Industries

Banking and Finance

AI solutions for finance aimed to improve operational performance through automated processes. Integrating AI services in finance may help collect and manage extensive data flows and produce real-time business reporting. AI algorithms may efficiently be used to distinguish fraud automatically and enhance security, deliver more personalized customer service as well as get more valuable insights through predictive analytics.
Automated advice is one of the most controversial topics in the financial services space. A robo-advisor attempts to understand a customer’s financial health by analyzing data shared by them, as well as their financial history. Based on this analysis and goals set by the client, the robo-advisor will be able to give appropriate investment recommendations in a particular product class, even as specific as a specific product or equity.
One of AI’s most common use cases includes general-purpose semantic and natural language applications and broadly applied predictive analytics. AI can detect specific patterns and correlations in the data, which legacy technology could not previously detect. These patterns could indicate untapped sales opportunities, cross-sell opportunities, or even metrics around operational data, leading to a direct revenue impact.

Healthcare

The power of AI technology may help solve many challenges in the healthcare industry. Automation of healthcare ensures increased diagnostics accuracy, highly advanced patient data analytics, and data security solutions to prevent medical fraud and patient data breach. AI can also simplify data management and improve the overall clinical workflow.

Transportation

AI can solve and automate many problems in transportation, like predict and mitigate real-time traffic issues, parking shortages, long commutes to work, monitor pedestrians and foot traffic, license plate reading and matching, monitor road and infrastructure conditions and more. It is also expected to play a crucial role in improving future transportation systems quality, safety, efficiency, and sustainability.

Agriculture

AI systems can conduct chemical soil analyses and provide accurate estimates of missing nutrients. AI can monitor the state of plants to spot and even predict diseases, identify and remove weeds, and recommend effective treatment of pests. With the help of AI, it’s possible to automate harvesting and even predict the best time for it. AI is useful for identifying optimal irrigation patterns and nutrient application times and predicting the optimal mix of agronomic products.

Retail

Artificial intelligence (AI) and machine learning (ML) significantly impact the retail world, particularly for companies that rely on online sales, where using some kind of AI technology is very common nowadays.
It is made possible by the generated data that helps unlock the opportunities to anticipate, adapt and meet constantly changing customer demands.
A typical machine learning model breaks large volumes of complex data into actionable insights with a better understanding of customer behavior and market trends. By leveraging these insights, an organization can estimate future demand, decide on competitive pricing and even personalize offerings for customers.

Traveling

In tourism and hospitality, ML has been used for revenue management, operational analytics and customer experience improvement. The objective of ML technology in hospitality is to assemble the arrangements of gathering data and learning from it and improve self-capability through experience without the involvement of human and plain reprograming. At first, experts gather, choose, organize, preprocess and transmute to the machine as data set and then build analytical models. These models can be used in the different utilities of hospitality such as hotel champ autopilot, forecast prices and customers demand with the uppermost exactitude rate by the ML model structure or a device application.

Government

AI has the potential to advance public sector organizations in many ways. The deployment of AI helps predict and prevent improper payments and tax fraud, improve public safety and healthcare, optimize traffic conditions, enhance agricultural outcomes, and natural resources management.

Oil and gas

In many companies, maintenance is addressed on a reactive basis rather than a proactive basis. Equipment is repaired when it fails instead of maintaining it before it fails. A reactive approach can lead to damaged equipment and unexpected downtimes.
Machine learning can help companies make the switch to predictive maintenance by modelling sensor data to find problematic equipment. If there are anomalies in the dataset, such as equipment operating outside its parameters, then the equipment can be maintained before it is damaged. Damaged equipment leads to safety issues and reduced production.
Predictive maintenance can also reduce environmental impact. Well-maintained equipment fails less, so fewer spills happen. Spills in the industry can be almost impossible to completely clean up and can have far-reaching effects on people, water, animals, and soil.