Unlocking enterprise agility in the API economy
Across industries, enterprises are increasingly adopting an on-demand approach to compute, storage, and applications. They are favoring digital services that are faster to deploy, easier to scale, and better integrated with partner ecosystems. Yet, one critical pillar has lagged: the network. While software-defined networking has made inroads, many organizations still operate rigid, pre-provisioned networks. As applications become increasingly distributed and dynamic—including hybrid cloud and edge deployments—a programmable, on-demand network infrastructure can enhance and enable this new era.
From CapEx to OpEx: The new connectivity mindset
Another, practical concern is also driving this shift: the need for IT models that align cost with usage. Rising uncertainty about inflation, consumer spending, business investment, and global supply chains are just a few of the economic factors weighing on company decision-making. And chief information officers (CIOs) are scrutinizing capital-expenditure-heavy infrastructure more closely and increasingly adopting operating-expenses-based subscription models.
Instead of long-term circuit contracts and static provisioning, companies are looking for cloud-ready, on-demand network services that can scale, adapt, and integrate across hybrid environments. This trend is fueling demand for API-first network infrastructure connectivity that behaves like software, dynamically orchestrated and integrated into enterprise IT ecosystems. There has been such rapid interest, the global network API market is projected to surge from $1.53 billion in 2024 to over $72 billion in 2034.
In fact, McKinsey estimates the network API market could unlock between $100 billion and $300 billion in connectivity- and edge-computing-related revenue for telecom operators over the next five to seven years, with an additional $10 billion to $30 billion generated directly from APIs themselves.
“When the cloud came in, first there was a trickle of adoptions. And then there was a deluge,” says Rajarshi Purkayastha, VP of solutions at Tata Communications. “We’re seeing the same trend with programmable networks. What was once a niche industry is now becoming mainstream as CIOs prioritize agility and time-to-value.”
Programmable networks as a catalyst for innovation
Programmable subscription-based networks are not just about efficiency, they are about enabling faster innovation, better user experiences, and global scalability. Organizations are preferring API-first systems to avoid vendor lock-in, enable multi-vendor integration, and foster innovation. API-first approaches allow seamless integration across different hardware and software stacks, reducing operational complexity and costs.
With APIs, enterprises can provision bandwidth, configure services, and connect to clouds and edge locations in real time, all through automation layers embedded in their DevOps and application platforms. This makes the network an active enabler of digital transformation rather than a lagging dependency.
For example, Netflix—one of the earliest adopters of microservices—handles billions of API requests daily through over 500 microservices and gateways, supporting global scalability and rapid innovation. After a two-year transition period, it redesigned its IT structure and organized it using microservice architecture.
Elsewhere, Coca-Cola integrated its global systems using APIs, enabling faster, lower-cost delivery and improved cross-functional collaboration. And Uber moved to microservices with API gateways, allowing independent scaling and rapid deployment across markets.
In each case, the network had to evolve from being static and hardware-bound to dynamic, programmable, and consumption-based. “API-first infrastructure fits naturally into how today’s IT teams work,” says Purkayastha. “It aligns with continuous integration and continuous delivery/deployment (CI/CD) pipelines and service orchestration tools. That reduces friction and accelerates how fast enterprises can launch new services.”
Powering on-demand connectivity
Tata Communications deployed Network Fabric—its programmable platform that uses APIs to allow enterprise systems to request and adjust network resources dynamically—to help a global software-as-a-service (SaaS) company modernize how it manages network capacity in response to real-time business needs. As the company scaled its digital services worldwide, it needed a more agile, cost-efficient way to align network performance with unpredictable traffic surges and fast-changing user demands. With Tata’s platform, the company’s operations teams were able to automatically scale bandwidth in key regions for peak performance, during high-impact events like global software releases. And just as quickly scale down once demand normalized, avoiding unnecessary costs.
In another scenario, when the SaaS provider needed to run large-scale data operations between its US and Asia hubs, the network was programmatically reconfigured in under an hour; a process that previously required weeks of planning and provisioning. “What we delivered wasn’t just bandwidth, it was the ability for their teams to take control,” says Purkayastha. “By integrating our Network Fabric APIs into their automation workflows, we gave them a network that responds at the pace of their business.”
Barriers to transformation — and how to overcome them
Transforming network infrastructure is no small task. Many enterprises still rely on legacy multiprotocol label switching (MPLS) and hardware-defined wide-area network (WAN) architectures. These environments are rigid, manually managed, and often incompatible with modern APIs or automation frameworks. As with any organization, barriers can be both technical and internal, and legacy devices may not support programmable interfaces. Organizations are often siloed, meaning networks are managed separately to application and DevOps workflows.
Furthermore, CIOs face pressure for quick returns and may not even remain in the company long enough to oversee the process and results, making it harder to push for long-term network modernization strategies. “Often, it’s easier to address the low-hanging fruit rather than go after the transformation because decision-makers may not be around to see the transformation come to life,” says Purkayastha.
But quick fixes or workarounds may not yield the desired results; transformation is needed instead. “Enterprises have historically built their networks for stability, not agility,” says Purkayastha. “But now, that same rigidity becomes a bottleneck when applications, users, and workloads are distributed across the cloud, edge, and remote locations.”
Despite the challenges, there is a clear path forward, starting with overlay orchestration, well-defined API contracts, and security-first design. Instead of completely removing and replacing an existing system, many enterprises are layering APIs over existing infrastructure, enabling controlled migrations and real-time service automation.
“We don’t just help customers adopt APIs, we guide them through the operational shift it requires,” says Purkayastha. “We have blueprints for what to automate first, how to manage hybrid environments, and how to design for resilience.”
For some organizations, there will be resistance to the change initially. Fears of extra workloads, or misalliance with teams’ existing goals and objectives are common, as is the deeply human distrust of change. These can be overcome, however. “There are playbooks on what we’ve done earlier—learnings from transformation—which we share with clients,” says Purkayastha. “We also plan for the unknowns. We usually reserve 10% of time and resources just to manage unforeseen risks, and the result is an empowered organization to scale innovation and reduce operational complexity.”
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.
The road to artificial general intelligence
Artificial intelligence models that can discover drugs and write code still fail at puzzles a lay person can master in minutes. This phenomenon sits at the heart of the challenge of artificial general intelligence (AGI). Can today’s AI revolution produce models that rival or surpass human intelligence across all domains? If so, what underlying enablers—whether hardware, software, or the orchestration of both—would be needed to power them?
Dario Amodei, co-founder of Anthropic, predicts some form of “powerful AI” could come as early as 2026, with properties that include Nobel Prize-level domain intelligence; the ability to switch between interfaces like text, audio, and the physical world; and the autonomy to reason toward goals, rather than responding to questions and prompts as they do now. Sam Altman, chief executive of OpenAI, believes AGI-like properties are already “coming into view,” unlocking a societal transformation on par with electricity and the internet. He credits progress to continuous gains in training, data, and compute, along with falling costs, and a socioeconomic value that is
“super-exponential.”
Optimism is not confined to founders. Aggregate forecasts give at least a 50% chance of AI systems achieving several AGI milestones by 2028. The chance of unaided machines outperforming humans in every possible task is estimated at 10% by 2027, and 50% by 2047, according to one expert survey. Time horizons shorten with each breakthrough, from 50 years at the time of GPT-3’s launch to five years by the end of 2024. “Large language and reasoning models are transforming nearly every industry,” says Ian Bratt, vice president of machine learning technology and fellow at Arm.
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.
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.”
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.
Battling next-gen financial fraud
From a cluster of call centers in Canada, a criminal network defrauded elderly victims in the US out of $21 million in total between 2021 and 2024. The fraudsters used voice over internet protocol technology to dupe victims into believing the calls came from their grandchildren in the US, customizing conversations using banks of personal data, including ages, addresses, and the estimated incomes of their victims.
The proliferation of large language models (LLMs) has also made it possible to clone a voice with nothing more than an hour of YouTube footage and an $11 subscription. And fraudsters are using such tools to create increasingly more sophisticated attacks to deceive victims with alarming success. But phone scams are just one way that bad actors are weaponizing technology to refine and scale attacks.
Synthetic identity fraud now costs banks $6 billion a year, making it the fastest-growing financial crime in the US Criminals are able to exploit personal data breaches to fabricate “Frankenstein IDs.” Cheap credential-stuffing software can be used to test thousands of stolen credentials across multiple platforms in a matter of minutes. And text-to-speech tools powered by AI can bypass voice authentication systems with ease.
“Technology is both catalyzing and transformative,” says John Pitts, head of industry relations and digital trust at Plaid. “Catalyzing in that it has accelerated and made more intense longstanding types of fraud. And transformative in that it has created windows for new, scaled-up types of fraud.”
Fraudsters can use AI tools to multiply many times over the number of attack vectors—the entry points or pathways that attackers can use to infiltrate a network or system. In advance-fee scams, for instance, where fraudsters pose as benefactors gifting large sums in exchange for an upfront fee, scammers can use AI to identify victims at a far greater rate and at a much lower cost than ever before. They can then use AI tools to carry out tens of thousands, if not millions, of simultaneous digital conversations.
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.