Using unstructured data to fuel enterprise AI success

Enterprises are sitting on vast quantities of unstructured data, from call records and video footage to customer complaint histories and supply chain signals. Yet this invaluable business intelligence, estimated to make up as much as 90% of the data generated by organizations, historically remained dormant because its unstructured nature makes analysis extremely difficult.

But if managed and centralized effectively, this messy and often voluminous data is not only a precious asset for training and optimizing next-generation AI systems, enhancing their accuracy, context, and adaptability, it can also deliver profound insights that drive real business outcomes.

A compelling example of this can be seen in the US NBA basketball team the Charlotte Hornets who successfully leveraged untapped video footage of gameplay—previously too copious to watch and too unstructured to analyze—to identify a new competition-winning recruit. However, before that data could deliver results, analysts working for the team first had to overcome the critical challenge of preparing the raw, unstructured footage for interpretation.

The challenges of organizing and contextualizing unstructured data

Unstructured data presents inherent difficulties due to its widely varying format, quality, and reliability, requiring specialized tools like natural language processing and AI to make sense of it.

Every organization’s pool of unstructured data also contains domain-specific characteristics and terminology that generic AI models may not automatically understand. A financial services firm, for example, cannot simply use a general language model for fraud detection. Instead, it needs to adapt the model to understand regulatory language, transaction patterns, industry-specific risk indicators, and unique company context like data policies.

The challenge intensifies when integrating multiple data sources with varying structures and quality standards, as teams may struggle to distinguish valuable data from noise.

How computer vision gave the Charlotte Hornets an edge 

When the Charlotte Hornets set out to identify a new draft pick for their team, they turned to AI tools including computer vision to analyze raw game footage from smaller leagues, which exist outside the tiers of the game normally visible to NBA scouts and, therefore, are not as readily available for analysis.

“Computer vision is a tool that has existed for some time, but I think the applicability in this age of AI is increasing rapidly,” says Jordan Cealey, senior vice president at AI company Invisible Technologies, which worked with the Charlotte Hornets on this project. “You can now take data sources that you’ve never been able to consume, and provide an analytical layer that’s never existed before.”

By deploying a variety of computer vision techniques, including object and player tracking, movement pattern analysis, and geometrically mapping points on the court, the team was able to extract kinematic data, such as the coordinates of players during movement, and generate metrics like speed and explosiveness to acceleration. 

This provided the team with rich, data-driven insights about individual players, helping them to identify and select a new draft whose skill and techniques filled a hole in the Charlotte Hornets’ own capabilities. The chosen athlete went on to be named the most valuable player at the 2025 NBA Summer League and helped the team win their first summer championship title.

Annotation of a basketball match

Before data from game footage can be used, it needs to be labeled so the model can interpret it. The x and y coordinates of the individual players, seen here in bounding boxes, as well as other features in the scene, are annotated so the model can identify individuals and track their movements through time.

Taking AI pilot programs into production 

From this successful example, several lessons can be learned. First, unstructured data must be prepared for AI models through intuitive forms of collection, and the right data pipelines and management records. “You can only utilize unstructured data once your structured data is consumable and ready for AI,” says Cealey. “You cannot just throw AI at a problem without doing the prep work.” 

For many organizations, this might mean they need to find partners that offer the technical support to fine-tune models to the context of the business. The traditional technology consulting approach, in which an external vendor leads a digital transformation plan over a lengthy timeframe, is not fit for purpose here as AI is moving too fast and solutions need to be configured to a company’s current business reality. 

Forward-deployed engineers (FDEs) are an emerging partnership model better suited to the AI era. Initially popularized by Palantir, the FDE model connects product and engineering capabilities directly to the customer’s operational environment. FDEs work closely with customers on-site to understand the context behind a technology initiative before a solution is built. 

“We couldn’t do what we do without our FDEs,” says Cealey. “They go out and fine-tune the models, working with our human annotation team to generate a ground truth dataset that can be used to validate or improve the performance of the model in production.”

Second, data needs to be understood within its own context, which requires models to be carefully calibrated to the use case. “You can’t assume that an out-of-the-box computer vision model is going to give you better inventory management, for example, by taking that open source model and applying it to whatever your unstructured data feeds are,” says Cealey. “You need to fine-tune it so it gives you the data exports in the format you want and helps your aims. That’s where you start to see high-performative models that can then actually generate useful data insights.” 

For the Hornets, Invisible used five foundation models, which the team fine-tuned to context-specific data. This included teaching the models to understand that they were “looking at” a basketball court as opposed to, say, a football field; to understand how a game of basketball works differently from any other sport the model might have knowledge of (including how many players are on each team); and to understand how to spot rules like “out of bounds.” Once fine-tuned, the models were able to capture subtle and complex visual scenarios, including highly accurate object detection, tracking, postures, and spatial mapping.

Lastly, while the AI technology mix available to companies changes by the day, they cannot eschew old-fashioned commercial metrics: clear goals. Without clarity on the business purpose, AI pilot programs can easily turn into open-ended, meandering research projects that prove expensive in terms of compute, data costs, and staffing. 

“The best engagements we have seen are when people know what they want,” Cealey observes. “The worst is when people say ‘we want AI’ but have no direction. In these situations, they are on an endless pursuit without a map.”

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

The overlooked driver of digital transformation

When business leaders talk about digital transformation, their focus often jumps straight to cloud platforms, AI tools, or collaboration software. Yet, one of the most fundamental enablers of how organizations now work, and how employees experience that work, is often overlooked: audio.

As Genevieve Juillard, CEO of IDC, notes, the shift to hybrid collaboration made every space, from corporate boardrooms to kitchen tables, meeting-ready almost overnight. In the scramble, audio quality often lagged, creating what research now shows is more than a nuisance. Poor sound can alter how speakers are perceived, making them seem less credible or even less trustworthy.

“Audio is the gatekeeper of meaning,” stresses Julliard. “If people can’t hear clearly, they can’t understand you. And if they can’t understand you, they can’t trust you, and they can’t act on what you said. And no amount of sharp video can fix that.” Without clarity, comprehension and confidence collapse.

For Shure, which has spent a century advancing sound technology, the implications extend far beyond convenience. Chris Schyvinck, Shure’s president and CEO, explains that ineffective audio undermines engagement and productivity. Meetings stall, decisions slow, and fatigue builds.

“Use technology to make hybrid meetings seamless, and then be clear on which conversations truly require being in the same physical space,” says Juillard. “If you can strike that balance, you’re not just making work more efficient, you’re making it more sustainable, you’re also making it more inclusive, and you’re making it more resilient.”

When audio is prioritized on equal footing with video and other collaboration tools, organizations can gain something rare: frictionless communication. That clarity ensures the machines listening in, from AI transcription engines to real-time translation systems, can deliver reliable results.

The research from Shure and IDC highlights two blind spots for leaders. First, buying decisions too often privilege price over quality, with costly consequences in productivity and trust. Second, organizations underestimate the stress poor sound imposes on employees, intensifying the cognitive load of already demanding workdays. Addressing both requires leaders to view audio not as a peripheral expense but as core infrastructure.

Looking ahead, audio is becoming inseparable from AI-driven collaboration. Smarter systems can already filter out background noise, enhance voices in real time, and integrate seamlessly into hybrid ecosystems.

“We should be able to provide improved accessibility and a more equitable meeting experience for people,” says Schyvinck.

For Schyvinck and Juillard, the future belongs to companies that treat audio transformation as an integral part of digital transformation, building workplaces that are more sustainable, equitable, and resilient.

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

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 Shure.

As companies continue their journeys towards digital transformation, audio modernization is an often overlooked but key component of any successful journey. Clear audio is imperative not only for quality communication, but also for brand equity, both for internal and external stakeholders and even the company as a whole.

Two words for you: audio transformation.

My guests today are Chris Schyvinck, President and CEO at Shure. And Genevieve Juillard, CEO at IDC.

Welcome Chris and Genevieve.

Chris Schyvinck: It’s really nice to be here. Thank you very much.

Genevieve Juillard: Yeah, thank you so much for having us. Great to be here.

Megan Tatum: Thank you both so much for being here. Genevieve, we could start with you. Let’s start with some history perhaps for context. How would you describe the evolution of audio technology and how use cases and our expectations of audio have evolved? What have been some of the major drivers throughout the years and more recently, perhaps would you consider the pandemic to be one of those drivers?

Genevieve: It’s interesting. If you go all the way back to 1976, Norman Macrae of The Economist predicted that video chat would actually kill the office, that people would just work from home. Obviously, that didn’t happen then, but the core technology for remote collaboration has actually been around for decades. But until the pandemic, most of us only experienced it in very specific contexts. Offices had dedicated video conferencing rooms and most ran on expensive proprietary systems. And then almost overnight, everything including literally the kitchen table had to be AV ready. The cultural norms shifted just as fast. Before the pandemic, it was perfectly fine to keep your camera off in a meeting, and now that’s seen as disengaged or even rude, and that changes what normalized video conferencing and my hybrid meetings.

But in a rush to equip a suddenly remote workforce, we hit two big problems. Supply chain disruptions and a massive spike in demand. High-quality gear was hard to get so low-quality audio and video became the default. And here’s a key point. We now know from research that audio quality matters more than video quality for meeting outcomes. You can run a meeting without video, but you can’t run a meeting without clear audio. Audio is the gatekeeper of meaning. If people can’t hear clearly, they can’t understand you. And if they can’t understand you, they can’t trust you and they can’t act on what you said. And no amount of sharp video can fix that.

Megan: Oh, true. It’s fascinating, isn’t it? And Chris, Shure and IDC recently released some research titled “The Hidden Influencer Rethinking Audio Could Impact Your Organization Today, Tomorrow, and Forever.” The research highlighted that importance of audio that Genevieve’s talking about in today’s increasingly virtual world. What did you glean from those results and did anything surprise you?

Chris: Yeah, well, the research certainly confirmed a lot of hunches we’ve had through the years. When you think about a company like Shure that’s been doing audio for 100 years, we just celebrated that anniversary this year.

Megan: Congratulations.

Chris: Our legacy business is over more in the music and performance arena. And so just what Genevieve said in terms of, “Yeah, you can have a performance and look at somebody, but that’s like 10% of it, right? 90% is hearing that person sing, perform, and talk.” We’ve always, of course, from our perspective, understood that clean, clear, crisp audio is what is needed in any setting. When you translate what’s happening on the stage into a meeting or collaboration space at a corporation, we’ve thought that that is just equally as important.

And we always had this hunch that if people don’t have the good audio, they’re going to have fatigue, they’re going to get a little disengaged, and the whole meeting is going to become quite unproductive. The research just really amplified that hunch for us because it really depicted the fact that people not only get kind of frustrated and disengaged, they might actually start to distrust what the other person with bad audio is saying or just cast it in a different light. And the degree to which that frustration becomes almost personal was very surprising to us. Like I said, it validated some hunches, but it really put an exclamation point on it for us.

Megan: And Genevieve, based on the research results, I understand that IDC pulled together some recommendations for organizations. What is it that leaders need to know and what is the biggest blind spot for them to overcome as well?

Genevieve: The biggest blind spot is this. If your microphone has poor audio quality, like Chris said, people will literally perceive you as less intelligent and less trustworthy. And by the way, that’s not an opinion. It’s what the science says. But yet, when we surveyed first time business buyers, the number one factor they used to choose audio gear was price. However, for repeat buyers, the top factor flipped to audio quality. My guess is they learn the lesson the hard way. The second blind spot is to Chris’s point, it’s the stress that bad audio creates. Poor sound forces your brain to work harder to decode what’s being said. That’s a cognitive load and it creates stress. And over a full day of meetings, that stress adds up. Now, we don’t have long-term studies yet on the effects, but we do know that prolonged stress is something that every company should be working to reduce.

Good audio lightens that cognitive load. It keeps people engaged and it levels the playing field. Whether you’re in a room or you’re halfway across the world, and here’s one that’s often overlooked, bad audio can sabotage AI transcription tools. As AI becomes more and more central to everyday work, that starts to become really critical. If your audio isn’t clear, the transcription won’t be accurate. And there’s a world of difference between working, for example, the consulting department and the insulting department, and that is an actual example from the field.

The bottom line is you fix the audio, you cut friction, you save time, and you make meetings more productive.

Megan: I mean, it’s just a huge game changer, isn’t it, really? I mean, and given that, Chris, in your experience across industries, are audio technologies being included in digital transformation strategies and also artificial intelligence implementation? Do we need a separate audio transformation perhaps?

Chris: Well, like I mentioned earlier, yes, people tend to initially focus on that visual platform, but increasingly the attention to audio is really coming into focus. And I’d hate to tear apart audio as a separate sort of strategy because at the same time, we, as an audio expert, are trying to really seamlessly integrate audio into the rest of the ecosystem. It really does need to be put on an equal footing with the rest of the components in that ecosystem. And to Genevieve’s point, as we are seeing audio and video systems with more AI functionalities, the importance of real-time translations that are being used, voice recognition, being able to attribute who said what in a meeting and take action items, it’s really, I think starting to elevate the importance of that clear audio. And it’s got to be part of a comprehensive, really collaboration plan that helps some company figure out what’s their whole digital transformation about. It just really has to be included in that comprehensive plan, but put on equal footing with the rest of the components in that system.

Megan: Yeah, absolutely. And in the broader landscape, Genevieve, in terms of discussing the importance of audio quality, what have you noticed across research projects about the effects of good and bad audio, not only from that company perspective, but from employee and client perspectives as well?

Genevieve: Well, let’s start with employees.

Megan: Sure.

Genevieve: Bad audio adds friction you don’t need, we’ve talked about this. When you’re straining to hear or make sense of what’s being said, your brain is burning energy on decoding instead of contributing. That frustration, it builds up, and by the end of the day, it hurts productivity. From a company perspective, the stakes get even higher. Meetings are where decisions happen or at least where they’re supposed to happen. And if people can’t hear clearly, decisions get delayed, mistakes creep in, and the whole process slows down. Poor audio doesn’t just waste time, it chips away at the ability to move quickly and confidently. And then there’s the client experience. So whether it’s in sales, customer service, or any external conversation, poor audio can make you sound less credible and yet less trustworthy. Again, that’s not my opinion. That’s what the research shows. So that’s quite a big risk when you’re trying to close a deal or solve a major problem.

The takeaway is good audio, it matters, it’s a multiplier. It makes meetings more productive and it can help decisions happen faster and client interactions be stronger.

Megan: It’s just so impactful, isn’t it, in so many different ways. I mean, Chris, how are you seeing these research results reflected as companies work through digital and AI transformations? What is it that leaders need to understand about what is involved in audio implementation across their organization?

Chris: Well, like I said earlier, I do think that audio is finally maybe getting its place in the spotlight a little bit up there with our cousins over in the video side. Audio, it’s not just a peripheral aspect anymore. It’s a very integral part of that sort of comprehensive collaboration plan I was talking about earlier. And when we think about how can we contribute solutions that are really more easy to use for our end users, because if you create something complicated, we were talking about the days gone by of walking into a room. It’s a very complicated system, and you need to find the right person that knows how to run it. Increasingly, you just need to have some plug and play kind of solutions. We’re thinking about a more sustainable strategy for our solutions where we make really high-quality hardware. We’ve done that account for a hundred years. People will come up to me and tell the story of the SM58 microphone they bought in 1980 and how they’re still using it every day.

We know how to do that part of it. If somebody is willing to make that investment upfront, put some high-quality hardware into their system, then we are getting to the point now where updates can be handled via software downloads or cloud connectivity. And just really being able to provide sort of a sustainable solution for people over time.

More in our industry, we’re collaborating with other industry partners to go in that direction, make something that’s very simple for anybody to walk into a room or on their individual at home setup and do something pretty simple. And I think we have the right industry groups, the right industry associations that can help make sure that the ecosystems have the proper standards, the right kind of ways to make sure everything is interoperable within a system. We’re all kind of heading in that direction with that end user in mind.

Megan: Fantastic. And when the internet of things was emerging, efforts began to create sort of these data ecosystems, it seems there’s an argument to be made that we need audio ecosystems as well. I wonder, Chris, what might an audio ecosystem look like and what would be involved in implementation?

Chris: Well, I think it does have to be part of that bigger ecosystem I was just talking about where we do collaborate with others in industry and we try to make sure that we’re all playing by the kind of same set of rules and protocols and standards and whatnot. And when you think about compatibility across all the devices that sit in a room or sit in your, again, maybe your at home setup, making sure that the audio quality is as good as it can be, that you can interoperate with everything else in the system. That’s just become very paramount in our day-to-day work here. Your hardware has to be scalable like I just alluded to a moment ago. You have to figure out how you can integrate with existing technologies, different platforms.

We were joking when we came into this session that when you’re going from the platform at your company, maybe you’re on Teams and you go into a Zoom setting or you go into a Google setting, you really have to figure out how to adapt to all those different sort of platforms that are out there. I think the ecosystem that we’re trying to build, we’re trying to be on that equal footing with the rest of the components in that system. And people really do understand that if you want to have extra functionalities in meetings and you want to be able to transcribe or take notes and all of that, that audio is an absolutely critical piece.

Megan: Absolutely. And speaking of bit of all those different platforms and use cases, that sort of audio is so relevant to Genevieve that goes back to this idea of in audio one size does not fit all and needs may change. How can companies also plan their audio implementations to be flexible enough to meet current needs and to be able to grow with future advancements?

Genevieve: I’m glad you asked this question. Even years after the pandemic, many companies, they’re still trying to get the balance right between remote, in office, how to support it. But even if a company has a strict return to office in-person policy, the reality is that work still isn’t going away for that company. They may have teams across cities or countries, clients and external stakeholders will have their own office preferences that they have to adapt to. Supporting hybrid work is actually becoming more important, not less. And our research shows that companies are leaning into, not away from, hybrid setups. About one third of companies are now redesigning or resizing office spaces every single year. For large organizations with multiple sites, staggered leases, that’s a moving target. It’s really important that they have audio solutions that can work before, during, after all of those changes that they’re constantly making. And so that’s where flexibility becomes really important. Companies need to buy not just for right now, but for the future.

And so here’s IDC’s kind of pro-tip, which is make sure as a company that you go with a provider that offers top-notch audio quality and also has strong partnerships and certifications with the big players and communications technology because that will save you money in the long run. Your systems will stay compatible, your investments will last longer, and you won’t be scrambling when that next shift happens.

Megan: Of course. And speaking of building for the future, as companies begin to include sustainability in their company goals, Chris, I wonder how can audio play a role in those sustainability efforts and how might that play into perhaps the return on investment in building out a high-quality audio ecosystem?

Chris: Well, I totally agree with what Genevieve just said in terms of hybrid work is not going anywhere. You get all of those big headlines that talk about XYZ company telling people to get back into the office. And I saw a fantastic piece of data just last week that showed the percent of in-office hours of the American workers versus out-of-office remote kind of work. It has basically been flatlined since 2022. This is our new way of working. And of course, like Genevieve mentioned, you have people in all these different locations. And in a strange way, living through the pandemic did teach us that we can do some things by not having to hop on an airplane and travel to go somewhere. Certainly that helps with a more sustainable strategy over time, and you’re saving on travel and able to get things done much more quickly.

And then from a product offering perspective, I’ll go back to the vision I was painting earlier where we and others in our industry see that we can create great solid hardware platforms. We’ve done it for decades, and now that advancements around AI and all of our software that enables products and everything else that has happened in the last probably decade, we can get enhancements and additions and new functionality to people in simpler ways on existing hardware. I think we’re all careening down this path of having a much more sustainable ecosystem for all collaboration. It’s really quite an exciting time, and that pays off with any company implementing a system, their ROI is going to be much better in the long run.

Megan: Absolutely. And Genevieve, what trends around sustainability are you seeing? What opportunities do you see for audio to play into those sustainability efforts going forward?

Genevieve: Yeah, similar to Chris. In some industries, there’s still a belief that the best work happens when everyone’s in the same room. And yes, face-to-face time is really important for building relationships, for brainstorming, for closing big deals, but it does come at a cost. The carbon footprint of daily commutes, the sales visits, the constant business travel. And then there’s the basic consideration, as we’ve talked about, of just pure practicality. The good news is with the right AV setup, especially high-quality audio, many of those interactions can happen virtually without losing effectiveness, as Chris said it, but our research shows it.

Our research shows that virtual meetings can be just as productive as in-person ones, and every commute or flight you avoid, of course makes a measurable sustainability impact. I don’t think, personally, that the takeaway is replace all in-person meetings, but instead it’s to be intentional. Use technology to make hybrid meetings seamless, and then be clear on which conversations truly require being in the same physical space. If you can strike that balance, you’re not just making work more efficient, you’re making it more sustainable, you’re also making it more inclusive, and you’re making it more resilient.

Megan: Such an important point. And let’s close with a future forward look, if we can. Genevieve, what innovations or advancements in the audio field are you most excited to see to come to fruition, and what potential interesting use cases do you see on the horizon?

Genevieve: I’m especially interested in how AI and audio are converging. We’re now seeing AI that can identify and isolate human voices in noisy environments. For example, right now, there are some jets flying overhead. It’s very loud in here, but I suspect you may not even know that that’s happening.

Megan: We can’t hear a thing. No.

Genevieve: Right. That technology, it’s pulling voices forward so that conversations like ours are crystal clear. And that’s a big deal, especially as companies invest more and more in AI tools, especially for that translating, transcribing and summarizing meetings. But as we’ve talked before, AI is only as good as the audio it hears. If the sound is poor or a word gets misheard, the meaning can shift entirely. And sometimes that’s just inconvenient, or it can even be funny. But in really high stakes settings, like healthcare for example, a single mis-transcribed word can have serious consequences. So that’s why our position as high quality audio is critical and it’s necessary for making AI powered communication accurate, trustworthy, and useful because when the input is clean, the output can actually live up to its promise.

Megan: Fantastic. And Chris, finally, what are you most excited to see developed? What advancements are you most looking forward to seeing?

Chris: Well, I really do believe that this is one of the most exciting times that I know I’ve lived through in my career. Just the pace of how fast technology is moving, the sudden emergence of all things AI. I was actually in a roundtable session of CEOs yesterday from lots of different industries, and the facilitator was talking about change management internally in companies as you’re going through all of these technology shifts and some of the fear that people have around AI and things like that. And the facilitator asked each of us to give one word that describes how we’re feeling right now. And the first CEO that went used the word dread. And that absolutely floored me because you enter into these eras with some skepticism and trying to figure out how to make things work and go down the right path. But my word was truly optimism.

When I look at all the ways that we are able to deliver better audio to people more quickly, there’s so many opportunities in front of us. We’re working on things outside of AI like algorithms that Genevieve just mentioned that filter out the bad sounds that you don’t want entering into a meeting. We’ve been doing that for quite a long time now. There’s also opportunities to do real time audio improvements, enhancements, make audio more personal for people. How do they want to be able to very simply, through voice commands perhaps, adjust their audio? There shouldn’t have to be a whole lot of techie settings that come along with our solutions.

We should be able to provide improved accessibility and a little bit more equitable meeting experience for people. And we’re looking at tech technology solutions around immersive audio. How can you maybe feel like you’re a bit more engaged in the meeting, kind of creating some realistic virtual experiences, if you will. There’s just so many opportunities in front of us, and I can just picture a day when you walk into a room and you tell the room, “Hey, call Genevieve. We’re going to have a meeting for an hour, and we might need to have Megan on call to come in at a certain time.”

And all of this will just be very automatic, very seamless, and we’ll be able to see each other and talk at the same time. And this isn’t years away. This is happening really, really quickly. And I do think it’s a really exciting time for audio and just all together collaboration in our industry.

Megan: Absolutely. Sounds like there’s plenty of reason to be optimistic. Thank you both so much.

That was Chris Schyvinck, President and CEO at Shure. And Genevieve Juillard, CEO at IDC, 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 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 enjoy this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thanks for listening.

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

How healthcare accelerator programs are changing care

As healthcare faces mounting pressures, from rising costs and an aging population to widening disparities, forward thinking innovations are more essential than ever.

Accelerator programs have proven to be powerful launchpads for health tech companies, often combining resources, mentorship, and technology that startups otherwise would not have access to. By joining these fast-moving platforms, startups are better able to rapidly innovate, enhance, and scale their healthcare solutions, bringing transformative approaches to hospitals and patients faster.

So, why are healthcare accelerators becoming essential to the evolution of the industry? There are key reasons why these programs are reshaping health innovation and explanations how they are helping to make care more personalized, proactive, and accessible.

Empowering growth and scaling impact       

Healthcare accelerator programs offer a powerful combination of guidance, resources, and connections to help early-stage startups grow, scale, and succeed in a complex industry. 

Participants typically benefit from: 

  • Expert mentorship from seasoned healthcare professionals, entrepreneurs, and industry leaders to navigate clinical, regulatory, and business challenges
  • Access to valuable resources such as clinical data, testing environments, and technical infrastructure to refine and validate health tech solutions
  • Strategic support for growth including investor introductions, partnership opportunities, and go-to-market guidance to expand reach and impact 

Speeding up innovation 

Accelerators help startups and early-stage companies bring their solutions to market faster by streamlining the path through one of the most complex industries: healthcare. Traditionally, innovation in this space is slowed by regulatory hurdles, extended sales cycles, clinical validation requirements, and fragmented data systems.  

Through structured support, accelerators help companies refine their product market fit, navigate compliance and regulatory landscapes, integrate with healthcare systems, and gather the clinical evidence needed to build trust and credibility. They also open doors to early pilot opportunities, customer feedback, and strategic partnerships, compressing what could take years into just a few months. 

By removing barriers and accelerating critical early steps, these programs enable digital health innovators to reach the market more efficiently, with stronger solutions and a clearer path to impact. 

Connecting startups with key stakeholders 

Today, many accelerator programs are developed by large healthcare organizations that are driving change from within. These accelerator programs are especially beneficial to startups since they have strong partnerships with hospitals, pharma companies, insurance providers, and regulators. This gives startups a chance to validate their ideas in real-world settings, gather clinical feedback early, and scale more effectively.  

Many accelerators also bring together people from different fields; doctors, engineers, data scientists, and designers, encouraging fresh perspectives on persistent problems like chronic disease management, preventative care, data interoperability, and patient engagement. 

Breaking barriers to global expansion 

Healthcare accelerator programs act as gateways for international digital health companies looking to enter the U.S. market, often considered one of the most complex and highly regulated healthcare landscapes in the world. These programs provide tailored support to navigate U.S. compliance standards, understand payer and provider dynamics, and tailor offerings to meet the needs of U.S. patients and care delivery models. 

Through market-specific mentorship, strategic introductions, and access to a robust health innovation ecosystem, accelerators help international startups overcome geographic and regulatory barriers, enabling global ideas to scale and make an impact where they’re needed most. 

Building the future of healthcare

The role of healthcare accelerator programs extends far beyond startup support. They are helping to redefine how innovation happens, shifting it from isolated efforts to collaborative ecosystems of change. By bridging gaps between early-stage technology and real-world implementation, these programs play a critical role in making healthcare more personalized, preventative, and equitable.

As the digital transformation of healthcare continues, accelerator programs will remain indispensable in cultivating the next generation of breakthroughs, ensuring that bold ideas are not only born, but brought to life in meaningful, measurable ways.

Spotlight: Mayo Clinic Platform_Accelerate

One standout example of this innovation-forward approach is Mayo Clinic Platform_Accelerate, a 30-week accelerator program designed to help health tech startups reach market readiness. Participants gain access to de-identified clinical data, prototyping labs, and guidance from experts across clinical, regulatory, and business domains.

By combining Mayo Clinic’s legacy of clinical excellence with a forward-thinking innovation model, the Mayo Clinic Platform_Accelerate program helps promising startups to refine their solutions and prepare for meaningful scale, transforming how care is delivered across the continuum.

Finding value in accelerator programs

In a time when healthcare must evolve faster than ever, accelerator programs have become vital to the industry’s future. By supporting early-stage innovators with the tools, mentorship, and networks they need to succeed, these programs are paving the way for smarter, safer, and more connected care.

Whether tackling chronic disease, reimagining patient engagement, or unlocking the power of data, the startups nurtured in accelerator programs are helping to shape a more resilient and responsive health system, one innovation at a time.

This content was produced by Mayo Clinic Platform. It was not written by MIT Technology Review’s editorial staff.

Fighting forever chemicals and startup fatigue

What if we could permanently remove the toxic “forever chemicals” contaminating our water? That’s the driving force behind Michigan-based startup Enspired Solutions, founded by environmental toxicologist Denise Kay and chemical engineer Meng Wang. The duo left corporate consulting in the rearview mirror to take on one of the most pervasive environmental challenges: PFAS.

“PFAS is referred to as a forever chemical because it is so resistant to break down,” says Kay. “It does not break down naturally in the environment, so it just circles around and around. This chemistry, which would break that cycle and break the molecule apart, could really support the health of all of us.”

Basing the company in Michigan was both a strategic and a practical strategy. The state has been a leader in PFAS regulation with a startup infrastructure—buoyed by the Michigan Economic Development Corporation (MEDC)—that helped turn an ambitious vision into a viable business.

From intellectual property analyses to forecasting finances and fundraising guidance, the MEDC’s programs offered Kay and Wang the resources to focus on building their PFASigator: a machine the size of two large refrigerators that uses ultraviolet light and chemistry to break down PFAS in water. In other words, “it essentially eats PFAS.”

Despite the support from the MEDC, the journey has been far from smooth. “As people say, being an entrepreneur and running a startup is like a rollercoaster,” Kay says. “You have high moments, and you have very low moments when you think nothing’s ever going to move forward.”

Without revenue or salaries in the early days, the co-founders had to be sustained by something greater than financial incentive.

“If problem solving and learning new talents do not provide sufficient intrinsic reward for a founder to be satisfied throughout what I guarantee will be a long duration effort, then that founder may need to reset their expectations. Because the financial rewards of entrepreneurship are small throughout the process.”

Still, Kay remains optimistic about the road ahead for Enspired Solutions, for clean water innovation, and for other founders walking down a similar path. “Often, founders are coached about formulas for fundraising, formulas for startup success. Learning those formulas and expectations is important, but it’s also important to not forget that it’s your creativity and innovation and foresight that got you to the place you’re in and drove you to start a company. Ultimately, people still want to see that shine through.”

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. 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 launching a technology startup in the US state of Michigan. Building out an innovative idea into a viable product and company requires knowledge and resources that individuals might not have. That’s why the Michigan Economic Development Corporation, or the MEDC, has launched an innovation campaign to support technology entrepreneurs.

Two words for you: startup ecosystem.

My guest is Dr. Denise Kay, the co-founder and CEO at Enspired Solutions, a Michigan-based startup focused on removing synthetic forever chemicals called PFAS from water.

Welcome, Denise.

Dr. Denise Kay: Hi, Megan.

Megan: Hi. Thank you so much for joining us. To get us started, Denise, I wondered if we could talk about Enspired Solutions a bit more. How did the idea come about, and what does your company do?

Denise: Well, my co-founder, Meng, and I had careers in consulting, advising clients on the fate and toxicity of chemicals in the environment. What we did was evaluate how chemicals moved through soil, water, and air, and what toxic impact they might have on humans and wildlife. That put us in a really unique position to see early on the environmental and health ramifications of the manmade chemical PFAS in our environment.

When we learned of a very novel and elegant chemistry that could effectively destroy PFAS, we could foresee the value in making this chemistry available for commercial use and the potential for a significant positive impact on maintaining healthy water resources for all of us.

Like you mentioned, PFAS is referred to as a forever chemical because it is so resistant to break down. It does not break down naturally in the environment, so it just circles around and around. This chemistry, which would break that cycle and break the molecule apart, could really support the health of all of us.

Ultimately, Meng and I quit our jobs, and we founded Enspired Solutions. Our objective was to design, manufacture, and sell commercial-scale equipment that destroys PFAS in water based on this laboratory bench-scale chemistry that had been discovered, the goal being that this toxic contaminant does not continue to circulate in our natural resources.

At this point, we have won an award from the EPA and Department of Defense, and proven our technology in over 200 different water samples ranging from groundwater, surface water, landfill leachate, industrial wastewater, [and] municipal wastewater. It’s really everywhere. What we’re seeing traction in right now is customer applications managing semiconductor waste. Groundwater and surface water around airports tend to be high in PFAS. Centralized waste disposal facilities that collect and manage PFAS-contaminated liquids. And also, even transitioning firetrucks to PFAS-free firefighting foams.

Megan: Fantastic. That’s a huge breadth of applications, incredible stuff.

Denise: Yeah.

Megan: You launched about four years ago now. I wondered what factors made Michigan the right place to build and grow the company?

Denise: That is something we put a lot of thought into, because I live in Michigan, and Meng lives in Illinois, so when it was just the two of us, there was even that, “Okay, what is going to be our headquarters?” We looked at a number of factors.

Some of the things we considered were rentable incubator space. By incubator, I mean startup incubators or innovation centers. The startup support network, a pool of future employees, and what position the state agencies were taking regarding PFAS.

While thinking about all those things and investigating our communities, in Michigan, we found a space to rent where we could do chemistry experiments in an incubator environment. Somewhere where we were surrounded by other entrepreneurs, which we knew was something we had to learn how to do. We were great chemists, but we knew that surrounding ourselves with those skills that could be a gap for us was going to be helpful.

Also, we know that Michigan has moved much faster than other states in identifying PFAS sources in the environment and regulating its presence. This combination was something we knew would be the right place for starting our business and having success.

Megan: It was a perfect setting for those two reasons. What were the first stages of your journey working with the Michigan Economic Development Corporation, the MEDC?

Denise: Well, both my co-founder, Meng, and I are first-time entrepreneurs. MEDC was one of the first resources I reached out to, starting from a Google search. They were an information resource we turned to initially, and then again and again for learning some fundamental skills. And receiving one-on-one expert mentorship for things like business contracts, understanding intellectual property landscapes, tracking and forecasting our business finances, and even how to approach fundraising.

Megan: Wow. It sounds like they were an invaluable resource in those early days. How did early-stage research and development progress from that point? What were the key MEDC services and programs you used to get started?

Denise: Well, our business is based on cutting-edge science, truly cutting-edge science. Understanding the intellectual property landscape, which is a term used to describe intellectual property, patents, trademarks, trade secrets that are related to the science we were founding our business on, it was very important. So that we knew we were starting on a path, that we wouldn’t hit a wall three years from now.

The MEDC performed an IP landscape survey for us. They searched the breadth of patents, and patent applications, and trademarks, and those things, and provided that for Meng and me to review and consider our position before really, really digging in and spending a lot of emotional time and money on the business.

The MEDC also helped us early on create a model in Excel for tracking business financing and forecasting, forecasting our future financial needs, so that we could be proactive instead of reactive to financial limitations. We knew it wasn’t going to be inexpensive to design and build a piece of equipment that’s the size of two very large refrigerators that had never been built before. That type of financial-forward modeling helped us figure out when we would need to start fundraising and taking in investments. As we progressed along that, the MEDC also provided support of an attorney who reviewed contract language to make sure that we really understood various agreements that we were signing.

Megan: Right. You mentioned that you and your co-founder were first-time entrepreneurs, as you put it. Tech acumen and business acumen are very different sets of skills. I wondered, what was the process like, developing this innovative technology while also building out a viable business plan?

Denise: Well, Meng is a brilliant individual. She is a chemical engineer who also has an MBA. Meng had fantastic training to help understand the basis of how businesses function, in addition to understanding both the engineering and the chemistry behind what we were trying to do.

I am an environmental toxicologist by training. I’ve had a longer career than Meng in that field. Over time, I have grown new offices and established new offices for different consulting firms I’ve worked for. I had the experience with people, space, culture, and running a business from that side. Meng has the financial MBA knowledge basis for a business. We’re both excellent chemists and engineers, and those types of things.

We had much of the necessary knowledge, at least to take the first steps forward. The challenge became the hard limit of 24 hours in a day and no revenue to hire any support. That’s when the startup support networks like the MEDC became invaluable.

It was simply impossible to do everything that needed to be done, especially while we were learning what we were doing. The MEDC and other programs provided support to take some of that load off us, but also helped us to learn to implement the new skills in an efficient manner, less stumbling.

Megan: So many things to juggle, isn’t there, in starting a company. I wondered, in that vein, could you share some successes and highlights from your journey so far? Any partnerships or projects that you’re excited about that you could share with us?

Denise: As people say, being an entrepreneur and running a startup is like a rollercoaster. You have high moments and you have very low moments when you think nothing’s ever going to move forward. I’d love to talk about some of the highlights. Our machine, which we call the PFASigator.

First of all, coming up with that name has a fun story behind it. The machine is, like I said, about the size of two large refrigerators. It’s very large, and it breaks down PFAS in water. The machine takes in water that has PFAS in it, we add a couple of liquid chemicals, then a very intense ultraviolet light shines on that water, which catalyzes a chemical reaction called reductive defluorination. When all of this is happening and the PFAS molecules are being broken apart to nontoxic compounds, to an outsider, it all still just looks like water with a light shining on it. But the machine is big, and it essentially eats PFAS.

Meng and I were bantering, and her young, six-year-old son was in the background at the time. We were throwing names around. Thomas called out, “The PFASigator!” We were like, “Ooh, there’s something there.”

Megan: It’s a great name.

Denise: It matches what we do, and it’s a memorable name. We’ve really had fun with that throughout. That was an early highlight, and we’ve stuck with that name.

The next highlight I’d say was standing next to our first fully functioning PFASigator. It was big. It was all stainless steel. Meng and I had never been part of building a physical, large object like that. Just standing there, and the picture we have of us, it was exhilarating. That was a magnificent feeling.

Selling our first machine was a day that everyone in the company, I think we were about eight at that point, received a bottle of champagne.

Megan: Fantastic.

Denise: For a startup to go from zero to one, they call it, you’ve sold nothing to you’ve sold something. That’s a real strong milestone and was a celebration for us.

I’d say most recently, Enspired has been awarded a very exciting project in Michigan. It is in the contracting phase, so I can’t reveal too many details. But it is with a progressive municipality that will have our PFASigator permanently installed, destroying PFAS. That kind of movement from zero to one, and then a significant contract that will raise the visibility of the effectiveness of our approach and machine, has really buoyed our energy and is pushing us forward. It’s amazing to know we are now having an impact on the sustainability of water resources. That’s what we started the company for.

Megan: Awesome. You have some incredible milestones there. But it’s a hard journey, as you’ve said as well, being an entrepreneur. I wondered, finally, what advice would you offer to burgeoning entrepreneurs given your own experience?

Denise: I would advise that if problem solving and learning new talents do not provide sufficient intrinsic reward for a founder to be satisfied throughout what I guarantee will be a long duration effort, then that founder may need to reset their expectations, because the financial rewards of entrepreneurship are small throughout the process.

Meng and I put [in] some of our personal funds and took no salary, and worked harder than we ever had in our lives for at least a year and a half before we were able to take a small salary. The financial rewards are small throughout the process of being a startup. The rewards are delayed, and in many cases, for many startups, the financial rewards never materialize.

It’s a tough journey, and you have to love being on that journey, and be intrinsically rewarded for that for the sake of the journey itself, or you’ll be a very unhappy founder.

Megan: It needs to be something you’re as passionate about as I can tell you are about the work you’re doing at Enspired Solutions.

Denise: There’s probably one other thing I’d like to add to that.

Megan: Of course.

Denise: Often, founders are coached about formulas for fundraising, formulas for startup success. Learning those formulas and expectations is important, but it’s also important to not forget that it’s your creativity and innovation and foresight that got you to the place you’re in and drove you to start a company. Ultimately, people still want to see that shine through.”

Megan: That’s fantastic advice. Thank you so much, Denise.

That was Dr. Denise Kay, the co-founder and CEO at Enspired Solutions, whom I spoke with from an unexpectedly sunny 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. 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. If you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thanks for listening.

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

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.

Finding value from AI agents from day one

Imagine AI so sophisticated it could read a customer’s mind? Or identify and close a cybersecurity loophole weeks before hackers strike? How about a team of AI agents equipped to restructure a global supply chain and circumnavigate looming geopolitical disruption? Such disruptive possibilities explain why agentic AI is sending ripples of excitement through corporate boardrooms. 

Although still so early in its development that there lacks consensus on a single, shared definition, agentic AI refers loosely to a suite of AI systems capable of connected and autonomous decision-making with zero or limited human intervention. In scenarios where traditional AI typically requires explicit prompts or instructions for each step, agentic AI will independently execute tasks, learning and adapting to its environment to refine decisions over time. 

From assuming oversight for complex workflows, such as procurement or recruitment, to carrying out proactive cybersecurity checks or automating support, enterprises are abuzz at the potential use cases for agentic AI. 

According to one Capgemini survey, 50% of business executives are set to invest in and implement AI agents in their organizations in 2025, up from just 10% currently. Gartner has also forecast that 33% of enterprise software applications will incorporate agentic AI by 2028. For context, in 2024 that proportion was less than 1%. 

“It’s creating such a buzz – software enthusiasts seeing the possibilities unlocked by LLMs, venture capitalists wanting to find the next big thing, companies trying to find the ‘killer app,” says Matt McLarty, chief technology officer at Boomi. But, he adds, “right now organizations are struggling to get out of the starting blocks.” 

The challenge is that many organizations are so caught up in the excitement that they risk attempting to run before they can walk when it comes to deployment of agentic AI, believes McLarty. And in so doing they risk turning it from potential business breakthrough into a source of cost, complexity, and confusion.

Keeping agentic AI simple 

The heady capabilities of agentic AI have created understandable temptation for senior business leaders to rush in, acting on impulse rather than insight risks turning the technology into a solution in search of a problem, points out McLarty. 

It’s a scenario that’s unfolded with previous technologies. The decoupling of Blockchain from Bitcoin in 2014 paved the way for a Blockchain 2.0 boom in which organizations rushed to explore the applications for a digital, decentralized ledger beyond currency. But a decade on, the technology has fallen far short of forecasts at the time, dogged by technology limitations and obfuscated use cases. 

“I do see Blockchain as a cautionary tale,” says McLarty. “The hype and ultimate lack of adoption is definitely a path the agentic AI movement should avoid.” He explains, “The problem with Blockchain is that people struggle to find use cases where it applies as a solution, and even when they find the use cases, there is often a simpler and cheaper solution,” he adds. “I think agentic AI can do things no other solution can, in terms of contextual reasoning and dynamic execution. But as technologists, we get so excited about the technology, sometimes we lose sight of the business problem.”

Instead of diving in headfirst, McLarty advocates for an iterative attitude toward applications of agentic AI, targeting “low-hanging fruit” and incremental use cases. This includes focusing investment on the worker agents that are set to make up the components of more sophisticated, multi-agent agentic systems further down the road. 

However, with a narrower, more prescribed remit, these AI agents with agentic capabilities can add instant value. Enabled with natural language processing (NLP) they can be used to bridge the linguistic shortfalls in current chat agents for example or adaptively carry out rote tasks via dynamic automation. 

“Current rote automation processes generate a lot of value for organizations today, but they can lead to a lot of manual exception processing,” points out McLarty. “Agentic exception handling agents can eliminate a lot of that.” 

It’s also essential to avoid use cases for agentic AI that could be addressed with a cheaper and simpler technology. “Configuring a self-manager, ephemeral agent swarm may sound exciting and be exhilarating to build, but maybe you can just solve the problem with a simple reasoning agent that has access to some in-house contextual data and API-based tools,” says McLarty. “Let’s call it the KASS principle: Keep agents simple, stupid.”

Connecting the dots

The future value of agentic AI will lie in its interoperability and organizations that prioritize this pillar at the earliest phase of their adoption will find themselves ahead of the curve. 

As McLarty explains, the usefulness of agentic AI agents in scenarios like customer support chats lies in their combination of four elements: a defined business scope, large language models (LLM), the wider context derived from an organization’s existing data, and capabilities executed through its core applications. These latter two rely on in-built interoperability. For example, an AI agent tasked with onboarding new employees will require access to updated HR policies, asset catalogs and IT. “Organizations can get a massive head start on business value through AI agents by having interoperable data and applications to plug and play with agents,” he says. 

Agent-to-agent frameworks like the model context protocol (MCP) – an open and standardized plug-and-play that connects AI models to internal (or external) information sources – can be layered onto an existing API architecture to embed connectedness from the outset. And while it might feel like an additional hurdle now, in the longer-term those organizations that make this investment early will reap the benefits. 

“The icing on the cake for interoperability is that all the work you do to connect agents to data and applications now will help you prepare for the multi-agent future where interoperability between agents will be essential,” says McLarty. 

In this future, multi-agent systems will work collectively on more intricate, cross-functional tasks. Agentic systems will draw on AI agents across inventory, logistics and production to coordinate and optimize supply chain management for example or perform complex assembly tasks. 

Conscious that this is where the technology is headed, third-party developers are already beginning to offer multi-agent capability. In December, Amazon launched such a tool for its Bedrock service, providing users access to specialized agents coordinated by a supervisor agent capable of breaking down requests, delegating tasks and consolidating outputs. 

But though such an off-the-rack solution has the advantage of allowing enterprises to bypass both the risk and complexity in leveraging such capabilities, the digital heterogeneity of larger organizations in particular will likely mean – in the longer-term at least – they’ll need to rely on their own API architecture to realize the full potential in multi-agent systems.

McLarty’s advice is simple, “This is definitely a time to ground yourself in the business problem, and only go as far as you need to with the solution.”

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.

Building community and clean air solutions

When Darren Riley moved to Detroit seven years ago, he didn’t expect the city’s air to change his life—literally. Developing asthma as an adult opened his eyes to a much larger problem: the invisible but pervasive impact of air pollution on the health of marginalized communities.

“I was fascinated about why we don’t have the data we need,” Riley recalls, “or why we don’t have the infrastructure to solve these issues, to understand where pollution is coming from, how it’s impacting our communities, so that we can solve these problems and make an equitable breathing environment for everybody.”

That personal reckoning sparked the idea for JustAir, a Michigan-based clean-tech startup building neighborhood-level air quality monitoring tools. The goal is simple but urgent: provide communities with access to hyper-local data so they can better manage pollution and protect public health. As Riley puts it, “JustAir is solving the problem of how to better manage local pollution so that we can make sure our communities, our lifestyles—where we work, where we play, and where we learn—are really protected.”

Founded during the height of the pandemic, when the connection between health disparities and air quality became impossible to ignore, JustAir now partners with local governments, health departments, and community residents to deploy monitoring networks that offer key data relevant to everything from policy to personal decision-making.

From the start, the Michigan Economic Development Corporation (MEDC) offered key support that helped turn JustAir’s bold vision into technical infrastructure. Through the MEDC’s early-stage funding partners and a network of mentorship and resources known as SmartZones, JustAir sharpened its product-market fit and gained critical momentum.

Success for Riley isn’t just about scale, it’s about impact. “It warms my heart, and it shows that we’re doing exactly what we said we wanted to do,” Riley says, “which is to make sure that communities have the data that they deserve to create the future, the clean, healthy future that they desperately need.”

To other burgeoning entrepreneurs, Riley sees a sense of community as key to lasting and impactful change. “When people are celebrating you with your head up, and then when people are helping you put your chin up when your head’s down, I think it’s so, so critical. I found that here in Michigan, and also found it here in our community, right here in Detroit. Passion and finding a community that’s going to help get you through the journey is all it takes.”

This episode of Business Lab is produced in association 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 technology startup in the U.S. state of Michigan. Taking an innovative idea to a full-fledged product and company requires resources that individuals might not have. That’s why the Michigan Economic Development Corporation, the MEDC, has launched an innovation campaign to support technology entrepreneurs.

Two words for you: startup ecosystem.

My guest is Darren Riley, the co-founder and CEO at JustAir, a clean air startup that began its journey in Michigan.

Welcome, Darren.

Darren Riley: Hi. Thanks for having me.

Megan: Thank you ever so much for being with us. To get us started, let’s just talk a bit about JustAir. How did the idea for the company come about, and what does your company do as well?

Darren: Yeah, absolutely. The real thesis of JustAir, is really a combination of one, my personal experience but also my professional experience. On the professional side, background in software engineering, graduated from Carnegie Mellon University, but I was always fascinated by how to use technology to really support and innovate and really push the frontier on issues that are near and dear to my heart. Coming from Houston, Texas, coming from communities that often are restricted with certain issues, systemic issues, is something that I always carried in my heart.

And on the personal side, it was around seven years ago when I moved to Detroit, in Southwest Detroit, where I developed asthma. Not growing up with asthma and not developing any issues, having that disease of the lungs really opened my eyes to just how much our environment impacts our health and well-being.

The combination of those, that pain point and also my background in technology, I was fascinated about why we don’t have the data we need or why we don’t have the infrastructure to solve these issues, to understand where pollution is coming from, how it’s impacting our communities, so that we can solve these problems and make an equitable breathing environment for everybody. That’s kind of what birthed JustAir in a way.

And actually, it was around COVID-19 where we really started to push forward, where we saw all this information and research around health disparities and a lot of the issues of mortality rates around COVID-19, which kind of coincides with COPD, asthma, and other diseases that are often overburdened in communities that look like ours, in Black and brown communities. That’s kind of where we got our start.

And what is JustAir today? JustAir is solving the problem of how to better manage local pollution so that we can make sure our communities, our lifestyles—where we work, where we play, and where we learn—are really protected. And, so, what JustAir does is build hyper-local neighborhood-level air quality monitoring networks. Communities have access to the data, policymakers and decision-makers can use that data to really influence and push things to help protect the community, but also other stakeholders can use the data to move the environment to a healthier state. So that’s where we are, and we’re four years strong, and I’m really excited to be a part of this journey here in Michigan.

Megan: So you launched about four years ago now. Why did you choose to build and grow just there in Michigan?

Darren: Yeah, I think a combination of things, the reason why I chose to start here and be intentional about building our team here. I think first is really around the ecosystem support around Michigan. So the MEDC has a network of what we call SmartZones that really offer funding, resources, mentorship, advisory on the different challenges that can range from capital, legal, and other issues that kind of hold an entrepreneur from just getting out there and putting their product in the market. First and foremost, I’m super thankful and grateful for just the state really focusing on and putting entrepreneurs first in that regard.

I think secondly is community. I really felt a strong sense of community here in Detroit. One of the founding members of an organization called Black Tech Saturdays, which sees over hundreds, 500-1,000 folks almost every Saturday of the month, just really sharing and really engaging with tech-curious folks from all different walks of life, but making intentional space for folks who are often left out of those rooms and out of those conversations. And just really seeing a peer network of entrepreneurs who come from a similar cultural background or a similar situation, really going after it together and helping each other navigate some issues.

And then lastly, I talk about this a lot, but problem-solution fit. Being here in Detroit where I developed asthma, where we have many issues and many around the environment that have hit some communities the hardest, right here in Detroit in my own backyard I really want to be very narrowly focused and make sure that I’m building something that actually solves the problem that got me on this journey in the first place. Not thinking about regional-wide, different country, international, et cetera, but how do we build something right here in the backyard that solves the problem for my neighbors and makes sure that we can make a real difference in the community. So, from the community to the problem that I really care about and make sure we solve, and then also just the ecosystem support is why we’re here in Michigan and why we plan to really grow and really be a part of this movement.

Megan: Fantastic. And you’ve touched on a few of those already, but as you were getting started, what specific resources, partnerships, or community support helped you navigate the early-stage research and development stages?

Darren: One example, really early, actually, I forgot about this for a while, but we have a Business Accelerator Fund here in Michigan where there’s funding offered to entrepreneurs for technical assistance. I used that to operationalize some of our technical roadmap processes to build out the infrastructure that we really intended to do. So, that real funding that was non-dilutive that the state provided helped accelerate some of those issues in the early days, where it was just myself and advisors going after this problem. And so now, where we are today, there are funds that receive funding from MEDC, so local funds and venture capital that help you get your first check. Those are really helpful as well. All that to say is basically a combination of funding primary source, but also strategically, that funding is going towards product positioning and product-market fit. Those were some of the two core examples that have been beneficial.

And then, I think the last thing I’ll mention as well, MEDC and a lot of the SmartZones within the state, these SmartZones are just bucketed in different regions and areas, so you have Ann Arbor, you’ve got Detroit, you have Grand Rapids, the whole nine yards, having these events and creating these clusters, if you will, of density of entrepreneurs, I think is super, super critical. I’ve experienced in New York, Chicago, and San Francisco, and other bigger ecosystems that density is so critical to where you’re constantly rubbing shoulders with the next entrepreneur, the next investor, the next customer, to really kind of accelerate that velocity of your journey.

Megan: Yeah. Having that ecosystem makes such a difference, doesn’t it?

Darren: Oh yeah, absolutely.

Megan: And tech acumen and business acumen are very different sets of skills. I wonder what was the process like developing out your technology whilst also building out a viable business plan?

Darren: I think I have a real unique opportunity. Having a software background, I code all the time, felt I had a lot of ideas, always joked that I had a Google Drive of 30 ideas that never worked, that I never showed anybody. I really felt I had that piece. What I was missing in my journey and why nothing ever came to fruition was just the simple principles of, are you solving a real problem, a real pain point for a customer?

Two things on the business acumen side are having an affinity for the problem. I truly believe that going on the entrepreneurial journey is lonely, it’s risky, it’s stressful, and tiring. The more I can wake up in the morning and think about [how] the problems that we solve could actually result in a breath of clean air for someone who may not have that awareness or have the tools to advocate on their behalf, just having that extra motivation and having that affinity towards a problem that I feel really deeply, I think does help.

But I think also from the business acumen side of things, I had the opportunity to work at an organization called Endeavor based here in Michigan, where I was on the other side of an entrepreneur resource support organization. I got to see founders from high-growth companies throughout Michigan, series A, series B, retail, fintech, the whole nine yards, health tech, and seeing where are the challenges, where are things going well and where things are going wrong, from co-founder struggles to missing the market timing or going through banking issues from a couple years ago and all that stuff. All those things really help build a muscle memory of, I don’t have all the answers, but being able to pull through those experiences and pattern matching does help as well, from how you actually build a business from zero, from product-market fit to scale and grow.

Megan: Yeah, absolutely. And as you say, it can be a stressful journey, life as an entrepreneur, but I wonder if you could also share some highlights from your journey so far, any partnerships or projects that you’re really excited about at the moment?

Darren: I think the first and foremost highlight [that] I didn’t realize I would come to enjoy so much is certainly my team. Being able to work with people who are aligned in passionate values and just kind of the culture and the focus is immensely valuable. If I’m going to spend this many hours in a week or in a year, I’d love to spend it with folks who are really passionate about it. I want to see them succeed. So I think first and foremost, I think the biggest success is really just the fortunate opportunity to work with people I really enjoy working with.

The others I’ll mention [are] we have one of the largest county-owned monitoring networks in the country within Wayne County. The Health Department of Wayne County and Executive Warren Evans established this partnership where we deployed 100 fixed monitors throughout Wayne County to understand the patterns of local pollution to where we can help combat some of these issues where we are ranked F in air quality from the Lung Association, or Detroit is the third-worst from Asthma and Allergy Foundation of America, the third-worst place to live in with asthma. So, how do we really look at this data and tell the story, and how can we really mitigate solutions, while also giving data to the public so that they can navigate the world that’s happening to them. That’s one of our critical partnerships.

We’re also very excited, we just got announced in Fast Company as one of the most innovative companies of 2025, so woo-hoo to that.

Megan: Congratulations.

Darren: It is really exciting, yeah, in the social impact, social good category. There are many, many more, but I think the last one, I’m so, so grateful for, and I tell our team this all the time, is that we’ve already succeeded. Going to community meetings, hearing people raise their hand, asking questions about the adjuster application or about their data, and I to emphasize that when you hear community members saying ‘our data’ and not an ask, but as something that they have obtained, it warms my heart, and it shows that we’re doing exactly what we said we wanted to do, which is to make sure that communities have the data that they deserve to create the future, the clean, healthy future that they desperately need.”.

Megan: Yeah, absolutely, what an incredible achievement. And what advice, finally, would you offer to other burgeoning entrepreneurs?

Darren: Yeah, I think really something you are passionate about. Repeat that point again, do something that you feel that you can really go through those pain points and struggles for, [because] you need some extra kick to get you through and navigate these challenges.

The second thing, and the most important thing that a lot of people take away is community, community, community. I wouldn’t be here today if I didn’t have people to call on when I’m at my lowest points, and call on people in my highest points. When people are celebrating you with your head up, and then when people are helping you put your chin up when your head’s down, I think it’s so, so critical. I found that here in Michigan, and also found it here in our community, right here in Detroit. Passion and finding a community that’s going to help get you through the journey is all it takes.

Megan: Fantastic. All great advice. Thank you ever so much, Darren.

Darren: Absolutely.

Megan: That was Darren Riley, the co-founder and CEO at JustAir 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 on 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. This episode was produced by Giro Studios. Thanks for listening.

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

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.

Shaping the future with adaptive production

Adaptive production is more than a technological upgrade: it is a paradigm shift. This new frontier enables the integration of cutting-edge technologies to create an increasingly autonomous environment, where interconnected manufacturing plants go beyond the limits of traditional automation. Artificial intelligence, digital twins, and robotics are among the powerful tools manufacturers are using to create dynamic, intelligent systems that not only perform tasks, but also learn, make decisions, and evolve in real-time.

Taking this kind of adaptive approach can transform a manufacturer’s productivity, efficiency, and innovation. But beyond the factory, it also has the potential to deliver society-wide benefits, by bolstering economic growth locally, creating more attractive and accessible employment opportunities, and supporting a sustainability agenda.

As efforts to revive and modernize local manufacturing accelerate in regions around the world, including North America and Europe, adaptive production could help manufacturers overcome some of their biggest obstacles—firstly, attracting and retaining talent. Nearly 60% of manufacturers cited this as their top challenge in a 2024 US-based survey. Highly automated, technology-led adaptive production methods hold new promise for attracting talent to roles that are safer, less repetitive, and better paid. “The ideal scenario is one where AI enhances human capabilities, leads to new task creation, and empowers the people who are most at risk from automation’s impact on certain jobs, particularly those without college degrees,” says Simon Johnson, co-director of MIT’s Shaping the Future of Work Initiative.

Secondly, the digitalization of manufacturing—embedded in the very foundation of adaptive production technologies—allows companies to better address complex sustainability challenges through process and resource optimization and a better understanding of data. “By integrating these advanced technologies, we gain a more comprehensive picture across the entire production process and product lifecycle,” explains Jelena Mitic, head of technology for the Future of Automation at Siemens. “This will provide a much faster and more efficient way to optimize operations and ensure that all the necessary safety and sustainability requirements are met during quality control.”

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.

Finding value with AI automation

In June 2023, technology leaders and IT services executives had a lightning bolt headed their way when McKinsey published the “The economic potential of generative AI: The next productivity frontier” report. It echoed a moment from the 2010s when Amazon Web Services launched an advertising campaign aimed at Main Street’s C-suite: Why would any fiscally responsible exec allow their IT teams to spend capex for servers and software when AWS only cost 10 cents per virtual machine? 

Vendors understand that these kinds of reports and aggressive advertising around competitive risks projected onto an industry sector would drive many calls from boards to their C-suite, rolling from C-suite to their staff all asking, “What are we doing with AI?” When asked to “do something with AI,” technical leadership and their organizations promptly responded — sometimes begrudgingly and sometimes excitedly — for work-sanctioned opportunities to get their hands on a new technology. At that point, there was no time to sort between actual business returns from applying AI and “AI novelty” use cases that were more Rube Goldberg machines than tangible breakthroughs. 

Today’s opportunity: Significant automation gains 

When leaders respond to immediate panic, new business risks and mitigations often emerge.  Two recent examples highlight the consequences of rushing to implement and publish positive results from AI adoption. The Wall Street Journal reported in April 2025 on companies struggling to realize returns on AI. Just weeks later, it covered MIT’s retraction of a technical paper about AI where the results that led to its publication could not be substantiated.  

While these reports demonstrate the pitfalls of over-reliance on AI without common-sense guardrails, not all is off track in the land of enterprise AI adoption. Incredible results being found from judicious use of AI and related technologies in automating processes across industries. Now that we are through the “fear of missing out” stage and can get down to business, where are the best places to look for value when applying AI to automation of your business?  

While chatbots are almost as pervasive as new app downloads for mobile phones, the applications of AI realizing automation and productivity gains line up with the unique purpose and architecture of the underlying AI system they are built on. The dominant patterns where AI gains are realized currently boil down to two things: language (translation and patterns) and data (new format creation and data search).  

Example one: Natural language processing  

Manufacturing automation challenge: Failure Mode and Effects Analysis (FMEA) is both critical and often labor intensive. It is not always performed prior to a failure in manufacturing equipment, so very often FMEA occurs in a stressful manufacturing lines-down scenario. In Intel’s case, a global footprint of manufacturing facilities separated by large distances along with time zones and preferred language differences makes this even more difficult to find the root cause of a problem. Weeks of engineering effort are spent per FMEA analysis repeated across large fleets of tools spread between these facilities.  

Solution: Leverage already deployed CPU compute servers for natural language processing (NLP) across the manufacturing tool logs, where observations about the tools’ operations are maintained by the local manufacturing technicians. The analysis also applied sentiment analysis to classify words as positive, negative, or neutral. The new system performed FMEA on six months of data in under one minute, saving weeks of engineering time and allowing the manufacturing line to proactively service equipment on a pre-emptive schedule rather than incurring unexpected downtime.  

Financial institution challenge: Programming languages commonly used by software engineers have evolved. Mature bellwether institutions were often formed through a series of mergers and acquisitions over the years, and they continue to rely on critical systems that are based on 30-year-old programming languages that current-day software engineers are not familiar with. 

Solution: Use NLP to translate between the old and new programming languages, giving software engineers a needed boost to improve the serviceability of critical operational systems. Use the power of AI rather than doing a risky rewrite or massive upgrade. 

Example two: Company product specifications and generative AI models 

Sales automation challenge: The time it takes to reformat a company’s product data into a specific customer RFP format has been an ongoing challenge across industries. Teams of sales and technical leads spend weeks of work across different accounts reformatting the same root data between the preferred PowerPoint or Word document formats. The customer response times were measured in weeks, especially if the RFPs required legal reviews. 

Solution: By using generative AI combined with a data extraction and prompting technique called retrieval augmented generation (RAG), companies can rapidly reformat product information between different customer required RFP response formats. The time spent moving data between different documents and different document types only to find an unforced error in the move is reduced to hours instead of weeks.  

HR policy automation challenge: Navigating internal processes can be time consuming and confusing for both HR and employees. The consequences of misinterpretation, access outages, and personal information or private data being exposed are massively important to the company and the individual. 

Solution: Combine generative AI, RAG, and an interactive chatbot that uses employee-assigned assets to determine identity and access rights, provides employees interactive query-based chat formats to answer their questions in real time. 

Finding your best use cases for AI 

In a world where 80% to 90% of all AI proof of concepts fail to scale, now is the time to develop a framework that is based on caution. Consider starting with a data strategy and governance assessment. Then find opportunities to compare successful AI-based automation efforts at peer companies through peer discussions. Clear, rules-based policies and processes offer the best opportunities to begin a successful AI automation journey in your enterprise. Where you encounter disparate data sources (e.g., unstructured, video, structured databases) or unclear processes, maintain tighter human-in-the-loop decision controls to avoid unexpected data or token exposure and cost overruns. 

As the AI hype cycle cools and business pressure mounts, now is the time to become practical. Apply AI to well-defined use cases and begin unlocking the automation benefits that will matter not just in 2025, but for years to come.

This content was produced by Intel. It was not written by MIT Technology Review’s editorial staff.