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.