Digital Trust articles

10 critical questions you need to answer to successfully implement industrial AI

Implementing AI in the industrial sector can be transformative. AI is not the end goal, but a means to achieve success. However, to ensure a successful and efficient implementation, it’s crucial to address several key questions. Here are 10 critical questions to guide your AI strategy.

• Energy

Improve asset lifecycle management by creating and integrating interoperable information models

The objective of this project was to model digital assets and demonstrate seamless collaboration between engineering and operator companies using standardized asset information models. The project aimed to automate data exchange, reduce manual input and enhance efficiency. The Proof of Concept delivered in December 2022 showcased significant improvements in reducing human errors and operational costs.

AI ‘red-teaming’ for critical infrastructure industries

Artificial intelligence (AI) is transforming industries, driving innovation and reshaping the future. However, with great power comes great responsibility. Ensuring the security and reliability of AI systems is paramount. Enter AI ‘red-teaming'—a proactive approach to identifying and mitigating vulnerabilities in AI systems. This article explores the concept, process, benefits, challenges and future of AI red-teaming.

• Energy

Inside the EU AI Act: Key Insights for AI Stakeholders

The AI Act entered into force – now what?

What is an artificial intelligence management system (AIMS)?

ISO/IEC 42001 offers a framework for establishing, implementing, and improving AI management systems.

• Other sectors

AI spells opportunity and manageable risk for the oil and gas industry

The use of Artificial intelligence (AI) in the oil and gas industry is growing as more operators digitalize for cost efficiency and lower carbon emissions during production, transmission, and distribution.

• Energy

Building trust in data for AI

The expression ‘garbage-in, garbage out’ describing the outcome of relying on ‘bad’ data has been used since the 1950s but has never been more appropriate than today, given our vastly greater computing power and reliance on it.

• Energy

What generative AI can do for the power sector

The power industry is no stranger to the concept of artificial intelligence (AI) and its subsets machine learning and deep learning. The industry has been applying neural networks and expert systems for decades. It uses machine learning methods for forecasting and optimization, data processing, and detecting anomalies in equipment and operations. 

• Energy

AI brings huge opportunities and new but manageable risks for the energy industry

Artificial intelligence (AI) and its subsets machine learning (ML) and deep learning (DL) are set to transform the way we manage and distribute energy so that power supply is reliable, affordable, and clean.

• Energy