor sport series, sustainability is obviously a real cornerstone of what Formula E is looking to do. Can you share with us how technology is helping you to achieve some of your ambitions when it comes to sustainability? 

Dan: We’ve been the only sport with a certified net-zero pathway, and we have to stay that part. It’s a really core fundamental part of our DNA. I sit on our management team here. There is a sustainability VP that sits there as well, who checks and challenges everything we do. She looks at the data centers we use, why we use them, why we’ve made the decisions we’ve made, to make sure that we’re making them all for the right reasons and the right ways. We specifically embed technology in a couple of ways. One is, we mentioned a little bit earlier, on our freight. Formula E’s freight for the whole championship is probably akin to one Formula One team, but it’s still by far, our biggest contributor to our impact. So we look about how we can make sure that we’ve refined that to get the minimum amount of air freight and sea freight, and use local wherever we can. That’s also part of our pledge about investing in the communities that we race in. 

The second then is about our staff travel. And we’ve done a really big piece of work over the last four to five years, partly accelerated through the covid-19 era actually, of doing remote working and remote TV production. Used to be traditionally, you would fly a hundred plus people out to racetracks, and then they would make the television all on site in trucks, and then they would be satellite distributed out of the venue. Now, what we do is we put in some internet connections, dual and diverse internet connections, and we stream every single camera back. 

Megan: Right. 

Dan: That means on site, we only need camera operators. Some of them actually, are remotely operated anyway, but we need camera operators, and then some engineering teams to just keep everything running. And then back in our home base, which is in London, in the UK, we have our remote production center where we layer on direction, graphics, audio, replay, team radio, all of those bits that break the color and make the program and add to that significant body of people. We do that all remotely now. Really interesting actually, a bit. So that’s the carbon sustainability story, but there is a further ESG piece that comes out of it and we haven’t really accommodated when we went into it, is the diversity in our workforce by doing that. We were discovering that we had quite a young, equally diverse workforce until around the age of 30. And then once that happened, then we were finding we were losing women, and that’s really because they didn’t want to travel. 

Megan: Right. 

Dan: And that’s the age of people starting to have children, and things were starting to change. And then we had some men that were traveling instead, and they weren’t seeing their children and it was sort of dividing it unnecessarily. But by going remote, by having so much of our people able to remotely… Or even if they do have to travel, they’re not traveling every single week. They’re now doing that one in three. They’re able to maintain the careers and the jobs they want to do, whilst having a family lifestyle. And it also just makes a better product by having people in that environment. 

Megan: That’s such an interesting perspective, isn’t it? It’s a way of environmental sustainability intersects with social sustainability. And Rohit, and your work are so interesting. And Rohit, can you share any of the ways that Infosys has worked with Formula E, in terms of the role of technology as we say, in furthering those ambitions around sustainability? 

Rohit: Yeah. Infosys understands that sustainability is at the heart of Formula E, and it’s a big part of why this partnership matters. Formula E is already net-zero certified, but now, they have an ambitious goal to cut carbon emissions by 45%. Infosys is helping in two ways. First, we have built AI-powered sustainability data tools that make carbon reporting accurate and traceable. Every watt of energy, every logistic decision, every material use can be tracked. Second, we use predictive analytics to model scenarios, like how changing race logistics or battery technology impact emissions so Formula E can make smarter, greener decisions. For us, it’s about turning sustainability from a report into an action plan, and making Formula E a global leader in green motor sport. 

Megan: And in April 2025, Formula E working with Infosys launched its Stats Centre, which provides fans with interactive access to the performances of their drivers and teams, key milestones and narratives. I know you touched on this before, but I wonder if you could tell us a bit more about the design of that platform, Rohit, and how it fits into Formula E’s wider plans to personalize that fan experience? 

Rohit: Sure. The Stats Centre was a big step forward. Before this, fans had access to basic statistics on the website and the mobile app, but nothing told the full story and we wanted to change that. Built on Infosys Topaz, the Stats Centre uses AI to turn race data into interactive stories. Fans can explore key stat cards that adapt to race timelines, and even chat with an AI companion to get instant answers. It’s like having a person race analyst at your fingertips. And we are going further. Next year, we’ll launch Race Centre. It’ll have live data boards, 2D track maps showing every driver’s position, overtakes and more attack timelines, and AI-generated commentary. Fans can predict podium finishes, vote for the driver of the race, and share their views on social media. Plus, we are adding video explainers for new fans, covering rules, strategies, and car technology. Our goal is simple: make every moment exciting and easy to understand. Whether you are a hardcore fan or someone watching Formula E for the first time, you’ll feel connected and informed. 

Megan: Fantastic. Sounds brilliant. And as you’ve explained, Dan, leveraging data and AI can come with these huge benefits when it comes to the depth of fan experience that you can deliver, but it can also expose you to some challenges. How are you navigating those at Formula E? 

Dan: The AI generation has presented two significant challenges to us. One is that traditional SEO, traditional search engine optimization, goes out the window. Right? You are now looking at how do we design and build our systems and how do we populate them with the right content and the right data, so that the engines are picking it up correctly and displaying it? The way that the foundational models are built and the speed and the cadence of which they’re updated, means quite often… We’re a very fast-changing organization. We’re a fast-changing product. Often, the models don’t keep up. And that’s because they are a point in time when they were trained. And that’s something that the big organizations, the big tech organizations will fix with time. But for now, what we have to do is we have to learn about how we can present our fan-facing, web-facing products to show that correctly. That’s all about having really accurate first-party content, effectively earned media. That’s the piece we need to do. 

Then the second sort of challenge is sadly, whilst these tools are available to all of us, and we are using them effectively, so are another part of the technology landscape, and that is the cybersecurity basically they come with. If you look at the speed of the cadence and severity of hacks that are happening now, it’s just growing and growing and growing, and that’s because they have access to these tools too. And we’re having to really up our game and professionalize. And that’s really hard for an innovative organization. You don’t want to shut everything down. You don’t want to protect everything too much because you want people to be able to try new things. Right? If I block everything to only things that the IT team had heard of, we’d never get anything new in, and it’s about getting that balance right. 

Megan: Right. 

Dan: Rohit, you probably have similar experiences? 

Megan: How has Infosys worked with Formula E to help it navigate some of that, Rohit? 

Rohit: Yeah. Infosys has helped Formula E tackle some of the challenges in three key ways, simplify complex race data into engaging fan experience through platforms like Stats Centre, building a secure and scalable cloud data backbone for the real-time insights, and enabling sustainability goals with AI-driven carbon tracking and predictive analytics. This solution makes the sport interactive, more digital, and more responsible. 

Megan: Fantastic. I wondered if we could close with a bit of a future forward look. Can you share with us any innovations on the horizon at Formula E that you are really excited about, Dan? 

Dan: We have mentioned the Race Centre is going to launch in the next couple of months, but the really exciting thing for me is we’ve got an amazing season ahead of us. It’s the last season of our Gen3 car, with 10 really exciting teams on the grid. We are going at speed with our tech innovation roadmap and what our fans want. And we’re building up towards our Gen4 car, which will come out for season 13 in a year’s time. That will get launched in 2026, and I think it will be a game changer in how people perceive electric motor sport and electric cars in general. 

Megan: It sounds like there’s all sorts of exciting things going on. And Rohit too, what’s coming up via this partnership that you are really looking forward to sharing with everyone? 

Rohit: Two things stand out for me. First is the AI-powered fan data platform that I’ve already spoken about. Second is the launch of Race Centre. It’s going to change how fans experience live racing. And beyond final engagement, we are helping Formula E lead in sustainability with AI tools that model carbon impact and optimize logistics. This means every race can be smarter and greener. Our goal is clear: help Formula E be the most digital and sustainable motor sport in the world. The future is electric, and with AI, it’s more engaging than ever. 

Megan: Fantastic. Thank you so much, both. That was Rohit Agnihotri, principal technologist at Infosys, and Dan Cherowbrier, CITO of Formula E, whom I spoke with from Brighton, England.  

That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor and host for Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print, on the web and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.  

This show is available wherever you get your podcasts. And if you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review and this episode was produced by Giro Studios. Thanks for listening. 

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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At a regional hospital, a cardiac patient’s lab results sit behind layers of encryption, accessible to his surgeon but shielded from those without strictly need-to-know status. Across the street at a credit union, a small business owner anxiously awaits the all-clear for a wire transfer, unaware that fraud detection systems have flagged it for further review.

Such scenarios illustrate how companies in regulated industries juggle competing directives: Move data and process transactions quickly enough to save lives and support livelihoods, but carefully enough to maintain ironclad security and satisfy regulatory scrutiny.

Organizations subject to such oversight walk a fine line every day. And recently, a number of curveballs have thrown off that hard-won equilibrium. Agencies are ramping up oversight thanks to escalating data privacy concerns; insurers are tightening underwriting and requiring controls like MFA and privileged-access governance as a condition of coverage. Meanwhile, the shifting VMware landscape has introduced more complexity for IT teams tasked with planning long-term infrastructure strategies. 

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This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

The past year has marked a turning point in the corporate AI conversation. After a period of eager experimentation, organizations are now confronting a more complex reality: While investment in AI has never been higher, the path from pilot to production remains elusive. Three-quarters of enterprises remain stuck in experimentation mode, despite mounting pressure to convert early tests into operational gains.

“Most organizations can suffer from what we like to call PTSD, or process technology skills and data challenges,” says Shirley Hung, partner at Everest Group. “They have rigid, fragmented workflows that don’t adapt well to change, technology systems that don’t speak to each other, talent that is really immersed in low-value tasks rather than creating high impact. And they are buried in endless streams of information, but no unified fabric to tie it all together.”

The central challenge, then, lies in rethinking how people, processes, and technology work together.

Across industries as different as customer experience and agricultural equipment, the same pattern is emerging: Traditional organizational structures—centralized decision-making, fragmented workflows, data spread across incompatible systems—are proving too rigid to support agentic AI. To unlock value, leaders must rethink how decisions are made, how work is executed, and what humans should uniquely contribute.

“It is very important that humans continue to verify the content. And that is where you’re going to see more energy being put into,” Ryan Peterson, EVP and chief product officer at Concentrix.

Much of the conversation centered on what can be described as the next major unlock: operationalizing human-AI collaboration. Rather than positioning AI as a standalone tool or a “virtual worker,” this approach reframes AI as a system-level capability that augments human judgment, accelerates execution, and reimagines work from end to end. That shift requires organizations to map the value they want to create; design workflows that blend human oversight with AI-driven automation; and build the data, governance, and security foundations that make these systems trustworthy.

“My advice would be to expect some delays because you need to make sure you secure the data,” says Heidi Hough, VP for North America aftermarket at Valmont. “As you think about commercializing or operationalizing any piece of using AI, if you start from ground zero and have governance at the forefront, I think that will help with outcomes.”

Early adopters are already showing what this looks like in practice: starting with low-risk operational use cases, shaping data into tightly scoped enclaves, embedding governance into everyday decision-making, and empowering business leaders, not just technologists, to identify where AI can create measurable impact. The result is a new blueprint for AI maturity grounded in reengineering how modern enterprises operate.

“Optimization is really about doing existing things better, but reimagination is about discovering entirely new things that are worth doing,” says Hung.

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This webcast is produced in partnership with Concentrix.

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Most organizations feel the imperative to keep pace with continuing advances in AI capabilities, as highlighted in a recent MIT Technology Review Insights report. That clearly has security implications, particularly as organizations navigate a surge in the volume, velocity, and variety of security data. This explosion of data, coupled with fragmented toolchains, is making it increasingly difficult for security and data teams to maintain a proactive and unified security posture. 

Data and AI teams must move rapidly to deliver the desired business results, but they must do so without compromising security and governance. As they deploy more intelligent and powerful AI capabilities, proactive threat detection and response against the expanded attack surface, insider threats, and supply chain vulnerabilities must remain paramount. “I’m passionate about cybersecurity not slowing us down,” says Melody Hildebrandt, chief technology officer at Fox Corporation, “but I also own cybersecurity strategy. So I’m also passionate about us not introducing security vulnerabilities.” 

That’s getting more challenging, says Nithin Ramachandran, who is global vice president for data and AI at industrial and consumer products manufacturer 3M. “Our experience with generative AI has shown that we need to be looking at security differently than before,” he says. “With every tool we deploy, we look not just at its functionality but also its security posture. The latter is now what we lead with.” 

Our survey of 800 technology executives (including 100 chief information security officers), conducted in June 2025, shows that many organizations struggle to strike this balance. 

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In 1913, Henry Ford cut the time it took to build a Model T from 12 hours to just over 90 minutes. He accomplished this feat through a revolutionary breakthrough in process design: Instead of skilled craftsmen building a car from scratch by hand, Ford created an assembly line where standardized tasks happened in sequence, at scale.

The IT industry is having a similar moment of reinvention. Across operations from software development to cloud migration, organizations are adopting an AI-infused factory model that replaces manual, one-off projects with templated, scalable systems designed for speed and cost-efficiency.

Take VMware migrations as an example. For years, these projects resembled custom production jobs—bespoke efforts that often took many months or even years to complete. Fluctuating licensing costs added a layer of complexity, just as business leaders began pushing for faster modernization to make their organizations AI-ready. That urgency has become nearly universal: According to a recent IDC report, six in 10 organizations evaluating or using cloud services say their IT infrastructure requires major transformation, while 82% report their cloud environments need modernization.

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This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Today’s IT leaders face competing mandates to do more (“make us an ‘AI-first’ enterprise—yesterday”) with less (“no new hires for at least the next six months”).

VMware has become a focal point of these dueling directives. It remains central to enterprise IT, with 80% of organizations using VMware infrastructure products. But shifting licensing models are prompting teams to reconsider how they manage and scale these workloads, often on tighter budgets.

For many organizations, the path forward involves adopting a LessOps model, an operational strategy that makes hybrid environments manageable without increasing headcount. This operational philosophy minimizes human intervention through extensive automation and selfservice capabilities while maintaining governance and compliance.

In practice, VMware-to-cloud migrations create a “two birds, one stone” opportunity. They present a practical moment to codify the automation and governance practices LessOps depends on—laying the groundwork for a leaner, more resilient IT operating model.

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

For decades, business continuity planning meant preparing for anomalous events like hurricanes, floods, tornadoes, or regional power outages. In anticipation of these rare disasters, IT teams built playbooks, ran annual tests, crossed their fingers, and hoped they’d never have to use them.

In recent years, an even more persistent threat has emerged. Cyber incidents, particularly ransomware, are now more common—and often, more damaging—than physical disasters. In a recent survey of more than 500 CISOs, almost three-quarters (72%) said their organization had dealt with ransomware in the previous year. Earlier in 2025, ransomware attack rates on enterprises reached record highs.

Mark Vaughn, senior director of the virtualization practice at Presidio, has witnessed the trend firsthand. “When I speak at conferences, I’ll ask the room, ‘How many people have been impacted?’ For disaster recovery, you usually get a few hands,” he says. “But a little over a year ago, I asked how many people in the room had been hit by ransomware, and easily two-thirds of the hands went up.”

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

There’s a lot at stake when it comes to understanding how AI is changing the economy at large. What’s the right outlook to have? Join Mat Honan, editor in chief, for a special conversation with David Rotman, editor at large, and Richard Waters, Financial Times columnist, exploring what’s happening across industries and the market.

Going live on December 9th at 18:00 GMT / 1:00 PM EDT / 10:00 AM PDT

Speakers:

Everything is a conspiracy theory now. MIT Technology Review’s series, “The New Conspiracy Age,” explores how this moment is changing science and technology. Watch a discussion with our editors and Mike Rothschild, journalist and conspiracy theory expert, about how we can make sense of them all.

Speakers: Amanda Silverman, Editor, Features & Investigations; Niall Firth, Executive Editor, Newsroom; and Mike Rothschild, Journalist & Conspiracy Theory Expert.

https://vimeo.com/1139060324?share=copy&fl=sv&fe=ci

Recorded on November 20, 2025

Related Content:

• The New Conspiracy Age
• What it’s like to be in the middle of a conspiracy theory (according to a conspiracy theory expert)
• How to help friends and family dig out of a conspiracy theory black hole

Digital resilience—the ability to prevent, withstand, and recover from digital disruptions—has long been a strategic priority for enterprises. With the rise of agentic AI, the urgency for robust resilience is greater than ever.

Agentic AI represents a new generation of autonomous systems capable of proactive planning, reasoning, and executing tasks with minimal human intervention. As these systems shift from experimental pilots to core elements of business operations, they offer new opportunities but also introduce new challenges when it comes to ensuring digital resilience. That’s because the autonomy, speed, and scale at which agentic AI operates can amplify the impact of even minor data inconsistencies, fragmentation, or security gaps.

While global investment in AI is projected to reach $1.5 trillion in 2025, fewer than half of business leaders are confident in their organization’s ability to maintain service continuity, security, and cost control during unexpected events. This lack of confidence, coupled with the profound complexity introduced by agentic AI’s autonomous decision-making and interaction with critical infrastructure, requires a reimagining of digital resilience.

Organizations are turning to the concept of a data fabric—an integrated architecture that connects and governs information across all business layers. By breaking down silos and enabling real-time access to enterprise-wide data, a data fabric can empower both human teams and agentic AI systems to sense risks, prevent problems before they occur, recover quickly when they do, and sustain operations.

Machine data: A cornerstone of agentic AI and digital resilience

Earlier AI models relied heavily on human-generated data such as text, audio, and video, but agentic AI demands deep insight into an organization’s machine data: the logs, metrics, and other telemetry generated by devices, servers, systems, and applications.

To put agentic AI to use in driving digital resilience, it must have seamless, real-time access to this data flow. Without comprehensive integration of machine data, organizations risk limiting AI capabilities, missing critical anomalies, or introducing errors. As Kamal Hathi, senior vice president and general manager of Splunk, a Cisco company, emphasizes, agentic AI systems rely on machine data to understand context, simulate outcomes, and adapt continuously. This makes machine data oversight a cornerstone of digital resilience.

“We often describe machine data as the heartbeat of the modern enterprise,” says Hathi. “Agentic AI systems are powered by this vital pulse, requiring real-time access to information. It’s essential that these intelligent agents operate directly on the intricate flow of machine data and that AI itself is trained using the very same data stream.” 

Few organizations are currently achieving the level of machine data integration required to fully enable agentic systems. This not only narrows the scope of possible use cases for agentic AI, but, worse, it can also result in data anomalies and errors in outputs or actions. Natural language processing (NLP) models designed prior to the development of generative pre-trained transformers (GPTs) were plagued by linguistic ambiguities, biases, and inconsistencies. Similar misfires could occur with agentic AI if organizations rush ahead without providing models with a foundational fluency in machine data. 

For many companies, keeping up with the dizzying pace at which AI is progressing has been a major challenge. “In some ways, the speed of this innovation is starting to hurt us, because it creates risks we’re not ready for,” says Hathi. “The trouble is that with agentic AI’s evolution, relying on traditional LLMs trained on human text, audio, video, or print data doesn’t work when you need your system to be secure, resilient, and always available.”

Designing a data fabric for resilience

To address these shortcomings and build digital resilience, technology leaders should pivot to what Hathi describes as a data fabric design, better suited to the demands of agentic AI. This involves weaving together fragmented assets from across security, IT, business operations, and the network to create an integrated architecture that connects disparate data sources, breaks down silos, and enables real-time analysis and risk management. 

“Once you have a single view, you can do all these things that are autonomous and agentic,” says Hathi. “You have far fewer blind spots. Decision-making goes much faster. And the unknown is no longer a source of fear because you have a holistic system that’s able to absorb these shocks and disruption without losing continuity,” he adds.

To create this unified system, data teams must first break down departmental silos in how data is shared, says Hathi. Then, they must implement a federated data architecture—a decentralized system where autonomous data sources work together as a single unit without physically merging—to create a unified data source while maintaining governance and security. And finally, teams must upgrade data platforms to ensure this newly unified view is actionable for agentic AI. 

During this transition, teams may face technical limitations if they rely on traditional platforms modeled on structured data—that is, mostly quantitative information such as customer records or financial transactions that can be organized in a predefined format (often in tables) that is easy to query. Instead, companies need a platform that can also manage streams of unstructured data such as system logs, security events, and application traces, which lack uniformity and are often qualitative rather than quantitative. Analyzing, organizing, and extracting insights from these kinds of data requires more advanced methods enabled by AI.

Harnessing AI as a collaborator

AI itself can be a powerful tool in creating the data fabric that enables AI systems. AI-powered tools can, for example, quickly identify relationships between disparate data—both structured and unstructured—automatically merging them into one source of truth. They can detect and correct errors and employ NLP to tag and categorize data to make it easier to find and use. 

Agentic AI systems can also be used to augment human capabilities in detecting and deciphering anomalies in an enterprise’s unstructured data streams. These are often beyond human capacity to spot or interpret at speed, leading to missed threats or delays. But agentic AI systems, designed to perceive, reason, and act autonomously, can plug the gap, delivering higher levels of digital resilience to an enterprise.

“Digital resilience is about more than withstanding disruptions,” says Hathi. “It’s about evolving and growing over time. AI agents can work with massive amounts of data and continuously learn from humans who provide safety and oversight. This is a true self-optimizing system.”

Humans in the loop

Despite its potential, agentic AI should be positioned as assistive intelligence. Without proper oversight, AI agents could introduce application failures or security risks.

Clearly defined guardrails and maintaining humans in the loop is “key to trustworthy and practical use of AI,” Hathi says. “AI can enhance human decision-making, but ultimately, humans are in the driver’s seat.”

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Manufacturing is getting a major system upgrade. As AI amplifies existing technologies—like digital twins, the cloud, edge computing, and the industrial internet of things (IIoT)—it is enabling factory operations teams to shift from reactive, isolated problem-solving to proactive, systemwide optimization.

Digital twins—physically accurate virtual representations of a piece of equipment, a production line, a process, or even an entire factory—allow workers to test, optimize, and contextualize complex, real-world environments. Manufacturers are using digital twins to simulate factory environments with pinpoint detail.

“AI-powered digital twins mark a major evolution in the future of manufacturing, enabling real-time visualization of the entire production line, not just individual machines,” says Indranil Sircar, global chief technology officer for the manufacturing and mobility industry at Microsoft. “This is allowing manufacturers to move beyond isolated monitoring toward much wider insights.”

A digital twin of a bottling line, for example, can integrate one-dimensional shop-floor telemetry, two-dimensional enterprise data, and three-dimensional immersive modeling into a single operational view of the entire production line to improve efficiency and reduce costly downtime. Many high-speed industries face downtime rates as high as 40%, estimates Jon Sobel, co-founder and chief executive officer of Sight Machine, an industrial AI company that partners with Microsoft and NVIDIA to transform complex data into actionable insights. By tracking micro-stops and quality metrics via digital twins, companies can target improvements and adjustments with greater precision, saving millions in once-lost productivity without disrupting ongoing operations.

AI offers the next opportunity. Sircar estimates that up to 50% of manufacturers are currently deploying AI in production. This is up from 35% of manufacturers surveyed in a 2024 MIT Technology Review Insights report who said they have begun to put AI use cases into production. Larger manufacturers with more than $10 billion in revenue were significantly ahead, with 77% already deploying AI use cases, according to the report.

“Manufacturing has a lot of data and is a perfect use case for AI,” says Sobel. “An industry that has been seen by some as lagging when it comes to digital technology and AI may be in the best position to lead. It’s very unexpected.”

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

The Ryder Cup is an almost-century-old tournament pitting Europe against the United States in an elite showcase of golf skill and strategy. At the 2025 event, nearly a quarter of a million spectators gathered to watch three days of fierce competition on the fairways.

From a technology and logistics perspective, pulling off an event of this scale is no easy feat. The Ryder Cup’s infrastructure must accommodate the tens of thousands of network users who flood the venue (this year, at Bethpage Black in Farmingdale, New York) every day.

To manage this IT complexity, Ryder Cup engaged technology partner HPE to create a central hub for its operations. The solution centered around a platform where tournament staff could access data visualization supporting operational decision-making. This dashboard, which leveraged a high-performance network and private-cloud environment, aggregated and distilled insights from diverse real-time data feeds.

It was a glimpse into what AI-ready networking looks like at scale—a real-world stress test with implications for everything from event management to enterprise operations. While models and data readiness get the lion’s share of boardroom attention and media hype, networking is a critical third leg of successful AI implementation, explains Jon Green, CTO of HPE Networking. “Disconnected AI doesn’t get you very much; you need a way to get data into it and out of it for both training and inference,” he says.

As businesses move toward distributed, real-time AI applications, tomorrow’s networks will need to parse even more massive volumes of information at ever more lightning-fast speeds. What played out on the greens at Bethpage Black represents a lesson being learned across industries: Inference-ready networks are a make-or-break factor for turning AI’s promise into real-world performance.

Making a network AI inference-ready

More than half of organizations are still struggling to operationalize their data pipelines. In a recent HPE cross-industry survey of 1,775  IT leaders, 45% said they could run real-time data pushes and pulls for innovation. It’s a noticeable change over last year’s numbers (just 7% reported having such capabilities in 2024), but there’s still work to be done to connect data collection with real-time decision-making.

The network may hold the key to further narrowing that gap. Part of the solution will likely come down to infrastructure design. While traditional enterprise networks are engineered to handle the predictable flow of business applications—email, browsers, file sharing, etc.—they’re not designed to field the dynamic, high-volume data movement required by AI workloads. Inferencing in particular depends on shuttling vast datasets between multiple GPUs with supercomputer-like precision.

“There’s an ability to play fast and loose with a standard, off-the-shelf enterprise network,” says Green. “Few will notice if an email platform is half a second slower than it might’ve been. But with AI transaction processing, the entire job is gated by the last calculation taking place. So it becomes really noticeable if you’ve got any loss or congestion.”

Networks built for AI, therefore, must operate with a different set of performance characteristics, including ultra-low latency, lossless throughput, specialized equipment, and adaptability at scale. One of these differences is AI’s distributed nature, which affects the seamless flow of data.

The Ryder Cup was a vivid demonstration of this new class of networking in action. During the event, a Connected Intelligence Center was put in place to ingest data from ticket scans, weather reports, GPS-tracked golf carts, concession and merchandise sales, spectator and consumer queues, and network performance. Additionally, 67 AI-enabled cameras were positioned throughout the course. Inputs were analyzed through an operational intelligence dashboard and provided staff with an instantaneous view of activity across the grounds.

“The tournament is really complex from a networking perspective, because you have many big open areas that aren’t uniformly packed with people,” explains Green. “People tend to follow the action. So in certain areas, it’s really dense with lots of people and devices, while other areas are completely empty.”

To handle that variability, engineers built out a two-tiered architecture. Across the sprawling venue, more than 650 WiFi 6E access points, 170 network switches, and 25 user experience sensors worked together to maintain continuous connectivity and feed a private cloud AI cluster for live analytics. The front-end layer connected cameras, sensors, and access points to capture live video and movement data, while a back-end layer—located within a temporary on-site data center—linked GPUs and servers in a high-speed, low-latency configuration that effectively served as the system’s brain. Together, the setup enabled both rapid on-the-ground responses and data collection that could inform future operational planning. “AI models also were available to the team which could process video of the shots taken and help determine, from the footage, which ones were the most interesting,” says Green.

Physical AI and the return of on-prem intelligence

If time is of the essence for event management, it’s even more critical in contexts where safety is on the line—for instance a self-driving car making a split-second decision to accelerate or brake.

In planning for the rise of physical AI, where applications move off screens and onto factory floors and city streets, a growing number of enterprises are rethinking their architectures. Instead of sending the data to centralized clouds for inference, some are deploying edge-based AI clusters that process information closer to where it is generated. Data-intensive training may still occur in the cloud, but inferencing happens on-site.

This hybrid approach is fueling a wave of operational repatriation, as workloads once relegated to the cloud return to on-premises infrastructure for enhanced speed, security, sovereignty, and cost reasons. “We’ve had an out-migration of IT into the cloud in recent years, but physical AI is one of the use cases that we believe will bring a lot of that back on-prem,” predicts Green, giving the example of an AI-infused factory floor, where a round-trip of sensor data to the cloud would be too slow to safely control automated machinery. “By the time processing happens in the cloud, the machine has already moved,” he explains.

There’s data to back up Green’s projection: research from Enterprise Research Group shows that 84% of respondents are reevaluating application deployment strategies due to the growth of AI. Market forecasts also reflect this shift. According to IDC, the AI market for infrastructure is expected to reach $758 billion by 2029.

AI for networking and the future of self-driving infrastructure

The relationship between networking and AI is circular: Modern networks make AI at scale possible, but AI is also helping make networks smarter and more capable.

“Networks are some of the most data-rich systems in any organization,” says Green. “That makes them a perfect use case for AI. We can analyze millions of configuration states across thousands of customer environments and learn what actually improves performance or stability.”

At HPE for example, which has one of the largest network telemetry repositories in the world, AI models analyze anonymized data collected from billions of connected devices to identify trends and refine behavior over time. The platform processes more than a trillion telemetry points each day, which means it can continuously learn from real-world conditions.

The concept broadly known as AIOps (or AI-driven IT operations) is changing how enterprise networks are managed across industries. Today, AI surfaces insights as recommendations that administrators can choose to apply with a single click. Tomorrow, those same systems might automatically test and deploy low-risk changes themselves.

That long-term vision, Green notes, is referred to as a “self-driving network”—one that handles the repetitive, error-prone tasks that have historically plagued IT teams. “AI isn’t coming for the network engineer’s job, but it will eliminate the tedious stuff that slows them down,” he says. “You’ll be able to say, ‘Please go configure 130 switches to solve this issue,’ and the system will handle it. When a port gets stuck or someone plugs a connector in the wrong direction, AI can detect it—and in many cases, fix it automatically.”

Digital initiatives now depend on how effectively information moves. Whether coordinating a live event or streamlining a supply chain, the performance of the network increasingly defines the performance of the business. Building that foundation today will separate those who pilot from those who scale AI.

For more, register to watch MIT Technology Review’s EmTech AI Salon, featuring HPE.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Training an AI model to predict equipment failures is an engineering achievement. But it’s not until prediction meets action—the moment that model successfully flags a malfunctioning machine—that true business transformation occurs. One technical milestone lives in a proof-of-concept deck; the other meaningfully contributes to the bottom line.

Craig Partridge, senior director worldwide of Digital Next Advisory at HPE, believes “the true value of AI lies in inference”. Inference is where AI earns its keep. It’s the operational layer that puts all that training to use in real-world workflows. Partridge elaborates, “The phrase we use for this is ‘trusted AI inferencing at scale and in production,'” he says. “That’s where we think the biggest return on AI investments will come from.”

Getting to that point is difficult. Christian Reichenbach, worldwide digital advisor at HPE, points to findings from the company’s recent survey of 1,775 IT leaders: While nearly a quarter (22%) of organizations have now operationalized AI—up from 15% the previous year—the majority remain stuck in experimentation.

Reaching the next stage requires a three-part approach: establishing trust as an operating principle, ensuring data-centric execution, and cultivating IT leadership capable of scaling AI successfully.

Trust as a prerequisite for scalable, high-stakes AI

Trusted inference means users can actually rely on the answers they’re getting from AI systems. This is important for applications like generating marketing copy and deploying customer service chatbots, but it’s absolutely critical for higher-stakes scenarios—say, a robot assisting during surgeries or an autonomous vehicle navigating crowded streets.

Whatever the use case, establishing trust will require doubling down on data quality; first and foremost, inferencing outcomes must be built on reliable foundations. This reality informs one of Partridge’s go-to mantras: “Bad data in equals bad inferencing out.”

Reichenbach cites a real-world example of what happens when data quality falls short—the rise of unreliable AI-generated content, including hallucinations, that clogs workflows and forces employees to spend significant time fact-checking. “When things go wrong, trust goes down, productivity gains are not reached, and the outcome we’re  looking for is not achieved,” he says.

On the other hand, when trust is properly engineered into inference systems, efficiency and productivity gains can increase. Take a network operations team tasked with troubleshooting configurations. With a trusted inferencing engine, that unit gains a reliable copilot that can deliver faster, more accurate, custom-tailored recommendations—”a 24/7 member of the team they didn’t have before,” says Partridge.

The shift to data-centric thinking and rise of the AI factory

In the first AI wave, companies rushed to hire data scientists and many viewed sophisticated, trillion-parameter models as the primary goal. But today, as organizations move to turn early pilots into real, measurable outcomes, the focus has shifted toward data engineering and architecture.

“Over the past five years, what’s become more meaningful is breaking down data silos, accessing data streams, and quickly unlocking value,” says Reichenbach. It’s an evolution happening alongside the rise of the AI factory—the always-on production line where data moves through pipelines and feedback loops to generate continuous intelligence.

This shift reflects an evolution from model-centric to data-centric thinking, and with it comes a new set of strategic considerations. “It comes down to two things: How much of the intelligence–the model itself–is truly yours? And how much of the input–the data–is uniquely yours, from your customers, operations, or market?” says Reichenbach.

These two central questions inform everything from platform direction and operating models to engineering roles and trust and security considerations. To help clients map their answers—and translate them into actionable strategies—Partridge breaks down HPE’s four-quadrant AI factory implication matrix (see figure):

Source: HPE, 2025

• Run: Accessing an external, pretrained model via an interface or API; organizations don’t own the model or the data. Implementation requires strong security and governance. It also requires establishing a center of excellence that makes and communicates decisions about AI usage.
• RAG (retrieval augmented generation): Using external, pre-trained models combined with a company’s proprietary data to create unique insights. Implementation focuses on connecting data streams to inferencing capabilities that provide rapid, integrated access to full-stack AI platforms.
• Riches: Training custom models on data that resides in the enterprise for unique differentiation opportunities and insights. Implementation requires scalable, energy-efficient environments, and often high-performance systems.
• Regulate: Leveraging custom models trained on external data, requiring the same scalable setup as Riches, but with added focus on legal and regulatory compliance for handling sensitive, non-owned data with extreme caution.

Importantly, these quadrants are not mutually exclusive. Partridge notes that most organizations—including HPE itself—operate across many of the quadrants. “We build our own models to help understand how networks operate,” he says. “We then deploy that intelligence into our products, so that our end customer gets the chance to deliver in what we call the ‘Run’ quadrant. So for them, it’s not their data; it’s not their model. They’re just adding that capability inside their organization.”

IT’s moment to scale—and lead

The second part of Partridge’s catchphrase about inferencing—”at scale”— speaks to a primary tension in enterprise AI: what works for a handful of use cases often breaks when applied across an entire organization.

“There’s value in experimentation and kicking ideas around,” he says. “But if you want to really see the benefits of AI, it needs to be something that everybody can engage in and that solves for many different use cases.”

In Partridge’s view, the challenge of turning boutique pilots into organization-wide systems is uniquely suited to the IT function’s core competencies—and it’s a leadership opportunity the function can’t afford to sit out. “IT takes things that are small-scale and implements the discipline required to run them at scale,” he says. “So, IT organizations really need to lean into this debate.”

For IT teams content to linger on the sidelines, history offers a cautionary tale from the last major infrastructure shift: enterprise migration to the cloud. Many IT departments sat out decision-making during the early cloud adoption wave a decade ago, while business units independently deployed cloud services. This led to fragmented systems, redundant spending, and security gaps that took years to untangle.

The same dynamic threatens to repeat with AI, as different teams experiment with tools and models outside IT’s purview. This phenomenon—sometimes called shadow AI—describes environments where pilots proliferate without oversight or governance. Partridge believes that most organizations are already operating in the “Run” quadrant in some capacity, as employees will use AI tools whether or not they’re officially authorized to.

Rather than shut down experimentation, it is now IT’s mandate to bring structure to it. And enterprises must architect a data platform strategy that brings together enterprise data with guardrails, governance framework, and accessibility to feed AI. Also, it’s critical to keep standardizing infrastructure (such as private cloud AI platforms), protecting data integrity, and safeguarding brand trust, all while enabling the speed and flexibility that AI applications demand. These are the requirements for reaching the final milestone: AI that’s truly in production.

For teams on the path to that goal, Reichenbach distills what success requires. “It comes down to knowing where you play: When to Run external models smarter, when to apply RAG to make them more informed, where to invest to unlock Riches from your own data and models, and when to Regulate what you don’t control,” says Reichenbach. “The winners will be those who bring clarity to all quadrants and align technology ambition with governance and value creation.”

For more, register to watch MIT Technology Review’s EmTech AI Salon, featuring HPE.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

For years, many chief information officers (CIOs) looked at VMware-to-cloud migrations with a wary pragmatism. Manually mapping dependencies and rewriting legacy apps mid-flight was not an enticing, low-lift proposition for enterprise IT teams.

But the calculus for such decisions has changed dramatically in a short period of time. Following recent VMware licensing changes, organizations are seeing greater uncertainty around the platform’s future. At the same time, cloud-native innovation is accelerating. According to the CNCF’s 2024 Annual Survey, 89% of organizations have already adopted at least some cloud-native techniques, and the share of companies reporting nearly all development and deployment as cloud-native grew sharply from 2023 to 2024 (20% to 24%). And market research firm IDC reports that cloud providers have become top strategic partners for generative AI initiatives.

This is all happening amid escalating pressure to innovate faster and more cost-effectively to meet the demands of an AI-first future. As enterprises prepare for that inevitability, they are facing compute demands that are difficult, if not prohibitively expensive, to maintain exclusively on-premises.

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

AI and quantum technologies are dramatically reconfiguring how cybersecurity functions, redefining the speed and scale with which digital defenders and their adversaries can operate.

The weaponization of AI tools for cyberattacks is already proving a worthy opponent to current defenses. From reconnaissance to ransomware, cybercriminals can automate attacks faster than ever before with AI. This includes using generative AI to create social engineering attacks at scale, churning out tens of thousands of tailored phishing emails in seconds, or accessing widely available voice cloning software capable of bypassing security defenses for as little as a few dollars. And now, agentic AI raises the stakes by introducing autonomous systems that can reason, act, and adapt like human adversaries.

But AI isn’t the only force shaping the threat landscape. Quantum computing has the potential to seriously undermine current encryption standards if developed unchecked. Quantum algorithms can solve the mathematical problems underlying most modern cryptography, particularly public-key systems like RSA and Elliptic Curve, widely used for secure online communication, digital signatures, and cryptocurrency.

“We know quantum is coming. Once it does, it will force a change in how we secure data across everything, including governments, telecoms, and financial systems,” says Peter Bailey, senior vice president and general manager of Cisco’s security business.

“Most organizations are understandably focused on the immediacy of AI threats,” says Bailey. “Quantum might sound like science fiction, but those scenarios are coming faster than many realize. It’s critical to start investing now in defenses that can withstand both AI and quantum attacks.”

Critical to this defense is a zero trust approach to cybersecurity, which assumes no user or device can be inherently trusted. By enforcing continuous verification, zero trust enables constant monitoring and ensures that any attempts to exploit vulnerabilities are quickly detected and addressed in real time. This approach is technology-agnostic and creates a resilient framework even in the face of an ever-changing threat landscape.

Putting up AI defenses 

AI is lowering the barrier to entry for cyberattacks, enabling hackers even with limited skills or resources to infiltrate, manipulate, and exploit the slightest digital vulnerability.

Nearly three-quarters (74%) of cybersecurity professionals say AI-enabled threats are already having a significant impact on their organization, and 90% anticipate such threats in the next one to two years. 

“AI-powered adversaries have advanced techniques and operate at machine speed,” says Bailey. “The only way to keep pace is to use AI to automate response and defend at machine speed.”

To do this, Bailey says, organizations must modernize systems, platforms, and security operations to automate threat detection and response—processes that have previously relied on human rule-writing and reaction times. These systems must adapt dynamically as environments evolve and criminal tactics change.

At the same time, companies must strengthen the security of their AI models and data to reduce exposure to manipulation from AI-enabled malware. Such risks could include, for instance, prompt injections, where a malicious user crafts a prompt to manipulate an AI model into performing unintended actions, bypassing its original instructions and safeguards.

Agentic AI further ups the ante, with hackers able to use AI agents to automate attacks and make tactical decisions without constant human oversight. “Agentic AI has the potential to collapse the cost of the kill chain,” says Bailey. “That means everyday cybercriminals could start executing campaigns that today only well-funded espionage operations can afford.”

Organizations, in turn, are exploring how AI agents can help them stay ahead. Nearly 40% of companies expect agentic AI to augment or assist teams over the next 12 months, especially in cybersecurity, according to Cisco’s 2025 AI Readiness Index. Use cases include AI agents trained on telemetry, which can identify anomalies or signals from machine data too disparate and unstructured to be deciphered by humans. 

Calculating the quantum threat

As many cybersecurity teams focus on the very real AI-driven threat, quantum is waiting on the sidelines. Almost three-quarters (73%) of US organizations surveyed by KPMG say they believe it is only a matter of time before cybercriminals are using quantum to decrypt and disrupt today’s cybersecurity protocols. And yet, the majority (81%) also admit they could do more to ensure that their data remains secure.

Companies are right to be concerned. Threat actors are already carrying out harvest now, decrypt later attacks, stockpiling sensitive encrypted data to crack once quantum technology matures. Examples include state-sponsored actors intercepting government communications and cybercriminal networks storing encrypted internet traffic or financial records. 

Large technology companies are among the first to roll out quantum defenses. For example, Apple is using cryptography protocol PQ3 to defend against harvest now, decrypt later attacks on its iMessage platform. Google is testing post-quantum cryptography (PQC)—which is resistant to attacks from both quantum and classical computers—in its Chrome browser. And Cisco “has made significant investments in quantum-proofing our software and infrastructure,” says Bailey. “You’ll see more enterprises and governments taking similar steps over the next 18 to 24 months,” he adds. 

As regulations like the US Quantum Computing Cybersecurity Preparedness Act lay out requirements for mitigating against quantum threats, including standardized PQC algorithms by the National Institute of Standards and Technology, a wider range of organizations will start preparing their own quantum defenses. 

For organizations beginning that journey, Bailey outlines two key actions. First, establish visibility. “Understand what data you have and where it lives,” he says. “Take inventory, assess sensitivity, and review your encryption keys, rotating out any that are weak or outdated.”

Second, plan for migration. “Next, assess what it will take to support post-quantum algorithms across your infrastructure. That means addressing not just the technology, but also the process and people implications,” Bailey says.

Adopting proactive defense 

Ultimately, the foundation for building resilience against both AI and quantum is a zero trust approach, says Bailey. By embedding zero trust access controls across users, devices, business applications, networks, and clouds, this approach grants only the minimum access required to complete a task and enables continuous monitoring. It can also minimize the attack surface by confining a potential threat to an isolated zone, preventing it from accessing other critical systems.

Into this zero trust architecture, organizations can integrate specific measures to defend against AI and quantum risks. For instance, quantum-immune cryptography and AI-powered analytics and security tools can be used to identify complex attack patterns and automate real-time responses. 

“Zero trust slows down attacks and builds resilience,” Bailey says. “It ensures that even if a breach occurs, the crown jewels stay protected and operations can recover quickly.”

Ultimately, companies should not wait for threats to emerge and evolve. They must get ahead now. “This isn’t a what-if scenario; it’s a when,” says Bailey. “Organizations that invest early will be the ones setting the pace, not scrambling to catch up.”

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

In this exclusive subscirber-only ebook you’ll learn how the emissions from individual AI text, image, and video queries seem small—until you add up what the industry isn’t tracking and consider where it’s heading next.

by James O’Donnell and Casey Crownhart May 20, 2025

Table of contents

• Part One: Making the model
• Part Two: A Query
• Part Three: Fuel and emissions
• Part Four: The future ahead

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Four years is a lifetime when it comes to artificial intelligence. Since the first edition of this study was published in 2021, AI’s capabilities have been advancing at speed, and the advances have not slowed since generative AI’s breakthrough. For example, multimodality— the ability to process information not only as text but also as audio, video, and other unstructured formats—is becoming a common feature of AI models. AI’s capacity to reason and act autonomously has also grown, and organizations are now starting to work with AI agents that can do just that.

Amid all the change, there remains a constant: the quality of an AI model’s outputs is only ever as good as the data
that feeds it. Data management technologies and practices have also been advancing, but the second edition of this study suggests that most organizations are not leveraging those fast enough to keep up with AI’s development. As a result of that and other hindrances, relatively few organizations are delivering the desired business results from their AI strategy. No more than 2% of senior executives we surveyed rate their organizations highly in terms of delivering results from AI.

To determine the extent to which organizational data performance has improved as generative AI and other AI advances have taken hold, MIT Technology Review Insights surveyed 800 senior data and technology executives. We also conducted in-depth interviews with 15 technology and business leaders.

Key findings from the report include the following:

• Few data teams are keeping pace with AI. Organizations are doing no better today at delivering on data strategy than in pre-generative AI days. Among those surveyed in 2025, 12% are self-assessed data “high achievers” compared with 13% in 2021. Shortages of skilled talent remain a constraint, but teams also struggle with accessing fresh data, tracing lineage, and dealing with security complexity—important requirements for AI success.

• Partly as a result, AI is not fully firing yet. There are even fewer “high achievers” when it comes to AI. Just 2% of respondents rate their organizations’ AI performance highly today in terms of delivering measurable business results. In fact, most are still struggling to scale generative AI. While two thirds have deployed it, only 7% have done so widely.

Download the report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Companies are pursuing climate solutions amid shifting U.S. politics and economic uncertainty. Drawing from MIT Technology Review’s 10 Climate Tech Companies to Watch list, this session highlights the most promising technologies—from electric trucks to gene-edited crops—and explores the challenges companies face in advancing climate progress today.

Speakers: Casey Crownhart, Senior Climate Reporter; James Temple, Senior Climate Editor; and Mary Beth Griggs, Science Editor

Recorded on October 28, 2025

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• How we picked promising climate tech companies in an especially unsettling year
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