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AI: Demystifying the Types and Real-World Applications

Discover how AI revolutionizes industries. Explore types and use cases for improved efficiency and customer experience.

Integrating cutting-edge technologies, new digital tools or using tech for good is pivotal to gaining an edge in today’s business world. Among those technologies exists Artificial Intelligence (AI), and it’s a transformative force!

P.S. This isn't AI -- it's just beautiful!

This post is about the 4 basic types of AI technology and applications in different industries. Throughout, we’ll only touch the surface of the impact AI has on efficiency, customer experience, and even decision-making.

Let’s get to it!

The Different Types of Artificial Intelligence

Narrow or Weak AI

Definition: Narrow AI is designed for specific tasks and operates within a limited domain.


  1. Virtual Assistant “Smart Speakers” (Siri, Alexa): These AI systems excel at natural language processing and voice recognition. AKA: smart devices and virtual assistants.

General or Strong AI

Definition: General AI possesses human-like cognitive abilities and can perform a wide range of tasks at human levels of intelligence.

“True” General AI or Strong AI isn’t here yet. At least, to my knowledge, there aren’t products on the market that use it. When that happens, leave a comment if you find something! Nonetheless, Narrow AI applications generally can perform specific tasks or functions at levels surpassing human capabilities. These AI systems are designed for specialized tasks and excel because they process vast amounts of data and perform repetitive tasks with high precision and speed.

P.S. AI isn't calculating world domination.

For instance, AI applications in fields like image recognition, natural language processing, and specific data analytics tasks have demonstrated the ability to outperform humans.

Machine Learning

Definition: Machine learning (ML) focuses on developing algorithms that allow computers to learn from and make predictions or more data-driven decisions.


  1. Healthcare Diagnosis: ML models analyze patient data to assist in detecting disease and predicting possible medical outcomes. For instance, Merative (previously known as IBM Watson) for Oncology uses machine learning to analyze large volumes of medical papers, clinical trial data, and patient records to provide oncologists with treatment recommendations for cancer patients. This helps health professionals make more informed decisions about personalized cancer treatments.
  2. E-commerce Recommendations: Some online retailers, like Amazon, use ML to provide personalized product suggestions to customers.

Deep Learning

Definition: Deep learning employs neural networks with many layers to analyze and make decisions about complex data.


  1. Autonomous Vehicles: Deep learning technology is pivotal for vehicle object detection and recognition. Companies like NVIDIA develop deep neural networks that enable vehicles to identify and classify objects on the road, such as pedestrians, cyclists, and other vehicles. This technology enhances the ability to navigate complex traffic scenarios safely.
  2. Speech Recognition: Deep learning is essential for automatic speech recognition systems in healthcare. For instance, deep learning algorithms convert spoken medical notes and patient records into text in medical transcription services, increasing efficiency and reducing the risk of errors. Companies like M*Modal utilize deep learning for accurate and timely medical transcription.

How AI is Used in Different Industries

AI is used in the automotive industry.

Let’s see how these AI types are applied in various industries. Please note that while some of the below sources directly support the examples, others provide broader insights into the respective industry’s use of AI technologies.

Automotive Industry

  • Machine Learning: In this example, AI helps with quality control and defect detection. Manufacturers like BMW employ computer vision systems powered by AI to inspect each car for imperfections during production. These AI systems can quickly identify and flag defects in paint, welding, or other critical components. This ensures vehicles meet strict quality standards before they head to the dealer.
  • Narrow AI: In the auto industry, Narrow AI is employed for advanced driver assistance systems (ADAS). Think adaptive cruise control and lane-keeping assistance. Various automakers, including Ford, use these AI-powered systems. For instance, Ford’s Co-Pilot360 technology includes features like adaptive cruise control with stop-and-go, which uses AI algorithms to maintain a safe following distance from the vehicle ahead and can even bring the car to a complete stop in traffic. This technology enhances safety and driver convenience.

AI in Healthcare

AI is helping the healthcare industry and not just with scans. #healthtech
  • Machine Learning: You’ll see it used in certain health facilities to help improve predictive analytics in the early detection of sepsis. Also, hospitals use machine learning to monitor patient vital signs and laboratory results continually. Plus, these algorithms can detect subtle changes that may indicate the onset of sepsis, allowing for early intervention and potentially saving lives.
  • Deep Learning: Deep learning plays a pivotal role in medical image analysis, particularly in radiology. For example, companies like Siemens Healthineers utilize deep learning algorithms to enhance the accuracy of medical image interpretation. These algorithms can detect subtle anomalies in X-rays, MRIs, and CT scans, aiding radiologists in diagnosing conditions such as fractures, tumors, and neurological disorders. This technology leads to more precise diagnoses and improved patient care.


  • Machine Learning: The retail industry loves dynamic pricing optimization. For example, airlines like Delta use ML to adjust ticket prices based on various factors, including seat availability, booking patterns, and weather conditions that might affect travel demand. This dynamic pricing strategy maximizes revenue while ensuring competitive pricing. (Pssst: the article is also an excellent example of why I’m writing a post like this because confusion abounds!)
  • Narrow AI: Chatbots as virtual shopping assistants are common in retail. Retailers such as Sephora have integrated chatbots into their digital platforms. Sephora’s chatbot, accessible via its mobile app, assists customers in selecting beauty products, providing personalized recommendations based on skin type, preferences, and prior purchases. Overall, this AI-driven virtual assistant enhances the shopping experience, making product selection more convenient and tailored to individual needs.


Me + AI + fintech
  • Machine Learning: A good example is algorithmic trading. Investment firms like Citadel Securities utilize ML algorithms to analyze vast datasets of financial market information. These algorithms can also identify subtle market trends and patterns, enabling automated trading systems to execute high-frequency trades precisely and quickly. This technology enhances trading strategies and optimizes investment portfolios.
  • Deep Learning: In finance, deep learning helps with fraud detection. For example, credit card companies like Visa leverage deep learning algorithms to detect real-time fraudulent transactions. These algorithms analyze numerous transaction parameters, including location, transaction history, and spending patterns. The system can immediately flag any transaction for further investigation or block it if anything appears suspicious. This technology helps protect customers and financial institutions from fraud.


  • Machine Learning: ML helps perform predictive maintenance. For example, aerospace companies like Boeing use machine learning algorithms to monitor the health of aircraft engines. These algorithms analyze sensor data, like temperature, pressure, and vibrations, to predict when maintenance is needed. By proactively addressing potential issues, maintenance is scheduled more efficiently to reduce downtime and ensure safety and reliability.
  • Narrow AI: Another application of Narrow AI in manufacturing is supply chain management. AI-driven supply chain systems optimize inventory levels, shipping, and production schedules. For instance, companies like Toyota use AI to improve supply chain efficiency and reduce costs. Below is how they plan to do part of that in-house.
Will Toyota’s future logistics be AI-based?

AI in Marketing

  • Machine Learning: Can help make content for web strategies. For example, ML methods can assist with natural language processing tasks like generating text and optimizing blog content. The kicker, these AI tools, for the most part, don’t require complex algorithms. Instead, they are meant to aid people, not replace us. P.S. If you’re curious and want an example, here are my thoughts on blogging with an AI tool.
  • Narrow AI: This type of artificial intelligence can be harnessed for content generation. Furthermore, content marketing platforms like Clearscope utilize AI to analyze vast amounts of online content related to specific topics. These algorithms can identify gaps in existing content, recommend better keywords, and generate content briefs for writers. This streamlines content creation and ensures that content is comprehensive, informative, and optimized for search engines.

The Takeaway: Businesses Should Embrace AI Opportunities

In its various forms, AI plays a crucial role in multiple industries. It enhances efficiency, customer experience, and decision-making. Also, as we can see, AI’s impact is profound, from autonomous vehicles and disease diagnosis to personalized shopping experiences in retail.

Embracing AI can give organizations a competitive advantage, deliver exceptional customer value, and help them navigate the complexities of the digital age. As we continually learn and AI continues to evolve, its role in business ops will become even more prominent, offering endless possibilities for innovation and growth.

Ultimately, businesses can position themselves for success in today’s competitive landscape by recognizing and harnessing AI’s potential.

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