Predictive Global Country Analysis Technology
- The launch of ChatGPT in November 2022 sparked a heated debate about how artificial intelligence will impact businesses and consumers around the world.
- Many businesses are already using AI or machine learning for a variety of purposes, from automating manual processes to predicting and fulfilling customer demand.
- Businesses need to identify and articulate the most profitable use cases to determine their future AI investment and development needs.
ChatGPT and generative artificial intelligence (AI) have been hot topics in recent months, but consumers and businesses alike have been using AI for several years. This is used when Amazon or Netflix provide recommendations or through the use of voice assistants such as Siri or Alexa. In this article, we take a look at how many different sectors and companies are already using AI as part of their daily business processes and services.
AI changes the future of agriculture
AI in agriculture is being used to improve agricultural productivity in three key ways: agricultural robotics, soil and crop monitoring, and predictive analytics.
John Deere has spent decades investing in technology and robotics to develop the first fully automatic tractor and launch it at CES 2022. By applying high-quality AI and machine learning training data tailored for agriculture, autonomous tractors plow, fertilize, harvest and plant with minimal human intervention. US-based startup Monarch Tractors is also developing an autonomous tractor with the backing of CNH Industrial.
Another important application of AI is precision agriculture using predictive analytics. AI, combined with real-time sensor data and visual analytics data from drones, can provide farmers with better crop yield predictions and proactive guidance to detect pest and disease infections. PepsiCo (US) recently partnered with software-as-a-service (SaaS) provider Cropin (India) to launch a crop intelligence model for India that uses predictive AI analytics to improve potato yields.
AI is driving innovation in the automotive sector
AI has a wide range of applications in the automotive industry, from design, production and vehicle maintenance to infotainment and autonomous driving.
AI algorithms, combined with input from sensors (including LiDAR) and cameras, are already capable of guiding self-driving cars within defined, self-contained areas. The BMW X5, Tesla Model S and GM’s Cadillac Escalade are currently operating at level 2 or 3 of his five levels of autonomous driving. This means it still requires full-time driver attention, but offers assistance with steering, braking, acceleration, and adaptive cruise control. Meanwhile, Cruise (US, owned by GM), Waymo (US, owned by Alphabet) and Pony.ai (China) are among the companies experimenting with Level 4 driverless robo-taxis in cities from Phoenix, Arizona to Beijing, China. is one.
From a manufacturing perspective, AI is being used in vehicle design, workflow solutions, and production line robotics. BMW (Germany), Toyota (Japan), and GM are among the companies that are using AI for automated inspection and deploying machine vision systems to help detect defective products. AI is also being used in in-vehicle infotainment systems to enable navigation, biometric security, and driving monitoring for insurance companies.
AI contributes to smooth logistics and retail
Amazon (US) has been using AI for predictive logistics for several years and patented the technology in 2014. The online retail giant analyzes customer data to predict demand for its products so it can be ready and shipped for on-time delivery. Only hours after purchase. Retailers such as Walmart (US) use AI tools to predict and plan inventory levels by scanning photos and videos from store cameras, as well as analyzing demand. Consumer companies use AI and location data to improve supply chain transparency, including meeting sustainability goals. Unilever (Netherlands) uses it to track deforestation.
Another use case for AI (particularly generative AI) is customer service, where many companies use AI-powered chatbots to answer customer questions, take orders, and help them shop. There is even However, some retailers struggle to monetize such services. Walmart shut down its experimental AI personal shopping assistant in 2020, three years after it launched, because it didn’t get enough adoption. AI-powered digital avatars are more successful. It is being used in China to replace government-monitored human online influencers, and is particularly popular with Western luxury brands such as Louis Vuitton (France) and Prada (Italy).
AI can improve grid resilience
The primary use of AI in the energy sector today is to improve grid management and efficiency in an increasingly volatile and flexible power grid. The output of wind, solar and hydro power plants fluctuates with the weather, requiring efficient grid management to avoid blackouts. In the US, the Department of Energy has made AI central to its smart grid strategy, and in the UK, the National Grid is working with IBM to develop cloud-based analytics. These efforts will enable real-time monitoring of the grid and the ability to anticipate and respond to surges in output or demand.
With the electrification of durable goods, this flexibility becomes even more important. Electric vehicles (EVs) are charged from the grid, but they can also provide additional power sources without being charged to the grid, so they need flexible power networks. As home appliances become more electrified and connected to smart meters, more flexible usage becomes possible. This includes washing machines that turn on automatically when electricity is cheaper.
Fraud Detection, Automated Investments Dominate Financial AI
Companies in the financial industry were early adopters of algorithms and AI, but the sector has lagged behind in recent years. One widely used one is fraud detection. Visa, Mastercard and PayPal (all in the US) use machine learning algorithms to analyze data on customer behavior collected over decades. Such analytics can detect anomalies in account activity and identify fraud within milliseconds at any point in the trading cycle. These systems occasionally generate false positives and block customers from genuine payments, but they are successful in reducing fraud.
Another prominent use of AI, once called algorithmic trading, now relies more on machine learning than human direction. Early adopters of these systems often made big profits, but they risked encouraging market-shaking herd behavior. These days, investment firms can deploy similar automated investment systems, namely robo-advisors, to take over tasks such as rebalancing portfolios, recovering tax losses, and investing their cash efficiently. Popular robo-advisors in the US include Vanguard’s Digital Advisor and SoFi’s Automated Investing bot.
Is AI already making its way into health care?
AI is already being used in healthcare facilities and the pharmaceutical industry in a variety of ways, including drug discovery, diagnostics, and resource allocation.
Pfizer (US), Genentech (US), and Sanofi (France) are among the companies that are using AI and machine learning to accelerate their R&D efforts. This can be done by tracing historical research papers and clinical trial data looking for undiscovered patterns, or by analyzing genetic data from both patients and diseases to generate new insights. This insight can also be used to design subsequent clinical trials using AI to help develop more personalized and effective drug candidates.
GE Healthcare (US) is one medical technology company using AI to help digitize healthcare services. Centralized command centers at Johns Hopkins Hospital (US) and Bradford Royal Infirmary (UK) use predictive AI analytics to support physician decision-making, patient flow management and research collaboration .
Diagnostics is also an area of potential, where AI is used to check patient symptoms against possible causes and analyze scans. Early adopters include Chinese health apps such as Ping’an’s Good Doctor, and hospitals in Shanghai, which particularly want to become hubs for medical AI.
Use cases should drive adoption
In all these areas, the pandemic has highlighted the need for digital transformation strategies, and AI is a key part of that process. This technology is evolving rapidly, but it’s still important for businesses to understand why they want to use it. That business need drives the necessary investments and innovations in the right direction, with less failure.
The analysis and forecasts presented in this article are available on the EIU’s Country Analysis Service. This integrated solution provides unparalleled global insight covering the political and economic outlook of nearly 200 countries, helping organizations identify future opportunities and potential risks.
Predictive Global Country Analysis Technology
