Listen to the session or watch below

Can AI design new life forms? In 2025, Stanford University PhD student Samuel King came up with a preliminary answer when he used a generative AI model to propose genetic blueprints for microscopic viruses. It isn’t yet an example of AI-generated life, but that could be next. Watch as senior AI reporter James O’Donnell interviews King about his work, being named one of MIT Technology Review’s Innovators Under 35 and new ways of seeing biology.

Speakers: James O’Donnell, AI Reporter, and Samuel King, Bioengineering PhD Candidate, Stanford University/Arc Institute

Recorded on Friday, October 16, 2026

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Industrial AI is entering a new phase. After decades of predictive analytics and other specialized applications, advances in foundation models, physical AI, and agentic AI are making it possible to automate more complex tasks across industrial environments. But unlike AI that operates purely in the digital world, industrial AI can interact directly with physical systems, where an unexpected decision can have consequences for safety, reliability, and critical infrastructure.

That makes responsible deployment central to the next wave of industrial automation. “How do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?” asks Arti Garg, chief technologist at AVEVA. The challenge is particularly acute as newer AI systems become more capable but also harder to predict and explain.

One foundation for making that transition work is data. Industrial systems often contain information across telemetry, service logs, engineering documents, and other disparate sources. Newer technologies can help connect and correlate that information more quickly, giving operators real-time support when diagnosing problems. AI-powered robots could take that a step further by gathering information in hazardous environments without requiring workers to enter them.

But greater autonomy also requires new approaches to governance. AVEVA’s framework for responsible AI emphasizes security, efficiency, and human safety and oversight. Garg argues that AI should augment rather than replace people in critical decision loops, with guardrails determining where automated systems can act and where human supervisors remain responsible.

Sustainability is another part of that equation. AI can help manage complex power systems as renewable generation grows, while organizations also need better ways to understand AI’s own environmental footprint. Garg is involved in an IEEE working group developing a standard methodology for measuring that impact across electricity, energy, resources, water, and carbon.

The next phase could bring industrial AI further into the physical world, from autonomous robots and drones to AI-assisted coding that allows domain experts to build new applications. But realizing that potential will require more than deploying new technology, says Garg. Organizations will need to rethink business processes, establish appropriate safeguards, and give experienced workers new ways to apply their expertise, creating a model of automation that is not only more autonomous, but safer, more efficient, and more sustainable.

“Autonomous systems, whether they’re robots or drones, are really going to change the way that we work in plants, in power systems, on mining sites,” says Garg. “In a way, that will make these types of operations more efficient, much safer for the human beings involved and more productive.”

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

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.

Industrial AI may not be new, but it is developing at a breakneck pace. From modern predictive analytics that can preempt equipment failures, to autonomous robots tasked with inspecting high risk machinery, emerging applications have the potential to transform operational efficiencies. But often used in high consequence environments with minimal margin for error, industrial AI deployed without clarity, security, or accountability could also have catastrophic consequences for critical infrastructure over time. As industries rush to embrace the latest technologies, the goal should always be responsible AI designed to support, rather than replace that all important human judgment.

Two words for you: sustainable automation.

My guest today is Arti Garg, chief technologist at AVEVA.

This podcast is produced in partnership with AVEVA.

Welcome, Arti.

Arti Garg: Thank you, Megan. It’s great to be here.

Megan: Thank you so much for joining us. And just to start, if we could set some context for our discussion, can you share a bit about the current state of industrial AI and what AVEVA is working on at the moment as well?

Arti: Yeah, more than happy to. And I want to go back to what you already stated, which is that in some ways, industrial AI is not new. It’s something that, even here at AVEVA, we’ve been working on for more than 20 years, really thinking about how AI can enhance applications in the industrial sector.

But what has changed quite substantially in maybe the last few years is the type of AI. We’ve really moved to these general purpose foundation models, which enable a lot more people to leverage advanced AI technologies. In addition to that, in just the last couple of years, there’s been an explosion in what’s known as physical AI and the types of AI that can really accelerate the capabilities of robotics and autonomous systems. And also agentic AI, which also allows for automation at more of the software level.

And what we’re seeing is that while in the past there has been some hesitancy to adopt models that don’t totally have predictable outcomes, there’s been a rapid acceleration in adoption of AI even in the industrial sector. One study suggests maybe that’s almost a 78% increase over the past just two years within the industrial sector. So yes, that’s an inflection point, but it’s almost like a step function if you think about it with respect to AI adoption.

And what we’re really thinking about now at AVEVA is, how do we think about this? How do we think about incorporating these new and very powerful AI technologies in environments where customers have mission-critical operations, human safety is a significant factor? Those are some of the primary concerns that our customers have when they think about adopting any new technology. We’re doing that in narrow where these newer types of AI aren’t necessarily predictable, in terms of how they behave. That’s one of the areas we’re really thinking about is, how do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?

Megan: There’s so many things to consider at once, isn’t it? And you outlined there some of the huge developments we’ve seen in this space in recent years. What makes this such a pivotal moment for industrial AI specifically? What has changed to make some of those perhaps more out there hypotheticals much more plausible?

Arti: Well, I think one of the big things, one of the big challenges within industrial AI, and this is something that as AVEVA, it’s in our blood, it’s in our core DNA, is that to really leverage any kind of digital technology in the industrial space, one of the first important things you often need to do is gather and correlate data from disparate systems, so that maybe I have telemetry coming off of a pump or a mixer, and I want to be able to correlate that with service logs so I know maybe the last time that someone had done some maintenance on that pump or mixer. And maybe I also want to be able to correlate that with the original design documentation and some of the original documentation on how to maintain that, so that if something is happening, I can do a better job diagnosing what’s going wrong and how to address it. This has always been a core challenge in the industrial space.

I think that what’s happening now is that with newer technologies, even things like graph databases and being able to leverage AI to really match disparate data sets much faster, we’re able to get to the point where if something goes wrong, instead of an operator maybe needing to spend a little bit of time doing this research to really understand how do these different components fit together of information, they may have just an iPad with an AI that can very quickly go and fetch that information, correlate it and help in real time, almost, diagnose what is happening.

Even a little bit more out there, but not as out there as you might think is, I still describe this with a human operator with potentially a mobile tablet, but imagine now I can also have a robot moving through that environment also gathering data and either with onboard computation or a quick connection to an operator who doesn’t necessarily now have to go out into a dangerous, potentially somewhat hazardous space, be able to gather data and enable that very quick diagnostic as well. And I think that’s really what’s exciting right now and what all of these new technologies that I’ve talked about are helping to enable.

And I think then from our perspective, trying to really leverage all of those means that we have to think a lot now about our governance approach to this. How do we leverage AI in a way that keeps it contained, if you will?

Megan: Yeah, absolutely. And on that governance point, how does AVEVA define responsible AI and what do you see as the primary benefits?

Arti: Yeah, so I think for us, we think about a triple mandate around responsible AI, and that means that it’s secure, it’s efficient, and that includes environmentally efficient, and then it really preserves human safety and human oversight above all. And for us, that’s what some of our core pillars of responsible AI include. And for us, that means we think about internally as we build tools, a multi-layered governance approach where we’ve got both for how we use AI as a company and then how we deploy AI into our products. We’ve got a similar framework that we leverage across both, a kind of joint governance model, both around how we’re leveraging AI and also how we’re infusing AI into our products to allow our customers to leverage AI in their operations.

But I would say one of the core foundational principles underneath all of these is that human beings still remain centered in how we think about how AI is leveraged. And so human judgment, human responsibility, human ethics are still core to how we think about where AI can add benefits, and we think about it more as augmenting rather than replacing human beings in critical decision loops, if you will.

Megan: Right, which is such an important distinction, isn’t it? And in terms of the next phase of development, there’s growing sentiment that agentic AI will soon allow these industrial systems to operate much more autonomously. I wonder, what makes the risks in industrial settings different from AI risk in a purely digital context? And I suppose on the flip side, what are the benefits and opportunities of that same software defined automation?

Arti: I think we can start with the risks and then go to the benefits. I think ultimately, I would say the biggest risk in the industrial setting is that we are interacting with real physical systems, often physical systems that are very capable of delivering important outcomes that keep our world running, whether that’s delivering power, whether that’s mining for natural resources. But usually. Those physical systems are also operating in hazardous environments, the equipment itself is capable of also having human safety implications. Anytime you’re interfacing software with a physical system that can have real world consequences, you have to be extra careful. And we’ve always had that, again, we’ve had that in our DNA at AVEVA from the beginning, being mindful of that end user and that end application, which is not just something on a computer screen.

Even as we’ve deployed AI over the past few decades into our systems, we’ve typically leaned toward really trying to pick the best fit model, really understand that model’s behavior if we’re going to use, for example, we have a proprietary anomaly detection model that works in a lot of operational environments. Now, as we’re thinking about some of these newer AI capabilities, which by design are hard to understand how they behave, they’re not fundamentally explainable in the way we’ve thought about even AI or statistical models in the past, and actually their behavior can change over time as they learn and get tuned to new capabilities. That’s really the potential risk of automating a physical system based on capabilities that can evolve over time is one of the things that we have to be really mindful of and really thoughtful around, where are we willing to put some of that increased automation into practice?

But at the same time, I think there’s a real opportunity there, because I glossed over this idea that, okay, well, models evolve, but so do human beings, and sometimes that’s a good thing. We talk about humans having expertise and they gain understanding over their careers of how a system works. If we can actually leverage the ability of some of these newer AI capabilities, these sort of reasoning models often have underlying agentic capabilities, to learn and gather experience faster, and potentially even more importantly or more valuably, take experience that’s learned at one site and apply it to another site, then that’s a real opportunity to take what we already know works for human beings, which is that sometimes you just have to learn by doing and be able to apply that and scale that through AI. That’s where I see potentially a real opportunity in this space.

One of the things that we’re really aware of in the industrial sector is just the way that our workforce is changing. I think that almost half, not quite half, of the industrial workforce is set to retire in the next five years, and that’s a lot of expertise and experience that means that we’re going to lose in the sector. If there’s ways to make sure that we can capture that in ways that are actionable, in ways that can also help a newer generation of workers that are used to experience things in a different way, apply that expertise, apply that knowledge, then I think that’s a huge opportunity within the industrial space.

Megan: Yeah, absolutely. Clearly, some huge opportunities there particularly against the backdrop of other market and workforce changes as you’ve outlined there. I suppose building on that, what is the potential for these autonomous industrial AI systems when built and deployed responsibly to facilitate even faster, more sustainable industrial processes?

Arti: I think there’s a couple different ways to think about this. One is, what are the demands of potentially more environmentally sustainable processes? I’ll use one example of something that my team has been working on in partnership with Idaho National Laboratory here in the U.S. as part of their testing for AI grid resilience project.

One of the challenges in the electric grid as we’re moving toward a future where we have a lot more intermittent renewable power generation sources, often distributed rooftop solar for example, is it’s becoming much more challenging to manage the grid both from really understanding where electric capacity is coming in, electric load is pulling off of the grid. In addition to that, being able to maintain just power quality because instead of having one spinning asset delivering the frequency of electricity that’s going across the grid, you’ve got a lot of smaller systems. AI is actually quite critical for being able to manage that grid of the future. And this is one of the things that we’ve been working in partnership is, how can we do that? How can we help grid operators better understand what’s happening across their system, identify where things might be behaving anomalously so they can detect that early and then remediate for it?

At the same time though, I always talk about that’s an example of where AI can potentially help us accelerate the transition to a lower carbon energy future. At the same time, I think there’s a lot of conversation around the sustainability of AI itself. What are the power requirements? What are the resource requirements needed to run AI? And before I get into how do we think about that at AVEVA, because it is definitely something we think about and I think a lot of actors in the space are thinking about, I want to point out, is one of the challenges is that there’s really no agreed upon method to measure the environmental impact of AI. You see a lot of these stories, like one query on some kind of chat interface is X amount of gallons of water or this amount of electricity use. But the truth is that community-wide, there’s no agreed upon standard.

One of the things that I’m involved with is a standards working group that was launched by the IEEE two years ago, a little over two years ago. I’m the chair of that working group, it’s called the P7100 Standards Working Group on measuring the environmental impact of AI. And one of the things we’re trying to do is really identify all the different areas over which we want to think about environmental sustainability associated with AI, and then having one standard methodology that works and that can be adopted both from a reporting perspective and potentially also from an oversight or regulatory perspective. That becomes really important for any sort of real understanding of how AI impacts the environment is just we have to know, what does it use? We want to look holistically at that. Our standards cover electricity and energy consumption, it covers resource usage, it covers water consumption, and it also covers carbon.

But all of that said, I think it’s important for us to understand the footprint of AI, but I think it’s also important for us to simultaneously recognize that a bigger model uses more compute and more compute probably leverages more resources. The more that we can be intentional and pick the right size model for the right application, the more we can already start down that path of being more environmentally efficient in the AI that we run. One of the potentially nice side effects of that is the more purpose-built a model, the less likely it is to misbehave in unpredictable ways, if you choose correctly your architecture. There’s some sort of ancillary benefits that go beyond environmental sustainability.

Megan: Fascinating. It’s really, really interesting to hear some of the work going on behind the scenes there, because I think we’ll all have heard some of the statistics around the environmental impact, as you say. So, it’s great to understand some of the work going on there to really clarify that.

And we’ve talked a little bit about AI augmenting humans earlier. As systems gain greater autonomy, how should organizations think about that balance between closed loop automation, human in the loop accountability? What guardrails need to be in place? And how are governments and cross-border actors approaching those concerns as well?

Arti: There’s a lot packed into that question, so I’ll try to answer it somewhat one by one. First, starting with that sort of balance between closed loop and human in the loop, automation and accountability. One of the things that we talk about in the industrial sector is from human operator to human supervisor of systems, of industrial systems. And if I’m honest about that, we’re still trying to figure that out. When a human’s in a loop in an automated system, you’re really thinking about a system may process all the data and then make a recommendation.

We’ve got a solution that we’ve been working on more jointly with a few different customers where we’re able to take in a lot of their operational data, also some simulations of how their systems work, and be able to provide, say, recommendations around, you should now operate at this set point instead of that set point based on some of the other conditions that are changing in your plant. There’s a lot of interest in moving from having that be a recommended set point to have an automation that can automatically adjust the set point. And we’ve actually had some successful real-world pilots around that as well.

But when you get into that, obviously it makes people very nervous if you’re changing set points on industrial equipment. That’s where you maybe continue to have some guardrails around like, you can’t go outside of a certain band, for example, of operations, or we only allow the automation in certain areas. That’s where the human supervisor, the same way a human supervisor might give employees a lot of bandwidth or a lot of flexibility to make decisions around certain things, around other things, they’re hard and fast, like this is the deadline or this is the sort of production target. It’s like that. So thinking about, how do you put the appropriate guardrails?

The challenge is, AIs are not human beings, and so the guardrails look different, and I think that’s one of the things that there’s still to think about. That question of, what guardrails should be put in place? I think that it’s going to be probably dependent a little bit on the application, but over time I think we’re all going to learn, and so being really upfront and thoughtful about how we do this I think is important.

But the opportunity though, potentially, is really great. I’ve already mentioned that AI can be very useful in synthesizing a lot of information and bringing to the forefront, this is what matters. That’s something that we do want to create some space to experiment around, but what’s then important is making sure that a human being understands the risk.

I’ll give a little bit of a personal story because it might be illustrative. This weekend, I bought a little toy robot and decided I wanted to program it to do some stuff, and I found AI very, very helpful to get me started to read all the documentation. Putting the robot together was straightforward, they had nice instructions, but there’s a pretty heavy software developer kit that’s already there, but going through and reading all that documentation can take a while. AI was super helpful to help me surface, this is the function that does this, to help me get started. But occasionally, it made really bad recommendations on the best way to troubleshoot something.

That’s where I think having the human being in the loop, being able to say, “I know that it’s not going to be the best software architect or the best troubleshooter,” is really helpful because I had the opportunity to leverage AI for what it’s good for, synthesizing and surfacing a lot of information, but being able to say, “I don’t think that’s the most efficient first step. Let’s try something else.” That’s where it’s a really different way of working, but it’s something that I think as humans get more comfortable with the power and limitations of AI, you can start to teach people how to work with it, and it’s very different from how you would work with other digital tools.

One of the things that is important when it comes to AI is recognizing just the breadth of things that it touches and the breadth of impacts that it’s going to have, whether it’s on the environment, as we’ve discussed, whether it’s on productivity as sort of implicit in this entire discussion, whether it’s on labor force, whether it’s on just how economies work. From my eye, and I’m by no means a policy expert in this area, but from my eye, what I’m seeing is that different governments are prioritizing different aspects of that from how they’re thinking about regulatory and other kind of governance approaches.

Megan: We’re absolutely seeing some really vastly different approaches to this, aren’t we, around the world? I wondered to illustrate some of this, if you could share perhaps some case studies you’ve seen, maybe talking us through what lessons they could hold for other organizations perhaps a little earlier in their own AI journeys.

Arti: I think a lot of the key areas where we’re seeing very clear benefits of adopting AI in the industrial sector, the core to all of them is actually getting the data right and getting the right foundation of data so that you can build AI on top of it.

One of our customers, SCG Chemicals, which is a petrochemical company in Thailand, they had this vision of producing a reliability platform for their operations that was as AI-driven as possible. But a core part of that was actually getting the data right, being able to have all of their information in the right place, leveraging some of AVEVA’s tools to do that. Bringing together operational data and also engineering data. And then putting on top of that AI capabilities, in this case, one of our capabilities called AVEVA Predictive Analytics that deploys some proprietary models to do things like detect anomalies, so that they could really much earlier identify potential operational risks. And instead of having unplanned downtime, translate that to planned downtime. When you translate unplanned downtime to planned downtime, you can get a huge improvement in plant reliability.

At this point they’re targeting something like 99% plant reliability and a very, very high return on investment from the platform that they put in place. Again, in early pilot days, it was almost a 9x ROI, and so that’s really quite impressive. But what I want to emphasize is that it’s kind of a multi-layered problem to get it right.

Megan: Those are some really striking results, though. I mean, for industrial leaders who perhaps are still hesitant to explore AI, I wonder, what do you think is the cost of taking a more wait and see approach?

Arti: I think, again, this is an area where the industrial sector has a little bit of a different calculus to apply to this type of problem. In general, I think you would hear most business experts say that you can’t afford to take a wait and see approach to AI because it is so transformative across every sector of society and economy, and certainly in the industrial space, we’re not immune to that. But I do think that some of the challenges that I’ve outlined today also puts leaders in the industrial space into a mindset of sometimes potentially being a fast follower rather than the first adopter of newer technologies, just because the risk is so high.

But that being said, I think one of the challenges is that the risks that we’ve covered today around infusing systems that don’t always behave predictably with physical systems is that I don’t know that there’s a lot of other sectors that are going to be solving those problems. That’s where what I’m seeing is actually a lot of interest and excitement in trying new things and willingness to do that because there’s a recognition that applied correctly and applied with the right guardrails and safeguards in place, these technologies really have truly transformative potential from a resource usage standpoint, from a human safety standpoint, from a productivity standpoint.

I think the calculus has shifted a little bit in the sector to we want to actually try these things out. But then it becomes more, how do we try these things out in environments that we can make a little bit more sandboxed or safe to test out what some of the unique challenges we’re going to face in the industrial sector are?

Megan: Yeah, absolutely. It’s just hard to ignore the potential nowadays, isn’t it? And just to close with a slightly future forward look, I suppose, what are you most enthusiastic about in terms of the long-term potential of responsible autonomous industrial AI systems?

Arti: Yeah. Well, I think that there’s a couple of different trajectories. One of the things that I think within the next 18 months for sure, we’re going to see an increase of people with deep domain expertise who maybe didn’t grow up with a software background, be able to adopt AI assisted coding techniques to really be able to build the things that they weren’t able to build before.

That’s really exciting in my career, which way back when I was actually an industrial data scientist, I would say I always felt the most energized and that I learned the most talking to the people that were on the front lines of operations, having to monitor a lot of different equipment and understanding how it all worked together. They really have a lot of expertise, and being able to put in their hands the ability to very quickly develop new applications is I think going to really lead to a lot of new ideas that many of us in the industrial software space maybe wouldn’t even have thought about and really understood how to put. I think that’s really exciting.

I think then looking beyond that, I was a little bit of a, not to say a robotic skeptic, because clearly robotics and autonomous systems are going to be important, especially given the types of environments, whether they’re hazardous or remote, that industrial equipment operates in. But I just actually think things are moving a lot faster than I had anticipated. I’m really interested to see how these physical embodiments of AI systems start to really transform, again, how we think about operations.

One of the things about any new technology in any environment is that to really gain value from it, you have to change the way you do things. I would say for close to 10 years now, I’ve been giving talks on AI adoption and I always say the biggest barrier to AI adoption is not anything to do with the technology. It’s not even to do with the data, although data are often the biggest sticking point, it’s really to do with, are you going to change your business processes to be able to work with the way this technology is good or not good at things?

Autonomous systems, whether they’re robots or drones, are really going to change the way that we work in plants, in power systems, on mining sites. And I think that will make these types of operations more efficient, much safer for the human beings involved and more productive. So, I’m quite excited about that.

I don’t know entirely what direction it will go, but one of the things that I think about a lot is that if you go back to I, Robot and Isaac Asimov’s book, the premise of those was that robotics would be the first broadly adopted AI, not computer-based systems. We went in the other direction and I think it’s really interesting to now see all of this converging.

Megan: Yeah, absolutely. See robotics catches up a bit. So many exciting things on the horizon, that’s for sure. Thank you so much, for your time.

That was Arti Garg, chief technologist at AVEVA, whom I spoke with from Brighton in England.

That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor at 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 us, 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. Thank you so much for listening. Goodbye.

Learn more at aveva.com.

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

For all the data that AI systems continually amass and analyze, enterprise AI agents often suffer from a curious shortcoming: a lack of knowledge. More than data, knowledge is the understanding of what the data means in the context of individual organizations. AI agents need this understanding to reason about situations, make decisions, and ultimately take actions. Without sufficient knowledge, agents are prone to making flawed and unreliable decisions.

A lack of knowledge, our research finds, is a major reason agentic AI use cases never make it to production. Competitive pressure is making it urgent to address this. Organizations need to deploy and scale more of their agentic projects to capture the efficiency gains AI promises. Falling short risks wasting the investment already sunk into these projects, and it cedes ground to rivals already putting their agents to work more effectively.

The purpose of this report, which is based on a survey of 300 data, AI, and other technology executives, is threefold. First, it seeks to gauge organizations’ agentic knowledge capabilities (i.e., their ability to give AI agents a full contextual understanding of the data they ingest) across semantic knowledge, episodic memory, and procedural knowledge.

Second, the report probes the challenges organizations face in improving access to knowledge and ultimately to getting more agent use cases into production. Third, it explores the measures organizations are taking to overcome these challenges.

The key findings include the following:

Data and knowledge weaknesses consistently stall AI agent progress. On average, only around a third (34%) of organizations’ agentic AI projects make it into production. Even high-tech firms struggle with this. Legacy data systems, security and privacy concerns, and a lack of knowledge and context are the key points of failure.

Strong knowledge capabilities correlate with agent success. A small group of production leaders (organizations where on average 61% of agentic projects advance beyond pilot) have stronger knowledge capabilities than the rest, especially when it comes to semantics. This advantage tracks closely with their higher production rate.

Fragmented data hugely complicates knowledge access. Data fragmentation (the inadequate sharing of data across systems) was most commonly cited as a top challenge to expanding agents’ access to knowledge (cited by 55%). Production leaders, by contrast, are more likely to see security and privacy concerns as a major concern (cited by 72% of this group).

Most firms aim to strengthen the link between data and agents. Among steps that can yield higher quality agent decisions, executives expect the biggest impact to come from strengthening the structural foundation between the organization’s data and its AI agents. The experts we interviewed see a knowledge layer as a prime way to achieve this.

Investment priorities to boost knowledge range from pipelines to knowledge graphs. To expand agent access to knowledge, organizations will prioritize investments in retrieval technologies, like ingestion pipelines, AI-ready APIs, and retrieval-augmented generation (RAG); in AI evaluation agents; and in knowledge graphs.

Download the report

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

In 2026, the question for enterprise AI is no longer whether predictive models can outperform statistical forecasts—that argument is settled. The big question now is how to enable predictive systems to act on their own conclusions without drifting from business intent. The frontier has moved from prediction to autonomous decision making, and the gap between leaders and laggards is widening accordingly.

“Enterprises are done with a backward-looking point of view; they want to be more forward-thinking,” says Vishal Gupta, partner at research firm Everest Group.

Intelligent analytics, powered by technologies like deep learning and generative AI, are making this possible. Real-time training allows AI to evolve continuously instead of waiting for quarterly refreshes. In addition, the data that newer predictive engines rely upon has expanded to encompass not just neat, numerical records but also messy, unstructured sources of insight-rich interactions. As a result, AI-powered analytics are moving enterprises from passive hindsight to pragmatic foresight.

AI takes predictive analytics—a broad discipline that includes predictive modeling, data prep, analysis workflows, interpretation of results, and decision-making applications—to new heights. “In many ways I think the word ‘analytics’ is giving way to AI,” says Gupta. “Everything is becoming AI.”

Download the report

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

Yossi Matias, Vice President & Head of Google Research, explores how AI is beginning to reshape biology, infrastructure, manufacturing, and science, and why its greatest impact may come when it intersects with other fields. 

Step inside the newsroom with our MIT Technology Review editors for sharp analysis and unpublished insights from the team that researches and reports on emerging tech daily. 

There’s much more to discover 

These are just two sessions from EmTech Future 2026.

Across three days, EmTech Future explored some of the most important questions shaping technology right now: how AI is impacting industries, where quantum is heading, what new energy systems could make possible, how robotics are evolving, and what challenges and opportunities arise when these technologies start to converge.  

Hear the ideas, insights, and perspectives you missed at EmTech Future 2026. 

The full program is now available on demand, giving you access to all the conversations and sessions from the event, including: 

• Quantum in context Hartmut Neven, founder and lead at Google Quantum AI, explores how quantum is advancing alongside other frontiers and why its significance may lie not only in what it can do on its own, but in how it begins to interact with the systems around it. 

• Energy Systems for the Future Evelyn Wang, VP for energy & climate at MIT, explores how energy, computing, infrastructure, and climate technologies are increasingly interconnected, and what these converging systems could mean for the future of industry and society. 

• Rethinking our relationship with AI –Cory Doctorow, bestselling author and award-winning writer, explores how AI is changing the way we work and think. 

As an MIT Technology Review subscriber, you’ll get a 20% discount, meaning you can access the full EmTech Future 2026 program for just $596. 

Enterprise AI is no longer a future ambition. It is in full operational flight. Model capabilities are advancing faster than most organizations can absorb, while the cost of performance continues to fall. Globally, AI investment is set to reach $2.5 trillion in 2026, up 44% from the previous year.

For many enterprises, this investment has produced fragmentation. Intelligence can accumulate in silos so that sales agents are unaware of open support tickets, for instance, or marketing systems are personalizing content without visibility into what finance already knows about a customer. Each function may perform well in isolation, but the enterprise as a whole learns little and has less information to act upon.

The shift from AI as a tool to AI as an operating model—what we call the “agentic shift” in this report—demands something more fundamental than better models or faster infrastructure. It requires connecting people, processes, and data in real time, along with the governance and control to act on that intelligence reliably.

This means rethinking both architecture and operating models simultaneously. First, rebuilding data infrastructure for accessibility rather than volume. Second, replacing fixed tech stacks with composable architectures that can evolve as models and tools change. And, lastly, resolving questions of AI sovereignty, including where intelligence runs, who controls it, and how it operates across organizational and jurisdictional boundaries.

Key findings include the following:

Enterprise AI’s scaling problem is structural. Process-first companies are pulling ahead. Global AI spending is rising sharply and model capabilities are advancing faster than most organizations can integrate them. Yet the majority of enterprises are still not growing revenue through AI or fundamentally rethinking how they operate. The companies generating sustained returns share a common discipline. They treat process redesign as the work that precedes model selection, building for how the technology will evolve rather than retrofitting roles and workflows after deployment. For them, the agentic shift begins with the operating model.

Data readiness, not data abundance, is what makes AI compoundable. Most enterprises discover too late that having data and having AI-ready data are very different things. A sovereign, composable foundation—one that queries and prepares data where it resides, without migration or centralization—can convert raw data estates into intelligence that AI agents can act upon. As data residency laws, multicloud environments, and structural complexity make centralization increasingly impractical, sovereign control over where models run and data lives is what keeps that adaptability intact.

Download the report.

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

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The US has spent billions building a “virtual wall” of surveillance towers along its southern border over the past 25 years, promising they will help detect and apprehend border crossers and save lives. But a groundbreaking investigation by MIT Technology Review has documented over a thousand people who moved through areas watched by these towers without being reached or apprehended, and who ultimately died there. Some were even under the watch of newly installed AI-powered towers designed to spot people automatically.

Our findings reveal a humanitarian crisis more visible than previously known, and repeated failures of the virtual wall’s basic security promise. Watch a conversation examining the failures of border surveillance technology and uncovering the stories of the people who die in the borderlands.

Speakers: Mat Honan, editor in chief, James O’Donnell, senior AI reporter, and Eileen Guo, senior features and investigations reporter

Recorded on September 28, 2026

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• The US spent billions on border surveillance. Why can’t it catch people before they die?
• How we made the first comprehensive map of deaths along the US border’s “virtual wall”
• 4 ways to address the failures we found along the US border’s “virtual wall”

The AI boom is becoming a materials challenge. As AI pushes computing into new territory, the materials behind that infrastructure are becoming just as crucial as the algorithms running on it. Semiconductors and data centers are approaching physical limits around performance, thermal management, electrical efficiency, and reliability, creating new demands for materials that can do more at once. At the same time, AI is giving materials scientists new ways to search the enormous universe of possible molecules and accelerate the development of solutions.

For Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, that convergence is transforming what advanced materials can enable. “AI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits,” he says.

As requirements accumulate, including high temperature, purity, electrical performance, chemical resistance, plasma resistance, and long-term stability, materials move toward what Finelli calls the “top of the pyramid.” Beyond supporting AI innovation, he contends that advanced materials are “actually increasingly defining what’s going to be possible.”

That challenge is playing out across the infrastructure powering the AI surge. Syensqo is developing materials for high-voltage data center architectures, advanced sealing materials for semiconductor manufacturing, and thermal-management solutions including fluids for direct immersion cooling. Some of those innovations can also cross industry boundaries. Materials developed for electric vehicles, for example, can help address the higher voltage and energy-density demands that are emerging in data centers.

The definition of performance is also changing. More customers are expecting materials to meet technical requirements while reducing environmental impact. “Our goal is to remove the trade-off between performance and sustainability,” Finelli says. That means considering sustainability at the beginning of the research process instead of treating it as an additional requirement once a material has been developed.

AI is changing how those materials are discovered, too. Syensqo is using AI agents to digitally synthesize millions of potential molecular combinations, predict their performance and sustainability characteristics, and narrow them to a much smaller group for laboratory testing. The result, Finelli says, is the ability to go “broader, deeper, and faster” while giving scientists more time to solve complex engineering problems.

Looking to the future, Finelli sees the possibility of a reinforcing cycle: AI helps develop materials that improve AI infrastructure, which in turn enables better AI to accelerate materials discovery. That feedback loop could create a cycle of innovation and expand what future technologies can achieve.

“You end up in this accelerated materials, innovative cycle of materials innovation,” says Finelli. “That really excites me, and it gives us the opportunity to continue enabling technologies that will shape the future.”

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

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.

This episode is produced in partnership with Syensqo.

Now asked to name the key enablers to AI advancement, many of us might list algorithms, data centers, or even computing power, but just as critical to the performance are the advanced materials that underpin each layer of that innovation. As AI continues to evolve, it’s pushing the likes of semiconductors and data centers to new physical limits, putting new pressure on the advanced material sector to keep pace. But the relationship goes both ways. As the sector rises to this challenge, AI is also emerging as a powerful tool for accelerating materials discovery and development, significantly shortening development timelines for new solutions.

Two words for you: materials innovation.

My guest today is Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo.

Welcome, Mike.

Mike Finelli: Thank you, Megan. Nice to be here.

Megan: Thank you so much for joining us. Mike, can I start by asking you to tell us a little bit more about Syensqo and the role it plays in developing advanced materials?

Mike: Yeah, absolutely. Syensqo is a global leader in specialty materials. Our job is to help customers solve their toughest technology challenges. We serve a lot of different markets, but the way I like to say it simply is if it flies, we’re on it. If it drives, we’re in it. In healthcare, our products literally are saving lives every day. And if you like your mobile devices, if you like AI, it’s our products that are actually enabling the advanced semiconductor chips that are required to produce all of this. Our role is to enable innovation through advanced chemistry. We develop materials that deliver higher performances, greater reliability, and increasingly more sustainable solutions. The way I would say this, it’s at the heart of our business. Actually, it’s in our name, Syensqo. And to put some numbers around it, 20% of our annual revenues come from new products and applications that we’ve launched in the last five years, which is really evidence of a really strong innovation engine.

Megan: Yeah, absolutely. And as you sort of described there, you’re in all sorts of different industries with an emphasis perhaps on electronics and semiconductors. Can you talk a bit more about that work and where those industries are headed perhaps?

Mike: Sure. So look, electronics and semiconductors have been strategic markets for Syensqo for literally decades. I don’t want to date myself, but 33 years ago when I started in the company, semiconductors were one of the first industries that I worked in. And we’ve supported successive waves of innovation from enabling smaller, more powerful mobile devices, helping the industry get to the smaller and smaller profiles and the chips. We’ve helped to advance hyperconnectivity, supporting increasingly sophisticated semiconductor manufacturing. And today we’re helping to advance the AI era.

We have one of the industry’s broadest portfolios of high performance polymers and advanced materials. We support applications across the entire electronics value chain from semiconductor fabrication, electronic components, to smart devices and telecommunications, even hyperconnectivity. And our materials are helping customers solve increasingly demanding challenges around miniaturization, thermal management, electrical performance, chemical resistance, higher and higher purities, and long-term reliability and sustainability. And today we work with leading semiconductor manufacturers and electronics companies all around the world.

Megan: Fantastic. And as you alluded to there in the last 30 years, we’ve seen huge evolutions in those sectors.

Mike: Oh my God, yes.

Megan: And now AI is putting these new demands on semiconductors and data centers. What does that mean for the materials they’re built from and to what extent will AI innovation be constrained or enabled by materials science finding a solution?

Mike: Yeah, so I mean, you’re absolutely right. But AI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits, and materials are becoming a key enabler of that continued progress.

The way I try to describe it, think of a pyramid, I call it the performance pyramid. You have commodity materials at the bottom of the pyramid and you have high performing specialty materials at the top of the pyramid. At Syensqo, all we do is we operate at the top of the pyramid and we’re continually trying to raise the top of that pyramid by bringing newer and newer and more higher performing materials out.

Now you might say, okay, but why doesn’t a data center or a semiconductor manufacturing fab need a specialty versus something in the commodity space? Well, I call it the and, and, and principle. If you just need a polymer or a material that can sit at the table at room temperature and stay there for 10 years and not change, well, there’s a lot of commodity materials that will do that and you don’t have a problem. The minute you start adding requirements, and I call it the and, and, and so if you need a polymer that can handle high temperature and have to have high purity and electrical performance and chemical resistance and plasma resistance and it’s got to have long-term stability, all of these ands, you start moving to the top of the pyramid.

Now what AI is doing with semiconductors, because of the speed at which it’s advancing, it’s requiring semiconductor chips and data centers, the number of requirements are increasing the number of ands which is pushing the limits of the materials. That’s where we come in. And I really believe that advanced materials, they’re no longer just supporting AI innovation, we’re actually increasingly defining what’s going to be possible.

Megan: Right. That’s fascinating. And in terms of rising to that challenge of focusing on that top of the pyramid and that and, and, and principle you’re talking about, could you talk us through perhaps an example or two of those top of the pyramid solutions you’ve created or that you’re working on at the moment?

Mike: Like I said, our focus is enabling higher performance, but it’s also without compromising on reliability or safety. We develop advanced polymers, elastomers, specialty fluids, fluids meaning lubricants and heat transfer fluids, and they’re used throughout the semiconductor manufacturing process and also increasingly in AI data center infrastructure. One example of our work on specialty materials for next generation AI data centers is the work we’re doing around high voltage architectures. Data centers are moving towards high voltage architectures because they can enable greater computing power while also improving energy efficiency. We know that’s a big issue for that segment of the industry, and these high voltage architectures will help them reduce and improve energy efficiency because it reduces energy losses and they can ultimately help lower the environmental footprint of the data centers. And we’re developing new materials that can help them get there.

Another example is our high performing sealing materials found inside semiconductor fabs and wafer tools. If you can picture, many people have seen what a semiconductor looks like during processing. It’s a big, big silicon disc that’s then later diced into the tiny little chips that go into the computer. But that wafer is put inside a giant chamber where it has a very extreme environment, aggressive plasmas, reactive chemicals, and they need higher and higher performing materials. And all of the seals that are around that chamber to keep those gases in the environment inside have to be able to withstand that environment. And that’s what we’re developing and we’re pushing the limits. They’re asking for higher temperatures, more aggressive environment with lower out gassing and purity. And that’s what we’re developing for this industry to allow that next chip to be developed and produced industrial.

Megan: It’s so fascinating that people wouldn’t give much though necessarily to the seal in something like that. As you’re outlining, it’s just absolutely critical in terms of performance. And in developing those solutions, I understand you also looked across different markets to see what may be applicable perhaps in more than one space, and that includes an overlap between the automotive sector and data centers, I understand. Can you tell us a little bit more about that?

Mike: As I mentioned just previously, the data centers are shifting to higher voltage architectures. This is the next generation data center, which can be more energy efficient, but it’s got a higher energy density. The power density increases, which increases temperatures. And many of the material challenges that we will be facing there, we’ve already developed for the automotive industry in electric vehicles. I’ll give you an example of an application. I mean, think about an electric vehicle. The powerhouse in electric vehicle is no longer the motor, it’s the battery. That’s where all the energy sits. And when you’re putting a hundred kilowatts of energy, driving that to the electric motor through wires and through what they call bus bars, you got to get that car up to 60 miles an hour pretty quick. You’re driving massive amounts of energy that’s increasing temperatures dramatically.

And all the electrical connections are in these bus bars that there’s a polymer that’s an insulating polymer with copper in between for all the connections. That’s got to withstand that temperature increase, which could come pretty rapidly. We’ve developed new materials there and those materials will be translatable over to these data centers where they’re going to have the higher voltages with a higher energy density.

Another thing we’ve been doing in automotive, we have a lot of knowledge in both automotive and semiconductor around fluid circulation and how to use dielectric materials to do direct immersion cooling. That’s something that will be very valuable for data centers and server farms. Using air to cool semiconductors is really inefficient and energy intensive. If you could submerse them in a liquid, you have direct immersion cooling, that’s extremely efficient, so that’s another thing we’re working on.

Another thing we developed in automotive that will be translated over is battery energy storage systems. Inside the battery, we’ve developed a binder. It’s the highest performing binder on the market, which is using the cathode of a lithium ion battery, and it keeps all the ingredients doing its job working together so that battery can actually last for 10 years and perform. Now that’s moving over to the data centers because they’re moving more towards renewables and they need to have these energy storage systems to smooth the peak loads and provide resilient backup power. That’s one of the things that we’re doing. By transferring our knowledge across the markets, we can accelerate new power and new thermal management solutions while supporting reliability required by next generation AI infrastructure.

Megan: Fantastic. So many transferable applications there that necessarily wouldn’t have sprung to mind. And it isn’t only technical advancements that you need to contend with, of course. Companies today are also demanding the materials are developed and manufactured more responsibly too. So how is sustainability shaping your innovation process?

Mike: Yeah, you’re absolutely right. I will say performance is still the entry ticket. Our customers want performance. Now what’s changing is that definition of performance is now broader and it is including sustainability targets and requirements. Our customers expect materials that deliver outstanding technical performance while also being developed and manufactured more responsibly.

At Syensqo, we believe that operating as a responsible company means we’re providing true sustainable business solutions to our customers. And this is why we developed what we call the Sustainable Portfolio Management tool, SPM. It’s a matrix, and it defines what a sustainable solution is. For us, it’s a product that in a given application improves our product’s social and environmental performance while also demonstrating a lower environmental impact in its production, creating values for our customers. In short, we want to develop products, and this is where it starts. Every one of our research projects before we even start them is assessed on whether it’s going to be a sustainable product or not.

And 88% of our portfolio now is a sustainable product. We’re developing materials that are better for the environment, lower environmental footprint when we produce it, but also they contribute to improvements for our customers as well so they could operate with a lower carbon footprint or they can operate in a safer way or less water consumption. There’s a lot of different lists in there.

Another example is our longer-term development of next generation heat transfer fluids. Semiconductor manufacturing and data centers have become more powerful. I mentioned before the heat that they’re generating, especially when they move to the higher voltage architectures. Managing that heat is increasingly important. And again, I talked about direct immersion cooling. We’re developing those solutions because today there are fluids out there that will work, but they got high global warming. That’s not good for the environment. We’re developing the next generation heat transferred fluids that will reduce the potential environmental impact compared to the fluids today. In the end, our goal is to remove the trade-off between performance and sustainability. You notice that’s another and, we can be performing and sustainable.

Megan: That’s so important, isn’t it though, to think about sustainability in terms of performance? As you say, when we’re thinking about commercially scaling up these solutions, it’s such an important part of it. And as I talked about in the introduction, AI isn’t only a challenge, but it’s also an opportunity within the advanced material space. I’d love to explore how you’re using AI tools at Syensqo to inform and accelerate the development of solutions as well.

Mike: Absolutely. We embarked on this journey about two years ago, where we’re using AI in our research and development, and we’ve partnered with Microsoft and their Microsoft discovery tool, and it’s helping us to rapidly identify and evaluate promising molecular candidates.

Now, in the normal research approach, historically, you would design your experiment and you’d look at all the potential combinations of materials and chemicals that you could make all these different molecules. And the combinations of potential and molecules that you could develop to solve a problem could be in the millions, but it’s impossible to develop a million molecules or tens of millions of molecules in your laboratory and actually physically do that. But you have to select a small area based on your expertise and knowledge, based on the literature searches, based on the state of the art that’s out there and looking at patents, et cetera. And you pick a small area and you go through the process, you develop the materials, you test them, you learn something, you go back to the drawing board, you start again. Eventually you find something that works, but it doesn’t mean you found the best possible combination that’s out there.

But what we’re doing with AI is we have developed AI agents with Microsoft that are literally digitally synthesizing the entire millions and millions of combinations of potential molecules. And we have another AI agents that are using physics-based simulation to look at all those molecules and predict the performance of them, and not just performance on physical chemical properties, but also on toxicity, on sustainability, et cetera. Then we have another agent that takes all that information and ranks them all. In the end, we have explored all of the potential molecules out there. We understand roughly what the performance should be, and we end up with a priority list of maybe a hundred, instead of millions and millions, a hundred that we actually synthesize in the lab.

And at the end, you end up getting the solution faster, much, much faster. You’ve explored the entire space. I basically say it allows us to go broader, deeper, and faster. And the important thing is it’s not replacing our scientists, it’s not replacing our scientific expertise. In a way, it’s giving them superpowers. It’s allowing them to spend less time searching and more time solving the industry’s toughest engineering challenges.

Megan: Amazing. It sounds like it’s genuinely a really transformative tool by what you’re explaining.

Mike: Completely, completely.

Megan: I mean, just to finish, Mike, it’d be great to take a look ahead if we could, because there’s so much activity in both AI and the advanced material space. I wonder what is coming down the pipeline that you are most excited about next?

Mike: I’ve talked a lot about AI and how we’re using AI to develop new materials. I think to me, what’s really exciting, and I’m starting to see it actually happen, I’m just curious how fast this is going to go, is that we’re using AI to develop new materials that will enable AI to get better, and then that AI will use the new AI to develop new materials to get AI to go better. I see this loop of developing for AI, for AI to improve, and then we use that AI to improve ourselves. You end up in this accelerated materials, innovative cycle of materials innovation. That really excites me, and it gives us the opportunity to continue enabling technologies that will shape the future. That’s what we do at Syensqo.

Megan: Fantastic. Yeah, real sort of virtuous circle of innovation, it sounds like that. Amazing. Thank you so much, Mike.

Mike: Thank you.

Megan: Thank you so much. That was Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, whom I spoke with from Brighton in 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 it, 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 so much for listening. Goodbye.

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

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Employees at the world’s leading AI labs are saying there’s a real possibility that advanced AI could destroy humanity. Are they right? Or is this more scaremongering and hype? Watch a conversation unpacking AI extinction fears: where they come from, whether they hold any water, and, if so, what we should do.

Recorded on September 15, 2026

Speakers: Niall Firth, Executive Editor, Will Douglas Heaven, Senior AI editor, and Grace Huckins, AI reporter

Since we couldn’t get to all of your questions during the session, Will and Grace answered the most popular ones in their piece: Could AI really kill us all? Your questions, answered.

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The era of AI inference has arrived. Imagine a healthcare system analyzing millions of data points in real time to accelerate life-saving medical research, or an intelligent assistant instantly resolving thousands of complex customer needs at once. These real-world breakthroughs rely on advanced infrastructure acting as the engine of continuous intelligence, powering real-time services while also supporting an increasingly intelligent edge of IoT and consumer devices. However, in this inference-driven landscape, every delay, bottleneck, or wasted watt directly affects human outcomes and operating costs. 

This shift changes what infrastructure must deliver. Performance, latency, memory bandwidth, storage throughput, and networking cannot be optimized in silos. Inference workloads are continuous, geographically distributed, and highly sensitive to response time, requiring systems designed for scale, resilience, and efficiency from the start.

“We tend to think of AI as a single workload, and it’s not. It’s thousands, it’s millions, it’s billions of different workloads,” says Jim McGregor, founder and principal analyst, Tirias Research. AI inference changes the optimization problem from one of raw compute to coordinated infrastructure—memory, storage, and networking.

For business leaders, the priority is clear: AI infrastructure decisions must balance cost, flexibility, and future readiness. The winners will be organizations that improve performance per watt, reduce environmental footprint, and remove memory and storage bottlenecks before they limit growth.

AI inference requires a new architectural approach

Systems for AI need to be rearchitected because shoehorning modern AI systems into legacy infrastructure limits AI’s transformative potential. Purpose-built architectures are essential to realize the true value of AI, from accelerating scientific discovery to creating truly autonomous digital agents.

Traditional enterprise IT has been able to rely on relatively stable infrastructure assumptions, but inference and agentic AI introduce new demands around latency, data movement, scalability, and utilization that make architecture choices far more consequential.

“Data centers must now support continuous, distributed, and increasingly real-time AI services—none of which are a single workload,” says McGregor. “They all require different requirements from a system-level perspective.”

To support real-time AI, enterprises can no longer view memory and storage merely as supporting hardware, but at the heart of the system. Organizations need to architect a data pipeline that can rapidly ingest, clean, transform, store, move, and deliver data. Inference workloads place sustained pressure on infrastructure in ways that look very different from earlier training-centric deployments, demanding continuous data retrieval and caching that traditional applications never required.

Accordingly, performance by itself is no longer the sole benchmark that matters. Enterprises increasingly must balance performance with efficiency, cost, and scalability, especially as they try to support different AI services without overbuilding infrastructure for peak conditions.

“You have to optimize the entire network, and that includes memory and storage, around the types of workloads you plan on running,” says McGregor. “You have to really have a detailed understanding of what those workloads are going to be.”

Any AI infrastructure strategy must start with workload awareness. Inference, agentic AI, and other emerging AI use cases require organizations to treat the data center as an integrated system.

Data movement is the new bottleneck and an opportunity for competitive advantage

As enterprises deploy advanced inference and agentic systems, the sheer volume of data being queried in real time has made data movement the most pressing constraint. Modern AI techniques like retrieval-augmented generation (RAG) require systems to constantly scan massive databases to generate accurate responses. This requires immense computing power, but more importantly, it requires immediate access to data.

McGregor says the focus shift to how efficiently data can be moved, cached, and delivered across the broader architecture elevates memory and storage from background infrastructure to strategic assets. “The biggest thing we’re doing right now is moving data from one place to another and making sure that we can use it effectively.”

Because AI is not a single workload category, simply buying the fastest processors is insufficient. Inference depends heavily on memory bandwidth, caching, storage proximity, and the ability to retrieve relevant information quickly and consistently. Understanding where each resource belongs in the stack and how those layers interact under real operating conditions has become a business imperative.

The most effective AI infrastructure looks less like a collection of best-in-class parts and more like a balanced system of compute, memory, storage, and networking, McGregor says, because bottlenecks tend to migrate from one layer to the next. “You have to architect all four together to be efficient, and that’s the challenge.”

The interdependence of data-plane design and network bandwidth means AI infrastructure planning has become a business decision just as much as an engineering one: latency is now inseparable from value. In robotics, financial services, healthcare, and customer-facing AI systems, delays are not merely technical imperfections; they can undermine safety, responsiveness, or trust. AI infrastructure performance becomes a matter of reputation management.

The organizations that gain the most from AI may not be those with the largest clusters, but those with the clearest understanding of how to align every infrastructure element to effectively execute AI workloads.

Building an AI infrastructure procurement framework

Planning AI infrastructure is not simply about choosing the fastest hardware. It is about how to scale without locking the organization into assumptions that may quickly become obsolete. “You need to be flexible because the demands are going to change rapidly and the technology is changing rapidly,” McGregor says.

Future-proofing AI infrastructure requires keeping your options open as workloads, economics, and architectures keep shifting:

• Define the AI workloads that are being optimized. Infrastructure choices must match business needs rather than what McGregor calls generic “AI readiness,” which risks overspending in some areas while leaving bottlenecks unresolved in others.
• Build a modular architecture for compute, memory, storage, power, and cooling so capacity can change as demand shifts rather than committing too early to a rigid architecture.
• Work with the full ecosystem of suppliers and integrators to reduce supply risk and improve access to the right components. McGregor says buyers can no longer assume their OEM or cloud provider alone will insulate them from supply constraints or architectural complexity.
• Reassess your procurement strategy continuously. AI requirements, hardware, and business models are changing too quickly for a fixed long-term design.
• Optimize for efficiency and ROI, not just peak performance. The most powerful setup may be too costly to sustain. Efficiency is also a public-facing metric—better utilization and more workload-aware system design can help companies respond to growing scrutiny around power consumption and water use.

The strategic goal of smarter AI data center design is not maximum performance at any cost, but an adaptable architecture that can deliver value, absorb change, and justify its footprint.

AI infrastructure is now a business strategy

AI data centers have quickly evolved from a back-end technical concern to becoming strategic business systems that help determine how effectively an organization can turn AI into revenue, improve human outcomes, and create a competitive advantage.

In the inference era, memory and storage are no longer passive repositories, explains McGregor, they are the active lifeblood of AI. The organizations that gain the most from AI will not necessarily be those with the largest computing footprint, but those that align infrastructure investments to business outcomes, reduce data bottlenecks, and build the flexibility to adapt as workloads evolve. He predicts that competitive advantage will increasingly belong to enterprises that treat compute, memory, storage, and networking as an integrated system designed to deliver AI efficiently, at scale, and with measurable ROI.

Procurement is now strategy and system design is a leadership issue, McGregor concludes. “One of the biggest questions every executive has to ask is how is AI going to change my business model?”

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

As agentic AI moves from experimentation toward enterprise deployment, the challenge is figuring out how agents can work together, connect to the systems and data they need, and operate safely across the workflows that run a business. Although agentic AI has been adopted by some 80% of Fortune 500 companies, progress toward meaningful scale remains uneven, with many organizations still working through isolated pilots.

For Arun Chandra, chief operating officer at NiCE, the first step is moving beyond experimentation for its own sake. “Everybody’s trying to figure out what can we do with this technology?” he says. But scaling requires a clearer connection to business strategy: Organizations need to define whether they are trying to increase revenue, reduce costs, or pursue another strategic or financial objective. From there, they need to rethink the workflows where agents will operate instead of just layering AI onto existing processes. “The last thing you want to do is to apply AI on an outdated or an inefficient workflow,” Chandra says.

That shift requires organizations to treat agentic AI as a cohesive system. Agents need access to the data, knowledge, and context required to make effective decisions, as well as connections to back-end systems if they are expected to take action. Fragmented information can undermine those capabilities: “The efficacy of these AI agents is purely a function of the context, the knowledge, and the data they can ingest and use,” Chandra says.

The organizational implications are equally noteworthy. Scaling agents can create a new form of fragmentation if teams build isolated systems that don’t connect with one another, while governance, privacy, security, and change management become more important as agents take on more consequential work. Chandra argues that AI agents should ultimately be held to the same standards as human workers, with organizations thinking of their workforce as a combination of humans and AI agents.

Looking ahead, that connected approach could enable agents to work proactively and even communicate with other agents to resolve customer needs. For organizations making the transition from pilots to scale, the priority is not to “boil the ocean,” Chandra says, but to instead build a connected strategy around high-value use cases, workflows, workforce changes, and measurable outcomes.

This webcast is produced in partnership with NiCE.

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

As companies scale, the technology supporting operations can become a liability just as quickly as it becomes an asset. Disconnected systems, site-specific tools, spreadsheets, and manual workarounds can create data silos that make it harder to spot problems early, coordinate responses, and make decisions with confidence. For Jabil, a global manufacturing company with more than 100 sites across more than 30 countries, the answer has been to make integration and simplification a priority.

The company adopted a “simplify-first, then-innovate mindset,” says Harish Manohar, SAP IT director at Jabil, recognizing that adding new technologies without first reducing complexity risks creating more risk. The goal is to standardize processes, consolidate where possible, and establish a more consistent data backbone across the organization. “Any innovation without simplification is going to add more complexity,” Manohar says.

That philosophy also changes how Jabil approaches modernization. “Any modernization or transformation should add measurable business value,” Manohar says. The company is focused on connecting processes end-to-end across its supply chain and creating a foundation that can scale consistently across regions. Integration comes first because, as Manohar puts it, “the backbone of any contemporary or modern organization is data.” Before organizations can optimize, automate, or apply AI, data needs to flow seamlessly across systems.

But doing that across a global organization is hardly straightforward. Jabil’s more than 100 sites operate with different levels of process maturity, legacy systems, and localized workflows, while regulated businesses bring additional compliance requirements. As such, standardizing across different regions and business environments means changing processes and governance without disrupting the operations already in place.

The value of that work extends beyond the technology to the people using it. Integrated workflows can offer employees shared visibility into data, reduce manual data reconciliation, and help them move from chasing information to acting on insights. For Jabil, the aim is also to improve real-time visibility into supply chain events, which can enable faster responses to disruptions and reduce operational risk.

Looking to the future, that foundation could make AI and automation all the more useful and scalable. With trusted data and integrated systems in place, Jabil is exploring predictive supply chain insights, intelligent exception handling, and AI-driven planning and forecasting. To Manohar, the takeaway is clear: “Simplicity at scale is a very competitive advantage,” and technology investments must ultimately connect to business value and operational resilience.

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

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.

Our topic today is enterprise technology integration, and how the benefits of consolidating tools and systems across the supply chain help organizations operate more reliably at scale. When companies reduce tool sprawl and connect their systems more effectively, they gain earlier visibility, faster response, and greater resilience across production lines.

My guest today is Harish Manohar, SAP IT Director at Jabil. Jabil has been on a journey to simplify its technology landscape by using SAP Integration Suite as the foundation to connect systems, retire fragmented tools, and enable more consistent operations globally.

This podcast is produced in partnership with SAP.

Welcome, Harish.

Harish Manohar: Hello, Megan. Good morning.

Megan: Thank you so much for being here, Harish. Just to start, if we could set some context, can you give us a quick overview of Jabil, the business and its overall transformation journey?

Harish: All right. So, about Jabil. Jabil is a global manufacturing company headquartered in St. Petersburg, Florida, USA. We have about 60 years of experience offering comprehensive engineering, supply chain, and manufacturing solutions across different industries. We have a global footprint of about over 100 different sites across 30-plus countries, 140,000-plus employees.

We are a trusted partner for more than 400 of the world’s top brands. That’s a little bit about Jabil.

Megan: Fantastic. And a lot of scale there, as you’re referring to some of the stats there. As things have got more complex, where did disconnected tools and systems start to slow you down, and what ultimately drove you to make integration a really strategic priority?

Harish: I talked about our global footprint across 100-plus sites. With a global footprint always comes complexity about site-specific tools. All of our sites have been in business for a long time, and over the period of years, they had their own tools for their own processes. It’s a little bit disconnected.

When we are looking to scale, the first thing we wanted to start looking at is what is this mix of site-specific tools, manual workarounds, spreadsheet-based processes, legacy applications, whatnot? That’s a big technical debt that we have had over the last 25 years. That’s where we started, and that is what led Jabil to make integration a strategic priority because we had limited ability to see issues early across plants, across regions, which would help us to coordinate responses consistently, and also to be able to scale those responses consistently. This complexity created data silos, and in a way delayed robust decision-making.

As the complexity increased globally, integration became extremely critical to a few things. It was critical to establishing a single trusted data backbone, which would directly enable faster coordinated responses across the network. We at Jabil, as part of our transformation journey, believe that having the right data at the right time fundamentally changes how we respond to disruptions, which is all about a manufacturing business. How we respond to disruptions. This is where we started our strategic priority towards having a integrated system that drove data consistency across our landscape.

Megan: Fantastic. As you have outlined there, there was obviously a real commercial need for this, but how did you think about bringing in those new technologies without adding even more complexity to the mix?

Harish: Great question. Whenever we talk about transformation, we talk about all these bleeding-edge technologies that are out there today when it comes to AI and data, cloud, et cetera. But it was very important for us to put a stake in the ground and say and adopt a simplify-first, then-innovate mindset. Because any innovation without simplification is going to add more complexity, just like you mentioned.

For us, SAP is our core digital platform. We want to focus on bringing more processes into SAP as much as possible. That is easier said than done because we have been in business for a while, global company, so not all processes exist within SAP at this point in time. We are slowly trying to standardize those processes, and having them under one single source of data would help us scale faster in terms of having data silos. We don’t want data silos across different systems.

This is where we started to introduce newer capabilities around SAP. From a cloud standpoint, we have been using SAP’s BTP and Integration Suite, which is proving to be the center stage of all integrations across Jabil. Well, it’s not there yet, but that is the direction that we want to pursue is we don’t want to have a slew of different integration platforms, rather try and see where Integration Suite fits best and where other smaller integration platforms would add more value.

Similarly, we have adopted an API-driven, event-based integration approach. That is our best practice that we have put down because we don’t want to keep moving data from one place to the other. That’s not good business practice in the IT world. Most of our integration architectures are API-driven and event-based. That is our focus.

Coming back to your new technologies perspective, we want to reuse as much as possible and standardize versus going out and buying these one-off tools that solve for point-in-case use cases. We really don’t want to go down that path. For major processes, we do adopt a best-of-breed approach, but for, let’s say, site-based use cases where a specific site has a particular need for a tool, we try and standardize that and reuse what exists in a different site, for example. There may be some need for a business process change, minor process changes, but that is our direction to make those process changes and reuse what is there already in a different location or a different region. So, that’s one.

Lastly, we are heavily aligned with SAP’s clean-core approach when it comes to customization. That has been the challenge for us over the last 25 years where we have been using SAP is our systems are heavily customized because we cater to different customers across the globe. Most of our demands are customer-driven, so we have to put in play these heavy customizations.

But now we are taking a pause, and we are saying, “You know what? We have customized so much so far, but now we are moving our systems into RISE, which would enable a clean-core journey in the future.” Now we have to put really good governance criteria and review processes that do not allow heavy customization of our system. We want to move away from that model as much as possible. Again, it’s not easy to do that at this point in time, but there is always a start.

Megan: I mean, it sounds like you took a very incremental, intentional approach to this. I mean, as you scaled globally then, what did modernization really look like at the company, and why start with integration?

Harish: To that point, we have always looked at transformation, modernization, very objectively. For us, it’s just not about upgrading a system. That’s not what it is. Any modernization or transformation should add measurable business value is our model, is our charter. Having said that, we don’t look at modernization in terms of just upgrades, but in what it gets our business in terms of value.

Most of our modernization transformation approaches are focused on connecting processes end-to-end across our supply chain, which is key for our business value. And then we also have a very concerted effort going on in the business community: how to standardize how our plants operate globally. Because, like I mentioned earlier, we have 100-plus plants, different processes, different legal regulations, different countries. It’s very hard for us to come up with one template across the globe, but we are trying to standardize as much as possible. And that’s where we are leveraging SAP’s Signavio, which is our business process management tool. We want to leverage Signavio’s capabilities in helping us standardize these global processes.

Now, back to your question, why did integration come first? Because the backbone of any contemporary or modern organization is data. And to get the right data at the right time, integration is the key aspect of the whole optimization exercise. Data needed to flow seamlessly before we start optimizing or automating or even applying AI use cases. This is where integration came first.

We wanted to create a single system of record across the operations. Well, when I say “single system of record,” it’s not just SAP, but the ability for us to create those data pipelines across those systems of record being supply chain, planning, inventory, et cetera, et cetera, in that operation space.

The result is we want to get to a foundation that helps us scale consistently across our different region. That is our main objective is to, how do we scale as the business grows, as we develop into this bigger organization across different industries? How do we set this foundation that will help us scale consistently? Simplicity, consolidation becomes strategic assets at scale.

Megan: Absolutely. And you touched on some of the complexities there of doing this at a scale that Jabil is at with its international footprint. What were some of the biggest challenges in your view in terms of rolling this out across regions, and how did that more standardized approach that you’ve mentioned there help?

Harish: Absolutely. I would like to reiterate some of those key challenges I mentioned. One hundred-plus sites, different sites have different maturity levels in terms of how they approach processes. They have a multitude of different legacy systems, localized processes, workarounds, spreadsheets, and the change management that exists within each site is very different. And we do have a footprint of highly regulated businesses. And when it comes to regulated businesses, that comes with its own set of challenges around qualification and CSD processes, et cetera. These are the key challenges that we are up against.

Now, the standardization helped us provide consistent workflows, data flows, and governance across sites. Now, we are not there at 100%, but we are working towards that, providing consistent workflows, data pipelines, and governance across sites. And we want to enable faster rollouts of our new bleeding edge technologies. For example, when I talked about SAP’s BTP or SAP Signavio or any other new SAP tool or non-SAP tool, traditionally our ability to deploy those had a challenge around the heavy customization that is required for each and every site. Now, the standardization approach takes that heavy customization out, which enables a faster rollout of those newer technologies.

And then lastly, we want to scale across all of our plants. I think initially we want a target of about 40-plus plants with shared processes that are consistent across the different regions. We want to shift from a site-by-site operations model to more of an enterprise-capability approach.

Megan: Right, and fascinating. And you touched on the people management aspect of this as well, because obviously this isn’t just about technology, it’s about people too. So, from the employee side, how did this shift to a more simplified landscape change the day-to-day experience for people compared to juggling multiple tools at once?

Harish: Great question. And I’ve been hearing direct feedback from our business community on some of these transformation initiatives on how those have changed their daily jobs significantly. Before we embarked on this transformation journey, any employee, any persona. You take a buyer, you take an inventory planner, you take a finance analyst, we go by personas. They had to deal with multiple tools, manual coordination, data reconciliation, especially in the finance space, inconsistent processes across different regions. And then the time spent reconciling data resulted in delay of making decisions, robust decisions. This was the before.

But now, since we are moving towards this newer standardization and more of an integration approach, we are able to achieve, to a certain extent, a single integrated workflow across different systems. We have built some key processes that will enable the single integrated workflows across systems. The users, our business community, irrespective of their roles in the organization, have clear visibility and shared data across their teams, which is very important. Earlier, they were dealing with different versions of the data, local workbooks, spreadsheets, and then the time spent talking to each other and reconciling what is the right data? What is the single source of truth? That we are trying to peel away that layer and get to that where the employees don’t have to deal with that kind of complexity.

This reduces manual effort and enables faster issue resolution when it comes to actual disruptions. Employees move from chasing information to focusing on acting on insights, which is where I think the new age of AI comes into play. I’ll talk about that in a little bit, but technology becomes an enabler of decision-making, and it’s no more an overhead. That’s where we want to go.

Megan: Fantastic. Such an important element of this, isn’t it, that people side of things? And we’ve touched briefly on this idea of value you’ve talked about before, because with an initiative of this scale, ROI is always front and center, of course. What benefits stood out most for you, and how important was better visibility in particular across systems?

Harish: Megan, I talked about how modernization and transformation for Jabil means measurable business value, which is directly connected to the ROI. We don’t do any transformation initiatives just because we want to do it from an IT standpoint. Any investment that we make in a transformation or a modernization initiative has to have a deliverable business case that is approved, signed off by business, because that is the only way true transformation happens, if IT and business are a partner as part of this transformation journey.

The biggest benefit that we have seen in this initiative is we are striving to reach, attain real-time visibility across our supply chain events. That is the biggest benefit that we see, faster response to disruptions and exceptions. And we are working to reduce our operational risk significantly by operating in this newer model.

One example I can give you is the unified workflows that I talked about earlier. It enabled earlier identification of missing materials and faster resolution across our sites. When it comes to a manufacturing company that has a global footprint, materials are the backbone of our whole supply chain process, right? Having a unified workflow, which is able to identify missing materials early in the game, was a game changer for our whole operations community.

Real-time analytics allow instant supply chain adjustments without delays. We are focusing a lot on getting analytics, a global analytic footprint in place that allows instant supply chain adjustments without any delays. That’s where AI is going to play a major role currently, and also in the near future.

And again, when you talk about visibility. Visibility is not just about reporting what is there in the system. Visibility directly should enable scenario modeling for our users to make strategic adjustments in their processes, which visibility also should make way for proactive decision-making, and also foster business continuity. This is how we look at visibility at Jabil.

Megan: Right. And you’re still on this journey, of course, but now that Jabil has a strong integration foundation in place, what does it unlock next for you, and how are you thinking about AI and automation as you’ve touched on a couple of times?

Harish: Yeah, we have talked about a couple of times around AI. So, we strongly believe at Jabil, a strong integration foundation enables event-driven, real-time processes, robust decision-making, scalable automation, and all of this enable easier adoption of AI use cases. And again, we are in the new age of AI. We are working towards getting to a AI-enabled enterprise, but having these foundations in place truly fast tracks our approach of AI use cases.

Our key focus areas, when I’m thinking about AI and automation in the immediate future, are predictive supply chain insights, intelligent exception handling, which is key to our business operations from a site operation standpoint. Intelligent exception handling is very, very key. On the supply chain side, I talked about predictive insights. That is also absolutely important. All of this enables AI-driven planning and forecasting capabilities.

For us, AI should augment true decision-making and robust decision-making, and deliver measurable value, not just experiment AI in use cases. We want to move beyond just experimenting AI in our business processes, but we want that AI that we implement to truly augment the decision-making process that we have, and also deliver key business value.

How does all this connect to integration? Integration ensures AI has access to trusted data, and also enables the ability to act across multiple systems in a global company like Jabil.

Megan: Fantastic. And if we could just finish, I suppose, with a little bit of advice for others, for other leaders, perhaps, dealing with tool sprawl at the moment, what are some key lessons you would say you’ve learned about prioritizing integration right from the start?

Harish: Absolutely. When it comes to tool sprawl, we can go all day about what are the different areas of tool sprawl? For example, application development, we have a multitude of tools; via integration, we have a multitude of tools. Data, we have a multitude of tools, but let’s just focus on integration. That’s the core topic here.

I would recommend folks that are in transformative roles in their organizations to start with integration as a foundation and not as an afterthought, right? Prioritize simplification over adding a slew of different tools to address different capabilities. Try and simplify as much as possible before we start your upgrade or your transformation journey. Standardize over locally optimizing tools. Try and get to that. Try and get the business community, your key SMEs in the business space, to understand the value of standardization and simplification of processes and how that enables your business to deliver value faster.

Second one, after prioritize: build a single source of truth for data as much as possible. I’m not saying it’s going to be always the case where an organization as in the scale of Jabil will be able to function just with SAP. They’re going to have different systems, but try and get to a model where you’re working with a single source of truth and not locally siloed data sources, right?

Next is focus on building a scalable integration architecture. Don’t just confine yourselves to the current state where you are, and build something in place that will only serve you for the next six months to a year. No, that’s not the goal. Anything that you build as an integration architecture should be scalable, and should serve the organization for the next three to five years. That’s how I look at it. When I’m putting in a new architecture pattern or a new event-driven insights, I look at, “Okay, where is Jabil going to be two years, three years down the line? Would this suffice for that scale?” That’s how I look at it.

Then focus on outcomes and not just technology. Focus on outcomes: speed, visibility, resilience, and not just technology deployment, because end of the day, IT and business should partner on the business value and not just technology upgrades.

Going back, simplicity at scale is a very competitive advantage, and technology investments must tie directly to business value and operational resilience. That’s how we look at Jabil in terms of our tool sprawl and how we prioritize integration right from the start. And that’s what I would suggest to other leaders that are looking to advance in this space.

Megan: Fantastic. Brilliant and very comprehensive advice. Thank you ever so much, Harish. And thank you ever so much for joining us. That was Harish Manohar, SAP IT Director at Jabil, whom I spoke with from Brighton in England.

That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor at 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 so much for listening. Goodbye.

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

For years, legacy technology has been a problem companies knew they needed to solve, but one they often struggled to tackle. The cost, complexity, and risk of replacing business-critical systems could make modernization feel like a disruption to manage instead of an opportunity to pursue. But with the rise in customer expectations and the changes AI brought to the economics of software development, that calculation is changing. Bupa’s modernization of its My Bupa mobile application offers a case study in what becomes possible when a legacy migration is treated as a business transformation rather than a technology rewrite.

Bupa CIO of health insurance Asifa Sherazi describes the risks of waiting for legacy systems to become an emergency: “The end-of-life technology is a risk that compounds quietly, and then arrives all at once.” For Bupa, moving its application from Xamarin to native Swift and Kotlin improved the app rating from 3.7 to 4.7, while the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. “What they’ll notice is that when they need us, often at a stressful moment, it just simply works,” Sherazi says.

Sanjeev Tripathi, senior vice president and region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys, contends that AI is helping change the equation. “The emergence of AI is fundamentally shifting the economics of modernization,” he says, reducing the effort, risk, and time traditionally associated with these programs. At Bupa, combining AI-assisted reverse engineering with forward engineering helped deliver the transformation in approximately 60% less time than would have been possible in the pre-AI era.

Sherazi and Tripathi also highlight the human dimension of modernization: preserving institutional knowledge, giving teams capacity to adapt, and creating an environment where employees can surface problems early.

Looking ahead, both experts see modernized platforms as foundations for more personalized, predictive and AI-driven experiences. The payoff of modernization may be less about replacing aging technology and more about building the flexibility needed for whatever comes next. 

“Modern platforms will become the base for far more intelligent AI-driven ecosystems, where AI is not just an add-on, but it is built into everything from design to operations. That’s how the modern platforms will evolve, and the customer experiences will become far more personalized and predictive,” says Tripathi. For Sherazi, that shift is already changing the questions organizations can ask: “It used to be, can our platform support that? Now, it’s: is that the right thing to do for our customers?”

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.

Our topic today is legacy modernization. Companies across industries continue to struggle to bring AI modernization to legacy technology stacks, increasing the risk of losing vendor support, degraded customer experience, and limited capacity for innovation.

Two words for you: future-ready foundation.

My guests are Asifa Sherazi, who is CIO of health insurance at Bupa, and Sanjeev Tripathi, who is senior vice president, region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys.

This podcast is produced in association with Infosys.

Welcome, Asifa and Sanjeev.

Asifa Sherazi: Thank you, Megan. Delighted to be here.

Sanjeev Tripathi: Thanks, Megan. Wonderful to be here. And good talking to you again, Asifa.

Megan: Thank you both so much for being here. Asifa, if I could start with you just to set the context for our conversation. There are seven million healthcare customers across the Asia-Pacific. Could you tell us a bit more about Bupa and the challenges it’s faced in its modernization plan?

Asifa: Yes, absolutely. Let me start with something about Bupa that shapes everything we do. We are a global healthcare organization. Think hospitals, clinics, dental, age care, digital health, alongside our insurance business. We reinvest back into the organization, so into our services, our capability, our teams, and our outcomes that we deliver for our customers. In Asia-Pacific, we serve around, as you said, seven million customers across health insurance and health services, individuals, families, corporate clients, patients. Our purpose, helping people live longer, healthier, happier lives is more than just a statement. It actually shapes our strategy, guides our investment decisions, and influences the choices our teams make every day.

Now, for most of our health insurance members, the day-to-day digital relationship with Bupa begins through My Bupa, our self-service platform. It’s the front door for managing cover, updating policy details, submitting claims, checking entitlements. Alongside of it, Blua plays a complimentary role. Where My Bupa helps members manage their cover, Blua helps them manage their health, digital healthcare services, clinical support, preventative health. And together, they let us move beyond transactional interactions towards more personalized, proactive care.

Here was our challenge. Our mobile app, My Bupa, was built on Xamarin, and Microsoft support for it ended sometime in 2024. We had extended support in place, so our customers remained protected throughout, but we were clear-eyed that this was a bridge, not a destination. The runway was finite, and it was shortening for us. We made the decision to modernize from a position of stability proactively for the long-term safety and experiences of our customer rather than waiting until circumstances forced our hand, because the platform carrying our most important customer relationship was in effect standing still while the world around it was moving.

Megan: Right. So you decided to act really proactively in that sense. And so, Asifa, what are some of the risks then of sticking with end-of-life technologies, and how will moving away from legacy technologies help you improve the customer experience?

Asifa: Yeah. Look, the end-of-life technology is a risk that compounds quietly, and then arrives all at once. We saw it in three ways. The first is security and compliance. Once a technology is out of vendor support, the flow of security updates and fixes changes fundamentally. In our case, we put extended support in place as a bridge so our customers stayed protected. But extended support buys you time, it doesn’t buy you a future. In healthcare, we hold some of the most sensitive information a person will ever share with an organization. That isn’t data to us, it’s trust. And trust is extraordinarily expensive to rebuild. We weren’t prepared to run that risk on a shortening runway.

The second is losing control of your own roadmap. How I’d explain that is iOS and Android don’t stand still. Every operating system release, every change to app store requirements becomes something that you react to rather than plan for. As each one slows you down a little further, over time, that opens a widening gap between what customers expect and what you can actually give them. We’re all customers. We don’t benchmark a health insurer against other health insurers. We benchmark against whatever app we used last.

And the third way was, and this is one we’d flag for peers, is the shrinking talent pool. Xamarin is legacy technology, and the engineering expertise available for it is limited. You end up with a critical customer platform supported by a narrowing group of specialists. That’s a workforce risk wearing a technology costume. We partnered with Infosys and we migrated the entire application estate from Xamarin to fully native Swift and Kotlin, and the customer outcomes are why we’re comfortable talking about this today. Our app rating moved from 3.7 to 4.7. Some of the stats that I’d love to share are that the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. The Android login success per visit doubled back up to 77%, and that’s one the team that is most proud of, because a login failure isn’t a technical event. It’s a person who wanted to check their cover and they couldn’t.

The team achieved 100% feature parity in a single release, and 90% of our active customer base moved to a new version, and they’ve, I think, downloaded nearly 1.8 million unique downloads. From our customers’ perspective, customers will never think about any of this as a technology change, and they shouldn’t have to either. What they’ll notice is that when they need us, often at a stressful moment, it just simply works, and that’s the outcome we were really after.

Megan: Those are some really striking results and statistics that you’ve shared there on the success of the migration. I mean, Sanjeev, could you talk a bit about why modernizing legacy technologies is so critical at this time, and how Infosys has approached that transformation journey with Bupa?

Sanjeev: Sure, Megan. To be honest, legacy modernization initiatives are not new, and there has always been a strong desire to drive modernization across the entire technology landscape. And Asifa covered all the points that I was going to cover about the risks that have been there. But I’ll reiterate, the reality is the industry has been held back by the cost complexity, and also the risks that have been associated with any legacy modernization initiative that has been undertaken historically.

As Asifa mentioned, customer expectations of what was acceptable five, 10 years back are simply not acceptable anymore. The customers today expect a seamless, intuitive, responsive interaction across every channel. And Asifa also covered the growing challenges around security, resilience, talent availability, and so on. Finding talent on legacy technology is extremely, extremely difficult, and that introduces risk in every organization and in every legacy platform, most of which are actually business-critical platforms as well. Security vulnerabilities are getting increasingly difficult to manage, and as I mentioned, finding deep expertise in older technologies is very, very difficult now.

So what has changed? What has changed is that we now have new tools that are available to us to address these challenges. The emergence of AI is fundamentally shifting the economics of modernization, and it’s doing that by helping organizations to reduce the effort, risk, and also the time that is traditionally taken for programs like these, and that is why we believe that the time is now for legacy modernization. In fact, in Infosys, we have six strategic value pools that we have identified in our AI-first value framework, which is publicly available, and legacy modernization is one of these six value pools. And in the market, we are seeing very strong interest across our client base, and they recognize that the opportunity to unlock both technical and business value is now.

With Bupa in particular, we approached the journey as a business transformation rather than simply a technology rewrite, and our approach had two key phases. One is reverse engineering, and then forward engineering. Let me just quickly cover what these two are.

Reverse engineering, what we did is we extracted and we understood the rules, the processes, and the logic within the legacy environment, and that’s a standard approach we took, we take in any legacy modernization program. What that does is it allows us to preserve the critical business functionality, but at the same time, it avoids the risks that often come with large-scale migration programs.

The second aspect is forward engineering, where we re-architected the solution to enable a reimagined customer experience. The objective is not just a feature-by-feature migration or ensuring feature parity, which is important, but it is even more important that since we’re investing this kind of money to create a modern platform that is scalable, maintainable, and is also capable for future innovation, and that’s what Asifa mentioned about you need to be in control of your own roadmap. You have to build a platform which is capable of supporting future innovation as well. We essentially ensured nothing was lost in translation, and we significantly improved the platform stability and long-term maintainability. And some of the metrics that Asifa mentioned reflects the meaningful improvement in customer experience as well.

Finally, just one more point before I close this question is the time to market. I spoke about the time is now, and because we’ve got the power of the tools that are available now. What AI allowed us is to accelerate the transformation dramatically. What would have traditionally been a long and complex modernization, was delivered in approximately 60% less time than what would have happened in pre-AI era, and that is the real story.

Megan: So AI in this context is a real enabler in terms of the economics and the speed and all of those things you’re talking about. If I could come back to you, Asifa, as much as modernization is a technology challenge, there is the human component as well, and I wondered if you could talk about how you prepared employees for these new technologies, and what challenges and solutions you faced on that front as well?

Asifa: The human side of it is what I’m really passionate about. Technology was only half the challenge. The real work was helping people move from what they knew to what was possible. Modernization isn’t just about replacing systems, it’s about giving teams the confidence, the capability, the clarity to embrace a different future, and that’s what determines whether change actually succeeds.

From that experience, there were three human challenges that stood out for us. The first one was scarcity of expertise on both sides of the transition, and like we said before, Xamarin is a legacy app. We were moving away from a legacy technology, supported by a rapidly shrinking specialist talent pool, and modernizing onto two native platforms. Documentation of that existing environment was limited. Much of the operational knowledge sat in individual experience and in the code base itself, and that created a real dependency on a small number of people. And for the team, it was a confronting reality and a powerful reminder that modernization isn’t just about technology imperative, it’s actually a resilience one.

What changed the dynamic was using AI to do the archeology. As Sanjeev mentioned, Infosys applied AI-assisted reverse engineering to harvest the legacy Xamarin code and extract the flows, the rules, the business logic, and generate native-ready user stories and acceptance criteria from it. The team did a lot of work. They mapped hundreds, I think nearly 1,500 regression scenarios to native epics, and that way, parity critical journeys were preserved by design rather than by memory. The human effect was just as important. Knowledge stopped living with a handful of individuals and became shared across the team, and our people could spend less energy holding institutional memory and more on designing and improving. So that was the first challenge.

The second challenge, Megan, was capacity and not willingness. Our business analysts were fully committed to ongoing feature delivery. And this is a live customer-facing app, and you cannot pause improving the customer experience while you rebuild underneath it. AI-driven discovery and documentation removed almost an estimated of 400 hours of manual BA effort, and that’s not a headcount story, that’s actually people not being asked to do two full-time jobs at once.

The third challenge that stood out was pace. Our original internal estimate was around 18 months, and thanks to Sanjeev and the team, almost like a one-team approach of how do we tackle this, the team delivered it in seven, and that’s exhilarating.

t’s also demanding, and both things need saying out loud. We mobilized cross-functional squads across engineering, architecture, testing, release, because managing parallel environments while protecting BAU commitments is such an emotional load as well as a logistical one. We leaned in hard alongside the team, being present rather than reporting from a distance, regular check-ins, genuinely listening to concerns, unblocking things quickly so people weren’t sitting waiting on a decision. And Megan, one thing I’ll say is when you’re compressing 18 months to seven, the most useful thing leadership can do is remove the friction in front of someone else and get out of their way.

But the thing that made the biggest difference was surprisingly simple, actually. We built a visual depiction of the transformation journey, and we updated it every month so the team could actually see how far they’ve come. Because when you deepen migration of this scale, it’s really easy to only see what’s left to be done, and being able to look back at the ground that’s already been covered gave people real intent and real momentum. And genuinely, it was exhilarating to watch. Watching the team’s pride became their fuel.

Megan: I love that idea of it being exhilarating, but exhausting. I think that’s a great description.

Asifa: Yeah. And the leadership lesson for all of us was just simpler than any of it. Programs like this have hard weeks, there’s going to be incidents, delay, difficult conversations. And Sanjeev, you and I have had those conversations many times. The job of leadership in those moments is to absorb the ambiguity rather than transmit anxiety, because if people feel safe telling you bad news early, there’s almost nothing you can’t fix. I say this plainly because it’s the truest thing about the whole program. I am so incredibly proud of this team, because what they achieved in seven months, while continuing to serve customers every single day without disruption, was genuinely remarkable. But what I’m really proud of isn’t the speed and it isn’t the engineering, it’s that the team never lost sight of who it was for, so every decision that we were making, and they came back, it just came back to the person at the other end of the app.

Megan: It sounds like you did an incredible job focusing on that people element just as much as the technology, which is so important. And Sanjeev, we’ve heard some of the incredible results Bupa has had with this, but more broadly, I suppose, looking across modernization use cases, where do you find that companies see the most ROI, and what advice do you have for leaders who need to focus on transformation at that legacy level?

Sanjeev: Thanks, Megan. That’s a very important and actually a very good question, because what we see is modernization ROI is sometimes viewed too narrowly through just a technology lens, and as Asifa mentioned, it is broader than just technology. Legacy modernization has aspects associated with business implications as well, so I’ll just cover that very quickly.

There are two dimensions, as I mentioned. One is technical ROI or technology-related ROI, and second is business ROI. On the technical side, and as you heard from Asifa as well, the biggest benefits come from faster time to market, platform stability and resilience, of course, and a lot of times, in fact, almost in all the cases, lower operating costs, and that is one of the aspects associated with some of the legacy modernization programs.

Besides this, access to broader, more readily available talent pool, and security management, and ensuring that the platforms are secure and free from, as much as possible, free from vulnerabilities in the current environment. At the same time, making it easier to innovate, releases become faster so that the feature delivery into the market becomes faster, and also able to respond to any new technology innovation that comes into play. But this is only on the technology side.

On the business side, however, the returns are often reflected in customer outcomes, and what we typically see are improvements in measures such as net promoter score, and in this case, for example, application ratings that you see on the app store. Both of which are actually very strong indicators of customer satisfaction and digital experience quality. I think it is important for us to cover, look at the ROI from both technology, but more importantly, from a business perspective.

The second part of your question was about what would be my advice to leaders, and I think Asifa covered it very well, where she mentioned that the one-team approach, the providing safe environment to the team to be able to say what is going well, but also what is not going well, and keeping your eye on the end outcome. I think those are very important things. But I would also like to add that don’t treat modernization as a technology initiative. It is a unique opportunity for us to rethink the business platform itself, and rather than pursuing a like-to-like migration, use the investment that you’re making to improve customer experience, simplify processes, and re-architect for capabilities such as real-time personalization and data-driven decision-making. The greatest returns come when technology transformation is directly linked to business outcomes.

And finally, and this is something that I have seen from personal experience multiple times, is you have to think from first principles. AI does not replace good engineering practices. It enables organizations to execute those practices faster, and it enables it faster, but also more consistently and at greater scale. The foundations of good architecture, sound engineering, and clear business objectives will continue to remain important, and in fact, their importance is going to increase as we progress. That, I think, is what I would say anybody embarking on a legacy modernization program should be focused on.

Megan: I love the idea that this is not just about migration. This a chance, as you say, to reimagine what you can deliver and what’s possible. Fantastic. Let’s close with a forward look. Asifa, what innovation are you looking forward to that wouldn’t have been possible before this, and what do you see on the horizon?

Asifa: There’s definitely lots of things on the horizon. But what excites us most isn’t specific technology, it’s that the question in our conversations has changed. It used to be, can our platform support that? Now, it’s is that the right thing to do for our customers? And that’s a profound shift.

There’s three things, Megan, that genuinely weren’t possible before, and we’ve touched on this a little bit, and Sanjeev’s touched on it as well. This first is speed as a permanent capability. Since launch, Android and iOS, the team has shipped multiple rapid-fire releases, including our migration to a new payment gateway. Builds are now completing four times faster, codes are reaching to testers in about an hour, and we’ve reduced our code base by 30%, and our application footprint’s reduced 18% as well. A simpler estate isn’t an aesthetic preference anymore, it’s what makes the next change cheap, and we’ve bought ourselves optionality to do that.

The second is quality at that speed, which is the part we’ve been most skeptical about five years ago. AI-driven triage and predictive defect analysis, we ran it across nearly 1,400 cases to focus on testing on the highest risk journeys, which meant we went live with zero security defects and zero high severity defects, and Sanjeev mentioned that just before. The old trade-off between moving fast and moving safely is being renegotiated right in front of us, so that’s the second point.

The third one is AI-driven accessibility testing, which the teams treated as a critical rather than an optional opportunity. In healthcare, people who most need to reach us are very often the people whom our poorly designed interface is a genuine barrier, and so being able to test that systematically at scale was a real advance for us as well.

And Megan, you mentioned on the horizon. It’ll be remiss of me not to take the name of agentic AI. The shift from AI that supports a task to AI that completes an outcome end-to-end with proper governance, human oversight at the key decision points. What this program showed us is that the constraint is no longer the models. It’s whether your platforms, data, processes are modern enough to let AI act safely, which is precisely why this work mattered now. Sanjeev alluded to it as well about that underlying architecture. Responsible AI as a source of advantage, not a compliance exercise, I would say. In health, if people don’t trust how you’re using their information, nothing else you build matters. We didn’t modernize to have modern technology. We modernized to earn the right to do the next thing, and to do it in weeks rather than years.

Megan: Absolutely. And now you have those foundations in place, like you say, all of these opportunities open up. Fantastic. And Sanjeev, just finally, as companies complete these migrations, what kinds of innovations and benefits are you seeing, and what do you expect in the next five years?

Sanjeev: Sure. Again, good question, Megan. The reason is there is no uniformity on how these migrations are being done even today. As I mentioned earlier, so where there is simple lift and shift of existing code base onto a new platform, a like-to-like replacement, the benefits generally tend to be limited. Where we apply first principles, thinking about re-imagining, re-architecting, and refactoring the system to establish foundations for a far more flexible system, where rules are not boxed into the architecture, but are managed in a way that changes can be incorporated faster, personalization can be achieved in real time, and time to market improves multifold. That’s where we are really seeing far more benefits coming through.

And to take the example of Bupa, it is a pretty clear step change, both in terms of customer experience and how fast teams can actually deliver now. I think the app ratings have gone up significantly. The customer experience has improved. Even simple things, such as login success rate, has improved quite significantly. And on the engineering side, we are now building and releasing features roughly about four times faster, and we are also seeing the migration of nearly 100% of the customers onto the new platform.

Now, if I look ahead over the next, you mentioned about next five years, I don’t know about five years, four years, but over the next few years at least, I think it is going to get very interesting, because modern platforms will become the base for far more intelligent AI-driven ecosystems, where AI is not just an add-on, but it is built into everything from design to operations. That’s how the modern platforms will evolve, and the customer experiences will become far more personalized and predictive. And even the way we build software will shift, with AI playing a much bigger role in the development process itself.

Megan: Fantastic. Really exciting changes on the horizon then. Thank you both so much.

That was Asifa Sherazi, who is the CIO of health insurance at Bupa, and Sanjeev Tripathi, senior vice president, region head of BFSI, healthcare, and public Sector for Australia, New Zealand, and Southeast Asia at Infosys, whom I spoke with from Brighton in England.

That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor at 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. Thank you so much for listening.

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

Each day, an airline transports tens of thousands of passengers on hundreds of flights. Often these are not straightforward point-to-point routes, with passengers requiring multiple connections. The airline can consider potentially hundreds of variables to price each of these journeys: demand, season, time of day, current events, global markets, and competitor airline activity to name just a few. It is a nuanced process that must constantly adapt to the goings on in the wider world.

Generative AI-powered market models are emerging as a means of handling complex tasks like this in real time. These deep learning models are trained on high-resolution numerical data and designed to analyze, simulate, and predict complex financial dynamics. Rather than relying on historical trends or static rules, the market model acts as an AI “brain,” consolidating a variety of data to simulate different market environments and make dynamic commercial decisions, such as pricing, inventory, or revenue management.

“It helps us make better, faster, more granular commercial decisions,” says Dominic Kennedy, senior vice president of revenue management, sales, and e-commerce at Virgin Atlantic about the market model his team is using to drive their generative pricing engines in some markets.

“It considers, on a real-time basis, a plethora of different inputs, whether it be demand, capacity, or booking. It has a really sophisticated way of evaluating our positioning relative to competitors, market conditions, and a whole raft of other things that have significance in how demand is manifested,” he adds.

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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.

Listen to the session or watch below

The “censorship-industrial complex” is an idea that a network of government, tech, and research groups is collaborating to suppress conservative online speech. This was fodder for the right-wing information sphere for years—then it began making its way into US policy. Watch a conversation exploring how it started, where it’s going, and what it means for the future of democracy and the internet.

Speakers: Amy Nordrum, Executive Editor, Operations, and Eileen Guo, Senior Reporter, Features & Investigations

Recorded on August 13, 2026

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• How ideas of a vast censorship network moved from the online fringe to Trump policy

Business and technology leaders need no convincing that the time of agentic AI is here. Organizations are rapidly adopting agents, and few executives doubt the technology’s potential to transform work. But many organizations find that realizing the desired return on investment (ROI) from AI hinges on having the right foundation, with inadequate infrastructure and data being major blockers.

Agentic AI places considerable new demands on enterprise data systems. The shift from answering questions to taking actions means AI agents need data from across the enterprise, in all its structured and unstructured forms, and with the right business context. To make decisions and act in real time, agents also need frictionless access to the organization’s operational systems—for example, those storing its supply chain, point-of-sale, or human resources data. Legacy data systems, even those updated just a few years ago, struggle to meet these demands.

As AI agents become embedded more widely in enterprise operations, the need to overcome the restrictions of legacy data systems grows more urgent. If Gartner’s prediction that AI agents will augment or automate 50% of business decisions by 2027 proves correct, organizations must eliminate bottlenecks or risk depriving agents of the data they need to make the right decisions at speed.

This report, based on a survey of 300 data and technology executives, explores how legacy systems are limiting the effectiveness of AI agents in many organizations. It finds that a handful of organizations—the data leaders—are having greater success with agentic AI and experiencing fewer data limitations as a result of legacy systems. These leaders offer a guide to creating the right data environment for agents to flourish and trusted systems to scale.

Key findings from the report include:

Few companies currently provide agentic AI with ample access to enterprise data. Across all the surveyed organizations, AI only has access to an average of 45% of company data. That number falls to 30% or less in organizations categorized as “data laggards”. A select group, however, ensures access to over 70% of their data. These “data leaders” are having greater success with their agents than the rest.

Trust in agent decisions is a reflection of data readiness. Today, only around half of surveyed organizations trust that the decisions their AI agents make are accurate and relevant. By contrast, 100% of the data leaders trust their agents’ decisions, a strong indicator that reliable AI requires a reliable data foundation.

Data leaders find it easier to achieve agent scale and speed. Two-thirds of data laggards say legacy data systems limit AI agent scaling (66%) and prevent agents from making decisions at speed (68%). Having largely overcome legacy data constraints, the leaders have mostly cleared these roadblocks, with just 8% reporting either constraint.

The pressure is on to make data estates agent-ready. Within two years, 100% of respondents plan to be using agentic AI, with 69% expecting to use it widely. Without removing data system constraints, agentic AI will fail to deliver the desired speed and efficiencies it promises.

Data access and context are top priorities. The most important initiative to enable scaling among all respondents is improving access to structured and unstructured data for AI agents. Also high on the list is enhancing data and AI governance with business context. Data leaders are also focusing heavily on the automation of data management.

Download the full 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. 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.

Imagine a healthcare system made up of multiple AI agents: one that manages symptom assessment, another scheduling, a third insurance, and a fourth pharmacy.

Each is an expert in its domain. But they all have their own distinct knowledge and objectives. Today they can exchange data, but they are not yet able to actually coordinate patient care without a human making the decisions.

“The intelligence is already there. What is missing is the connective tissue that turns four strangers into one team,” explains Vijoy Pandey, senior vice president and general manager of Outshift by Cisco.

This “connective tissue” comes from adding a semantic layer—what Outshift calls the “Internet of Cognition”—that enables agents across domains to work together and, critically, “think” together through shared intent, context, and reasoning.

This semantic layer relies on a connectivity layer beneath it called the “Internet of Agents,” which allows autonomous agents to discover one another, prove identity, and exchange messages across domains.

When used together, they enable “the next step on the road to distributed artificial superintelligence,” says Pandey.

From solo silicon savants to the ‘Internet of Cognition’

For years, the AI industry has been focused on growth. Scaling vertically has led to bigger models, trained on more data with more compute. This has produced the reasoning capabilities that can be like a “brain” for AI agents, which can perceive, reason, and act in digital environments.

While vertical scaling can produce more capable agents perpetually, to enable agentic problem solving across different systems, companies, and platforms the next axis of scale must be horizontal, says Pandey.

Multi-agent systems are already being explored in areas like software engineering, drug discovery, and scientific simulations, but their performances so far have been underwhelming. One study finds a failure rate of between 41% and around 87% when evaluating seven open-source multi-agent systems.

“Connected agents handle coordinated action well; taking a task whose shape they have seen, divided and passed around,” Pandey explains. “What they cannot do is hold a goal in common and reason toward something none of them was trained to solve.”

“The gap is architectural, not a prompting problem,” Pandey adds. “Without the right coordination layer, naive multi-agent setups can perform worse than a single agent. The step change is that team of agents converging on its own, on a new problem, with no human stitching the seams.”

To reach this goal, Pandey says Outshift has built a connectivity layer called AGNTCY, an open-source project now under the Linux Foundation. AGNTCY allows agents across different systems, companies, and platforms to find each other, prove identity, and exchange messages through open, standardized protocols.

And, as Pandey explains, this allows the Internet of Cognition thesis to take a step further. It creates a semantic layer that allows agents to align goals (share intent), pool institutional knowledge and compound memory (share context), and make collective trade-offs (share reasoning).

Pandey likens this progression to that of humans: “For hundreds of thousands of years humans got individually smarter, and the gains died with each person who made them,” he explains. “Around 70,000 years ago that changed, when humans learned to share intent, build cumulative knowledge, and reason collectively. That is when scattered individuals became civilization.

“Agents are at the same threshold. We have built the silicon geniuses and given them agency. What they lack is the layer that let humans go collective,” he says.

First steps to distributed superintelligence

Enabling agents to work collectively rests on three pillars in the tech stack:

Shared intent through cognition state protocols: Cognition state protocols are the semantic handshake that allow agents to agree on a goal before they act and then negotiate toward it. Outshift has created an open-source coordination layer called Mycelium, which organizations can clone and use against their own agents.

“We found that unstructured groups reached a decision about a third of the time across 14 scenarios,” says Pandey, speaking about internal testing. “A coordination protocol that makes agents declare a goal, surface missing information, and resolve conflicts before acting raised that to 93%.”

Shared context through cognition fabric: A cognition fabric is a shared institutional memory and communication mesh that allows agent insight to compound over time rather than resetting each session. This policy-governed context layer solves the problem of “organizational amnesia,” says Pandey, by ensuring the baseline intelligence of the systems only ever goes up.

Shared reasoning through cognitive amplifiers and guardrail technologies: Two kinds of cognition engine can be used together to enable shared reasoning. Cognitive amplifiers speed up shared reasoning and modeling, and guardrail technologies (GATs) create security, cost, and compliance frameworks. Humans are active contributors to this layer, making judgment calls the system routes to them (rather than reviewing outputs after the fact).

Cognition sharing in multi-agent systems can create new risks, including unintended delegations, malicious prompt injections or memory poisoning, or over-privileged agents with access to permissions and data far beyond what their tasks require. Environment-specific controls are therefore needed to protect against unintended actions or consequences.

“Agents have human-like attributes but operate at machine speed and scale,” says Pandey. “Everything we built for twenty years—access control, identity, compliance—was built for humans or machines, not both.”

Continuous Agent Semantic Authorization (CASA)—an open-source reference implementation developed by Outshift—is a GAT that works to ensure agent actions remain securely aligned with the user’s original goal through a process of continuous authorization. It does this by reading what the agent is trying to accomplish then checking each tool request against that task.

In the case of a healthcare system, for example, an agent told to summarize a patient record may start by querying a whole database. This could lead to CASA denying the call, because the request no longer matches the task it was authorized for.

“Today’s controls are scoped to a role or a session not to the task so an agent granted a tool can use it for anything,” explains Pandey. “Roughly 90% of the time, an agent has no way to confirm it is even cleared for the job it was handed.”

Experimentation for cross-domain innovation

When horizontally scaling intelligence in the enterprise, businesses should begin by experimenting with one cross-functional workflow that spans three or four teams and currently needs a human authorizing the handoffs, Pandey advises.

“Stand it up as a small multi-agent system on open, interoperable infrastructure, with a measurable baseline,” he says. “Keep building bigger models, add the horizontal axis on top of them, and change what you measure. Track where one agent’s insight made another agent better—that is the signal the horizontal axis is working.”

By starting to experiment now with intent, context, and reasoning layers, organizations can get ahead of the curve. “The problems are open, and the infrastructure is still being written,” says Pandey. “This is the moment to build it.”

For more information on the Internet of Cognition, visit Outshift.com.

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.

Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage.

Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law. Today, bringing a new drug to market takes an average of 10-15 years and costs anywhere from $1 billion to $2.5 billion, with failure rates upward of 90%.

AI has become the pharmaceutical industry’s biggest bet on bringing success rates up and timelines down. The faster drug companies can identify, test, and optimize new chemical compounds, the lower the risk of costly failures later in development.

“The main cost in drug discovery is still the clinical phase, so trying to reduce risk and increase your success rates there is obviously hugely beneficial,” says Paul Belcher, director of protein research strategy at global life sciences company Cytiva. “AI is one approach that drug companies hope will not only save time and compress timelines, but enable better quality candidates to reach the clinic.”

Early use of AI in drug discovery shows potential, but also highlights the need for robust and authentic data, as well as integration in lab systems.

AI brings efficiency to the lab

One of the most promising early-stage applications of AI in drug discovery is in hit identification. This involves screening libraries of molecular entities against a disease-related target, such as a protein, to find molecules that bind to it. A successful hit gives researchers a starting point for further testing and refinement, with the aim of eventually developing a viable drug.

Belcher has seen a shift from empirical screening to predictive design: Instead of physically screening libraries, drug companies are now using AI to design drug candidates from scratch and predict how they will interact with disease targets before committing anything to research and development (R&D).

This means companies are no longer limited by how much they can physically screen to identify starting points. “AI does away with that,” says Belcher. “And it can help eliminate low-quality candidates before you have to physically test them, saving time and resources.”

What AI can’t do yet is reliably predict kinetics or developability of new compounds, says Belcher. This means every AI-generated candidate still needs to be validated in the lab.

Traditional screening workflows were built to identify hits at scale, not to profile large numbers of complex candidates in detail. This is placing more pressure on lab teams, who now have to test, characterize, and purify a growing volume of more diverse, AI-generated compounds.

“The current techniques used in hit identification can screen hundreds of thousands, sometimes millions of compounds, using binary or threshold-based techniques producing low-fidelity data—yes-or-no responses,” Belcher explains. “AI can increase the number of hits you get and potentially give you better quality hits as well. That increases demand for higher-throughput, information-rich technologies to then validate and characterize those hits.”

Models need complete, quality data

As AI has accelerated demand for data-rich lab systems, it has also highlighted a fundamental need for better, more complete data.

Many earlier AI models were trained on publicly available datasets and are now hitting what Belcher calls a data wall. Because models have access to the same data, they all reach similar conclusions, with diminishing returns over time. Additionally, the datasets weren’t built with AI in mind, meaning they lack the structure, labeling, and diversity needed to keep models accurate and free of bias.

Publication bias reinforces the problem. “Most publicly available datasets and scientific publications focus exclusively on positive results,” says Belcher. “No one wants to share their failures. This bias is almost like having one hand tied behind your back. AI models can identify patterns associated with success, but they lack the comprehensive understanding of failures that would make predictions more reliable.”

The data Belcher believes would markedly improve models—the failed experiments, the compounds that don’t bind—remains frustratingly difficult to come by. “We often joke that there should be a journal of negative data,” he says. “It’s often buried in lab notebooks, and it’s never used to inform or guide future research.”

This lack of negative data creates a fundamental problem: Without access to a broad range of data, models can’t be adequately trained to avoid bias. “In all machine learning applications, the model’s performance relies heavily on the quality and scope of the training data,” notes Belcher.

Fabrication has also become much easier with AI, compounding concerns around data integrity. Take Western blots, for example. These are part of a standard technique for identifying proteins in blood or tissue samples, and they are among the most common targets for manipulation in biomedical research. Belcher cites research by Dutch microbiologist Elisabeth Bik, who found that almost 4% of biomedical papers contained duplicated or manipulated images. This was back in 2016, before generative AI made fabrication trivial.

“Manipulated or faked data has always been a problem in science, but in the AI world, especially when used to train models, it could have potentially disastrous consequences,” says Belcher. “There needs to be more tools to verify that data is not manipulated.”

Some vendors are starting to tackle this challenge. Belcher points to solutions like Cytiva’s Image Integrity Checker, for instance, which uses secure hash algorithms—the same technology used in blockchain—to detect whether scientific images have been tampered with. “We’re starting to see a lot of interest from publishing houses that want to adopt this as standard because it’s a quick way to ensure that what gets published in the literature is genuine,” he adds.

Autonomous labs could accelerate breakthroughs

Belcher describes the future state of drug discovery as fully autonomous labs that run with minimal human intervention. Foundational to this vision is consistency in data and infrastructure.

These AI-driven dark labs, or labs-in-the-loop, operate around the clock. They cycle through prediction, testing, and optimization, and then feed results back into AI models to guide the next round of experiments. This can improve the success rates of drug candidates entering clinical trials, says Belcher. Better starting points, combined with more rounds of optimization, should result in better candidates with fewer liabilities reaching the clinic.

But automating a lab depends heavily on integration. That means interoperable systems, highly structured and comprehensive datasets, and information flowing easily in and out. Most labs aren’t there yet. “Today, a lot of the instruments in labs are standalone,” Belcher notes. “You can have the best technology in the world, but if it’s a closed ecosystem—if the user can’t get the data out—it doesn’t do any good.”

An integrated infrastructure can enable labs to generate FAIR (findable, accessible, interoperable, and reusable) data at scale. This would not only inform individual lab reports, but could also train subsequent generations of AI models, effectively closing the loop between the computational, AI-driven dry lab and the physical wet lab.

“Our goal is to help scientists and researchers accelerate their breakthroughs and make that future state of autonomous labs a real possibility,” says Belcher. “We want to help them generate reliable data, simplify workflows in discovery, and hopefully enable what they’re working on to become tomorrow’s life-changing therapies, faster and with greater confidence.”

On costs and what comes next

AI-driven drug discovery is still in its early days. Notably, no drug discovered primarily through AI-driven design has yet received full FDA approval—although Belcher expects that to change in the next two to three years.

How big of an impact could AI eventually have on drug discovery? “The holy grail would be full in silico prediction of efficacy and toxicity, eliminating the need for the vast majority of physical wet lab work,” says Belcher. But there are many barriers to this beyond the maturity of the models, including regulatory hurdles and cost challenges.

A Stanford study found that the cost of training frontier AI models has more than doubled every year since 2016, adding more financial pressure to a sector already defined by exceptionally high R&D spend.

Belcher acknowledges the tension, but remains optimistic about what’s ahead. “I think we’ll get to a point where there’s a balance between AI and wet work, from a cost perspective and a risk perspective,” he says. “As long as the cost of compute doesn’t ever outweigh the cost of clinical development, I think AI is going to be an advantage.”

Learn more about how Cytiva is using faster discovery to reshape protein purification workflows.

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