The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials.
Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and higher reliability. Every increase in computing performance increases the physical demands placed on the systems that make and run AI.
Delivering these gains depends not only on advances in chip design and system architecture, but on advances in the materials that enable them to perform under extreme conditions.
As AI continues to push the physical limits of semiconductors and data center infrastructure, advanced materials are no longer simply supporting innovation in this area; they are defining the limits of what is possible.
Advanced materials exist to solve performance challenges. As AI raises the bar, these challenges are becoming more demanding.
Manufacturing a semiconductor chip today requires thousands of tightly controlled process steps, with almost no room for error. Tiny variations in temperature or chemical instability can create defects that reduce yield and drive up manufacturing costs. With every new generation of semiconductor chips, manufacturers seek advanced materials that can deliver greater purity, higher chemical and plasma resistance, and better stability under increasingly harsh operating conditions.
These are familiar engineering challenges being pushed to new extremes. And it’s here that materials innovation makes the difference with continuous advances in polymers, elastomers, specialty fluids, and other advanced materials that make each new generation of technology possible.
For materials companies, it’s not about reinventing semiconductor manufacturing but about ensuring the materials supporting the industry continue to evolve alongside it. This same principle applies beyond the semiconductor fabrication floor. As AI workloads become more demanding, the physical infrastructure that powers them is evolving rapidly.
Increasing computing density is transforming data center design, driving the need for more sophisticated thermal management, higher-voltage power architectures, increased data storage, and faster, more reliable data transmission. Every part of the system is under greater pressure, from cooling and power management to critical electronic components, such as connectors, capacitors, and hard disk drives.
At Syensqo, we’re building on our expertise in electronic and electrical components, along with insights from other markets, to meet these emerging needs.
For example, as data centers shift to higher-voltage architectures and greater power density, many of the materials challenges we face closely mirror those of electric vehicles. Fluid-circulation know-how from semiconductor and automotive coolant systems, for instance, can be adapted to direct liquid-cooling designs for AI servers. By transferring knowledge across markets, we can accelerate new power and thermal management solutions while supporting the reliability required by next-generation AI infrastructure.
Whether we’re talking about semiconductor fabrication or hyperscale server farms, the challenge for materials science companies is the same: enabling greater performance without compromising reliability.
While performance remains the first priority, the way performance is defined is changing.
In addition to meeting the increasingly demanding technical requirements of next-generation semiconductors and data centers, there is now an expectation that these materials are developed and manufactured more responsibly.
Perfluoroelastomers, for example, are used to seal semiconductor manufacturing equipment. These materials operate under extreme temperatures, aggressive plasma, and highly reactive chemicals.
To make the process more sustainable, at Syensqo, our next generation of perfluoroelastomers use a fluorosurfactant-free manufacturing process. Our goal was to make a better-performing material, produced in a better way, ensuring manufacturers no longer have to choose between higher performance and a more responsible way of producing the materials that enable it.
This approach reflects a broader reality across the industry.
New materials aren’t adopted simply because they are new. Qualification can take years, and manufacturers only make changes when a material solves a genuine engineering challenge or enables new technology.
Performance remains the price of entry. The difference today is that the definition of performance has expanded. Success increasingly depends on delivering technical excellence through more responsible manufacturing from the outset.
As the performance bar rises, the way we innovate must evolve with it.
Developing advanced materials has traditionally involved a lengthy process of hypothesis, synthesis, testing, and iteration. While this process remains unchanged, new digital tools are helping researchers move through these cycles faster. By helping researchers identify the most promising candidates earlier, AI can reduce the number of physical experiments required and accelerate the earliest stages of materials discovery.
AI isn’t replacing scientific expertise. It’s helping scientists apply that expertise more effectively, allowing them to spend less time searching for answers and more time solving the industry’s toughest challenges.
At Syensqo, we’re putting this approach into practice through use of several AI tools, including the Microsoft Discovery platform, which are helping researchers identify and evaluate promising molecular candidates for next-generation heat transfer fluids, used in semiconductor manufacturing and data centers.
AI helps our researchers rapidly identify and evaluate promising molecular candidates based on the properties they need to achieve. This allows us to focus laboratory work where it has the greatest potential to deliver results, accelerating discovery and reducing the time needed to turn promising materials into solutions customers can qualify and deploy.
The journey from laboratory discovery to a qualified material will always require scientific expertise, rigorous testing, and close collaboration with customers. But by accelerating the earliest stages of discovery, AI can help materials innovation keep pace with the evolving needs of industries such as semiconductors, electronics, and data centers.
The future of artificial intelligence will depend on better algorithms, more powerful chips, and larger computing infrastructure. But sustaining that progress will also require advances in the materials that make those technologies possible.
Whether in semiconductor manufacturing or AI infrastructure, progress is earned. Every new generation of technologies raises the bar, and every new material must prove it can deliver the performance, reliability, and efficiency needed before it earns its place.
For materials companies, that remains both the challenge and the opportunity.
This content was produced by Syensqo. It was not written by MIT Technology Review’s editorial staff.
With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future.
Returning to the foundational elements of AI architecture—the structural framework required for deploying and managing reliable, integrated AI systems at scale—allows technology leaders to make astute decisions today while supporting a future of AI agents that can retrieve information, make decisions, and execute complex workflows across systems.
The following capabilities provide a stable compass on the path to production-ready deployment, regardless of how the underlying technology evolves.
Models are only as reliable as the data they can access, and poor data quality leads to AI hallucinations, bias, and unreliable outputs.
Most enterprises rely on legacy systems, inconsistent data structures, fragmented ownership, and incomplete datasets, making it difficult to scale AI effectively. Powerful as it is, AI itself cannot solve these underlying data problems.
As Adnan Adil, CIO of Elastic, explains: “The data is a durable part of AI architecture because without it, these models won’t run, won’t provide the right context, or won’t give the right level of services that we’re looking to implement.” Industry surveys consistently cite data quality as one of the greatest barriers to AI success. “The data quality has to be good; otherwise, the user loses confidence in the system,” says Adil.
An effective AI strategy begins with connecting data across the organization and ensuring it is organized, accurate, governed, and accessible in real time. These considerations are most effective when built into models and architecture from the start. Scalable data architecture allows AI systems to evolve alongside the business and connect reliably to the internal information needed to deliver meaningful value.
Gartner predicts that companies will abandon 60% of all AI projects through 2026 if they are not supported by AI-ready data. Avoiding that outcome includes clear data standards and ownership, clean and labeled data, and pipelines that support real-time retrieval.
Context engineering ensures that the model draws on the most pertinent information for each query, selecting and organizing the data needed to produce accurate answers efficiently.
Effective context engineering shapes the inputs that guide AI reasoning and action. While prompt engineering focuses on how a request is worded, context engineering designs the entire information environment around the model: retrieving the right data and presenting it in a structured, machine-readable way. Many organizations are discovering that reliable AI depends as much on context quality as on the strength of the model.
Context engineering relies on a modernized, unified data foundation as well as retrieval and memory systems such as retrieval augmented generation (RAG) and vector databases. It also requires careful prioritization to determine what information matters most, what should be excluded, and when different types of information should be used. Feeding models too much context can dilute relevant details, increase costs, and slow response times.
“Minimum context, correct and current data, and machine-readable information are critical to effective context engineering,” Adil says.
Strong governance and LLM observability help organizations maintain control over how AI systems use data, monitor system performance, and identify problems before they affect operations.
In the absence of clear controls around retrieval, workflows, and model usage, AI systems often process far more information than necessary. This inefficiency also drives up operating costs by requiring additional computing resources, often reflected in higher token consumption and API charges.
Governance also works in tandem with robust security. AI expands the attack surface, introducing risks such as prompt-based data leakage, model vulnerabilities, and adversarial inputs. Protecting sensitive information requires strong access controls, monitoring, and oversight.
Adil notes that essential controls — including those related to security, granular cost management, project controls, data security, and architecture—are frequently insufficient.
For governance systems to support transparent, compliant, trustworthy, and cost-effective AI, organizations cannot leave them as a layer to add later. Governance structures need to be embedded into architecture, workflows, and decision-making processes from the outset.
When governance is established from the start, it enables robust observability. Observability helps organizations understand how AI applications are performing in practice. Mechanisms for LLM observability and benchmarking allow teams to assess accuracy and utility over time, monitor adoption patterns, and adjust systems as conditions change. Observability also helps organizations gain trust by increasing visibility of model performance, behavior, and failure points.
Furthermore, observability is essential to get ROI of AI initiatives, as the benefits of it are often indirect and business value depends heavily on how systems are adopted and used. Real-time visibility into AI behavior allows organizations to measure performance against expectations, identify gaps between intent and reality, and continuously refine systems as requirements evolve.
In a 2026 report from Elastic, 85% of IT decision makers expect to enable LLM observability for their internal generative AI apps.
“Observability is actually huge. We can use observability data for cost control, decision-making, and engineering efficiency,” Adil says.
The thoughtful design, integration, and governance that maximize AI value demand specialized in-house expertise. Nearly 70% of respondents in Deloitte’s 2025 Tech Executive Survey report plan to grow teams in direct response to generative AI, a clear contrast to widely reported AI-related cuts. Adil agrees: “We think the people aspect is largely what’s going to make AI impactful going forward.”
As AI systems become more embedded in operations, organizations need people who can govern workflows, evaluate outputs, redesign processes, and adapt systems as conditions change. Evolution toward increasingly autonomous tools requires teams skilled in prompt engineering, orchestration, and change management.
Talent adept at critical thinking and prepared to adapt with technology’s rapid advances will be in high demand. Although turnover brings in fresh thinking, it also presents high costs in system continuity, institutional understanding, and innovation. Human-centered strategy needs to be built into AI execution stages to ensure smooth implementation.
As Adil says, “Many aspects of the stack are moving very, very fast, but institutional knowledge and the ability to adapt remain durable.
As AI systems evolve from single-task assistants to increasingly autonomous agents, the organizations best positioned to benefit will be those that invest in the underlying systems, governance, and expertise that make AI reliable at scale.
Tech leaders who focus on these fundamentals can move effectively from experimentation to reliable, production-level deployment in the medium term, confident that these elements will remain relevant and adaptable amid constant advancements.
“We fundamentally believe that with these tools, velocity of work will get much faster,” Adil says. “We are really focused on how we can do work with these tools in ways we had not thought of before.”
Learn more about how Elastic is building an AI-first enterprise with these core foundational components.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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Frameworks like Lean Six Sigma and business process management (BPM) first gained traction because they promised clarity in the chaos—a structured way to bring order to messy, sprawling operations. Lean Six Sigma emphasized statistical rigor and quality control; BPM created end-to-end maps of how work should flow across departments. Both offered a repeatable way to embed habits of measurement, analysis, and accountability into day-to-day company culture.
But today, those time-tested playbooks are evolving as companies seek to embed AI into established process excellence methodologies. By some estimates, the market for AI-powered process optimization is projected to exceed $113 billion within the next decade. In one study, a full 88% of business leaders anticipated increasing investments into AI-infused process intelligence in the next 12 to 18 months.
Yet without the right foundations, many of those investments may not fully deliver on their potential. Companies that already operate with discipline have an edge. They can channel new tools into proven systems rather than bolting them onto shaky foundations. Organizations with mature process disciplines are also better positioned to translate AI ambition into real outcomes, as they are already accustomed to data-driven decision-making and process discipline—precisely the cultural foundation AI systems need to deliver value.
Simply put: AI can accelerate process excellence, but existing process excellence is what makes AI truly impactful. Technology and process are no longer separate levers, and only organizations that pull them together stand to realize the full value of both.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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.
Full Transcript:
Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.
This episode is produced in partnership with Infosys.
Now, when people think about artificial intelligence, they often picture chatbots or productivity tools, but some of the most sophisticated and high impact uses of AI are actually happening far from consumer apps, inside complex industrial environments where safety, reliability, and physical systems matter. The global energy sector is a prime example.
Companies like Woodside Energy, a global energy producer headquartered in Western Australia, have been applying AI for more than a decade now, from advanced analytics and operations, to remote decision support, to smarter maintenance, and energy efficiency across large scale assets. Today, Woodside is scaling that experience, embedding AI more deeply across its operations and the enterprise with a strong focus on governance, data quality, and human accountability.
Two words for you: technological fuel.
My guest today is Andrew Melouney, vice president for digital at Woodside Energy. Welcome, Andrew.
Andrew Melouney: Thanks, Megan. It’s great to be here.
Megan: Lovely to have you. Now, Andrew, as I said there, the energy sector has approached AI quite differently from technology or consumer businesses. Early value has emerged in operational and industrial environments, rather than consumer-facing generative AI tools. Why is that? And what differentiates the energy sector’s AI journey?
Andrew: Megan, I think it really comes down to the nature of the work we do. Energy operations and what Woodside does is very asset intensive, it’s very safety critical, and it’s highly physical. And when you think about how Woodside operates, we operate across the full value chain. We do exploration through to drilling and subsurface work, to project development, all the way through to operating assets, which are often operated in harsh and remote locations, and then global energy portfolio marketing and trading as well.
We’ve always had very large volumes of operational data coming from the equipment and the plants and the assets that we operate, and those have created really clear, quite high-value use cases for us. When you think about reliability, when you think about safety and efficiency, those are really critical things for a company like Woodside. We’ve been doing traditional AI for many years now. If you think about analytics, if you think about optimization, if you think about things like predictive models, those techniques we’ve been applying to our data sets and to our business since around 2015.
And more recently with the advent of generative AI, we’ve really found that we’ve got a pretty strong and awesome foundation to build on top of and to really solve problems in the service of improving the business. And again, whether that is keeping people safe, keeping the environments we operate in safe, or improving returns for the organization.
Megan: Fantastic. I mean you touched on it there, but how has this reality shaped your own AI strategy at Woodside? Where did you start, and where did the technology prove most impactful in those early days?
Andrew: Well, like I said, we’ve had a very long journey, in terms of understanding our operational data, recognizing the value of it, and collecting it at scale so that we can use it. And we’ve been very deliberate in that approach, Megan. We’ve really thought about where the value is and where the risks were manageable. And we’ve started looking at, in today’s world from an agentic AI perspective, we’ve started looking at the problems that were solved with traditional AI and machine learning and data science in the past. And we’ve started to think about, where can we then layer agentic AI over the top to provide an even better outcome?
For our asset intensive industry and organization, we’re looking at areas such as maintenance optimization. We’re looking at areas such as, how do we ensure our LNG plants start up reliably, consistently, and safely? And we’re considering really our frontline workforce and making sure that we’re giving people on the frontline the tools required to do their jobs. When we think about AI, 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? I think over time, this has just evolved from what has been traditional analytics to now artificial intelligence and generative AI. And we’ve learned along the way that the technology is important, but it’s about aligning people, processes, and the technology together.
We’ve spent a long time not only in collecting the data and having a well-curated data set that we can build on top of, but we’ve also spent a lot of time teaching people how to work in agile ways, how to do design thinking, how to problem solve, and how to really make sure that the technology that, say, my team can bring to bear to the organization is adopted effectively and purposefully. And I think once we had that solid foundation in place from a technology perspective, from a data perspective, once we got strong trust built between our digital teams and the organization, we really saw quite a material uptick and the scaling of technology occur more broadly across the enterprise.
Megan: Fantastic. That people piece so important, isn’t it? It’s just a tool, technology, that needs to be in the right hands. And you touched on data there; industrial AI obviously depends on vast amounts of data. Can you walk us through how you’ve approached data at Woodside in a little more detail? How it’s structured and governed, and how tools like maintenance intelligence as well fit into that.
Andrew: Well, data is really foundational and fundamental to everything we do, particularly from a technology perspective. It gives us the ability to innovate at pace when we are building over the top of a strong foundation. As I said before, we’ve had the benefit of a long-term investment in our underlying operational data. I think the way we think about data is that it’s an asset for us.
And when you think about operating a facility where you’ve got sensors everywhere, you’ve got data streaming in real time, you’ve got operators needing to make decisions in real time, we have consciously made a decision over many, many years to invest in that enterprise scale data platform to make sure that it’s secure. We’ve got well-structured data assets, and we’ve got strong governance over the top of that data so that when it is used, when it’s built in a data science application or an AI agent, that we’ve got a level of trust in it that it’s going to be used responsibly. And that when it’s used, it can be trusted to give the outcome that we expect.
We have developed platforms that continuously ingest really high frequency data from the assets and from our enterprise systems. Once we’ve been able to develop solutions on top of that, parts of the business that might own the systems that collect that data, they see the value in it.
When you look at something like maintenance intelligence is a really good example of how we’ve been able to take something that we’ve been working on for a long time. Woodside does a lot of maintenance, it’s a very important part of our business, and it occurs across all of our operating assets. But we have been looking at how we do predictive analytics and predictive maintenance for a long time across that data set that we own. And something like maintenance intelligence is a solution that gives us the ability to optimize how we do that maintenance. And what it does is it analyzes historical maintenance records, alongside the performance of the equipment. And again, by having that data set well-governed and in one place, we get the ability to correlate different data sets, such as maintenance records out of SAP, alongside say equipment and performance coming from our time series data lake.
And when we build over the top of that, something like maintenance intelligence gives us the opportunity to recommend to the assets what the optimal timing for maintenance activities might be, and really give what is quite a simple aim, which is do the right work at the right time. And with something like maintenance intelligence, we have seen the opportunity, and we have the opportunity to reduce maintenance hours by up to 15% over five years on one of the assets that we’ve piloted this on. And as we’ve built out that underlying analytical model, we’re now able to put agentic AI over the top of that and provide better insights and optimize that solution more.
It really comes down to providing our asset teams and our operational teams with the right decision support capability that ensures they’re still accountable to make the decision and to ensure the right work is being done, but we are giving them the best possible opportunity to use their judgment and experience with the data that we provide to make the right decision.
Megan: Sounds like a really impactful change. Last year also marked a milestone in moving from early AI learnings to scale, using AI more deliberately as a force multiplier. What transition were you trying to make and how did you approach it?
Andrew: Well, Megan, we’ve had a philosophy for a long time in Woodside from an innovation perspective, where we really want to think big, we want to prototype small, and we want to scale fast. We want to find big opportunities that we can go after, but we want to ensure that we look at how we deploy those on a small scale first, and then provide the right learning and insight that then can scale it everywhere. Something like maintenance intelligence is a good example of that, or our Startup Advisor, where we know that we’ve got multiple plants that we need to start up. We know that we’ve got multiple assets that need to do maintenance, so we have a big, bold ambition about how we can improve and optimize that. We start with a small prototype; it might be one subsystem, it might be just a part of an asset, and then we scale it out, we learn, and we scale faster.
I think from an AI learning perspective, one of the key things we’ve learned is really the transition from moving from isolated AI solutions to a more coordinated enterprise-wide capability. If you look back maybe 18 months, two years, in our generative AI journey, we rarely started by deploying AI as broadly as we could in the organization from a personal productivity perspective. And probably being quite open in terms of the problems that we will solve, the business problems that we’ll solve with AI. That had a lot of benefits for us in terms of allowing our organization to get to know AI, get to know the capabilities, to build the trust in it.
What we’ve learned though is that we’ve needed to pivot from that to being a little bit tighter in terms of where we are going to invest our time and resources and more higher value solutions. How do we then enable and empower the rest of the organization so that they can actually effectively problem solve with technology in their domain or in their personal productivity without having to come to a central team?
When we think about that, think big, prototype small, scale fast, has been something really important for us. The transition from a more broader approach to use case development and solution development to now a narrower focus on the high value priorities. We’ve seen that paying dividends to us and allowing us to go after solutions and opportunities, things like Startup Advisor.
And so our Startup Advisor is a agentic AI solution that really aims to optimize and empower and better support our operators that sit in front of a panel and have to start up LNG plants, which are incredibly technical facilities and require really specialist skills to start up. And so our Startup Advisor is almost like a copilot that sits alongside those operators, and it gives them the ability to be able to play back previous startups. It gives them the ability to look at how the current startup is progressing, and it provides them better insights to optimize how they start up that facility. And again, starting up an LNG facility is incredibly complex.
Megan: I can imagine.
Andrew: When we think about opportunities like Startup Advisor, again, it goes back to that think big, prototype small, and scale fast. We started with a very bold vision of, how do we start up all of our LNG plants in a much more structured and optimized fashion? How do we better support our panel operators? How do we make, say, a more junior panel operator have a copilot that can help them almost like an experienced panel operator sitting next to them? And when we think about that vision and the ability then to prototype on a small scale and then scale fast, I think it’s been really successful for us.
As we scale, we’ve just naturally expanded into more agent-based solutions. Today, we’ve got around 50 AI agents in production, supporting both our operating assets and our enterprise workflows. These tools have been proven in live environments, and we have really seen the benefit of being able to shift from point solutions that maybe solve small scale problems in specific areas, to AI and agentic solutions with agency that can really work across our workflows.
We’re able to do this because we’ve standardized on the platform that we build on and we’ve got repeatable patterns. That’s been another really important learning for us, is that we don’t want to build 50 solutions in 50 different ways. We really want to be empowering our organization and our technical teams and the users of our solutions to roll them out quickly, to roll them out safely, and to do it in a patternized and platform manner.
But the last point I’ll make, Megan, from a learning perspective is that we’ve really understood that a strong governance around how AI is deployed and developed is critical for us, and it’s critical for us to go fast as well. The traditional ways of governing how we roll out different solutions or digital systems isn’t going to scale to the breadth that we need when we are thinking about AI. Being able to have a clear philosophy around how we innovate, transitioning from isolated solutions to that enterprise-wide capability, and making sure that we’ve got strong platforms with strong patterns and clear governance are the three really critical things that we’ve learned.
Megan: Such important pillars, all of them. And you’ve been working with Infosys on this journey. How has that partnership helped accelerate scaling and embedding AI across the business?
Andrew: Well, Infosys is our managed service provider, and so they play a really critical role in the operations of our core business. One of the things that I like to say is that our license to innovate is based on our license to operate. And so, for my team to be able to turn up to an operating asset or a corporate function and have the trust that’s needed to be able to innovate and reimagine and redesign how work gets done, to be able to do that, we need to make sure that our core platforms, our core systems, our applications are running really reliably, safely, and consistently every day. Having an experienced partner like Infosys looking after those core operations in partnership with our internal teams is really, really important to us.
As we move from pilots to enterprise-wide deployment, the ability to partner with someone like Infosys also gives us the ability to scale. And so being from Perth and Western Australia, while we’ve got a really strong local team in Western Australia, and we’ve also got a very strong team in some of our other operating locations, like everyone, we’re struggling to find people that can fill AI roles. Being able to partner with Infosys and have a number of different operating models at our disposal becomes really important for us. Having co-mingled teams where they are staff, they are Infosys staff, Woodside staff, and some of our other partners, really just brings diversity of thought and experience to how we solve problems.
Fundamentally, the partnership has allowed us to operate and innovate with more confidence. While Woodside always retains ownership of the strategy and where we’re going and the governance and my teams remain accountable for the outcomes, we can’t do what we do without strong partnerships like the one we have with Infosys.
Megan: Fantastic. And as AI adoption scales, you mentioned yourself, governance becomes increasingly important. How challenging has that been, and what guardrails have you put in place at Woodside?
Andrew: So, Megan, governance is really important to us, and we operate in a well-regulated environment. That means we’ve got to make really deliberate and well-reasoned decisions when we’re thinking about how we deploy technology into our organization, whether it’s artificial intelligence or anything else, for that matter. And so, governance is really central to how we approach the execution of our AI strategy at Woodside.
We’ve got maybe two or three really key things that we’ve put in place. The first one is just making sure that every AI use case goes through a structured assessment, and that’s making sure it meets our privacy controls, our cyber controls. We’re also asking the question, not just, could we do this, but should we do this? We’ve really got to bring together safety, ethics, transparency, accountability, and make sure that we make an informed decision. When an AI solution is going through that structured assessment, if there are concerns about how we might use that solution, it then goes to an AI council that’s made up of senior leaders across the organization. That council and that group really oversee some of the prioritization and risk management. That’s where we can have really strong, robust debates around, again, could we do something, should we do it, and how do we mitigate any of the risks that we might introduce here?
I think the last one, Megan, is really around lifecycle management. When you start thinking about, we’ve got 50 at the moment, but if we had 500 agents working in our organization, really amplifying the experience and the decision-making and the value creation of our staff, we really want to have an ability to manage the lifecycle of how those agents operate. We want to know, how many people are using them? What’s the efficacy and the outcome? Is there model drift? Do we need to retune or retrain? I think that’s an area where many organizations, including Woodside, are still leaning into and still figuring out the best way to do this. We can do it quite easily with 50 agents, but 500, 5,000, 50,000 becomes an opportunity for us. Again, thinking about how we partner with others, solving problems like that really present an opportunity to co-create and to co-solve with some of our partners, like with Infosys.
Megan: Fantastic. Just to close, what’s your long-term vision for AI at Woodside? How do you see this evolving over the years ahead, and what could it unlock for the sector in your view?
Andrew: So Megan, I think 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. The outcome that we want to get from that is to protect our people, to protect the environments we operate in, and to be able to provide energy at a lower cost to the world. When we think about that ambition, we can really see that being applied to almost all of the areas that Woodside work in. Whether that’s from exploration through to project developments, through to operations or marketing, the scale of the opportunity in front of us and the ability for us to really change the way that work flows through the organization is really exciting.
For us, there’s three things that we have to get right in terms of being able to execute on that ambition. The first one is really thinking about how the work gets done in the organization so that we’re not just bolting AI onto an existing process, but we’re deeply thinking about how that work needs to be reimagined. We’ve also got to think about how we enable our workforce to work differently. Providing them with the skills and the tools and the ability to really harness the power of the technology that we provide.
Secondly, we’ve got to continue to move from and restrain ourselves from deploying point solutions that solve very narrow problems, to having more connected, agentic systems of systems that can interact with each other. To do that, and if we do that successfully, that’s where we really get the high value unlock from agents being able to interact with workflows and really change how the work gets done.
And lastly, Megan, it’s about how we must continue our philosophy of thinking big, prototyping small, and scaling fast.
Megan: Which is a fantastic lens to which to make all these decisions. Thank you so much, Andrew. That was Andrew Melouney, vice president for digital at Woodside Energy, whom I spoke with from Brighton in England.
That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor and host for Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.
This show is available wherever you get your podcasts. And if you 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 ever so much for listening. Goodbye.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork.
The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. Research shows AI-enabled predictive models can improve crop yield by 26%, reduce water use by 41%, and cut chemical usage by 33%.
However, what AI vendors usually won’t tell you is that these solutions are only effective if you have a clean, solid data foundation. However, at Reltio, we have experience in this area, including leading technology strategy at a major agricultural distributor and building a data platform used by enterprises worldwide–we’ve seen it first hand.
Vendor conversations in agriculture tend to follow a familiar pattern. The pitch leads with grand promises around using AI to monitor crop health in real time, optimize irrigation, and squeeze more yield from every acre.
The promise is compelling, but what rarely comes up is the question of whether the data foundation underneath those promises is accurate and complete. If not, there is a real and significant risk that AI will generate misleading outputs that seem authoritative but inspire action that is, at best, counterproductive.
For instance, a yield prediction model fed inconsistent historical data will generate imprecise forecasts. Similarly, a precision irrigation system drawing on fragmented sensor data will make watering decisions that waste resources instead of saving them.
In each case, the AI is failing because the data it was trained on was not sufficient to produce trustworthy outputs. In agriculture, every AI hallucination is a liability, and the likelihood of error is high.
The data landscape across a modern agricultural operation or a large distributor serving thousands of growers is extraordinarily complex.
Modern farming environments make extensive use of IoT devices and machinery. Irrigation systems are automated, tractors navigate fields autonomously, and drones capture field imagery at scale.
However, machine data is disparate by nature. Add in external sources, including weather feeds, U.S. Department of Agriculture data, and third-party market information, and the question of how you bring all of it together into something coherent becomes a significant undertaking.
Agricultural AI also needs to understand more than just customer attributes; it needs to understand the land: GPS coordinates, farm boundaries, field blocks, and soil variation across a single property. Where do you apply fertilizer, and at what rate, and in which specific area of the farm? Not all parts of a field are the same, and an AI system that treats them as if they are will produce recommendations that are at best imprecise and at worst damaging.
There is also a compliance dimension due to the chemicals and the responsibility involved. Operational AI in agriculture needs significantly more checks and governance than it might in a lower-stakes environment. When a flawed recommendation gets acted upon in the field, the consequences can be severe.
Data readiness is the difference between AI delivering on its promise vs. a “garbage in, garbage out” scenario. Fundamentally, being ready for AI means having a data model that accurately reflects how the business operates.
For a company like Wilbur-Ellis, a 104-year-old, family-owned agricultural distributor, that means understanding who your customers are, which fields they farm, which inputs they need, which suppliers those inputs come from, what they paid last season, and how all of that connects to margin. That information needs to be current, consistent, and accessible across the organization, rather than locked in separate systems that were never designed to talk to each other.
Similarly, for farming operations themselves, data readiness means having a reliable, connected picture of what is happening across every field: soil health records, input application histories, yield data from previous seasons, equipment performance, and real-time sensor readings from irrigation systems.
Governance matters just as much as structure. Prices change, relationships evolve, and suppliers come and go. An AI system drawing on data that was accurate six months ago but has not been maintained will make recommendations based on a version of the business that no longer exists.
The good news is that the path to data readiness is feasible. It starts with a strong data model: a single, governed source of truth that connects customers, suppliers, products, pricing, orders, and margins in a way that reflects how the organization operates.
From there, it requires data pipelines fast enough to deliver insights when decisions need to be made, governance frameworks that keep that data trustworthy over time, and security controls that ensure sensitive commercial information is accessible to the right people under the right conditions.
This is precisely the challenge that Reltio, an SAP company, was built to solve. Reltio enables companies to unify their fragmented data so AI agents and systems can operate from a complete picture of the business. Reltio builds a trusted system of context, known as the context intelligence layer, that brings all entities, relationships, rules together under one roof and makes business data easy to access and interpret.
For Wilbur-Ellis, building that trustworthy data foundation has meant being able to ask more complex questions and trust the answers, which is the precondition for any AI system to be genuinely useful.
The question worth asking before the next AI conversation is not whether the use case is promising. It almost certainly is. The question is whether the underlying data foundation is strong enough to make the output trustworthy.
Agriculture has always required its leaders to make high-stakes decisions under uncertainty, and AI offers the genuine prospect of making those decisions faster and better informed. That prospect is only achievable for organizations that have done the foundational work first, and the businesses that will get the most from AI are the ones investing in that foundation now.
This content was produced by Reltio. It was not written by MIT Technology Review’s editorial staff.
Apple. Anthropic. Disney Research. Google. Meta. Microsoft. NVIDIA. OpenAI. Few places outside Silicon Valley can claim R&D hubs from all of these companies. Fewer still are concentrated in a city of just over 400,000 people—roughly half the size of San Francisco.
Over the past two decades, however, many of the world’s most influential technology companies have established R&D operations in and around Zurich, Switzerland. What began with Google’s decision to build its largest R&D hub outside the United States has evolved into one of the world’s most concentrated centers for AI research, talent, and commercialization, in certain areas at a higher density than Silicon Valley.
The question is why so many technology leaders keep choosing the same place to build and scale.
Located at the center of Europe, Greater Zurich Area, a region spanning the cantons of Glarus, Graubünden, Schaffhausen, Schwyz, Solothurn, Tessin, Uri, Zug, and Zürich, the region of Winterthur, and the city of Zurich, combines access to major markets with political stability, regulatory predictability, and strong intellectual property protection. And Zurich Airport connects the region directly with key business hubs across Europe, North America, and Asia, making it an efficient base for international operations.
The country’s innovation performance reinforces this position. Switzerland has ranked first in the Global Innovation Index for more than a decade, leads the world in patents per capita, and invests over 3.3% of GDP in research and development. Earlier this year, google.org pledged a $1 million grant to the Swiss National AI Institute, a joint effort to advance AI research for the public good.
Switzerland’s venture ecosystem reflects a similar focus. Over 60% of Swiss venture capital is invested in deep tech—the highest share globally by a large margin and nearly twice the share of major economies like Germany, France, and the UK. And, according to the Swiss Deep Tech Report 2026, at $1,470 invested per capita, Switzerland commits more to deep tech per capita than any other country in Europe.
While Switzerland is one of Europe’s most expensive locations for talent and operations, salaries remain at a fraction of those in Silicon Valley. The talent pool is small by global standards. Scaling a team quickly is harder in Zurich than in London, Paris, or Amsterdam. For early-stage companies that need to hire fast and burn lean, that trade-off is real. For companies building specialized AI capabilities, however, the equation works: The objective is to assemble the right team, not the largest one.
Switzerland’s economy is built around high-value, knowledge-intensive work. Productivity is among the highest in the world, and companies concentrate on functions that depend on specialized expertise rather than large workforces. For companies developing advanced AI capabilities, cost is often weighed against factors that are harder to replicate elsewhere: direct access to leading universities and research institutions, regulatory stability, and a quality of life that helps attract and retain skilled international talent.
Within Switzerland, the Greater Zurich Area concentrates many of the ingredients required to build and deploy AI systems.
The defining characteristic of this region is density. Many of the world’s leading AI companies, research institutions, investors, and startups operate in close proximity, creating connections between talent, capital, and ideas.
For example, Google engineers teach at ETH Zurich. ETH graduates join companies such as Anthropic. Researchers launch startups, while former employees of global technology firms go on to found new ventures of their own. Investors, founders, academics, and corporate teams encounter each other repeatedly through shared networks, industry events, and professional circles. In a region of this size, collaboration often happens less through formal introductions than through proximity. While talent flows freely, it rarely leaves the ecosystem.
One indicator of the region’s maturity is its ability to convene. Events such as the Zurich AI Festival will bring together more than 6,500 guests this September 28 to October 3. With more than 35 confirmed events across AI and the arts, AI literacy, health, technology, and policy, it is designed as a platform for cross-sector exchange. Its flagship events, the AI + X Summit, AI + Environment, and the AI + Policy Summit, will bring together internationally recognized leaders alongside researchers, policymakers, venture capitalists, and entrepreneurs, convening international voices and fostering dialogue across sectors.
At the center of the country’s AI capabilities are institutions such as ETH Zurich, the University of Zurich, École Polytechnique Fédérale de Lausanne (EPFL), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), and Zürcher Hochschule für Angewandte Wissenschaften (ZHAW).
ETH Zurich ranks among Europe’s leading universities for deep tech commercialization, generating more than 40 spin-offs and startups in 2025 alone, helping create some of the continent’s most valuable technology companies.
The Stanford AI Index 2026 reinforces that picture: Switzerland ranks first globally for AI researchers and inventors per capita, with 110.5 per 100,000 inhabitants—ahead of Singapore (109.5), Sweden (80.6), and the United States (64.8). And the IMD World Talent Ranking ranked Switzerland as number 1 for the 10th consecutive year, leading globally in investment, development, and talent appeal.
Engineers, researchers, and founders move frequently between universities, startups, and established technology firms, creating strong knowledge flows across organizations. That density is increasingly attracting companies from outside the region too. Even before formally announcing their Zurich office, Exa.ai received a strong pipeline of candidate applications. ‘To assemble the greatest search team in the world, you’ve got to meet people where they are,’ says Will Bryk, the company’s CEO and co-founder. ‘And many are in Greater Zurich.’
Former Google Switzerland employees alone have founded approximately 210 companies and created around 2,600 jobs over the past two decades. For a country of around nine million inhabitants, the multiplier effect is significant. Large technology firms contribute not only through direct employment, but also through the creation of new companies and the transfer of expertise.
For many technology companies, Switzerland is not a substitute for Silicon Valley. The two serve different functions within the AI value chain.
Silicon Valley remains unmatched in scale, venture capital, and frontier model development, but for global technology companies, an R&D presence in Switzerland has increasingly become a strategic complement: a way to access specialized talent, stay close to leading research, and build capabilities that will shape the next generation of products and services.
This is particularly relevant for companies working at the intersection of AI and the physical world. Switzerland offers direct access to leading universities, industrial partners, and sectors such as healthcare, finance, manufacturing, and robotics, where reliability, compliance, and precision are often as important as raw model performance.
Global AI leaders came to the Greater Zurich Area because the region concentrates capabilities that are often distributed across multiple locations: world-class research, specialized talent, industrial partners, capital, and pathways to deployment. Those advantages were built over decades, not years.
For companies evaluating where to build the next generation of AI products, the answer may not be another larger ecosystem. It may be one where the distance between research, talent, capital, and deployment is measured in minutes rather than hours.
Learn more about the Greater Zurich Area.
This content was produced by the Greater Zurich Area. It was not written by MIT Technology Review’s editorial staff.
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