How Machine Learning help organizations?

Machine Learning is a subfield of Artificial Intelligence that gives computers the ability to learn without being programmed. ML algorithms have the ability to analyze data and make predictions based on that data. ML algorithms learn from data, improve as more data is added to the system, and are capable of being reprogrammed to solve different problems.
This component can help organizations in many ways, it can help improve the customer experience, enable real-time decision making and enable predictive modeling. It can also help companies in financial services, healthcare, manufacturing, real-time data analysis, and marketing.
Below we present how Machine Learning will impact these industries in the coming years.

Machine Learning in financial services

• It is expected that the financial services sector will generate huge amounts of data in the coming years. Industry adoption of AI and ML will help sift through this data and extract business insights. ML algorithms can be used to provide a better customer experience and enable real-time decision making. Financial service providers can use ML algorithms to provide customers with a better online experience. Algorithms can help improve the online onboarding process and allow customers to complete their transactions seamlessly.
  Financial services providers can use ML algorithms to analyze customer data and use the insights to personalize the customer experience. These algorithms can analyze data generated from various sources and provide additional insights. Algorithms can help financial service providers take action on their customers' credit limits, fraud detection and risk scoring.

Machine Learning in Healthcare and Pharmaceuticals

• The pharmaceutical and healthcare industries are embracing AI and ML at an unprecedented pace. Healthcare organizations are increasingly turning to these technologies to drive operational efficiency and improve patient care.
  Healthcare organizations can use ML algorithms to discover insights from data and provide recommendations to improve healthcare outcomes. For example ML can be used to improve drug discovery and development by suggesting appropriate compounds.
  Healthcare organizations can also use ML algorithms to improve their operations for example to predict patient flow and recommend changes to reduce waiting times in hospitals.

Machine learning in manufacturing and supply chain management

• Manufacturing and supply chain industries generate an enormous amount of data from their production operations. AI and ML can be used to sift through this data and generate insights that can help companies in these industries improve their operations.
  Manufacturers can use ML algorithms to analyze data from their production operations and identify opportunities for improvement such as helping manufacturers with supply chain management identifying potential issues with their assembly lines, suggesting corrective actions and optimization of the supply chain.
  Likewise, manufacturers can use ML algorithms to optimize their supply chain, i.e. algorithms can help manufacturers identify potential problems with the supply chain and recommend corrective actions such as helping to identify problems such as product shortages or incorrect inventory at retail outlets.

Machine Learning in Marketing & Advertising

• The marketing and advertising industries are generating massive amounts of data. AI and ML can help these industries filter data and generate additional insights. Algorithms can be used to help marketing systems make personalized content recommendations and deliver personalized ads to their customers.
  Applications can use ML algorithms to analyze customer data and make personalized recommendations to help them make better business decisions such as recommending buy and sell actions to optimize inventory.
  Systems can use ML algorithms to deliver personalized ads to your customers based on your data, i.e. to deliver targeted social media ads or email campaigns to a specific customer segment.

Real-Time Data Analytics

• The real-time data analytics industry is expected to generate big data in the coming years. Organizations can use AI and ML algorithms to sift through this data and generate real-time insights. Algorithms can be used to predict customer behavior and recommend assertive actions based on the data. Real-time data analytics companies can use ML algorithms to analyze data generated from multiple sources and suggest actions such as forecasting product sales and recommending options such as reprocessing inventory.
  Algorithms can also be used to predict customer behavior and recommend optimal options based on the data, i.e. to track a customer's browsing behavior on a retail website and recommend a personalized offer.