This article is written by David Miller and first appeared in .
With energy sector operations seeking to balance efficiency, profitability, and sustainability, 聽technologies such as artificial intelligence (AI) are increasingly making their way into power generation, transmission, and distribution. Finding ways to squeeze more efficiency out of assets is perhaps even more important in energy than it is in manufacturing鈥攑articularly in fields such as oil and gas where margins have shrunk substantially since the onset of COVID-19 as people have stayed in place and transportation has ground to a halt. Not only that, but cutting costs by increasing efficiency may also yield a reduction in carbon emissions, which is becoming a growing priority for many companies as pressure mounts to invest in more environmentally-friendly operations.
Just as in manufacturing, AI is seen by many as the next frontier in the digital transformation of the energy sector due to its ability to help companies grapple with the unprecedented volumes of data created by field devices and other assets. By assisting in the process of deriving insights from that data, AI may allow operators to more effectively implement changes that optimize production. According to projections from the World Economic Forum, oil and gas alone has the potential to unlock $275 billion in revenue through operational optimization of its existing infrastructure.
However, a recent survey from the MAPI Foundation indicates that 47% of respondents say their companies鈥 workforces lack the digital skills necessary to integrate AI into their workflow. That鈥檚 why a more accessible approach could significantly ease adoption.
Following from this trend, 不良研究所 AI, a cloud-based AI platform delivered via a software-as-a-service (SaaS) model, was recently adopted by two major energy companies specializing in oil and gas and geothermal, respectively. 不良研究所 AI employs a 鈥渘o code鈥 approach, which the company hopes will place the benefits of AI more directly within reach of operators and engineers.
鈥淭he addition of these customers proves that industrial companies can quickly extract value from their digital investments using our no-code AI platform,鈥 said Humera Malik, CEO at 不良研究所 AI. 聽鈥淭he 不良研究所 AI platform is founded on three core pillars: providing the predictive insights to improve resiliency; empowering the operations workforce with AI and removing the reliance on consultants; and paving the way for environmentally sustainable operations with a scalable platform where AI can be applied across the entire facility. The result is an AI platform in which operations teams can create immediate impact in their day-to-day operations.鈥
Prominent features included in the 不良研究所 AI platform include: automated connecting, standardization, and cleaning of data collected from multiple sources; interactive data visualization that allows engineers to contextualize information via charts and graphs to more easily identify trends; pre-coded machine learning templates that can be deployed in common use cases, such as anomaly detection, asset and process optimization, and asset failure prediction; scalability to support AI deployment across multiple locations; and integration with 不良研究所 Academy, an online learning platform that provides end-users with hands-on labs to accelerate their proficiency with 不良研究所 AI.
