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Industrial AI is entering a new phase. After decades of predictive analytics and other specialized applications, advances in foundation models, physical AI, and agentic AI are making it possible to automate more complex tasks across industrial environments. But unlike AI that operates purely in the digital world, industrial AI can interact directly with physical systems, where an unexpected decision can have consequences for safety, reliability, and critical infrastructure. That makes responsible deployment central to the next wave of industrial automation. “How do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?” asks Arti Garg, chief technologist at AVEVA. The challenge is particularly acute as newer AI systems become more capable but also harder to predict and explain. One foundation for making that transition work is data. Industrial systems often contain information across telemetry, service logs, engineering documents, and other disparate sources. Newer technologies can help connect and correlate that information more quickly, giving operators real-time support when diagnosing problems. AI-powered robots could take that a step further by gathering information in hazardous environments without requiring workers to enter them. But greater autonomy also requires new approaches to governance. AVEVA’s framework for responsible AI emphasizes security, efficiency, and human safety and oversight. Garg argues that AI should augment rather than replace people in critical decision loops, with guardrails determining where automated systems can act and where human supervisors remain responsible. Sustainability is another part of that equation. AI can help manage complex power systems as renewable generation grows, while organizations also need better ways to understand AI’s own environmental footprint. Garg is involved in an IEEE working group developing a standard methodology for measuring that impact across electricity, energy, resources, water, and carbon. The next phase could bring industrial AI further into the physical world, from autonomous robots and drones to AI-assisted coding that allows domain experts to build new applications. But realizing that potential will require more than deploying new technology, says Garg. Organizations will need to rethink business processes, establish appropriate safeguards, and give experienced workers new ways to apply their expertise, creating a model of automation that is not only more autonomous, but safer, more efficient, and more sustainable. “Autonomous systems, whether they're robots or drones, are really going to change the way that we work in plants, in power systems, on mining sites,” says Garg. "In a way, that will make these types of operations more efficient, much safer for the human beings involved and more productive.” This episode of Business Lab is produced in partnership with AVEVA. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The AI boom is becoming a materials challenge. As AI pushes computing into new territory, the materials behind that infrastructure are becoming just as crucial as the algorithms running on it. Semiconductors and data centers are approaching physical limits around performance, thermal management, electrical efficiency, and reliability, creating new demands for materials that can do more at once. At the same time, AI is giving materials scientists new ways to search the enormous universe of possible molecules and accelerate the development of solutions. For Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, that convergence is transforming what advanced materials can enable. “AI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits,” he says. As requirements accumulate, including high temperature, purity, electrical performance, chemical resistance, plasma resistance, and long-term stability, materials move toward what Finelli calls the “top of the pyramid.” Beyond supporting AI innovation, he contends that advanced materials are “actually increasingly defining what's going to be possible.” That challenge is playing out across the infrastructure powering the AI surge. Syensqo is developing materials for high-voltage data center architectures, advanced sealing materials for semiconductor manufacturing, and thermal-management solutions including fluids for direct immersion cooling. Some of those innovations can also cross industry boundaries. Materials developed for electric vehicles, for example, can help address the higher voltage and energy-density demands that are emerging in data centers. The definition of performance is also changing. More customers are expecting materials to meet technical requirements while reducing environmental impact. “Our goal is to remove the trade-off between performance and sustainability,” Finelli says. That means considering sustainability at the beginning of the research process instead of treating it as an additional requirement once a material has been developed. AI is changing how those materials are discovered, too. Syensqo is using AI agents to digitally synthesize millions of potential molecular combinations, predict their performance and sustainability characteristics, and narrow them to a much smaller group for laboratory testing. The result, Finelli says, is the ability to go “broader, deeper, and faster” while giving scientists more time to solve complex engineering problems. Looking to the future, Finelli sees the possibility of a reinforcing cycle: AI helps develop materials that improve AI infrastructure, which in turn enables better AI to accelerate materials discovery. That feedback loop could create a cycle of innovation and expand what future technologies can achieve. “You end up in this accelerated materials, innovative cycle of materials innovation,” says Finelli. “That really excites me, and it gives us the opportunity to continue enabling technologies that will shape the future.” This episode of Business Lab is produced in partnership with Syensqo. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

As companies scale, the technology supporting operations can become a liability just as quickly as it becomes an asset. Disconnected systems, site-specific tools, spreadsheets, and manual workarounds can create data silos that make it harder to spot problems early, coordinate responses, and make decisions with confidence. For Jabil, a global manufacturing company with more than 100 sites across more than 30 countries, the answer has been to make integration and simplification a priority. The company adopted a “simplify-first, then-innovate mindset,” says Harish Manohar, SAP IT director at Jabil, recognizing that adding new technologies without first reducing complexity risks creating more risk. The goal is to standardize processes, consolidate where possible, and establish a more consistent data backbone across the organization. “Any innovation without simplification is going to add more complexity,” Manohar says. That philosophy also changes how Jabil approaches modernization. “Any modernization or transformation should add measurable business value,” Manohar says. The company is focused on connecting processes end-to-end across its supply chain and creating a foundation that can scale consistently across regions. Integration comes first because, as Manohar puts it, “the backbone of any contemporary or modern organization is data.” Before organizations can optimize, automate, or apply AI, data needs to flow seamlessly across systems. But doing that across a global organization is hardly straightforward. Jabil’s more than 100 sites operate with different levels of process maturity, legacy systems, and localized workflows, while regulated businesses bring additional compliance requirements. As such, standardizing across different regions and business environments means changing processes and governance without disrupting the operations already in place. The value of that work extends beyond the technology to the people using it. Integrated workflows can offer employees shared visibility into data, reduce manual data reconciliation, and help them move from chasing information to acting on insights. For Jabil, the aim is also to improve real-time visibility into supply chain events, which can enable faster responses to disruptions and reduce operational risk. Looking to the future, that foundation could make AI and automation all the more useful and scalable. With trusted data and integrated systems in place, Jabil is exploring predictive supply chain insights, intelligent exception handling, and AI-driven planning and forecasting. To Manohar, the takeaway is clear: "Simplicity at scale is a very competitive advantage,” and technology investments must ultimately connect to business value and operational resilience. This episode of Business Lab is produced in partnership with SAP. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

For years, legacy technology has been a problem companies knew they needed to solve, but one they often struggled to tackle. The cost, complexity, and risk of replacing business-critical systems could make modernization feel like a disruption to manage instead of an opportunity to pursue. But with the rise in customer expectations and the changes AI brought to the economics of software development, that calculation is changing. Bupa’s modernization of its My Bupa mobile application offers a case study in what becomes possible when a legacy migration is treated as a business transformation rather than a technology rewrite. Bupa CIO of health insurance Asifa Sherazi describes the risks of waiting for legacy systems to become an emergency: “The end-of-life technology is a risk that compounds quietly, and then arrives all at once.” For Bupa, moving its application from Xamarin to native Swift and Kotlin improved the app rating from 3.7 to 4.7, while the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. “What they’ll notice is that when they need us, often at a stressful moment, it just simply works,” Sherazi says. Sanjeev Tripathi, senior vice president and region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys, contends that AI is helping change the equation. “The emergence of AI is fundamentally shifting the economics of modernization,” he says, reducing the effort, risk, and time traditionally associated with these programs. At Bupa, combining AI-assisted reverse engineering with forward engineering helped deliver the transformation in approximately 60% less time than would have been possible in the pre-AI era. Sherazi and Tripathi also highlight the human dimension of modernization: preserving institutional knowledge, giving teams capacity to adapt, and creating an environment where employees can surface problems early. Looking ahead, both experts see modernized platforms as foundations for more personalized, predictive and AI-driven experiences. The payoff of modernization may be less about replacing aging technology and more about building the flexibility needed for whatever comes next.  “Modern platforms will become the base for far more intelligent AI-driven ecosystems, where AI is not just an add-on, but it is built into everything from design to operations. That's how the modern platforms will evolve, and the customer experiences will become far more personalized and predictive,” says Tripathi. For Sherazi, that shift is already changing the questions organizations can ask: “It used to be, can our platform support that? Now, it’s: is that the right thing to do for our customers?” This episode of Business Lab is produced in partnership with Infosys. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In industries where physical infrastructure, operational continuity, and safety are paramount, AI is becoming a core operating layer. With its sprawling industrial systems and constant stream of operational data, the energy sector offers a glimpse into what that future could look like. At Woodside Energy, AI adoption did not begin with generative models or enterprise copilots. The company has spent years building predictive analytics, optimization systems, and machine learning tools across exploration, drilling, maintenance, and plant operations. “We’ve always had very large volumes of operational data coming from the equipment and the plants and the assets that we operate,” says the company's vice president for digital Andrew Melouney. “Those have created really clear, quite high-value use cases for us.” That long-term investment in infrastructure and governance is now enabling a broader shift toward agentic AI systems that can support complex industrial workflows. Rather than replace human operators, Woodside designs AI systems to augment expertise in high-stakes environments. A prime example is its “Startup Advisor,” an AI copilot that helps operators manage the complex process of starting liquefied natural gas (LNG) plants. “We’re really thinking about, how does it support the people in the organization in terms of empowering them to make better decisions, to make faster decisions,” Melouney explains. The company’s approach reflects a wider evolution taking place across industrial AI: graduating from isolated experiments to enterprise-wide systems built on standardized platforms, governed data, and repeatable deployment patterns. That transition, Melouney argues, requires organizations to rethink both their technology stacks and how work itself gets done. “We’re not just bolting AI onto an existing process,” he says. “We’re deeply thinking about how that work needs to be reimagined.” Melouney's motto has become: “Think big, prototype small, and scale fast." As AI systems become more autonomous and interconnected, the companies poised to succeed may be those that spent years building the operational foundations beneath the hype. "Our ambition is really for an autonomous enterprise, where we have agents with agency that are able to really deeply interact with our core workflows," says Melouney. This episode of Business Lab is produced in partnership with Infosys. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Artificial intelligence may be dominating boardroom agendas, but many enterprises are discovering that the biggest obstacle to meaningful adoption is the state of their data. While consumer-facing AI tools have dazzled users with speed and ease, enterprise leaders are discovering that deploying AI at scale requires something far less glamorous but far more consequential: data infrastructure that is unified, governed, and fit for purpose. That gap between AI ambition and enterprise readiness is becoming one of the defining challenges of this next phase of digital transformation. As Bavesh Patel, senior vice president of Databricks, puts it, “the quality of that AI and how effective that AI is, is really dependent on information in your organization.” Yet in many companies, that information remains fragmented across legacy systems, siloed applications, and disconnected formats, making it nearly impossible for AI systems to generate trustworthy, context-rich outputs. “Really, the big competitive differentiator for most organizations is their own data and then their third-party data that they can add to it,” says Patel. For enterprise AI to deliver value, data must be consolidated into open formats, governed with precision, and made accessible across functions. Without that foundation, businesses risk “terrible AI,” as Patel bluntly describes it. That means moving beyond siloed SaaS platforms and disconnected dashboards toward a unified, open data architecture capable of combining structured and unstructured data, preserving real-time context, and enforcing rigorous access controls. When the groundwork is laid correctly, organizations can move toward measurable outcomes, unlocking efficiencies, automating complex workflows, and even launching entirely new lines of business. That value focus is critical, says Rajan Padmanabhan, unit technology officer at Infosys, especially as enterprises seek precision in the outputs driving business decisions. Rather than treating AI initiatives as isolated innovation projects, leading companies are tying AI deployment directly to business metrics, using governance frameworks to determine what delivers results and what should be abandoned quickly. “We see this big opportunity just with AI literacy with business users, where they're very eager to understand how they should be thinking about AI,” adds Patel. “What does AI mean when you peel the covers? What are the pieces and the building blocks that you need to put in place, both from a technology and a training and an enablement standpoint?” The possibilities ahead are substantial. As AI agents evolve from copilots into autonomous operators capable of managing workflows and transactions, the organizations that win will be those that build the right foundation now. “What we are seeing as a new way of thinking is moving from a system of execution or a system of engagement to a system of action,” notes  Padmanabhan. “That is the new way we see the road ahead.” The future of AI in the enterprise will be determined by whether businesses can turn fragmented information into a strategic asset capable of powering both smarter decisions and entirely new ways of operating. This episode of Business Lab is produced in partnership with Infosys Topaz. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

When work went remote, the sound of business changed. What began as a scramble to make home offices functional has evolved into a revolution in how people hear and are heard. From education to enterprises, companies across industries have reimagined what clear, reliable communication can mean in a hybrid world. For major audio and communications enterprises like Shure and Zoom, that transformation has been powered by artificial intelligence, new acoustic technologies, and a shared mission: making connection effortless.  Necessity during the pandemic accelerated years of innovation in months.   "Audio and video just working is a baseline for collaboration," says chief ecosystem officer at Zoom, Brendan Ittelson. "That expectation has shifted from connecting people to enhancing productivity and creativity across the entire ecosystem."   Audio is a foundation for trust, understanding, and collaboration. Poor sound quality can distort meaning and fatigue listeners, while crisp audio and intelligent processing can make digital interactions feel nearly as natural as in-person exchanges.  "If you think about the fundamental need here," adds chief technology officer at Shure, Sam Sabet, "It's the ability to amplify the audio and the information that's really needed, and diminish the unwanted sounds and audio so that we can enhance that experience and make it seamless for people to communicate."   For both Ittelson and Sabet, AI now sits at the center of this progress. For Shure, machine learning powers real-time noise suppression, adaptive beamforming, and spatial audio that tunes itself to a room’s acoustics. For Zoom, AI underpins every layer of its platform, from dynamic noise reduction to automated meeting summaries and intelligent assistants that anticipate user needs. These tools are transforming communication from reactive to proactive, enabling systems that understand intent, context, and emotion.  "Even if you're not working from home and coming into the office, the types of spaces and environments you try to collaborate in today are constantly changing because our needs are constantly changing," says Sabet. "Having software and algorithms that adapt seamlessly and self-optimize based on the acoustics of the room, based on the different layouts of the spaces where people collaborate in is instrumental."  The future, they suggest, is one where technology fades into the background. As audio devices and AI companions learn to self-optimize, users won’t think about microphones or meeting links. Instead, they’ll simply connect. Both companies are now exploring agentic AI systems and advanced wireless solutions that promise to make collaboration seamless across spaces, whether in classrooms, conference rooms, or virtual environments yet to come.  "It's about helping people focus on strategy and creativity instead of administrative busy work," says Ittelson.  This episode of Business Lab is produced in partnership with Shure. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In an era where business, education, and even casual conversations occur via screens, sound has become a differentiating factor. We obsess over lighting, camera angles, and virtual backgrounds, but how we sound can be just as critical to credibility, trust, and connection. That’s the insight driving Erik Vaveris, vice president of product management and chief marketing officer at Shure, and Brian Scholl, director of the Perception & Cognition Laboratory at Yale University. Both see audio as more than a technical layer: It’s a human factor shaping how people perceive intelligence, trustworthiness, and authority in virtual settings. "If you're willing to take a little bit of time with your audio set up, you can really get across the full power of your message and the full power of who you are to your peers, to your employees, your boss, your suppliers, and of course, your customers," says Vaveris. Scholl’s research shows that poor audio quality can make a speaker seem less persuasive, less hireable, and even less credible. "We know that [poor] sound doesn't reflect the people themselves, but we really just can't stop ourselves from having those impressions," says Scholl. "We all understand intuitively that if we're having difficulty being understood while we're talking, then that's bad. But we sort of think that as long as you can make out the words I'm saying, then that's probably all fine. And this research showed in a somewhat surprising way, to a surprising degree, that this is not so." For organizations navigating hybrid work, training, and marketing, the stakes have become high. Vaveris points out that the pandemic was a watershed moment for audio technology. As classrooms, boardrooms, and conferences shifted online almost overnight, demand accelerated for advanced noise suppression, echo cancellation, and AI-driven processing tools that make meetings more seamless. Today, machine learning algorithms can strip away keyboard clicks or reverberation and isolate a speaker’s voice in noisy environments. That clarity underpins the accuracy of AI meeting assistants that can step in to transcribe, summarize, and analyze discussions. The implications across industries are rippling. Clearer audio levels the playing field for remote participants, enabling inclusive collaboration. It empowers executives and creators alike to produce broadcast-quality content from the comfort of their home office. And it offers companies new ways to build credibility with customers and employees without the costly overhead of traditional production. Looking forward, the convergence of audio innovation and AI promises an even more dynamic landscape: from real-time captioning in your native language to audio filtering, to smarter meeting tools that capture not only what is said but how it’s said, and to technologies that disappear into the background while amplifying the human voice at the center. "There's a future out there where this technology can really be something that helps bring people together," says Vaveris. "Now that we have so many years of history with the internet, we know there's usually two sides to the coin of technology, but there's definitely going to be a positive side to this, and I'm really looking forward to it. In a world increasingly mediated by screens, sound may prove to be the most powerful connector of all. This episode of Business Lab is produced in partnership with Shure. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

When the ABB FIA Formula E World Championship launched its first race through Beijing’s Olympic Park in 2014, the idea of all-electric motorsport still bordered on experimental. Batteries couldn’t yet last a full race, and drivers had to switch cars mid-competition. Just over a decade later, Formula E has evolved into a global entertainment brand broadcast in 150 countries, driving both technological innovation and cultural change in sport.   "Gen4, that's to come next year," says Dan Cherowbrier, Formula E’s chief technology and information officer. "You will see a really quite impressive car that starts us to question whether EV is there. It's actually faster—it's actually more than traditional [internal combustion engines] ICE."  That acceleration isn’t just happening on the track. Formula E’s digital transformation, powered by its partnership with Infosys, is redefining what it means to be a fan. “It's a movement to make motor sport accessible and exciting for the new generation,” says principal technologist at Infosys, Rohit Agnihotri.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. It does not break down naturally in the environment, so it just circles around and around. This chemistry, which would break that cycle, break the molecule apart, could really support the health of all of us," says Kay.  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, 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 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, for clean water innovation, and for other founders walking down a similar path. "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." This episode of Business Lab is produced in partnership with the Michigan Economic Development Corporation. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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 on why don't we have the data that we need,” Riley recalls, “or why don't we have the infrastructure to really solve these issues, to understand where pollution is coming from, how's it impacting our communities so that we can really solve those 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 that problem of how do we better manage local pollution so that we can make sure that our communities, our lifestyle, where we work, where we play, 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, Michigan Economic Development Corporation (MEDC) offered key support that helped turn JustAir’s bold vision into technical infrastructure. Through MEDC’s early-stage funding partners and a network of mentorship and advisory known as SmartZones, JustAir sharpened its product-market fit and gain 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 shared ownership as key to lasting and impactful change. “Do something that you feel that you can really go through those pain points and struggles for, you need some extra kick to get you through, and navigate these challenges. The most important thing that a lot of people take away is community, community, community.” This episode of Business Lab is produced in association with the Michigan Economic Development Corporation. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Michigan may be best known as the birthplace of the American auto industry, but its innovation legacy runs far deeper, and its future is poised to be even broader. From creating the world’s largest airport factory during World War II at Willow Run to establishing the first successful polio vaccine trials in Ann Arbor to the invention of the snowboard in Muskegon, Michigan has a long history of turning innovation into lasting impact.  Now, with the creation of a new role, chief innovation ecosystem officer, at the Michigan Economic Development Corporation (MEDC), the state is doubling down on its ambition to become a modern engine of innovation, one that is both rooted in its industrial past and designed for the evolving demands of the 21st century economy.   “How do you knit together risk capital founders, businesses, universities, and state government, all of the key stakeholders that need to be at the table together to build a more effective innovation ecosystem?” asks Ben Marchionna, the first to hold this groundbreaking new position.  Leaning on his background in hard tech startups and national security, Marchionna aims to bring a “builder's thinking” to the state government. “I'm sort of wired for that—rapid prototyping, iterating, scaling, and driving that muscle into the state government ecosystem,” he explains. But these efforts aren’t about creating a copycat Silicon Valley. Michigan’s approach is uniquely its own. “We want to develop the thing that makes the most sense for the ingredients that Michigan can bring to bear to this challenge,” says Marchionna.  This includes cultivating both mom-and-pop businesses and tech unicorns, while tapping into the state’s talent, research, and manufacturing DNA.  In an era where economic development often feels siloed, partisan, and reactive, Michigan is experimenting with a model centered on long-term value and community-oriented innovation. “You can lead by example in a lot of these ways, and that flywheel really can get going in a beautiful way when you step out of the prescriptive innovation culture mindset,” says Marchionna. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In the U.S., two-thirds of the country’s 150,000 convenience stores are run by independent operators. Mom-and-pop shops, powered by personal relationships and local knowledge, are the backbone of the convenience sector. These neighborhood operators have long lacked the resources needed to compete with larger chains when it comes to technology, operations, and customer loyalty programs.  As consumer expectations evolve, many small business owners find themselves grappling with outdated systems, rising costs, and limited digital tools to keep up. “What would happen if these small operations could combine their knowledge of their market, of their neighborhood, with the state-of-the-art technology?” asks GM of digital products, mobility, and convenience for the Americas at bp, Tarang Sethia. That question is shaping a years-long, multi-pronged initiative to bring modern retail tools, like cloud-connected point-of-sale systems and personalized AI, into the hands of local convenience store operators, without stripping their independence.  Sethia’s mission is to close the digital gap. bp's newly launched Earnify app centralizes loyalty rewards for convenience stores across the country, helping independent stores build repeat business with data-informed promotions. Behind the scenes, a cloud-based operating system can proactively monitor store operations and infrastructure to automate fixes to routine issues and reduce costly downtime. This is especially critical for businesses that double as their own IT departments.  "We've aggregated all of that into one offering for our customers. We proactively monitor it. We fix it. We take ownership of making sure that these systems are up. We make sure that the systems are personalizing offers for the customers," says Sethia.  But the goal isn’t to corporatize corner stores. “We want them to stay local,” says Sethia. “We want them to stay the mom-and-pop store operator that their customers trust, but we are providing them the tools to run their stores more efficiently and to delight their guests.” From personalizing promotions to proactively resolving technical issues to optimizing in-store inventory, the success of AI should be measured, says Sethia, by its ability to make frontline workers more effective and customers more loyal. The future, Sethia believes, lies in thoughtful integration of technology that centers humans rather than replacing them.  “AI and other technologies should help us create an ecosystem that does not replace humans, but actually augments their ability to serve consumers and to serve the consumers so well that the consumers don't go back to their old ways.” This episode of Business Lab is produced in association with Infosys Cobalt. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Our topic today is digital transformation, from back office operations to infrastructure in the field like oil rigs, companies continue to look for ways to increase profit, meet sustainability goals, and invest in the latest and greatest technology.  Two words for you: enabling innovation.  My guest is Luis Niño, who is the digital manager of technology ventures, and innovation at Chevron. This podcast is produced in association with Infosys Cobalt. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.