Top 5 Emerging AI & ML Trends to Watch Out for in 2021

Gartner’s report reveals that around 37% of businesses across the industries are leveraging AI and ML technologies in some form to scale up the busines

Navin L

Team lead - Marketing

Gartner’s report reveals that around 37% of businesses across the industries are leveraging AI and ML technologies in some form to scale up the business. To back this report — it’s predicted that 80% of modern technologies use in an IT organization will be complemented by AI and ML by 2022.

AI and ML undoubtedly keep disrupting the new technology adoption that would certainly change the way we think, live, and work in the near future. Hard to believe this if this is real? Think of IBM’s Chef Watson; it has analyzed 9,000-odd recipes in the Bon Appetit database and capable of creating quintillion different recipe combinations from just four ingredients. Also, meet nurse Angel an AI-powered virtual nurse assistant, from saving lives and costs to assisting less invasive procedures for conducting open-heart surgery. Interesting isn’t it?

To be more specific about AI and ML disruption, businesses are significantly increasing AI and ML investments and headcount for 2021. The survey report revealed that 83% of organizations have increased their budget limit for AI/ML adoption, which in-turn increased the data scientist’s employment rate by around 76% year-on-year. Thus, IT organizations are exploring unique use cases, especially the new opportunities where they can achieve top – and bottom-line business benefits during times of economic uncertainty and unprecedented instances like Covid-19.

If AI and ML Disruption are real and businesses across the industry are investing aggressively, then what these technologies cater in 2021? What are the top 5 emerging AI & ML trends to watch out for in 2021?

1. Growth of Augmented Intelligence

Augmented analytics brings the unique flexibility to utilize capabilities of both human and technology to help organizations to boost the efficiency and performance of their workforce. Gartner predicts that, by the year 2023 – 40% of infrastructure and operations teams in enterprise-grade businesses will use AI-augmented automation while improving higher productivity.

Moreover, Augmented intelligence is a design pattern and alternative conceptualization of artificial intelligence that plays an AI’s assistive role. And then, emphasizes the fact that it helps to enhance human intelligence rather than replacing it.

2. AI & ML powered Hyperautomation

The pandemic in 2020 has massively accelerated the use of the advanced concept called Hyperautomation, where AI & ML are termed as key components and its major drivers. Few renowned research and advisory firms state that –

Hyperautomation is an emerging technology trend – Gartner

Hyperautomation is a new methodology of Digital Process Automation – Forrester

Hyperautomation is synonyms to Intelligent Process Automation – IDC

To simply put, Hyperautomation is an application of advanced technologies, such as:

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

And combines the right mix of technologies for the specific business need for automating, simplifying, discovering, designing, measuring, and managing workflows to deliver quick results across the organization setup.

3. AI/ML and IoT all set to bring a new breeze

As per Gartner’s report, 80% of IoT projects would use AI in some form by 2022. Thus, the combination of AI & ML is increasingly intertwined with IoT. For example, in an industrial setup, IoT networks collecting various data like operational or performance information extracted from manufacturing plant, which is later analyzed by AI & ML technologies to understand the production system performance to enhance productivity and predict the instances when machines need maintenance. In both cases, these terminologies complement each other; where AI & ML need a mountain of data to perform efficiently – the other way what IoT sensors and device networks provide.

4. Increase in Reinforcement Learning (RL) adoption

Though Reinforcement Learning (RL) is derived from ML techniques and powered by the essence of AI terminology, its ability to self-learning in an interactive environment using frequent feedbacks received from its own actions and experiences makes it one of the unique applications for modern days businesses.

A chatbot answering basic user queries or requests such as product details, consultation calls, etc., are mere examples of reinforcement learning in action. In other words, RL is capable of separating prospective customers and moving calls to the appropriate service agent. Hence these features and flexibility make it one of the most anticipated trending technologies of 2021 under the wings of AI&ML.

5. Use of AI & ML in Cybersecurity Applications

AI & ML powered cybersecurity system and tools are designed to collect data from company’s communication networks, transaction systems, digital process and workflows, and additional public sources, and then use a set of algorithms to identify patterns and spot the suspicious activity – such as finding out doubtful IP addresses and possible data breaches.

Data being a valuable asset for modern-day businesses, it’s equally important to protect it in all possible manner. Since AI & ML already reserved its role in strengthening cybersecurity systems for both home and corporate systems, the year 2021 and ahead will only see improved and robust security systems.

Wait, we are not done yet!

If you’re a business owner and planning to adopt any trending AI & ML technology or use a case in your workflow, here is the message in bold that you should consider and mull over your plan before investing aggressively in AI & ML technologies.

“Regardless of the increase in budgets and hiring, businesses were seen dramatically spending more time and resources on model deployment.”

The research report found that 38% of businesses, data scientists spend more than 50% of their time on model deployment, besides the time required to deploy a trained model into production has seen an increase in process execution time. These insights give hints on businesses using their resources to manually scale AI/ML efforts, instead of improving operational efficiency. And this is an alarming factor for new players planning to invest in AI & ML for the first time.

Then, what’s the solution to this problem?

The best part of AI & ML is that the window to access these technologies is resilient, where businesses can utilize their capabilities fullest to solve their business problems. Consequently, businesses planning to deploy models at scale were supposed to build and maintain their own MLOps infrastructure from scratch. However, now you can get started with AI & ML at pace and efficiently with third-party solutions irrespective of your business size and prerequisites.

Buying third-party solutions cost 19 – 21% less than building your own.

Third-party MLOps solutions can greatly minimize the manual process and reduce the AI & ML barriers in model deployment. And the survey report shows that 19 to 21% savings in the deployment process and relatively improved outcomes.

Wrapping Up!

The emerging trends of AI & ML in 2021 and ahead pose diverse business applications starting with smaller and bigger use cases as business need demands. The trends discussed in this blog would open the door for many practical scenarios that you can relate to your business need, leverage accurate forecasts, and a lot more. Forward-thinking companies must look into the latest trends of AI & ML, research, and develop a suitable solution for their business to scale bigger in the long run.

About Author

Navin is a technology storyteller. In his role at Affine, he gives words and wings and breaks down complex AI scenarios into stories that resonate with customers and the ecosystem.

Navin L

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