Artificial intelligence (AI) business usage has seen a vast increase across all industries over the past few years
Management in business sectors such as manufacturing, transport, finance, and medicine are investing in order to develop a competitive edge.
The use cases range from automating their activities, forecasting demand and enhancing the decision-making process.
This blog looks at the business impact of AI and opportunities for artificial intelligence to improve business processes.
Examples of industries using machine learning and deep learning
Oil and gas
The oil and gas industry has seen a fair amount of turbulence over the last few years. However, as oil prices increase, employees in the industry are certainly on firmer ground.
However, the prospect of new digital trends presents a challenge for oil and gas. Doing so is key to attracting and retaining talent with the skills needed to revolutionise the sector.
One way to do this is through data science. The offshore oil and gas industry can easily use AI to access the data used for oil and gas exploration.
An example of AI adoption in the oil and gas industry is BP’s investment in Belmont Technology in 2019. BP made the move to work with the technology start-up to bolster its AI capabilities, developing a cloud-based platform nicknamed “Sandy”.
The platform enabled BP to derive actionable insights from geophysics, geology, historic and reservoir project information. BP could then consult the data using neural networks to interpret simulation results.
Renewable energy is shaping our future as governments worldwide look for new ways to reduce carbon emissions and improve efficiency.
As renewable energy sources become more prevalent in the energy mix, it’s becoming more and more critical to predict capacity levels to ensure stable grids.
However, we are witnessing a decrease in generation from older sources like coal, which is responsible for grid inertia due to heavy rotating equipment such as steam turbines. With no grid inertia, we risk less stable power grids, thus making them more prone to power cuts.
AI provides a deeper understanding of these risks using real-time data collected by sensor technologies and satellite imagery. AI can then predict downtime periods and capacity levels, allowing the company to act accordingly.
The mining industry increasingly uses AI to optimise processes, improve safety, enhance decision-making, and derive value from data.
One way mining companies are using AI is to learn more about the environment. AI can map out and predict terrain more accurately than a human, preventing potential errors.
AI is also being used to identify new areas to mine through computer vision systems, pattern matching and predictive data analytics. These allow mining professionals to analyse large amounts of data to predict where to find the best resources.
Engineers have a lot of work to carry out across a range of industries. They can use AI to free up their time from working on low-value tasks. Machine learning algorithms help them to discover patterns to make accurate judgments in the long run.
As machines become more sophisticated, they can support manufacturing tasks, production lines. Vehicle engineers have been using robotics on the production line to handle precise moves without human intervention.
AI also helps to break down silos between departments and helps to manage data effectively.
Human developers need to work on various processes and apply AI at every stage of the software development cycle. It has the tools to transform human language into code and machine language, automatically offering accurate results.
AI algorithms provide intelligent software analysis, testing, development and decision support systems. These tools can support existing software development processes constructed for human-intensive software development.
AI has helped the chemicals industry increase operational efficiency, reduce costs, and improve the customer experience. Chemical engineering fields apply it for modelling, classification, process control, fault detection, and diagnosis.
Artificial intelligence can be applied to early product development stages to increase innovation. For example, it can improve research productivity by enabling access to previous relevant data during the initial design stage.
It also enables optimisation at each value chain stage and offers the objective base for value extraction.
What’s more, 75% of healthcare executives believe that AI initiatives are even more important now due to the pandemic.
AI has many use cases for the healthcare industry, including the potential to detect dementia before symptoms even appear. A team at the University of Cambridge and the Alan Turing Institute have developed machine learning tools that can spot dementia in patients at a very early stage.
The technology uses brain scans from patients who developed dementia, with machine learning able to detect structural changes in the brain. This, combined with the results from memory tests, generated a prognostic score that revealed the likelihood of the patient having Alzheimer’s disease.
At present, there are very few drugs available to help treat dementia. Still, this ability to identify individuals at the earliest possible stage could allow researchers to develop new medicines that could be tested before the patient’s symptoms are too severe.
Artificial intelligence has a variety of practical uses in fintech and the wider finance field. According to Autonomous Research, AI technology could allow financial services to reduce their operational costs by 22% by 2030.
An example is banks using data to decide whether a client is creditworthy. AI enables institutions to look at customer data and make credit decisions without the risk of over or undercharging.
Another good use case for the fintech industry is fraud detection. Machine learning solutions can react to data in real-time to find patterns and relationships and recognise the fraudulent activity.
AI is changing the world through digitisation
There are several solutions for using AI to help change the world with the digitised process. For example, AI helps in rural areas to boost access to medical care, also known as telemedicine.
Other use cases include enhancing operational efficiencies in the maritime shipping industry and introducing autonomous vehicles for transportation.
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This post was written by: Albert Kahlow, Global Director - IT & Technology Division & Global Head of Sustainability at Airswift