Machine learning has moved from just science fiction stuff to a staple in modern business as organizations in every industry vertical try to implement machine learning technologies for better operations. We can now see how the doctors are using machine learning to make a more accurate diagnosis of their patients, retailers using it to get the right merchandise to the stores at the right time, researchers using ML to develop effective new medicines, etc.
What we can see is just a sliver of the use cases, which are emerging largely at all sectors from utilities to energy, travel, hospitality, logistics, and manufacturing, etc. There are various functions within all types of organizations that increasingly deploy machine learning at work. It has worked wonders for them and brought them substantial results. If you are thinking of
choice help is another region where AI can help organizations transform the plenty of information they have into significant bits of knowledge that convey esteem. Here, calculations prepared on chronicled information and some other pertinent informational indexes can break down data and go through different potential situations at a scale and speed inconceivable for people to make suggestions on the best game-plan to take.
Dan Miklovic, originator and head examiner, Lean Manufacturing Research LLCDan Miklovic
“It doesn’t supplant individuals, yet rather it assists individuals with improving; it can make individuals substantially more viable,” said Dan Miklovic, author and head examiner at Lean Manufacturing Research LLC, and an individual from The Analyst Syndicate.
Machine learning is considered to be a subset of artificial intelligence, which uses computers and algorithms to gain better insights from data and allow the machines to identify patterns. This is a capability which organization can put into use in various ways.For example, experts say that machine learning will enable businesses to perform tasks on a scale and scope previously impossible to accomplish. As a result, one can speed up the pace of work, improve accuracy, and empower the employees, stakeholders, and customers alike. Moreover, our innovative organizations are now finding more ways to harness machine learning capabilities, drive efficiencies and improvements in their processes, and fuel new business opportunities, which can help differentiate their brand in the competitive marketplaces.
Here we will discuss a few real-time applications of machine learning in business, which are used effectively to solve problems and deliver more tangible business benefits.
One of the earliest forms of automation using machine learning was chatbots, which helped bridge the communication gap between people by allowing technology to take over the task.People started to converse with machines and chatbots, which can take actions based on the requirements and requests of humans effectively. The first generation chatbots followed scripted rules, which told the boats what type of actions to be taken based on the keywords or inputs by the users. However, machine learning and NLP (natural language processing) enabled chatbots to be more interactive and responsive.
This new generation of chatbots can better respond and converse increasingly like real humans. We can see examples of real-time digital assistants like Amazon Alexa, Apple Siri, Google Assistant, etc., which are based on machine learning algorithms. These technologies are now finding ways in customer service and engagement platforms that can replace the traditional chatbots. Chatbots are among the most widely used ML applications in businesses now. A few examples of company chatbots that are working amazingly include the following.
- Watson Assistant, developed by IBM, provides fast and straightforward answers when asking for clarity and judging a request like a human being.
- Another example is the music streaming platform of Facebook Messenger, which lets the userssearch, listen, and share music by getting recommendations.
- Riders request service through chat platforms or by voice and can send images to the driver’s license plate and the car model to support their rides.
Machine learning in decision support
Decision support is another crucial area where machine learning helps businesses turn data into actionable insights and deliver value. Here, the algorithms trained based on historical data and other relevant data sets can easily analyze information and run through various possible scenarios at scale.This can also speed up human-impossible processes to make recommendations on the best course of action.
Machine learning is not replacing real humans, but rather it helps people do things better. It will effectively make people more productive and accurate in decision-making. Let us further explore some examples of decision support systems using machine learning.
- In the Healthcare industry, the clinical decision support processes incorporate machine learning which gets clinicians more treatment and diagnosis options by improving the overall efficiency of patient care.
- In farming and agriculture, machine learning will enable decision-making with support tools that can incorporate data on energy, climate, resources, and other variable factors to help the farmers make appropriate decisions on crop management.
- In business management, decision support systems will help understandfuture trends and identify the problems to speed up decisions. Here, information is ideally represented through executive dashboards in charts and graphs for easy understanding.
Customer recommendation engines
Machine learning can also power customer recommendations with tools designed to enhance user experience and offer personalized suggestions. In such usecases, the algorithms can process the data points about various customers like past purchases, the company’s current inventory, demographic, and other buying tendencies based on the situations to determine which products and services to recommend for each individual. Let us explore a few examples of some companies applying recommendation engines.
E-Commerce giants like Walmart and Amazon now use recommendation engines to personalize the user’s shopping experience and expedite customer service.
- Another example of ML application is Netflix, a streaming and entertainment service using customer viewing history with similar entertainment interests and information about individual shows or data points to deliver personalized recommendations.
- YouTube is the number one online video platform that uses recommendation engine technology to help its users to find their most interesting videos.
- Here are a couple of instances of organizations whose plans of action depend on proposal motors:
- Huge web based business organizations like Amazon and Walmart use suggestion motors to customize and assist the shopping experience.
- Another notable deployer of this AI application is Netflix, the real time amusement administration, which utilizes a client’s survey history, the review history of clients with comparative diversion interests, data about singular shows and other information focuses to convey customized proposals to its clients.
- Online video stage YouTube utilizes suggestion motor innovation to assist clients with discovering recordings that fit their preferences.
- Other use cases of machine learning and business operations include customer churn modeling, dynamic pricing tactics, customer segmentation, market research fraud detection, image recognition and image classification, operational efficiencies, information extraction, etc. In addition, organizations can effectively use machine learning to process everything from invoices to tax forms and legal contacts and bring increased efficiency and accuracy to every process. In all these use cases above, machine learning comes into action as a supportive technology and the mainstream of operation, which can add real value to any business.