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AI Applications & Use Cases: Revolutionizing Major Industries

AI Applications & Use Cases: Revolutionizing Major Industries
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    11 min
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    June 17, 2024 Last Updated: June 17, 2024


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Artificial intelligence is rapidly transforming our world, and its impact is being felt across various industries. From revolutionizing healthcare to streamlining manufacturing processes, AI is creating a new landscape of possibilities. Thus, businesses are increasingly turning to AI development services to capitalize on this potential.

And why not when it has so much to offer?

Whether we talk about advanced driverless vehicles, virtual doctors, chatbots, or so on, all these demonstrate the wide range of ways that AI has been used to penetrate modern society.

If you're keen to understand how AI applications are reshaping global industries and how to harness them for substantial business revenue, this blog is your guide.

Let’s Begin!

AI Technology Market Analysis & Research 

According to Statista, the global AI market is experiencing explosive growth, projected to reach a staggering value of over US$184.00bn by the end of 2024. It will further grow resulting in a market volume of US$826.70bn by 2030 with a CAGR of 28.46%.

Market Share Graph

Apart from this, here are some amazing global stats on AI you must know

  • 57% of Americans anticipate that AI will eventually replace housework.
  • More than 97 million people will be employed in the AI industry by 2025.
  • Netflix generates $1 billion a year in revenue from tailored algorithmic suggestions.
  • 48% of firms use AI in some way to efficiently exploit big data. 
  • 38% of healthcare professionals use computers to aid with diagnosis.
  • 83% of businesses claim that AI is a major priority in their business strategy.

Ai Market Size By Industry

Moreover, according to Accenture, AI can boost economic growth rates across 16 industries by a weighted average of 1.7 percentage points by 2035. So it would not be wrong to say that AI has a solid grasp over industries worldwide.

AI Applications & Use Cases Across Major Industries

Artificial intelligence is frequently used in a variety of sectors, including those related to education, healthcare, finance, transportation, and so on. Here are a few examples of real-world uses for artificial intelligence.

AI Applications & Industries

1AI in Healthcare

Technology behemoths like Microsoft, Google, Apple, IBM, and so on have made important contributions to the healthcare sector, which is crucial for the entire healthcare industry. AI applications are transforming major industries worldwide, and in healthcare, it’s utilized for data mining, pattern recognition, and precise medical diagnosis and treatment. 

Now, let’s see some of the applications of AI in healthcare:

  • Medical imaging: Artificial intelligence has significantly improved medical imaging by enhancing the accuracy and speed of diagnosis. AI algorithms can analyze large volumes of imaging data, such as X-rays, MRI scans, and CT scans, to identify patterns and anomalies. For instance, PathAI, a healthcare company, uses AI to improve diagnostics and achieve high accuracy in analyzing data, particularly in drug development for complex diseases.
  • Personalized treatment: AI plays a crucial role in personalized medicine by analyzing genetic information and medical history to tailor individual treatment plans. AI algorithms can accurately analyze genetic data to identify specific genetic markers associated with certain conditions. 
  • Electronic Health Records (EHRs): AI is used to improve the management and analysis of EHRs, reducing errors and enhancing the overall efficiency of healthcare services. AI algorithms can process and analyze large volumes of EHR data, identifying patterns and trends that can inform clinical decisions and improve patient care. 
  • Fraud detection: AI models can predict the likelihood of fraudulent claims based on historical data and other factors. It can identify patterns in claims data that may indicate fraudulent activity, allowing for early detection and prevention of fraud. 
  • Virtual assistants and chatbots: AI-powered virtual assistants and chatbots are used to improve patient engagement and provide support for various healthcare tasks. With the help of chatbots, AI in healthcare can assist patients with simple transactions like refilling prescriptions, making appointments, and answering basic health queries
  • Remote patient care: AI algorithms can analyze data from wearable devices and sensors to monitor patients remotely. It can help tailor remote care to individual patient’s needs, ensuring that they receive the most appropriate support and interventions based on their unique health profiles. 

Ai application and use cases

2AI in Banking & Finance

Due to the emergence of AI in banking and financial services sector is going through a significant transformation. There are a ton of AI use cases in this field.

Given below are some important applications

  • Fraud detection and prevention: AI plays a crucial role in detecting and preventing fraudulent activities in the fintech industry. Advanced algorithms analyze patterns in credit history and spending behavior to identify suspicious transactions in real-time. 
  • Credit scoring and risk assessment: AI-driven credit scoring systems have revolutionized the way financial institutions assess creditworthiness. Machine learning models process and analyze vast amounts of data to provide more accurate risk assessments, reducing bias and expediting the loan approval process. 
  • Chatbots for customer service: AI-powered chatbots can provide 24/7 support, answering customer inquiries and resolving issues efficiently. These chatbots can analyze customer data to offer personalized financial advice and recommendations, enhancing the overall customer experience. 
  • Algorithmic trading and robo-advisors: Algorithmic trading and robo-advisors are two significant applications of AI in fintech. Algorithmic trading uses AI algorithms to analyze market trends and make trading decisions, reducing the need for human intervention. Robo-advisors, like Wealthfront, use AI to manage investment portfolios, providing personalized financial advice and investment strategies to customers. 
  • Anti-Money Laundering (AML) compliance: AI is also used to enhance AML compliance in the fintech industry. AI-powered systems can analyze large volumes of data to identify and prevent money laundering activities. This includes monitoring transactions for suspicious patterns and flagging potential illegal activities.

3AI in Manufacturing

Manufacturing is one of the prime sectors that has undergone the most significant transformation as a result of AI. It was one of the first industries to embrace automation and robots.

According to a McKinsey analysis, AI may raise industrial forecasting accuracy by 10% to 20%, which equates to a 5% decrease in inventory costs and a 2% to 3% rise in revenues. Sincerely, the use of AI in the manufacturing industry is revolutionary.

A few compelling applications of AI in manufacturing include:

  • Defect detection: AI-powered vision systems can recognize defects in products, enabling manufacturers to pull products from the production line and fix issues before they are shipped to customers. This technology is particularly useful in industries such as semiconductor manufacturing, where quality control is crucial. 
  • Real-time monitoring and analysis: AI systems can analyze data from sensors and machinery on the factory floor to anticipate potential failures and breakdowns. This enables manufacturers to schedule maintenance and repairs in advance, reducing downtime and optimizing logistical planning. For example, companies like Pepsi and Colgate have implemented AI-driven predictive maintenance systems to detect issues with manufacturing machinery before they cause breakdowns. 
  • Design and manufacturing: AI algorithms can be used in generative design to create new product designs based on specific parameters. Designers input parameters like materials, size, weight, manufacturing methods, and cost constraints, and the algorithms generate blueprints and instructions. This technology accelerates product development processes and fosters design innovation, particularly in the context of new additive manufacturing processes. 
  • Factory automation: AI can be used to automate tasks in manufacturing, such as quality control inspections using computer vision-enabled cameras. Collaborative robots (cobots) are designed to work alongside humans safely, performing tasks like picking components, manufacturing operations, and quality control inspections. For instance, cobots are used by companies like BMW and Ford for tasks like gluing and welding, and by consumer goods manufacturers like Procter & Gamble for tasks like assembling and packaging products.
  • Product development: AI-based product development uses augmented reality AR & VR technologies to test multiple product models before production. This technology simplifies maintenance and bug fixing, allowing manufacturers to enhance and accelerate their innovation. 
  • Warehouse management: AI can be used to optimize warehouse operations by tracking inventory levels and automating purchase requests. AI-based systems can handle the complexity of multiple order types from various sales channels, streamlining and improving order and inventory management.

4AI in Agriculture

Since the invention of agriculture, quite a few technical revolutions have affected farming. However, the recent growth in population has created some significant hurdles for the industry, as the need for global food supply is anticipated to rise by 50% by 2050.

Well in this scenario, AI-driven solutions can boost productivity by enhancing crop quantity and quality thus enabling a quicker go-to-market. These advantages will help the AI market for agriculture grow at a CAGR of 21.52% from 2021 to 2026, with a potential valuation of $2.468 billion.

Some of the most prominent applications of AI in Agriculture:

  • Crop Disease Detection: Crop disease detection is a crucial application of AI in agriculture. AI algorithms can analyze data from various sources such as satellite imaging, drones, and soil sensors to identify early signs of disease in crops. For instance, AI can analyze the color, texture, and shape of leaves to detect signs of fungal infections or bacterial diseases. This technology can significantly reduce the use of pesticides and other chemicals.
  • Automated Weed Control Systems: AI can analyze images of fields to identify areas with high weed concentrations and then direct automated sprayers to target those areas specifically. These systems use computer vision and machine learning to identify and target specific weeds, reducing the need for manual labor and herbicides.
  • Livestock Health Monitoring: Livestock health monitoring is an essential application of AI in agriculture, particularly for large-scale farming operations. AI algorithms can analyze data from sensors, cameras, and other sources to monitor the health and behavior of livestock. This includes monitoring vital signs such as temperature, heart rate, and respiration rate, as well as detecting signs of stress or illness. 
  • Predictive Analysis for Crop Yield: AI can enable farmers to make informed decisions about planting, irrigation, and harvesting, optimizing their operations for maximum yields. AI-powered predictive analysis can also help farmers identify areas where crops are under stress, allowing them to take corrective action to improve yields. 
  • Precise Irrigation System: Precise irrigation systems are a key application of AI in agriculture, particularly in areas where water is scarce. AI algorithms can analyze data from sensors, weather stations, and other sources to optimize irrigation schedules, ensuring that crops receive the right amount of water at the right time. This approach reduces water waste and minimizes the risk of overwatering, which can lead to crop damage and disease. 
  • Drone-Assisted Aerial Surveillance: Drones equipped with AI-powered sensors can capture high-resolution images of fields, which are then analyzed to identify areas of stress or disease. This enables farmers to take targeted action to address these issues, reducing the need for manual labor and chemical pesticides. 
  • Supply Chain and Demand Forecasting: AI algorithms can analyze data from various sources such as weather forecasts, market trends, and supply chain logistics to predict demand and optimize supply. This enables farmers to make informed decisions about planting, harvesting, and marketing their crops. AI-powered supply chain forecasting can also help farmers identify areas where they can improve their operations, such as optimizing transportation routes or streamlining inventory management.

AI App Development

5AI in Gaming

Needless to say, AI plays a crucial role in the gaming sector. Due to the growth of AR/VR technology and the requirement from sectors where simulations have become a necessity, its impact has multiplied further.

Gaming companies look for innovative technical solutions to improve their products and customers’ experiences. Other AI-powered technologies are being heavily incorporated into the sector as a result. Investment in AI will be further stimulated by the gaming market’s projected expansion to a valuation of $398.95 billion by 2026, at a CAGR of 11.0% from 2021 to 2026. 

Explore the range of AI use cases in the gaming industry:

  • Non Player Characters (NPCs): AI has revolutionized the development of non-player characters (NPCs) in video games. Advanced AI NPCs are designed to engage in dynamic conversations, understand human emotions, and respond accordingly. This technology has transformed the way NPCs interact with players, making the gaming experience more immersive and realistic. 
  • Image Enhancements and AI Upscaling: AI can be used to enhance graphics and upscale image quality in games. For example, NVIDIA successfully used AI to improve the graphics in Cyberpunk 2077.
  • Procedural Content Generation (PCG): AI can generate procedural content, such as levels, enemies, or items, to create a more dynamic and varied gaming experience. This technology can help reduce development time and costs while increasing the replayability of games. 
  • Player-Experience Modeling (PEM): AI can analyze player behavior and predict their preferences, allowing game developers to tailor the game experience to individual players. This can include adjusting game complexity, suggesting tips, and providing personalized recommendations. 
  • Data-Mining and Real-Time Analytics: AI can be used to analyze player data in real-time, providing insights into player behavior and preferences. This can help game developers optimize game design, improve player engagement, and identify potential issues. 
  • Player Sentiment Analysis: AI can analyze player sentiment and feedback, helping game developers understand player opinions and preferences. This can inform game development decisions and improve overall player satisfaction. 

6AI in Real Estate

Artificial Intelligence in the real estate sector is creating new opportunities for brokers, clients, and agents alike. Brokers are getting more strategic in their approach, agents are becoming more successful and efficient, and consumers feel more empowered than ever. Brokers and agents can more effectively meet the needs of those wishing to buy, rent, or sell their properties because of AI-powered bots.

Ai in real estate

Discover the various use cases of AI in real estate:

  • Listing Descriptions Generation: AI can generate detailed and personalized property listings by analyzing vast datasets, including property attributes, user preferences, and market trends. This enables real estate agents to create comprehensive and engaging descriptions that cater to individual buyer needs. 
  • Virtual Tour: AI-powered virtual assistants and chatbots provide immersive 3D property views, allowing prospective tenants to explore homes virtually. This technology includes zoom-in tools, spoken notes, and panoramic views. 
  • Lead Generation: AI can identify genuine leads by analyzing customer data and inspection patterns. This helps real estate agents focus on serious buyers and automate interactions with casual browsers, streamlining their workflow and improving conversion rates. 
  • Property Management: AI technologies automate various tasks in property management, such as streamlining accounting processes, ensuring timely rental payments, and preventing tenant disputes. This enhances the efficiency and accuracy of property management, reducing the risk of human errors. 
  • Property Search: AI-driven property search platforms leverage advanced machine learning algorithms to provide personalized property recommendations based on user preferences, budget, location, and essential criteria. 

7AI in Education

The education sector is one of the most influenced sectors by humans, but artificial intelligence has also started to make inroads there. This gradual adoption of AI in education has increased faculty productivity and allowed them to focus more on students than on administrative or office labor. 

AI Applications in Education is transforming the industry: 

  • Intelligent tutoring systems: AI-based tutoring systems are designed to provide personalized support to students. These systems use machine learning algorithms to analyze student data and adapt to their learning styles and needs.
  • Automated grading and assessment: AI-powered grading systems can significantly reduce the time spent by teachers on evaluating assignments and tests. These systems use computer vision and natural language processing. This automation can save teachers up to 90% of their grading time, allowing them to focus on more important tasks
  • Interactive and learning games: AI-driven educational games can enhance the learning experience by incorporating interactive elements, such as gamification and virtual reality. These games can engage students more effectively and make learning more enjoyable. 
  • Personalized learning: AI-based platforms can collect and analyze student data to create personalized learning pathways. These platforms can adapt to individual students’ progress in real-time, providing tailored content and exercises to meet their unique needs. This approach can lead to improved learning outcomes and increased student motivation. 
  • Task automation: AI can automate various administrative tasks, such as scheduling, grading, and data analysis, freeing up teachers to focus on more important aspects of their work. This automation can also help reduce errors and increase efficiency, making it a valuable tool for educational institutions. 
  • Smart Content Creation: AI can assist in the creation of high-quality educational content by generating interactive and engaging materials. These tools can help teachers develop customized curricula and lesson plans, making learning more effective and enjoyable for students. 

8AI in Retail & eCommerce

Retail and eCommerce stand out as the sectors where AI’s use is most obvious and observable, making its effects feel real to most end users. Retail businesses always search for methods to identify patterns in consumer behavior because of the fierce rivalry in the market. By doing this, they may match their business strategy with customer demands and beat rivals.

Use cases of AI across departments within a retail company: 

Merchandising: AI in merchandising helps retailers optimize product offerings and inventory management. It uses machine learning algorithms to analyze customer behavior, market trends, and competitor pricing to make informed decisions about product assortment and inventory levels. This includes:

  • Personalized Product Recommendations
  • Dynamic Pricing

Store operations management: AI in-store operations management streamlines processes, improves efficiency, and enhances customer experiences. Key use cases include:

  • Chatbots and Virtual Assistants
  • Predictive Maintenance

Production management: AI in production management optimizes production processes, ensuring timely and cost-effective delivery of products. Key use cases include:

  • Predictive Analytics
  • Quality Control

Inventory and supply chain:  AI in inventory and supply chain management optimizes inventory levels, reduces waste, and improves delivery times. Key use cases include:

  • Inventory Optimization
  • Predictive Inventory Management

Logistics department: AI in logistics management streamlines transportation and delivery processes, improving efficiency and reducing costs. Key use cases include:

  • Route Optimization
  • Predictive Logistics

Marketing and advertising: AI in marketing and advertising enhances customer engagement, personalization, and targeting. Key use cases include:

  • Personalized Marketing
  • Predictive Analytics

Also Read: How Much Does Artificial Intelligence Cost in 2024?

How Blocktunix Can Help You Utilize AI To Its Fullest?

No matter which industry you belong to, AI technology can prove to be a game changer for your business. If we take a look at the year 2023, AI has marked its presence in almost every sector as shown in the pie chart.

This percentage is further going to increase in the year 2023 and beyond. But with growing market competition, it will not be easy to make your presence in the global AI community. You are required to do something different. This is where Blocktunix can help you. As a seasoned AI development company. Our skilled AI developers possess a deep understanding of various AI algorithms and tools. We’ll help you design and build a custom AI solution that seamlessly integrates with your existing infrastructure and data systems. 

Therefore, by partnering with us you gain access to a team of AI specialists, project managers, and designers who are dedicated to your success by keeping your vision and budget in mind. 

So, what next?

Get in touch now and know how Blocktunix can help you climb the ladder of success swiftly!

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