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Cover image for Top Impacts of Artificial Intelligence in 2022
Pavel Tantsiura
Pavel Tantsiura

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Top Impacts of Artificial Intelligence in 2022

AI-based technologies have been the focus of attention over the last few years. The numbers attest to the sudden spike of intelligence software. Thus, by 2028, the revenue of the global AI market is projected to reach over $997 billion.

Artificial intelligence has proven to be the disruptive element of the digital data-driven era. Backed by tech giants like Amazon and Google, its dominance is now future-proof.

Today, we’ll have a look at the most significant use cases of automation in 2021. You’ll also learn about the game-changing impact of COVID-19 on the AI market.

Artificial Intelligence VS COVID-19: What’s the link?

The global pandemic has crippled a great number of businesses and laid bare tech gaps in multiple industries. However, soon after the Covid-19 pandemic was declared, the World Health Organization suggested that artificial intelligence (AI) could be a crucial asset for fighting back the virus. Companies have also realized the positive impact of replacing error-prone human enforcement with advanced automated systems.

And just like that, AI came into the fray as a powerful healthcare and general business tool that allows companies to detect and diagnose virus strains fast, as well as battle pandemics by using personal data.

Thus, based on typical computed tomography pictures, an artificial intelligence system recognized 17 out of 25 COVID-positive patients in a 2020 NCBI study, while specialists diagnosed all patients as COVID-negative.

With that said, let’s have a recap of the most important AI application areas that never ceased to amaze us in 2021 and will continue to do so in 2022.

Artificial intelligence and healthcare

While on this note, we’ll have a closer look at how automation and algorithms bring us closer to precise medicine and better patient outcomes.

According to a study, one in 10 people with a major vascular event, infection, or cancer is misdiagnosed. However, intelligent algorithms are uniquely positioned to minimize the error rate. With its ability to comb through oceans of data, AI holds the potential to make medical examinations more accurate and faster.

Let’s take the high-speed diagnosis of COVID-19 as an example. The whole diagnostic process usually needs a careful examination by an experienced professional that takes hours. If we pit human abilities against AI, the latter exchanges data with hospital CT scanners and analyses images of the lungs taken by a CT scanner. Then it discerns the signs of coronavirus and assesses the lesions - all these in mere seconds.

Moreover, trailblazing applications in AI in healthcare also include:

  • More accurate medical imaging
  • AI-assisted surgeries
  • Remote patient monitoring (e.g telemedicine)
  • Automated gene editing
  • Virtual nursing assistants
  • Chatbot-assisted triage, and others.

Overall, the complexity and advent of data in healthcare bode well for the proliferation of advanced AI algorithms for more efficient and accessible medical services.

Automated marketing

Granular personalization used to be a far-fetched dream of marketers all over the world. Today, it’s a dream come true thanks to Artificial Intelligence. AI-driven customer segmentation allows brands to divide the audience into meaningful groups based on their preferences. This, in turn, leads to more contextual marketing efforts that rely on the specific customer persona.

According to Forbes, AI-enabled marketing efforts boost sales by 52% and help retain more customers. In the case of new products, it increases its chances for success by almost 50%. Also, automation goes a long way in the industry - from user data collection to content generation.

Faster data analysis, streamlined marketing efforts, and cost savings have secured intelligent marketing into the future. From email marketing to dynamic pricing, we’ll see more AI adding to brand sales and getting actionable marketing insights.

Manufacturing and business processes

According to Forbes, the pandemic-induced crisis demonstrated manufacturers an opportunity to strengthen production and make it more sustainable by adopting new resources. Hence, a growing number of manufacturers are doubling investments in artificial intelligence-based analytics and intelligent systems.

Thus, the global AI in the manufacturing market size stood at $1.82 billion in 2019 and is expected to grow to $9.89 billion by 2027. Heralded as a driving force behind Industry 4.0, artificial intelligence is widely used in quality control, inventory management, and monitoring.

Predictive maintenance has also proved to be an important application of AI. According to Capgemini, 29% of AI implementations in manufacturing are for maintaining machinery and production assets. Thanks to data analysis tools and computer vision, smart systems can detect anomalies and possible defects well before they lead to costly downtimes.

Thus, General Motors analyzes images from cameras mounted on assembly robots, to identify signs of faulty robotic components. Shell has also been spearheading the adoption of predictive maintenance. The latter helps the petrochemical manufacturer to boost equipment reliability and extend the overall operational life of its assets.

Logistics and supply chain management

Intelligent algorithms are hitting new adoption levels each year, with logistics being another focus in the AI race. Supply chain disruptions to the sector caused by the pandemic solidified the need for more automation and data-driven management. Besides crisis management, the benefits of AI for transportation are manifold.

Smart tools and solutions help analyze huge data sets in real-time, thus balancing supply and demand gaps and assisting companies in planning production activities. AI solutions allow carriers to analyze existing routes, identify bottlenecks and focus on the best route. This, in turn, reduces both time and the overall cost of warehousing and delivery. AI- and ML-based data processing tools help capture details related to the shipment of the goods in real-time and estimate delivery windows.

Moreover, around 40% of respondents state that artificial intelligence can help improve inventory management. The foretelling power of AI can help suppliers forecast the demand, optimize stock levels, and reduce lead times for customers. Warehouse robots have also become popular for performing essential warehouse tasks, such as picking, sorting, and transportation.

Banking and financial sector

The banking and financial services industry is one of those brick-and-mortar fields that are opposed to digital transformation. Nevertheless, artificial intelligence has managed to permeate this industry and disrupt legacy processes. According to McKinsey, the potential of AI for banking is the greatest across industries. Thus, artificial intelligence can potentially unlock $1 trillion of value for banks, annually.

Besides improved customer experience and reduced costs, smart systems can avert frauds, improve loan decisioning, and automate the investment process.

Today, AI takes the form of document automation software and banking chatbots. Also, most financial services companies have implemented the technology in areas like risk management (56%) and revenue generation through new products and processes (52%).

However, as decentralization comes into the limelight, we’ll certainly see more of this duo revolutionizing the banking industry.

The Future of Artificial Intelligence

Artificial intelligence has stepped in as the driving force behind the majority of emerging technologies. Big data, analytics, robotics, and IoT are all relying on intelligence to change our world for the better.

In the foreseeable future, global industries are set to witness more AI adoption as the acute need for automation is surfacing due to the pandemic crisis. Runner-up companies will be those that have managed to adapt, become more flexible, and offer a better customer experience. Artificial intelligence can address those needs by providing more automated applications and taking the form of business intelligence and robotic automation.

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