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During the past few years, artificial intelligence (AI) and its technologies have wiggled their way into so many computer systems and applications. At the end of 2020, AI software, hardware, and services were projected to reach a revenue of nearly $157 billion worldwide. With AI becoming so popular, it can be hard to stay up to date with trends since they are constantly evolving. Not only is AI changing the type of applications they go into, but they’re also changing the way they are developed and being used. Let’s take a look at current trends in AI to look out for this year.

 

Cybersecurity

Whether it is home security or corporate systems, artificial intelligence has found its way into the cybersecurity world. The developers of cybersecurity systems constantly find ways to update their technology to help them with threats and attacks. Cybersecurity powered by AI can collect data from a company’s communications network, digital activity, transactions systems, and external sources. With the data, AI can point out patterns and suspicious activity that can lead to potential data breaches.

 

Hyperautomation

The popular IT trend Hyperautomation surrounds the idea that basically anything in a company or organization can be automated. Many companies picked up on this concept during the pandemic and used digital processing automation and intelligent process automation. Artificial intelligence is a significant factor of hyperautomation as it assists the data generation process and helps the systems improve over time. Automated processes need to be able to change with concise notice, so with the help of AI, they will be able to recognize algorithms and speed up their operations. 

 

Engineering

This year you can expect more businesses and organizations to realize the importance of AI engineering finally. Creating a solid strategy and process can help improve the performance and reliability of prototypes. Only a little over half of AI projects get passed through prototyped and then into production. AI engineering includes DataOps, DevOps, and ModelOps that can be specialized for different projects.

 

There are so many ways for businesses and organizations to apply AI into their everyday functions. The biggest struggle of it all is finding a way AI will safely and efficiently push you to complete short-term and long-term goals.