Teaching AI to run with the turbines
Teaching AI to run with the turbines
让人工智能助力涡轮机运行
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. 人工智能或许已通过聊天机器人和图像生成器俘获了公众的想象力，但其一些最具影响力的应用场景却发生在远离消费级工具的地方。在那些物理基础设施、运营连续性和安全性至关重要的行业中，人工智能正成为核心运营层。凭借其庞大的工业系统和源源不断的运营数据流，能源行业为我们提供了一窥未来图景的窗口。
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.” 在伍德赛德能源公司（Woodside Energy），人工智能的应用并非始于生成式模型或企业级副驾驶（Copilot）。该公司多年来一直致力于在勘探、钻井、维护和工厂运营等领域构建预测分析、优化系统和机器学习工具。该公司数字业务副总裁安德鲁·梅洛尼（Andrew Melouney）表示：“我们运营的设备、工厂和资产一直产生海量的运营数据，这些数据为我们创造了非常清晰且极具价值的应用场景。”
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. 这种对基础设施和治理的长期投入，如今正推动公司向能够支持复杂工业工作流的代理式人工智能（Agentic AI）系统转型。伍德赛德设计人工智能系统的初衷并非取代人类操作员，而是为了在高风险环境中增强专业能力。一个典型的例子是其“启动顾问”（Startup Advisor），这是一个帮助操作员管理液化天然气（LNG）工厂复杂启动流程的人工智能副驾驶。梅洛尼解释道：“我们真正思考的是，它如何通过赋能组织中的人员，帮助他们做出更好、更快的决策。”
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.” 该公司的做法反映了整个工业人工智能领域正在发生的更广泛的演变：从孤立的实验转向基于标准化平台、受控数据和可重复部署模式的企业级系统。梅洛尼认为，这种转型要求组织重新思考其技术栈以及工作本身完成的方式。他说：“我们不仅仅是将人工智能附加到现有流程上，我们还在深入思考如何重构这些工作。”
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. 梅洛尼的座右铭已变为：“大处着眼，小处试错，快速扩展。”随着人工智能系统变得更加自主和互联，那些在炒作之下花费数年时间夯实运营基础的公司，或许更有可能取得成功。梅洛尼表示：“我们的目标是实现自主企业，即拥有具备自主权的智能体，能够与我们的核心工作流进行深度交互。”
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. 梅根·塔图姆（Megan Tatum）： 欢迎收听《麻省理工科技评论》的《商业实验室》（Business Lab），我是主持人梅根·塔图姆。本节目旨在帮助商业领袖理解那些从实验室走向市场的创新技术。本期节目由印孚瑟斯（Infosys）合作制作。当人们想到人工智能时，往往会联想到聊天机器人或生产力工具，但人工智能一些最复杂、最具影响力的应用，实际上发生在远离消费类应用的地方——即那些安全性、可靠性和物理系统至关重要的复杂工业环境中。
Megan: 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. 梅根： 全球能源行业就是一个典型的例子。像总部位于西澳大利亚的全球能源生产商伍德赛德能源公司，应用人工智能已经超过十年了。从高级分析与运营，到远程决策支持，再到更智能的维护和大型资产的能源效率提升，他们都有涉足。如今，伍德赛德正在扩展这些经验，将人工智能更深入地嵌入到其运营和企业管理中，并高度重视治理、数据质量和人类问责制。送你两个词：技术燃料。今天我的嘉宾是伍德赛德能源公司的数字业务副总裁安德鲁·梅洛尼。欢迎你，安德鲁。
Andrew Melouney: Thanks, Megan. It’s great to be here. 安德鲁·梅洛尼： 谢谢你，梅根。很高兴来到这里。
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? 梅根： 很高兴你能来。安德鲁，正如我刚才所说，能源行业对待人工智能的方式与科技或消费类企业截然不同。早期的价值出现在运营和工业环境中，而不是面向消费者的生成式人工智能工具中。这是为什么呢？能源行业的人工智能之旅有何独特之处？
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 portfolio marketing and trading as well. 安德鲁： 梅根，我认为这归根结底取决于我们工作的性质。能源运营以及伍德赛德所做的工作是资产密集型的，对安全性要求极高，且具有高度的物理属性。当你思考伍德赛德的运营方式时，会发现我们覆盖了整个价值链。我们从勘探到钻井和地下作业，再到项目开发，一直到运营资产——这些资产通常在恶劣和偏远的地区运行，此外还涉及全球能源组合的营销和交易。
Andrew: 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. 安德鲁： 我们一直拥有来自所运营设备、工厂和资产的海量运营数据，这些数据为我们创造了非常清晰且高价值的应用场景。当你考虑可靠性、安全性和效率时，这些对于像伍德赛德这样的公司来说至关重要。我们从事传统人工智能研究已经很多年了。如果你想到分析、优化或预测模型，这些技术我们从2015年左右就开始应用于我们的数据集和业务中了。
Andrew: 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. 安德鲁： 最近随着生成式人工智能的出现，我们发现自己拥有一个非常强大且出色的基础，可以在此之上进行构建，并真正解决问题以改善业务。无论是保障人员安全、维护运营环境安全，还是提高组织回报，都是如此。
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? 梅根： 太棒了。你刚才提到了这一点，但这种现实是如何塑造伍德赛德的人工智能战略的？你们是从哪里开始的？在早期阶段，这项技术在哪些方面证明了其最大的影响力？
Andrew: Well, like I said, we’ve had a very long journey… 安德鲁： 嗯，正如我所说，我们已经走过了一段很长的旅程……