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Artificial Intelligence

This is how companies are using AI today

Technology is developing fast, companies should invest only after understanding their needs to avoid mistakes



ChatGPT and generative AI (Artificial Intelligence) have become hot topics in recent months, but consumers and companies have been using AI for several years. For example, Amazon and Netflix use AI to provide personalised recommendations, and voice assistants like Siri and Alexa use AI to understand and respond to user queries.

Let’s explore how AI is being used by different sectors and companies in their daily business processes and offerings.

AI is revolutionising agriculture by enhancing productivity through three key avenues: agricultural robotics, soil, and crop monitoring, and predictive analytics.

For instance, John Deere, one of the largest and leading agricultural and construction equipment manufacturers in the world, has dedicated years to harnessing technology and robotics to introduce fully automated tractors, showcased at CES 2022.

These autonomous tractors, equipped with top-notch AI and machine learning tailored for agriculture, can perform tasks like cultivating, fertilizing, harvesting, and planting with minimal human intervention. Monarch Tractor, a US startup, is also in the process of developing autonomous tractors, with support from CNH Industrial.

AI also plays a pivotal role in precision farming through predictive analysis. By leveraging real-time sensor data and visual analytics from drones, AI empowers farmers with proactive guidance to enhance crop yield predictions and detect pest or disease outbreaks. PepsiCo, for instance, partnered with India’s Cropin to launch a predictive AI-based crop intelligence model aimed at improving potato yields in India.

AI is significantly impacting design, production, vehicle maintenance, infotainment, and autonomous driving. AI algorithms, combined with sensor inputs and cameras, currently enable self-driving vehicles to operate at levels 2 or 3 on the autonomy scale, offering assistance with steering, braking, accelerating, and adaptive cruise control. Companies like Cruise, Waymo, and are even experimenting with level 4 driverless robotaxis in various cities.

In manufacturing, AI is employed for vehicle design, workflow optimization, and robotics on production lines. Machine-vision systems, powered by AI, facilitate automatic inspection and the detection of faulty products. Additionally, AI enhances in-vehicle infotainment systems, enabling navigation, biometric security, and driving monitoring for insurers.

AI has transformed logistics and retail, with companies like Amazon and Walmart employing predictive logistics and inventory management tools. AI-driven chatbots assist customers with queries and orders, while digital avatars have become popular, especially in China, for marketing luxury brands.

Another application of AI, particularly generative AI, is in the realm of customer service. Numerous businesses employ AI-driven chatbots to handle customer inquiries, process orders, or assist in shopping. Nevertheless, some retailers encounter challenges in monetizing these services. For instance, Walmart discontinued its experimental AI personal shopping assistant in 2020, three years after its launch, due to insufficient adoption. On the other hand, AI-powered digital avatars have achieved greater success. They are employed in China to replace human online influencers who are subject to government oversight and have gained significant popularity, particularly among Western luxury brands like Louis Vuitton (France) and Prada (Italy).

In the energy sector, AI aids grid management and efficiency, crucial for handling the variability of renewable energy sources. The US Department of Energy and the UK’s National Grid employ AI for real-time grid monitoring and response to surges in output or demand, particularly as electric vehicles and smart appliances are integrated into the grid.

The flexibility of the power network is becoming increasingly important as more and more durable goods are electrified. Electric vehicles (EVs), for example, need a flexible power network because they can be both charged from and discharged into the grid. This means that EVs can help balance the grid by providing extra power when needed or by absorbing excess power when the grid is oversupplied.

Household appliances are also becoming more electrified and connected to smart metres. This allows for more flexible usage of electricity, such as washing machines that turn on automatically when electricity is cheap. By making the power network more flexible, we can better accommodate the needs of these new electrified devices and ensure that the grid remains reliable and efficient.

The financial industry was an early adopter of algorithms and artificial intelligence (AI), but it has arguably fallen behind in recent years. One widespread use of AI in finance is fraud detection. Companies like Visa, Mastercard, and PayPal use machine learning algorithms to analyze data on customer behavior captured over several decades. This analysis can detect anomalies in account activity and identify fraudulent activity in milliseconds at any point in the transaction cycle. While these systems can sometimes generate false positives, they have been successful in reducing fraud.

Another prominent use of AI in finance is algorithmic trading. These systems used to rely on human instructions, but they now rely more on machine learning. Early adopters of these systems often made a lot of money, but they also risked triggering herd behavior that could jolt markets. More recently, investment firms have deployed similar systems for automated investing, or Robo-advisors. These Robo-advisors can take over tasks such as portfolio rebalancing, tax-loss harvesting, and efficient investment of cash holdings. Popular US Robo-advisors include Vanguard’s Digital Advisor and SoFi’s Automated Investing bot.

AI has found its way into various facets of the healthcare sector and pharmaceutical industries. Its applications encompass drug discovery, diagnostics, and resource allocation. Leading pharmaceutical companies like Pfizer (USA), Genentech (USA), and Sanofi (France) have harnessed the power of AI and machine learning to accelerate their research and development endeavors.

This involves delving into historical research papers and clinical trial data to uncover hidden patterns and analysing genetic information from both patients and diseases to gain fresh insights. Such insights facilitate the creation of more personalised and efficacious drug candidates. Additionally, AI plays a role in designing subsequent clinical trials.

In the realm of medical technology, companies like GE HealthCare (USA) have integrated AI into their strategies to facilitate the digital transformation of healthcare services. Notable implementations include the employment of centralised command centers, as seen at institutes like Johns Hopkins Hospital (US) and Bradford Royal Infirmary (UK). These command centers employ predictive AI analytics to assist physicians in decision-making, manage patient flows, and foster research collaborations.

Diagnostics represents another promising area for AI application. AI is employed to cross-reference patients’ symptoms with potential causes or to scrutinize medical scans. Early adopters of AI in diagnostics include Chinese health apps such as Ping An’s Good Doctor and several hospitals in Shanghai, which aspire to become hubs for healthcare AI innovation.

Although technology is developing rapidly, businesses should first understand their own needs before investing in it. This will help them make better decisions about which technologies to adopt and how to use them, resulting in fewer mistakes.

The analysis and forecasts featured in this piece can be found in EIU’s Country Analysis service.

Shalini is an Executive Editor with Apeejay Newsroom. With a PG Diploma in Business Management and Industrial Administration and an MA in Mass Communication, she was a former Associate Editor with News9live. She has worked on varied topics - from news-based to feature articles.