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

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

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

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

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

Making a network AI inference-ready

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

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

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

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

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

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

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

Physical AI and the return of on-prem intelligence

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

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

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

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

AI for networking and the future of self-driving infrastructure

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

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

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

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

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

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

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

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

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

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

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

Key findings from the report include the following:

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

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

Download the report.

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

Amid the turbulence of the wider global economy in recent years, the pharmaceuticals industry is weathering its own storms. The rising cost of raw materials and supply chain disruptions are squeezing margins as pharma companies face intense pressure—including from countries like the US—to control drug costs. At the same time, a wave of expiring patents threatens around $300 billion in potential lost sales by 2030. As companies lose the exclusive right to sell the drugs they have developed, competitors can enter the market with generic and biosimilar lower-cost alternatives, leading to a sharp decline in branded drug sales—a “patent cliff.” Simultaneously, the cost of bringing new drugs to market is climbing. McKinsey estimates cost per launch is growing 8% each year, reaching $4 billion in 2022. 

In clinics and health-care facilities, norms and expectations are evolving, too. Patients and health-care providers are seeking more personalized services, leading to greater demand for precision drugs and targeted therapies. While proving effective for patients, the complexity of formulating and producing these drugs makes them expensive and restricts their sale to a smaller customer base.

The need for personalization extends to sales and marketing operations too as pharma companies are increasingly needing to compete for the attention of health-care professionals (HCPs). Estimates suggest that biopharmas were able to reach 45% of HCPs in 2024, down from 60% in 2022. Personalization, real-time communication channels, and relevant content offer a way of building trust and reaching HCPs in an increasingly competitive market. But with ever-growing volumes of content requiring medical, legal, and regulatory (MLR) review, companies are struggling to keep up, leading to potential delays and missed opportunities. 

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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. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Artificial intelligence is fundamentally reshaping how the world operates. With its potential to automate repetitive tasks, analyze vast datasets, and augment human capabilities, the use of AI technologies is already driving changes across industries.

In health care and pharmaceuticals, machine learning and AI-powered tools are advancing disease diagnosis, reducing drug discovery timelines by as much as 50%, and heralding a new era of personalized medicine. In supply chain and logistics, AI models can help prevent or mitigate disruptions, allowing businesses to make informed decisions and enhance resilience amid geopolitical uncertainty. Across sectors, AI in research and development cycles may reduce time-to-market by 50% and lower costs in industries like automotive and aerospace by as much as 30%.

“This is one of those inflection points where I don’t think anybody really has a full view of the significance of the change this is going to have on not just companies but society as a whole,” says Patrick Milligan, chief information security officer at Ford, which is making AI an important part of its transformation efforts and expanding its use across company operations.

Given its game-changing potential—and the breakneck speed with which it is evolving—it is perhaps not surprising that companies are feeling the pressure to deploy AI as soon as possible: 98% say they feel an increased sense of urgency in the last year. And 85% believe they have less than 18 months to deploy an AI strategy or they will see negative business effects.

Companies that take a “wait and see” approach will fall behind, says Jeetu Patel, president and chief product officer at Cisco. “If you wait for too long, you risk becoming irrelevant,” he says. “I don’t worry about AI taking my job, but I definitely worry about another person that uses AI better than me or another company that uses AI better taking my job or making my company irrelevant.”

But despite the urgency, just 13% of companies globally say they are ready to leverage AI to its full potential. IT infrastructure is an increasing challenge as workloads grow ever larger. Two-thirds (68%) of organizations say their infrastructure is moderately ready at best to adopt and scale AI technologies.

Essential capabilities include adequate compute power to process complex AI models, optimized network performance across the organization and in data centers, and enhanced cybersecurity capabilities to detect and prevent sophisticated attacks. This must be combined with observability, which ensures the reliable and optimized performance of infrastructure, models, and the overall AI system by providing continuous monitoring and analysis of their behavior. Good quality, well-managed enterprise-wide data is also essential—after all, AI is only as good as the data it draws on. All of this must be supported by AI-focused company culture and talent development.

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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 entirely 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.

Artificial intelligence models that can discover drugs and write code still fail at puzzles a lay person can master in minutes. This phenomenon sits at the heart of the challenge of artificial general intelligence (AGI). Can today’s AI revolution produce models that rival or surpass human intelligence across all domains? If so, what underlying enablers—whether hardware, software, or the orchestration of both—would be needed to power them?

Dario Amodei, co-founder of Anthropic, predicts some form of “powerful AI” could come as early as 2026, with properties that include Nobel Prize-level domain intelligence; the ability to switch between interfaces like text, audio, and the physical world; and the autonomy to reason toward goals, rather than responding to questions and prompts as they do now. Sam Altman, chief executive of OpenAI, believes AGI-like properties are already “coming into view,” unlocking a societal transformation on par with electricity and the internet. He credits progress to continuous gains in training, data, and compute, along with falling costs, and a socioeconomic value that is
“super-exponential.”

Optimism is not confined to founders. Aggregate forecasts give at least a 50% chance of AI systems achieving several AGI milestones by 2028. The chance of unaided machines outperforming humans in every possible task is estimated at 10% by 2027, and 50% by 2047, according to one expert survey. Time horizons shorten with each breakthrough, from 50 years at the time of GPT-3’s launch to five years by the end of 2024. “Large language and reasoning models are transforming nearly every industry,” says Ian Bratt, vice president of machine learning technology and fellow at Arm.

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

This content was researched, designed, and written entirely 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.

Michigan may be best known as the birthplace of the American auto industry, but its innovation legacy runs far deeper, and its future is poised to be even broader. From creating the world’s largest airport factory during World War II at Willow Run to establishing the first successful polio vaccine trials in Ann Arbor to the invention of the snowboard in Muskegon, Michigan has a long history of turning innovation into lasting impact. 

Now, with the creation of a new role, chief innovation ecosystem officer, at the Michigan Economic Development Corporation (MEDC), the state is doubling down on its ambition to become a modern engine of innovation, one that is both rooted in its industrial past and designed for the evolving demands of the 21st century economy.  

“How do you knit together risk capital founders, businesses, universities, and state government, all of the key stakeholders that need to be at the table together to build a more effective innovation ecosystem?” asks Ben Marchionna, the first to hold this groundbreaking new position. 

Leaning on his background in hard tech startups and national security, Marchionna aims to bring a “builder’s thinking” to the state government. “I’m sort of wired for that—rapid prototyping, iterating, scaling, and driving that muscle into the state government ecosystem,” he explains.

But these efforts aren’t about creating a copycat Silicon Valley. Michigan’s approach is uniquely its own. “We want to develop the thing that makes the most sense for the ingredients that Michigan can bring to bear to this challenge,” says Marchionna. 

This includes cultivating both mom-and-pop businesses and tech unicorns, while tapping into the state’s talent, research, and manufacturing DNA. 

In an era where economic development often feels siloed, partisan, and reactive, Michigan is experimenting with a model centered on long-term value and community-oriented innovation. “You can lead by example in a lot of these ways, and that flywheel really can get going in a beautiful way when you step out of the prescriptive innovation culture mindset,” says Marchionna.

This episode of Business Lab is produced in partnership with the Michigan Economic Development Corporation.

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. 

Today’s episode is brought to you in partnership with the Michigan Economic Development Corporation. 

Our topic today is building a statewide innovation economy. Now, the U.S. state of Michigan has long been recognized as a leader in vehicle and mobility innovation. Detroit put it on the map, but did you know it’s also the birthplace of the snowboard or that the University of Michigan filed more than 600 invention disclosures in 2024, second only to the Massachusetts Institute of Technology, or that in the past five years, 40% of the largest global IPOs have been Michigan built companies?

Two words for you: innovation ecosystem. 

My guest is Ben Marchionna, chief innovation ecosystem officer at the Michigan Economic Development Corporation, the MEDC. 

Ben, thank you ever so much for joining us.

Ben Marchionna: Thanks, Megan. Really pleased to be here.

Megan: Fantastic. And just to set some context to get us started, I wondered if we could take a kind of high-level look at the economic development landscape. I mean, you joined the MEDC team last year as Michigan’s first chief innovation ecosystem officer. In fact, you were the first to hold such a role in the country, I believe. I wondered if you could talk a bit about your unique mission and how this economic development approach differs from efforts in other states.

Ben: Yeah, sure would love to. Probably worth pointing out that while I’ve been in this role for about a year now, it was indeed a first-of-its-kind role in the state of Michigan and first of its kind in the country. The slight difference in the terminology, chief innovation ecosystem officer, it differs a little bit from what folks might think of as a chief innovation officer. I’m not all that focused on driving innovation within government, which is what some other chief innovation officers would be focused on around the country. Instead, you can think of my role as Michigan’s chief architect for innovation, if you will. So, how do you knit together risk capital founders, businesses, universities, and state government, all of the key stakeholders that need to be at the table together to build a more effective innovation ecosystem? I talk a lot about building connective tissues that can achieve one plus one equals three outcomes.

Michigan’s got all kinds of really interesting ingredients and has the foundation to take advantage of the moment in a really interesting way over the next decades as we look to supercharge some of the growth of our innovation ecosystem development.

My charter is relatively simple. It’s to help make sure that Michigan wins in a now hyper-competitive global economy. And to do that, I end up being super focused on orienting us towards a growth and innovation-driven economy. That can mean a lot of different things, but I ultimately came to the MEDC and the role within the state with a builder’s mindset. My background is not in traditional economic development, it’s in not government at all. I spent the last 10 years building hard tech startups, one in Ann Arbor, Michigan, and another one in the Northern Virginia area. Before that, I spent a number of years at, think of it like, an innovation factory at Lockheed Martin Skunk Works in the Mojave Desert, working on national security projects.

I’m sort of wired for that, builder’s thinking, rapid prototyping, iterating, scaling, and driving that muscle into the state government ecosystem. I think it’s important that the government also figure out how to pull out all the stops and be able to move at the speed that founders expect. A bias towards action, if you will. And so this is ultimately what my mission is. There are a lot of real interesting things that the state of Michigan can bring to bear to building our innovation ecosystem. And I think, tackling it with this sort of a mindset, I am absolutely optimistic for the future that we’ve got ahead of us.

Megan: Fantastic. It almost sounds like your role is sort of building a statewide startup incubator of sorts. As we mentioned in the opening, Michigan actually has a really interesting innovation history even in addition to the advances in the automotive industry. I wondered if you could talk a bit more about that history and why Michigan, in particular, is poised to support that sort of statewide startup ecosystem.

Ben: Yeah, absolutely. And I would even broaden it. Building the startup ecosystem is one of the essential layers, but to be able to successfully do that, we have to bring in the research universities, we have to bring in the corporate innovation ecosystem, we have to bring in the risk capital, et cetera. So yes, absolutely, startups are important. And equally as important are all of these other elements that are necessary for a startup ecosystem to thrive, but are also the levers that are just sitting there waiting for us to pull them.

And we can get into some of the details over the course of our chat today on the auto industry and how this fits into it, but Michigan does a lot more than just automotive stuff. And you noted, I think, the surfboard as an example in the intro. Absolutely correct. We have a reputation as Motor City, but Michigan’s innovation record is a lot weirder in a fun way and richer than just cars.

Early 20th century, mostly industrial moonshot innovation. So first paved mile of concrete was in Detroit in 1909. A few years later, this is when the auto sector started to really come about with Henry Ford’s moving assembly line. Everyone tends to know about those details. But during World War II, Willow Run Airport sort of smack between Detroit and Ann Arbor, Michigan they had the biggest airplane factory in the world. They were cranking out B-24 bombers once every 63 minutes, and I’ve actually been to the office that Henry Ford and Charles Lindbergh shared. It’s still at the airport. And it was pretty cool because Henry Ford had a window built into the office that looked sort of around the corner so that he could tick off as airplanes rolled out of the hanger and make sure that they were following the same high rate production mentality that the auto sector was able to develop over the decades prior. 

And so they came in to help make sure that you could leverage that industrial sector to drive very rapid production, the at-scale mentality, which is also a really important part of the notion of re-industrialization that is taking hold across the country now. Happy to get into that a bit, but yeah, Willow Run, I don’t think most folks realize that that was the biggest airplane factory in the world sitting right here in Michigan.

And all of this provided the mass production DNA that was able to help build the statewide supplier base. And today, yes, we use that for automotive, EVs, space hardware, batteries, you name it. But this is the foundation, I think, that we’ve got to be able to build on in the future. In the few decades since you saw innovations in sports, space, advanced materials, it’s like the sixties to the eighties. You said the snowboard. That was invented in Muskegon on the west side of the state in 1965.

Dow Chemical’s here in a really big way. They’ve pioneered silicone and advanced plastics in Michigan. University of Michigan’s Dr. Thomas Francis is the world’s first successful polio vaccine trials that were pioneered out of Ann Arbor, and that Big 10 research horsepower that we’ve got in the state, between the University of Michigan, Michigan State University. We also have Wayne State University in Detroit, which is a powerhouse. And then Michigan Tech University in the Upper Peninsula just recently became an R1 research institution, which essentially means those top-tier research powerhouses and that culture of tinkering matter a lot today.

I think in more recent history, you saw design and digital innovations emerge. I don’t think a lot of people appreciate that Herman Miller and Steelcase reinvented office ergonomics on the west side of the state, or that Stryker is based in Kalamazoo. They became a global medical device powerhouse over the last couple of decades, too. Michigan’s first unicorn, Duo Security, the two-factor authentication among many other things that they do there, was sold to Cisco in 2018 for 2.35 billion.

Like I said, the first unicorn in the few years since we’ve had another 10 unicorns. And I think probably what would be surprising to a lot of people is it’s in sectors well beyond mobility, it’s marketplace like StockX, FinTech, logistics, cybersecurity, of course. It’s a little bit of everything, and I think that goes to show that some of the fabric that exists within Michigan is a lot richer than what people think of, Motor City. We can scale software, we can scale life sciences innovation. It’s not just metal bending, and I talked about re-industrialization earlier. So I think about where we are today, there’s a hard tech renaissance and a broad portfolio of other high-growth sectors that Michigan’s poised to do really well in, leveraging all of that industrial base that has been around for the last century. I’m just super excited about the future and where we can take things from here.

Megan: I mean, genuinely, a really rich and diverse history of innovation that you’ve described there.

Ben: That’s right.

Megan: And last year, when Michigan’s Governor Whitmer announced this new initiative and your position, she noted the need to foster this sort of culture of innovation. And we hear that a lot that terminal in the context of company cultures. It’s interesting to hear in the context of a U.S. state’s economy. I wonder what your strategy is for building out this ecosystem, and how do you foster a state’s innovation culture?

Ben: Yeah, it’s an awesome point, and I think I mentioned earlier that I came into the role with this builder’s mentality. For me, this is how I am wired to think. This is how a lot of the companies and other founders that I spent a lot of time with, this is how they think. And so bringing this to the state government, I think of Blue Origin, Jeff Bezos’ space company, their motto, the English translation at least of it, is “Step by Step, Ferociously.” And I think about that as a lot as a proxy for how I do that within the state government. There’s a lot of iterative work that needs to happen, a lot of coaching and storytelling that happens to help folks understand how to think with that builder’s mindset. The wonderful news is that when you start having that conversation, this is one of those in these complicated political times, this is a pretty bipartisan thing, right?

The notion of how to build small businesses that create thriving main street communities while also supporting high-growth, high-tech startups that can drive prosperity for all, and population growth, while also being able to cover corporate innovation and technology transfer out of universities. All of these things touch every corner of the state.

And Michigan’s a surprisingly large and very geographically diverse state. Most of the things that we tend to be known for outside the state are in a pretty small corner of Southeast Michigan. That’s the Motor City part, but we do a lot and we have a lot of really interesting hubs for innovation and hubs for entrepreneurship, like I said, from the small mom-and-pop manufacturing shop or interest in clothing business all the way through to these insane life sciences innovations being spun out of the university. Being able to drive this culture of innovation ends up being applicable really across the board, and it just gets people really fired up when you start talking about this, fired up in a good way, which is, I think, what’s really fantastic.

There’s this notion of accelerating the talent flywheel and making sure that the state can invest in the cultivation of really rich communities and connections, and this founder culture. That stuff happens organically, generally, and when you talk about building startup ecosystems, it’s not like the state shows up and says, “Now you’re going to be more innovative and that works.” That is not the case.

And so to be able to develop those things, it’s much more about this notion of ecosystem building and getting the ingredients and puzzle pieces in the right place, applying a little bit of funding here and there, or loosening a restriction here or there, and then letting the founders do what they do best, which is build. And so this is what I think I end up being super passionate about within the state. You can lead by example in a lot of these ways, and that flywheel that I mentioned really can get going in a beautiful way when you step out of the prescriptive innovation culture mindset.

Megan: And given that role, I wonder what milestones the campaign has experienced in your first year? Could you share some highlights and some developing projects that you’re really excited about?

Ben: We had a recent one, I think that was pretty tremendous. Just a couple of months ago, Governor Whitmer signed into law a bipartisan legislation called the Michigan Innovation Fund. This was a multi-year effort that resulted in the state’s biggest investment in the innovation ecosystem development in over two decades. A lot of this funding is going to early stage venture capital firms that will be able to support the broad seeding of new companies and ideas, keep talent within the state from some of those top tier research institutions, bring in really high quality companies that early stage, growth stage companies from out of state, and then develop or supercharge some of that innovation ecosystem fabric that ties those things together. So that connective tissue that I talked about, and that was an incredible win to launch the year with.

This was just back in January, and now we’re working to get some of those funds out over the course of the next month or two so we can put them to use. What was really interesting about that was, it wasn’t just a top-down thing. This was supported from the top all the way up to and including Governor Whitmer. I mentioned bipartisan support within Michigan’s legislature and then bottom-up from all of the ecosystem partners, the founders, the investors advocating as a whole block, which I think is really powerful. Rather than trying to go for one-off things, this huge coalition of the willing got together organically and advocated for, hey, this is why this is such a great moment. This is the time to invest. And Governor Whitmer and the legislators, they heard that call, and we got something done, and so that happened relatively quickly. Like I said, biggest investment in the last two decades, and I think we’re poised to have some really great successes in the coming year as well.

Another really interesting one that I haven’t seen other states do yet, Governor Whitmer, around a year ago, signed an executive order called the Infrastructure for Innovation. Essentially, what that does is it opens up state department and agency assets to startups in the name of moving the ball forward on innovation projects. And so if you’re a startup and you need access to some very hard-to-find, very expensive, maybe like a test facility, you can use something that the state has, and all of the processes to get that done are streamlined so that you’re not beating your head against a wall. Similarly, the universities and even federal labs and corporate resources, while an executive order can’t compel those folks to do that, we’ve been finding tremendous buy-in from those stakeholders who want to volunteer access to their resources.

That does a lot of really good things, certainly for the founders, that provides them the launchpad that they need. But for those corporations and universities, and whatnot, a lot of them have these very expensive assets sitting around wildly underutilized, and they would be happy to have people come in and use them. That also gives them exposure to some of the bleeding-edge technology that a lot of these startups today are developing. I thought that was a really cool example of state government leadership using some of the tools that are available to a governor to get things moving. We’ve had a lot of early wins with startups here that have been able to leverage what that executive order was able to do for them.

Here we are talking about the MIT Technology Review to tie in an MIT piece here, we also started a Team Michigan for MIT’s REAP program. It’s the Regional Entrepreneurship Acceleration Program, and this is one of the global thought leaders on best practices for innovation ecosystem development. And so we’ve got a cohort of about a dozen key leaders from across all of those different stakeholders who need to have a seat at the table for this ecosystem development.

We go out to Cambridge twice a year for a multi-day workshop, and we get to talk about what we’ve learned as best practices, and then also learn from other cohorts from around the world on what they’ve done that is great. And then also get to hear some of the academic best practices that the MIT faculty have discovered as part of this area of expertise. And so that’s been a very interesting way for us to be able to connect outside of the state government boundaries, if you will. You sort of get out there and see where the leading edge is and then come back and be able to talk about the things that we learned from all of these other global cohorts. So always important to be focused on best practices when you’re trying to do new things, especially in government.

Megan: Sounds like there are some really fantastic initiatives going on. It sounds like a very busy first year.

Ben: It’s been a very busy first year couldn’t be more thrilled about it.

Megan: Fantastic. And in early 2023, I know that Newlab partnered with Michigan Central to establish a startup incubator too, which brought in more than a hundred startups just in its first 14 months. I wonder if you could talk a bit about how the incubator fits in with the statewide startup ecosystem and the importance of partnerships, too, for innovation.

Ben: Yeah, a key element, and I think the partnerships piece is essential here. Newlab is one of the larger components of the Southeast Michigan and especially the Detroit innovation ecosystem development. They will hit their two-year launch anniversary in just a couple of weeks, here I think. This will be mid-May, it will be two years and in that time, they’ve now got 140 plus startups all working out of their space, and Newlab they’re actually headquartered in Brooklyn, New York, but they run this big startup accelerator incubator out of Detroit as well and so this is sort of their second flagship location. They’ve been a phenomenal partner, and so speaking of the partnerships, what do those do?

They de-risk the technologies to help enable broader adoptions. Corporations can provide early revenues, the state can provide non-dilutive grant matching. Universities can bring IP and this renewable source of talent generation, and being able to stitch together all of those pieces can create some really interesting unlocks for startups to grow. But again, also this broader entrepreneurship and innovation ecosystem to really be able to thrive.

Newlab has been thrilled with their partnership in Southeast Michigan, and I think it’s a model that can be tailored across the state so that, depending on what assets are available in your backyard, you can make sure that you can best harness those for future growth.

Megan: Fantastic. What’s the long-term vision for the state’s innovation landscape when you think about it in five, 10 years from now? What do you envisage?

Ben: Amazing question. This is probably what I get most excited about. I think earlier we talked about the Willow Run B-24 bomber plant. That is what made Michigan known as the arsenal of democracy back in the day. I want Michigan to be the arsenal of innovation. We’re not trying to recreate a Silicon Valley. Silicon Valley does certain things, not trying to recreate what El Segundo wants to do in hard tech or New York City in FinTech, and all of these other things. We want to develop the thing that makes the most sense for the ingredients that Michigan can bring to bear to this challenge.

I think that becoming the Midwest arsenal of innovation, that’s something that Michigan is very well poised to use as a springboard for the decades to come. I want us to be the default launch pad for building a hard tech company, a life sciences company, an agricultural tech company. You name it. If you’ve got a design prototype and want to mass produce something, don’t want to hop coast, you want to be somewhere that has a tremendous quality of life, an affordable place, somewhere that government is at the table and willing to move fast, this is a place to do that.

That can be difficult to do in some of the more established ecosystems, especially post-covid, as a lot of them are going through really big transition periods. Michigan’s already a top 10 state for business in the next 10 years. I want us to be a top 10 state for employment, top 10 state for household median income for post-secondary education attainment, and net talent migration. Those are my four top tens that I want to see in the next 10 years. And we covered a lot of topics today, but I think those are the reasons that I am super optimistic about being able to accomplish those.

Megan: Fantastic. Well, I’m tempted to move to Michigan, so I’m sure plenty of other people will be now, too. Thank you so much, Ben. That was really fascinating.

Ben: Thanks, Megan. Really delighted to be here.

Megan: That was Ben Marchionna, chief innovation ecosystem officer at the Michigan Economic Development Corporation, whom I spoke with from Brighton, England. 

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

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

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

This content was researched, designed, and written entirely 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.

Around the world, countries are facing the challenges of aging populations, growing rates of chronic disease, and workforce shortages, leading to a growing burden on health care systems. From diagnosis to treatment, AI and other digital solutions can enhance the efficiency and effectiveness of health care, easing the burden on straining systems. According to the World Health Organization (WHO), spending an additional $0.24 per patient per year on digital health interventions could save more than two million lives from non-communicable diseases over the next decade.

To work most effectively, digital solutions need to be scaled and embedded in an ecosystem that ensures a high degree of interoperability, data security, and governance. If not, the proliferation of point solutions— where specialized software or tools focus on just one specific area or function—could lead to silos and digital canyons, complicating rather than easing the workloads of health care professionals, and potentially impacting patient treatment. Importantly, technologies that enhance workforce productivity should keep humans in the loop, aiming to augment their capabilities, rather than replace them. 

Through a survey of 300 health care executives and a program of interviews with industry experts, startup leaders, and academic researchers, this report explores the best practices for success when implementing integrated digital solutions into health care, and how these can support decision-makers in a range of settings, including laboratories and hospitals. 

Key findings include: 

Health care is primed for digital adoption. The global pandemic underscored the benefits of value-based care and accelerated the adoption of digital and AI-powered technologies in health care. Overwhelmingly, 96% of the survey respondents say they are “ready and resourced” to use digital health, while one in four say they are “very ready.” However, 91% of executives agree interoperability is a challenge, with a majority (59%) saying it will be “tough” to solve. Two in five leaders say balancing security with usability is the biggest challenge for digital health. With the adoption of cloud solutions, organizations can enjoy the benefits of modernized IT infrastructure: 36% of the survey respondents believe scalability is the main benefit, followed by improved security (28%). 

Digital health care can help health care institutions transform patient outcomes—if built on the right foundations. Solutions like AI-powered diagnostics, telemedicine, and remote monitoring can offer measurable impact across the patient journey, from improving early disease detection to reducing hospital readmission rates. However, these technologies can only support fully connected health care when scaled up and embedded in ecosystems with robust data governance, interoperability, and security. 

Health care data has immense potential—but fragmentation and poor interoperability hinder impact. Health care systems generate vast quantities of data, yet much of it remains siloed or unusable due to inconsistent formats and incompatible IT systems, limiting scalability. 

Digital tools must augment, not overload, the workforce. With global health care workforce shortages worsening, digital solutions like clinical decision support tools, patient prediction, and remote monitoring can be seen as essential aids rather than threats to the workforce. Successful deployment depends on usability, clinician engagement, and training. 

Regulatory evolution, open data policies, and economic sustainability are key to scaling digital health. Even the best digital tools struggle to scale without reimbursement frameworks, regulatory support, and viable business models. Open data ecosystems are needed to unleash the clinical and economic value of innovation. Regulatory and reimbursement innovation is also critical to transitioning from pilot projects to high-impact, system-wide adoption.

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.

This content was researched, designed, and written entirely 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.