This article will cover the artificial intelligence (AI) and machine learning (ML) trends forecasted for the business landscape in 2022, but readers need to keep in mind that businesses are still contending with the pandemic, as well as labor shortages, economic crisis, and many other problematic factors. While some businesses worldwide have certainly come out stronger during these global crises, many have not, but for nearly everyone advanced technologies have revolutionized the way we live and work.
2020 and 2021 made us realize that technology is potentially an advantageous savior and certainly an important guide during a crisis. Artificial intelligence, machine learning, and associated technologies have the potential to resurrect traditional business models from total chaos to a highly streamlined, cost-friendly, and efficient workflow.
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The “intelligent” component of the intelligent digital mesh mostly refers to AI, ML (and related technologies), as these two drive the “brains” of smart machines to deliver business value. AI and ML collaboratively play a critical role in the intelligent digital world of business — enabling machines to mimic human thinking and human tasks. Businesses have learned to trust advanced technologies and endorse technology-enabled business models.
This discussion of artificial intelligence and machine learning trends in 2022 begins with statistics, which show the progression of ideas featured here:
- Ninety-three percent of the environmental sustainability goals (as defined by the United Nations) can be achieved with artificial intelligence
- Sustainable AI systems, with the current pace of adoption, have the potential to create 38.2 million jobs across the globe
- From 2020 to 2027, the global AI-driven cybersecurity market has been projected to grow at a CAGR of 23.6%, reaching $46 billion by the end of the projection period
- While 51% of enterprises have plans to implement AI for automated processes, 25% of companies are already doing so
- In 2020, 80% of executive staff were busy accelerating the automation initiatives of business processes
- By 2023, 40% of infrastructure and operations (I&O) teams in large organization will use AI-powered augmented solutions, with the intention of freeing up the busy IT staff for more strategic work
- According to a 2020 McKinsey Report, 66% of businesses gained higher revenue due to their AI systems
- In 2021, 74% of companies allocated $50,000 or more for AI projects, which is a significant 55% increase in AI budget from 2020
- In 2022, every company is predicted to have 35 AI projects in development
- By 2023, AI professionals must demonstrate solid understanding of responsible AI principles to secure their careers
Factors Affecting AI Adoption Businesses in 2022
“Launching pilots is deceptively easy but deploying them into production is notoriously challenging … Although the potential for success is enormous, delivering business impact from AI initiatives takes much longer than anticipated.” — Chirag Dekate, Senior Director Analyst at Gartner
Here are some factors that can have high impact on AI project implementation throughout organizations in 2022:
- Increased IT budgets for AI, as reflected by statistics provided by Appen, Gartner, McKinsey, or World economic Forum
- Pandemic-triggered accelerated digitization of businesses in an unprecedented manner, thus creating a market of highly skilled IT workforce in 2021
- Fifty countries including the US, UK, and China have national AI strategies in development. This may encourage other countries to initiate similar efforts
- Some environmental issues are necessitating the use of AI, such as the effort to tackle climate change
- In 2020, the FBI received 69% more cybercrime complaints than in 2019. The explosive rise of cybercrimes has created an urgent need for AI-driven cyber-security solutions
- In 2020, only 11% of AI adopters saw ROI from their AI projects. An Alegion and Dimensional Research survey indicates that 78% of all AI/ML projects never experience full implementation, possibly due to low C Suite buy-in
Which AI Trends Are About to Emerge in 2022?
This TechTarget article indicates AI is moving toward “conceptual design, smaller devices and multi-modal applications,” which will collectively dominate industry sectors. Quantum AI has been used as an example of that forward trend.
As mentioned earlier, multi-modal learning, AutoML, conceptual design, Democratized AI, Responsible AI, or Quantum ML — all present in the 2021 AI research landscape — will show more impact through industry applications in 2022.
A long list of AI trends is likely to dominate the business landscape in 2022. Here are five major AI trends to watch for:
- Augmented Business Processes and Systems: 2022 will give a boost to all types of automated systems powered by AI, like augmented Data Management and augmented analytics, to achieve operational excellence, cost efficiencies, and resilience. The combined impact of cloud, robotic process automation (RPA), and IoT, will make AI-augmented automation a dream come true for businesses.
- Rise of Responsible AI: “Responsible AI helps achieve fairness …”,says Svetlana Sicular, Research VP at Gartner. Increasingly, certain industry sectors are demanding that automated systems spitting out decisions must be able to explain the logic behind the decisions. Additionally, such decisions must be totally free of bias (fair). Available industry publications clearly indicate “ethical, responsible artificial intelligence usage will be one of the defining AI trends” for 2022.
- Use of AI in Cybersecurity: AI algorithms have already been used for preventing cyber attacks, monitoring corporate networks, detecting malicious software, and other applications. Now business users are troubled by smart hackers who manipulate data used in model training, access sensitive data by reverse engineering AI systems, or detect security weak spots in corporate systems. To counter these cyber threats, businesses now want AI solutions to closely screen all data used for model training and to inject special security elements in the AI models.
- Use of AI for Environmental Threats: In 2022, businesses and governments deployed powerful AI solutions to combat carbon emissions, use of fossil fuels, global warming, and deforestation. One case study is from Google, which applied deep learning to their data-center cooling technology, and achieved a 40%. reduction in energy consumption.
- Hyperautomation in Healthcare: Healthcare delivery systems will allow care providers to make quicker and more accurate decisions; help drug companies bring high quality products to market in record time; streamline healthcare system workflow; and reduce costs by automating human labor.
In this Forbes post, author and industry insider Bernard Marr talks about other AI Trends of 2022 not mentioned here.
Machine Learning (ML) Trends to Watch Out For in 2022
Machine learning solutions, when powered by Data Science, allow models to mimic human tasks and complete them more precisely, more efficiently. To remain competitive in the cut-throat world of business, it is imperative that organizations embrace and implement ML-powered solutions in their operations.
Here are some recent ML trends that can benefit businesses in 2022:
- Codeless ML: Because codeless ML is not exposed to time-consuming processes like modeling, algorithm development, collecting data, retraining, debugging, and so on, it is economical, simple, and easy to deploy and implement. This system of solution development does not require expert Data Science staff. The latest developments in ML technology, like biometric facial recognition, have revolutionized the way ML solutions are developed now.
- Tiny ML: Because ML algorithms processed on large servers can be time consuming due to data traveling back and forth, a better method is to use ML algorithms on edge devices. The many benefits of this TinyML approach include low power consumption, low bandwidth, high privacy, and low latency.
- Full-stack Deep Learning: This method leads to the libraries and frameworks to automate specific tasks for enhanced agility.
- Generative Adversarial Network (GAN): In a GAN, two competing neural networks complement each other’s roles. While one network generates images (the generative network), the other (the discriminative network) evaluates the images. This way, GANs do not require human intervention of any sort. Machines teach themselves with image samples.
- One Shot, Few Shot, Zero Shot Learning: Usually to make an ML model learn, a lot of data have to be provided. In some cases, it may get too complicated and redundant to use massive piles of images to teach the model. Thus the current practice is to use a single image, a few images, or no image to teach the model. In one shot learning, two subnetworks compare a image to be identified against a reference image. The level of similarity between the two images guides the model’s decision. Two other practices are to use few images. The ultimate goal of this learning method is to use limited data to train a model.
This Becoming Human article also describes how some other machine learning trends initiating in 2021 will impact businesses in 2022.
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