Artificial Intelligence: 3 ways the pandemic accelerated its adoption

The need for organizations to quickly create new business models and marketing channels has accelerated AI adoption throughout the past couple of years. This is especially true in healthcare, where data analytics accelerated the development of COVID-19 vaccines. In consumer-packaged goods, Harvard Business Reviewreportedthat Frito-Lay created an e-commerce platform,Snacks.com, in just 30 days.

The pandemic also accelerated AI adoption in education, as schools were forced to enable online learning overnight. And wherever possible, the world shifted to touchless transactions, completely transforming the banking industry.

Three technology developments during the pandemic accelerated AI adoption:

[ Also readArtificial Intelligence: How to stay competitive. ]

Lets look at the pros and cons of these developments for IT leaders.

Even 60 years after Moores Law, computing power is increasing, with more powerful machines and more processing power through new chips from companies like NVidia.AI Impactsreports that computing power available per dollar has probably increased by a factor of ten roughly every four years over the last quarter of a century (measured in FLOPS or MIPS). However, the rate has been slower over the past 6-8 years.

Pros: More for less

Inexpensive computing gives IT leaders more choices, enabling them to do more with less.

Cons: Too many choices can lead to wasted time and money

Consider big data. With inexpensive computing, IT pros want to wield its power. There is a desire to start ingesting and analyzing all available data, leading to better insights, analysis, and decision-making.

But if you are not careful, you could end up with massive computing power and not enough real-life business applications.

As networking, storage, and computing costs drop, the human inclination is to use them more. But they dont necessarily deliver business value to everything.

Before the pandemic, the terms data warehouses and data lakes were standard and they remain so today. But new data architectures like data fabric and data mesh were almost non-existent. Data fabric enables AI adoption because it enables enterprises to use data to maximize their value chain by automating data discovery, governance, and consumption. Organizations can provide the right data at the right time, regardless of where it resides.

Pros: IT leaders will have the opportunity to rethink data models and data governance

It provides a chance to buck the trend toward centralized data repositories or data lakes. This might mean more edge computing and data available where it is most relevant. These advancements result in appropriate data being automatically available for decisioning critical to AI operability.

Cons: Not understanding the business need

IT leaders need to understand the business and AI aspects of new data architectures. If they dont know what each part of the business needs including the kind of data and where and how it will be used they may not create the correct type of data architecture and data consumption for proper support. ITs understanding of the business needs, and the business models that go with that data architecture, will be essential.

Statistaresearch underscores the growth of data: The total amount of data created, captured, copied, and consumed globally was 64.2 zettabytes in 2020 and is projected to reach more than 180 zettabytes in 2025. Statista research from May 2022 reports, The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic. Big data sources include media, cloud, IoT, the web, and databases.

Pros: Data is powerful

Every decision and transaction can be traced back to a data source. If IT leaders can use AIOps/MLOps to zero in on data sources for analysis and decision-making, they are empowered. Proper data can deliver instant business analysis and provide deep insights for predictive analysis.

Cons: How do you know what data to use?