A Crucial Year for AI/ML
The way we work and live has been constantly changing in the last few years. Google CEO Sundar Pichai predicts that the advancement in artificial intelligence and machine learning will be even more revolutionary than the invention of fire.
According to Comptia, 86% of CEOs report that AI is considered mainstream technology in their offices as of 2021. Businesses across the globe are battling labour shortages, economic crises, and many other hurdles that affect business efficiency. Intelligent and comprehensive digital solutions include the use of artificial intelligence and machine learning as they are referred to as the ‘brains’ of smart machines that will help businesses deliver increased business productivity & constructive solutions. Many predictions in the field of artificial intelligence and machine learning are being made that we will see below:
Predictions about AI/ML in Business
- Accessibility and Democratization of Processes: Artificial intelligence and machine learning are no longer the responsibility of a single employee in the IT department. It is available to engineers, support representatives, sales engineers, and other professionals that can make use of it to solve everyday business problems. Machine learning will soon emerge to be the standard tool that is used to solve certain complex computational problems. It will help in personalizing customer experiences and provide an enhanced insight into customer behaviours.
- Enhanced Security for Data Access: AI & ML tools can track and analyze higher network traffics and recognize threat patterns to prevent cyber-attacks. This can be done in conjunction with monitoring the networks in question, detecting malware activities, and other related practices. Enterprises can adopt advanced AI solutions to both monitor data and construct special security mechanisms in their AI models. AI can help by recognizing patterns and suggesting business intentions using smart algorithms. AI-powered security will reach new heights in the days to come.
- Deep Learning to Aid Data Analysis: Deep learning happens after the creation of multiple layers of artificial neural networks to use for processing large amounts of unstructured data. This allows the machine to learn how to analyze and categorize inputs without being specifically instructed on how to handle the task. The use cases for deep learning range from industries such as predictive maintenance to product strategies in software development companies. Some autonomous locomotive and automobile enterprises are already implementing deep learning capabilities into their products. In the future, businesses across industries will increasingly leverage deep learning for data analysis.
- Natural Language Processing Enhancing Use Cases: Natural Language Processing involves both computational linguistics, and the general model of the human knowledge- paired with machine learning, statistical learning, and deep learning models all working closely with each other. NLP can help in making one aware of the subconscious patterns in the organization’s processes- this can help identify strategies to boost business efficiency. It is used both in the legal and commercial space, as dense legal contracts and documents and can be analyzed with speed.
Having got an insight into the probable trends for Artificial intelligence and machine learning, here we discuss a few use cases that are driving the use of AI/ML forward:
Use Cases for AI/ML in 2022
- Machine Learning in Finance: Machine learning techniques are paramount to enhancing the security of transactions by detecting patterns and possibilities of fraud in advance. Credit card fraud detection is an example of improving transactional and financial security through machine learning. These solutions work in real-time to constantly ensure security and generate alerts. Organizations across the globe use machine learning techniques to conduct sentiment analysis for stock market price predictions. In this instance, business trading is aided by the algorithm, where various data sources such as social media data help to perform sentiment analysis.
- Machine Learning in Marketing: Machine learning can aid with considering customer and business objectives while considering purchase patterns, pricing, comparison with other businesses, and mapping marketing points that can align with customer objectives. Content curation and development is an essential component in an era of digital marketing. There are tools that can help to customize the content as per the customer’s preferences and also tools that can help effectively organize content for customers for better engagement. Customization, understanding customers, and creating a memorable experience are all aided by machine learning as seen in the examples of chatbots that use AI technologies.
- Machine Learning in Healthcare: Administrative tasks can be delegated to natural language processing software, which can effectively reduce the physician’s and other healthcare staff’s overall workload. This can help the healthcare staff concentrate better on the patient’s health and spend less time going through legal and manual administrative work. NLP tools can help generate electronic health records and with managing critical administrative tasks in the healthcare industry. The tools would automatically find words and phrases to include in the electronic health record at the patient’s visit. They can create visual charts and graphs that can help the physician understand the patient’s health better.
Also Read: 10 Promising Enterprise AI Trends 2022
AI/ML Paving the Road Ahead for Growth
In 2022, along with the help of artificial intelligence and machine learning technologies, businesses will increasingly try to automate repetitive tasks and processes that involve sifting through large volumes of data and information. It is also possible that businesses will bring down their dependence on the human workforce to improve the overall accuracy, speed, and reliability of the information that is being processed.
AI/ML is usually called disruptive technologies as they are powerful enough to elevate industry practices by assisting organizations in achieving business objectives, making important decisions, and developing innovative services and products. Data specialists, analysts, CIOs, and CTOs alike should consider using these opportunities to efficiently scale their business capabilities to have an edge in the business.