Digitalization & New Technologies

Artificial Intelligence is moving from cloud servers into the devices we use every day from medical instruments and industrial robots to energy sensors and connected vehicles. But there is one crucial step that determines whether an AI project succeeds or fails: hardware selection.

As AI moves closer to real-world applications, one challenge remains and that is trust. Customers do not just want intelligent devices. They want secure, reliable, and compliant AI systems that protect privacy, operate offline, and meet evolving regulations.  

Artificial Intelligence (AI) has moved beyond research labs and now powers everyday products and industrial systems alike. From medical instruments and energy sensors to connected vehicles and factory machinery, AI enables devices to sense, decide, and improve in real time.

Only 5% of AI pilots deliver measurable business value. Discover how industrial companies can succeed not through technology alone, but by transforming how people and processes work alongside AI.

What’s the difference between the two Data & AI platforms Azure and Fabric? Read on for a playful comparison: the Fabric camper van vs. the Azure holiday home and rental car.

Companies in the manufacturing and process industries, as well as the energy and utilities sectors, face growing challenges in optimizing their processes, operations, and decision-making. Luckily, they are also sitting on a goldmine of data, which could help with solving the challenges. This article presents five ways Industrial AI and custom AI agents can help companies achieve key business goals.

Most industrial companies have no shortage of data. A constant stream of information feeds operational systems, such as ERP and MES systems, as well as SCADA, which perform their own well-defined roles within the enterprise architecture. Yet, Etteplan’s Matti Partanen says that companies are missing opportunities to make operational data actionable, derive business value, and become more data- and AI-driven simultaneously. AI pilots are started, but few solutions end up in production. What is the right way forward?

The energy and utilities sectors face growing complexity, regulatory demands, and rising expectations for reliability and efficiency. From managing distributed energy systems to maintaining critical water infrastructure, performance and cost control are constant challenges. When supported by high-quality data, AI technologies such as machine learning and advanced analytics enable predictive maintenance, demand forecasting, smart asset management, and much more. These capabilities offer a powerful path to improve efficiency, reduce risk, and unlock new value across operations, says Etteplan’s Artur Mroczkowski.

Companies aiming for AI-driven efficiency and business gains must start by assessing their data maturity. Without sufficient availability, accessibility, and quality of data, any AI projects will face difficulties and disappointing results. AI is only as strong as the data that feeds it.