Top 5 AI and Machine Learning Trends For 2022

Artificial intelligence and machine learning are becoming a dominating part of the tech industry by helping businesses achieve goals, drive critical decisions, and create innovative products and services.

In 2022, companies are predicted to have an average of 35 artificial intelligence projects in their organizations.

In fact, the AI and ML market is likely to grow $9 billion by 2022, at a CAGR of 44%.

In recent years, AI and ML technologies saw several breakthroughs. Let’s go through the top trends in AI and ML for 2022 that will give you ideas on how to control your market:

1. Increased Role of AI, Data Science, and ML in Hyper Automation

Hyper Automation is the process of using advanced technologies to automate tasks. It is also called Digital Process Automation and Intelligent Process Automation.

Nowadays, companies are working with lots of data and data extraction requires automation. Data science and analysis can be found everywhere. We have entered a new era of data science generation because data science tools are more accessible nowadays.

Data Scientist, Enterprise Architect, Machine Learning Scientist, Applications Architect, and Data Engineer are some of the careers that are in great demand. Data science is being used in a variety of industries such as finance companies, manufacturing firms, insurance agencies, marketing firms, and others.

Intelligent automation is used by organizations for conducting research to boost their bottom line.

Advanced technologies generally used in hyper automation are:

• Robotic Process Automation (RPA).
• Artificial Intelligence (AI).
• Machine Learning (ML).
• Cognitive process automation.
• Intelligent Business Process Management Software (iBPMS).

The concept is to combine the right technologies to simplify, design, automate, and manage processes across the organization instead of using tools that are script-based and designed for narrow use cases.

Here are the ways to apply hyper automation in your organization:

• Better customer support: Providing better customer support involves answering customer emails, questions, and queries. Companies can combine conversational AI and RPA to automatically respond to customer queries and improve their CSAT score.
• Improve employee productivity: By automating time-consuming processes, you can reduce the manual work of your employees and increase their productivity.
• System integration: Hyper Automation helps organizations to integrate their digital technologies across processes.

2. Usage of AI and ML for Cybersecurity Applications

AI and ML technologies are becoming a crucial part of information security. With the help of AI and ML, organizations are developing new methodologies to make cybersecurity more automated and risk-free. Ai is helping organizations to power up their cloud migration strategy and improve the performance of big data technologies.

In fact, the use of AI and ML in cybersecurity is likely to reach USD 38.2 billion by 2026. 

How AI and ML can improve cybersecurity:

Cybersecurity involves a lot of data points. Thus, AI can be used in cybersecurity for data clustering, classifying, processing, and filtering.

On the other hand, ML can analyze the past data and present optimum solutions for the present and future. Based on the past data, the system will provide instructions on various patterns to detect threats and malware. Thus, AI and ML will disrupt the essence of any party trying to break into the system.

Here is how you can analyze high volume data with the help of AI and ML:

• Use AI and ML to organize data in a specific pattern helping you correlate various data sets and scan any threats.
• To audit your data protection techniques to see if the placed restrictions are working effectively or not. It will help you to safeguard your users and other parties.
• The use of AL and ML helps you to detect malware and threats by setting a security platform that scans huge amounts of data.

3. The Intersection of AI and ML With loT

AI and Ml are increasingly utilized to make IoT devices and services smarter and more secure.

As per Gartner, over 80% of the IoT projects in organizations will incorporate AI and ML by 2022.

The Internet of things is to have all the devices connected to the internet to be able to respond to various situations based on the data collected.

The importance of AI and ML in this context is the ability to quickly gain insights from data. They automatically identify patterns and detect anomalies in the data generated by smart sensors and devices. The information can be about temperature, pressure, humidity, air quality, sound, speech recognition, and computer vision.

Here are the major segments where you can see the intersection of AI and ML:

• Wearables: Wearables include fitness, health trackers, heart rate monitoring applications, and AR/VR devices that use AIoT, such as smartwatches, AR & VR goggles, and wireless earbuds. 
• Smart home: These devices include lights, thermostats, smart TV, or smart speakers that learn from users’ habits to provide home support.
• Smart city: AIoT is used to make cities more safe and convenient to live in. For example, smart energy grids, smart street lights, and smart public transportation.
• Smart industry: AIoT is used as a real-time data analytics to optimize operations, logistics, and supply chain.

4. Business Forecasting and Analysis

Business forecasting and analysis by implementing AI and ML have turned out to be a lot easier than any previous method and technology.

With AI and ML, you can consider thousands of matrics to make more accurate predictions and forecasts.

For example, Fintech companies are using AI to forecast demand for various currencies depending on the market conditions and consumer behavior in real-time. It is helping Fintech companies to have the right amount of supply to meet the demand.

5. Rise of Augmented Intelligence

Augmented intelligence is the amalgamation of machines and humans to enhance cognitive performance.

As per Gartner, 40% of infrastructure and operations teams will use AI-augmented automation by 2023  for higher IT productivity. In fact, the contribution of digital workers will grow by 50% by 2022.

Augmented intelligence helps platforms to collect all types of data including structured and unstructured from various sources and present it to give a complete 360-degree view of customers.

Good examples of sectors where the use of augmented intelligence is increasing are financial services, healthcare, retail, and travel.

Final Thoughts

Above are the five main trends that are going to be in play in the coming year. Other functions that might be included are machine learning in voice assistance and regulation of digital data.

Trades and companies can forecast stresses and make quick choices with the help of advanced AI and ML solutions. Management of complex tasks and maintaining correctness is crucial to business success, and AI and L are spotless in doing the same. The dynamic scopes of ever-growing industries further drive the significance of artificial intelligence and machine learning trends.