Revisit Top AI, Machine Learning And Data Trends Of 2021

In a year that felt far from normal, data management and digital transformation played large roles in enterprises’ efforts to adjust. Review the top AI, machine learning and data trends of 2021.

Terri Coles, Contributor

December 16, 2021

4 Min Read
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This past year has been a strange one in many respects: an ongoing pandemic, inflation, supply chain woes, uncertain plans for returning to the office, and worrying unemployment levels followed by the Great Resignation. After the shock of 2020, anyone hoping for a calm 2021 had to have been disappointed.

Data management and digital transformation remained in flux amid the ups and downs. Due to the ongoing challenges of the COVID-19 pandemic, as well as trends that were already underway prior to 2021, this retrospective article has a variety of enterprise AI, machine learning and data developments to cover.

Automation Picks Up Pace

Automation was a buzzword in 2021, thanks in part to the advantages that tools like automation software and robotics provided companies. As workplaces adapted to COVID-19 safety protocols, AI-powered automation proved beneficial. Since March 2020, two-thirds of companies have accelerated their adoption of AI and automation, consultancy McKinsey & Company found, making it one of the top AL and data trends of 2021.

In particular, robotic process automation (RPA) gained traction in several sectors, where it was put to use for tasks like processing transactions and sending notifications. RPA-focused firms like UiPath and tech giants like Microsoft went in on RPA this year. RPA software revenue will be up nearly 20% in 2021, according to research firm Gartner. 

But while the pandemic may have sped up enterprise automation adoption, it appears RPA tools have lasting power. For example, Research and Markets predicted the RPA market will have a compound annual growth rate of 31.5% from 2021 to 2026. If 2020 was a year of RPA investment, 2021 and beyond will see those investments going to scale.

Systems Engineer


Micro-automation is one of the next steps in this area, said Mark Palmer, senior vice president of data, analytics and data science products at TIBCO Software, an enterprise data company. “Adaptive, incremental, dynamic learning techniques are growing fields of AI/ML that, when applied to the RPA’s exhaust, can make observations on the fly,” Palmer said. “These dynamic learning technologies help business users see and act on ‘aha’ moments and make smarter decisions.”

Implementation Of Hybrid Work Models

Automation also played an increasingly critical role in hybrid workplace models. While the tech sector has long accepted remote and hybrid work arrangements, other industries now embrace these models, as well. Automation tools can help offsite employees work efficiently and securely -- for example, by providing technical or HR support, security threat monitoring, and integrations with cloud-based services and software.

However, remote and hybrid workers do represent a potential pain point in one area: cybersecurity. With more employees working outside the corporate network, even if for only part of the work week, IT professionals must monitor more equipment for potential vulnerabilities. 

The hybrid workforce influenced data trends in 2021. The wider distribution of IT infrastructure, along with increasing adoption of cloud-based services and software, added new layers of concerns about data storage and security. In addition, the surge in cyberattacks during the pandemic represented a substantial threat to enterprise data security. As organizations generate, store and use ever-greater amounts of data, an IT focus on cybersecurity is only going to become increasingly vital. 

Digital Transformation Gains Momentum

All together, these developments point to an overarching enterprise AI, ML and data trend for 2021: digital transformation. Spending on digital transformation is expected to hit $1.8 trillion in 2022, according to Statistica, which illustrates that organizations are willing to invest in this area. 

As companies realize the value of data and the potential of machine learning in their operations, they also recognize the limitations posed by their legacy systems and outdated processes. The pandemic spurred many organizations to either launch or elevate digital transformation strategies, and those strategies will likely continue throughout 2022.

How did the AI, ML and data trends of 2021 change the way you work? Tell us in the comments below.

About the Author(s)

Terri Coles


Terri Coles is a freelance reporter based in St. John's, Newfoundland. She has worked for more than 15 years in digital media and communications, with experience in writing, editing, reporting, interviewing, content writing, copywriting, media relations, and social media. In addition to covering artificial intelligence, machine learning, big data, and other topics for IT Pro Today, she writes about health, politics, policy, and trends for several different publications.

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