Top Three Ways COVID-19 Revved the Deployment of Artificial Intelligence

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New algorithms, as well as more accessible and reasonably priced processing power, are enabling Artificial Intelligence (AI) to become more and more commonplace. It has been over 70 years since AI technology began to evolve. The pandemic pushed the adoption of AI rather than its development.

According to the IBM Global AI Adoption Index 2021, nearly a third of IT companies worldwide are now embracing AI. The COVID-19 pandemic, according to over 43% of the IT experts polled worldwide, caused their organizations to expedite the use of AI.

Here are a few ways COVID-19 has sped up the deployment of AI.

Also Read: Four Pitfalls Businesses Need to Avoid while Adopting Artificial Intelligence

New data structures

The names “data warehouses” and “data lakes” were widely used before the pandemic and are still in use today. However, brand-new data structures like “data fabric” and “data mesh” were scarce. Because data fabric automates data discovery, governance, and consumption, it enables businesses to leverage data to maximize their value chain. No matter where the data is, organizations can deliver it at the right moment.

IT leaders will get a chance to reconsider data models and data governance. It offers an opportunity to defy the trend toward data lakes or centralized data stores. More edge computing and data accessibility where it matters most may result from this. These developments make the right data automatically available for decision-making, which is essential to the functionality of Artificial Intelligence (AI).

The business and AI elements of new data architectures are essential for IT leaders to grasp.

They might not design the necessary form of data architecture and data consumption for adequate support if they don’t know what each component of the company requires, including the type of data and where and how it will be utilized. It will be crucial for IT to comprehend business demands and the business models associated with that data architecture.

Also Read: Three Potent Ways Artificial Intelligence Can Assist With Pricing

New sources of data

Research from Statista highlights the expansion of data: Globally, 64.2 zettabytes of data were generated, copied, and used in 2020; by 2025, that number is expected to rise to more than 180 zettabytes. According to a Statista study from May 2022, the COVID-19 pandemic-related spike in demand is what drove the growth to be larger than anticipated. Media, the cloud, the web, IoT, and databases are big data sources.

Every choice and action can be linked to a specific data source. IT leaders will have more influence if they can utilize AIOps/MLOps to focus on specific data sources for analysis and decision-making. With the right data, firms can perform immediate business analyses and get in-depth insights for predictive analysis.

Continued access to affordable computing power

Even 60 years after the discovery of Moore’s Law, computing power continues to grow thanks to more potent machines and new chips produced by businesses. According to industry experts, during the past quarter-century, the amount of processing power accessible per dollar has likely expanded. Over the past six to eight years, the rate has, however, slowed down.

IT executives now have additional options thanks to affordable computing, allowing them to accomplish more with less. IT professionals want to use big data’s potential, though, as it offers inexpensive computing, according to businesses. All accessible data should be ingested and analyzed since this will improve insights, analysis, and decision-making. However, if firms are not attentive, they risk having a lot of computing power but not enough practical commercial applications. The human tendency is to use networking, storage, and computing more as their costs decline. However, not everything they offer has business value.

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Umme Sutarwala is a Global News Correspondent with OnDot Media. She is a media graduate with 2+ years of experience in content creation and management. Previously, she has worked with MNCs in the E-commerce and Finance domain