Rust detection in the heavy-asset energy industry is a cumbersome and costly process. Aize’s new asset management SaaS solution, powered by machine learning and computer vision, speeds up the process by auto-detection of rust anomalies.
How can AI and other technologies be used to execute complex projects in an effective manner in the energy sector? During the industrial digitalization conference Ignite, I discussed the potential of AI and machine learning to automate and improve maintenance together with experts from Aize.
Energibransjen er i rask utvikling. Økt konkurranse og kompleksitet, endrede kundekrav og større forventninger til alternativer innen fornybar energi skaper utfordringer for flere bedrifter. Kan den nordiske energisektoren bruke AI, data og analyse for å få en dypere forståelse av kundebehov, forbedre etterspørselsplanlegging og gjøre det mulig å implementere en smartere infrastruktur?
Etterhvert som verdikjedene blir mer komplekse, og kundene forventer mer verdi til en lavere pris, leter nordiske produksjons- og logistikkselskaper etter måter å forbedre planlegging, prognoser og kundekunnskap. Flere og flere vender seg til AI og analyse, og pandemien har ytterligere understreket dette behovet.
Bank, finans- og forsikringsselskaper utfordres av disruptive konkurrenter og stadig økende krav fra kunder og leter derfor etter nye måter å raskt tilpasse seg på. En infrastruktur full av teknisk gjeld hindrer imidlertid innovasjon. Så hvordan kan AI og analyse hjelpe disse store selskapene til å frigjøre seg fra gammel moro?
Challenged by disruptive players and ever-increasing customer demands, banking, finance and insurance companies are looking for new ways to quickly adapt. However, an infrastructure full of technical debt is holding back innovation. How can AI and analytics help these big companies break free?
This sector is surrounded by challenges; the world is looking for new energy sources and everyone's focus is on increased sustainability, while oil and gas prices are volatile. It’s a strong imperative to make fundamental changes. AI and analytics are critical to succeed with digital transformation and essential parts of the modernization journey.
As value chains are becoming more complex, and customers expect more value for a smaller cost, Nordic manufacturing and logistics companies are looking for ways to improve planning, forecasting and customer knowledge. More and more are turning to AI and analytics, and the pandemic has further emphasized the need.
The utility industry is rapidly evolving. Increased competition and complexity, changed customer demands and stronger expectations on renewable energy options are challenging companies. Can the Nordic utility sector use AI, data and analytics to gain a deeper understanding of customer needs, improve demand planning, and enable a smarter infrastructure?
How can Nordic retailers meet challenges such as customer demands on hyper-personalized offerings, fierce competition, and the pandemic-fueled shift from brick-and-mortar to online sales? Learn more about enabling the seamless user experience and improving operations with AI and analytics.
The enemy of business? Incomplete data sets that can’t be collated to get a full picture of opportunities or threats. How companies collect, integrate, analyze and visualize data is hugely important, but most have built static data architectures that take considerable time to build and change.
Businesses know they need to digitally transform to become more data-driven, but the building blocks, tools and approaches behind it aren’t always that obvious. Welcome to a co-hosted event, where Microsoft, Cloudera, and Cognizant together cover the fundamentals for a truly data-driven enterprise.
It’s the core of any digital disruptor, it’s among the top 5 digital trends worldwide, and it’s a key enabler for creating new business. I’m talking about Big Data and Analytics. Big Data is relevant to all companies and if they are not ready for Big Data, the consumers are. Hence, it is high time to catch up. What about maturity? Some have already experienced major ROI, some have just about started their journey, while many are still stuck into the tech complexity or struggling to come up with a strong business case internally.
