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Practical use of AI: From assistant to a critical part of business

Artificial intelligence (AI) is reshaping our world, infiltrating everything from our smartphones to complex business systems. While some AI applications contribute to everyday assistance, others play a critical role at the core of business operations. In this post, we explore how AI operates across two main domains: as assistants to end-users and as drivers in business-critical operations.
6/6/24 2:15 PM Christoffer Haukås


AI assistants

Definition and examples

AI assistants, such as ChatGPT, Siri, and Alexa, are programmed systems designed to streamline interactions between humans and digital devices by providing information or performing tasks. These assistants have been integrated into our phones, homes, and self-driving cars, enhancing accessibility and interactivity with technology.



AI assistants are widely used to automate customer service, enhance user experiences, and ensure the availability of services 24/7. Tools like ChatGPT assist end-users with tasks ranging from sorting and providing information to more complex activities such as programming and image generation.


Advantages and limitations

For example, using AI in a customer service chatbot can increase cost-effectiveness and enhance customer service, although these assistants often struggle to grasp complex human contexts and may offer varying degrees of accuracy in their responses. In other scenarios, AI can equip end-users with powerful tools to boost their efficiency. However, it is crucial to maintain a critical eye and not accept all the information provided by the assistant.



AI in critical business processes: Industrial AI

Definition and examples

Industrial AI refers to the application of artificial intelligence technology in monitoring, controlling, and optimizing industrial processes. These AI systems are embedded in machines and production lines to enhance efficiency and reduce operating costs.


Examples and applications

Predictive maintenance: Many industrial companies, including those in the manufacturing sector, use AI to predict when equipment needs maintenance or is likely to fail. This significantly reduces downtime and maintenance costs. For example, by employing sensors and AI algorithms, a factory can collect and analyze data from machines to anticipate and prevent potential failures before they occur.


Optimization of production lines: AI systems can optimize operations and energy consumption in production processes. By analyzing data from production lines, AI can identify inefficiencies and recommend adjustments to enhance both the speed and quality of production.


Examples from the shipping industry

Route optimization: The shipping industry uses AI to analyze weather data and sea conditions to determine the most fuel-efficient route. This is not only cost-effective but also helps reduce the environmental impact of vessels.


Cargo handling and logistics: AI technology assists in the optimization of loading and unloading ships. By analyzing data on cargo weight and size, as well as dock location, AI systems can plan and execute loading processes in a way that maximizes efficiency and safety.


Benefits and challenges


The benefits of industrial AI include improved operational efficiency, reduced costs, and enhanced production quality. These systems enable companies to make more informed decisions based on data analysis and insights that would otherwise be inaccessible. However, implementing these technologies also poses challenges, such as the need for large amounts of high-quality data and concerns about data security and job disruption.




Artificial intelligence represents both enormous opportunities and challenges. Understanding AI's potential and limitations is crucial to maximizing its benefits while mitigating its risks. By continuing to explore and implement AI thoughtfully, we can ensure that the technology enhances our daily lives and serves as a powerful driver for business innovation.



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