Published On: 3 August 2019By Categories: Tags: 9.2 min read

Artificial Intelligence Applications

Every time you click on a new movie or TV series on Netflix, conduct a Google Search, ask Siri for help or browse Amazon’s recommended products, you’re subtly being helped by artificial intelligence in the background.

According to Statista, the global artificial intelligence market has grown by 154% in 2019 and has reached revenues of $9.5bn worldwide. Businesses that use artificial intelligence (AI) and related technologies will gain an unprecedented competitive advantage over businesses that lack behind. Given the complicated nature of AI, many organisations still lack the foundational knowledge to create value from AI on an enterprise level. For example, knowing exactly where their AI opportunities lie, how to implement it, and how to define a strategy for extracting value from these opportunities.

Although AI was first introduced as a concept since the 1950s, the technology has only recently begun to find real-world applications (such as digital personal assistants). Tech giants and start-ups have exponentially increased their investments in artificial intelligence applications. Artificial intelligence opportunities exist in every sector and across many industries.

There are three factors that have contributed heavily to the advancement of AI technologies, namely:

  • The vast amount of data generated from e-commerce, Internet-of-Things, science, governments, social media, businesses, etc.
  • Greater computer processing power and rise of cloud-based services, which help run complicated machine learning algorithms.
  • Improvement in machine learning algorithms due to the availability of large amounts of big data.

In this article we will be discussing a few applications of artificial intelligence in various industries that demonstrate how omnipresent the technology has become in our everyday lives.

(If you don’t know what Artificial Intelligence is, check out our introductory article to learn about the basics of AI)

Enterprise-level Artificial Intelligence Applications

Artificial intelligence is rapidly entering a new phase within enterprises. An increasing number of businesses are turning their trial AI programs into enterprise-wide implementations. This movement is largely based on the early successes of pilot programs, as well as the continuing advancements in artificial intelligence software and hardware. Heineken, a 150 year old company is using AI to drive marketing decisions and initiatives by utilising the vast amount of data they collect. By using AI, they also improve operations and customer service. From managing global supply chains to optimising delivery routes, artificial intelligence is helping organisations of all sizes and in all sectors to improve productivity and the triple bottom line at every stage of the business lifecycle from sourcing material to sales and accounting to customer service. It’s allowing companies optimise their design, production and delivery capabilities like never before.

CKC Enterprise Level AI Applications
Figure 1: Business functions in which AI has been adopted, by industry and shown in percentages. Source

Because there exists many different artificial intelligence applications, AI can be split into five different categories to help identify what kind of AI should be used. These five categories are listed as follows:

  • Reasoning: The ability to solve problems through logical deduction. e.g. financial asset management, legal assessment, financial application processing, autonomous weapons systems, games. This kind of AI should be able to look at historic data and come up with its own reasoning to solve an issue without pre-programmed rules. (this is a form of deep learning)
  • Knowledge: The ability to present knowledge about the world. e.g. financial market trading, purchase prediction, fraud prevention, drug creation, medical diagnosis, media recommendation
  • Planning: The ability to set and achieve goals. e.g. inventory management, demand forecasting, predictive maintenance, physical and digital network optimisation, navigation, scheduling, logistics
  • Communication: The ability to understand spoken and written language. e.g. real-time translation of spoken and written languages, real-time transcription, intelligent assistants, voice control
  • Perception: The ability to infer things about the world via sounds, images, and other sensory inputs. e.g. medical diagnosis, autonomous vehicles, surveillance
CKC Five Application Categories of AI
Figure 2: Five different categories of artificial intelligence applications

In order to showcase the range of sectors that AI is affecting, here are 7 different industries and their AI applications:

CKC AI Industry Applications
Figure 3: Seven different industries for Artificial intelligence applications


  • Chatbots have become an essential aspect to any bank’s online service offering. Powered by natural language processing, chatbots can serve banking customers quickly and efficiently by answering common inquiries and providing information promptly. Chatbots help reduce the workload on customer service representatives as they help filter non-complex questions, if a customer has a complicated issue, they can be soon connected with a human.
  • Fraud detection is one of the most useful application of artificial intelligence in financial services. By analysing data, AI can identify trends, outliers, and identify fraudulent transaction and flag them for manual review. Mastercard uses Decision Intelligence technology to analyse various data points to improve real-time approval accuracy, reduce errors, and improve their overall service offering.
  • Investment companies such as Nomura Securities and Aidya use artificial intelligence algorithms to conduct trading autonomously and robot-traders to perform high frequency trading to increase their profit margins.
  • Financial companies and organisations use AI-based natural language processing tools to analyse brand perception from social media platforms and provide an actionable strategy.
  • Personal banking apps offer personalised financial advice and help individuals achieve their financial goals. These intelligent systems track income, recurring expenses, and spending habits to come up with an optimised plan and financial tips.


  • Machine learning personalises promotions to match shopper’s preferences and purchasing habits.
  • Facial recognition software enables virtual agents to greet shoppers personally – improving the service experience of the shopper.
  • Companies like Sephora uses artificial intelligence to make finding makeup easier. Color IQ scans a customer’s face and provides a personalised recommendation for foundation and concealer shades, while Lip IQ does the same except for lipstick. This improves the shopper experience by reducing the amount of frustration caused by finding the right shade of makeup through trial and error.
  • Amazon eliminates the need for cashiers with AI. In an Amazon Go store, a customer can simply walk into the store, take what they desire from the shelves and walk out without going through a cashier. The store uses computer vision, deep learning algorithms and sensors to track what customers purchase and their Amazon account is charged when they leave. These kind of artificial intelligence applications are minor but they create a quick and seamless shopping experience so customers aren’t frustrated waiting in line, thus improving quality of life.
  • Olay customers can get personalised skincare treatment without having to visit a dermatologist. With Olay’s Skin Advisor, customers take a selfie of their face and the app uses artificial intelligence to tell the true age of their skin. The app evaluates skin health and makes recommendations for problem areas with personalised skin care regimen recommendations.


  • NuMedii is a biopharma company that developed a platform called Artificial Intelligence for Drug Discovery (AIDD) that uses big data and AI to identify the link between diseases and drugs at the systems level.
  • GNS Healthcare uses ML algorithms to match patients with the most effective treatments for them.
  • Google worked with the University of California, Stanford University, and the University of Chicago to generate an artificial intelligence system that predicts the outcomes of hospital visits. This acts as a way to prevent readmissions and lessen the amount of time patients are kept in hospitals.
  • AI-enabled assistants are helping doctors free up their schedules, reducing time and cost by streamlining processes and opening up new possibilities for the industry. In addition, AI-powered technology helps pathologists in analysing tissue samples and making more accurate diagnoses.
  • Medecision developed an algorithm that detects 8 different variables in diabetes patients to determine if treatment is necessary.


  • Tesla created TeslaBot, an intelligent virtual assistant that is integrated with Tesla cars. It allows users to interact with their car from their phone or desktop.
  • Ericsson predicts that 5G technology will vastly improve vehicle-to-vehicle communication where sensors will be implemented in airport runways, roads, and railways.
  • Uber AI labs is working on developing self-driving vehicles. These self-driving cars are aimed at replacing drivers to optimise their service offering for the customer. Self-driving cars are leading examples of how artificial intelligence is impacting the automotive industry. A large portion of autonomous vehicles are connected through the Internet-of-Things and thus are able to share important data with each other.
  • nuTonomoy’s created nuCore, which allows for flexible and human-like vehicle handling. The software enables vehicles to navigate even the most complicated traffic situations. The company’s aim is to create autonomous cars that can ensure safer roads, less traffic and reduced environmental impact.

Data Security

  • AI-based antivirus software in many cases uses anomaly detection to study program behaviour. Antivirus systems using AI focus on detecting unusual behaviour generated by programs rather than matching signatures of known malware.
  • AI-based cybersecurity systems can be used to detect a pattern of malicious behaviour in users in order to safeguard against them. In doing so, they can detect and prevent network or data breaches before they happen.


  • Netflix integrates artificial intelligence and machine learning into their recommendation system. Every time you press play and spend time watching a TV series or a movie, Netflix is collecting data that informs the algorithm and refreshes it. The more you watch the more up to date the algorithm is.
  • Movie production involves multiple steps such as creating storyboards, screenwriting, location scouting, budgeting, scheduling, recording, edits, etc. These procedures are often time-consuming and complicated. AI can offer a platform that can automate tasks such as breaking down scripts, storyboarding, generating shot-lists, creating schedules, and managing movie budgets.
  • Artificial Intelligence is commonly used in games to control non-player characters (NPCs). NPCs react to a player’s movement Pathfinding is a common use for artificial intelligence in strategy games. Pathfinding can be described as a method of determining how to get an NPC from one point to another, while taking into consideration the obstacles and terrain. Personalisation is one of the more important key business drivers in the entertainment industry today. People want personalised content that can cater to their preferences. Whether its music, games or movies, services are expected to deliver content that suits a consumer’s personal taste. AI is able to achieve this by viewing patterns in consumer data and in turn, personalise content for every user.


  • One primary concern of manufacturing is downtime. AI technology helps keep things running in a more streamlined fashion. By implementing preventive maintenance AI, machine parts can be monitored constantly. Software can send signals, data and alerts to specialists if there are any problems that need adjusting.
  • With human error taking place on the manufacturing plant, a step towards artificial intelligence means fewer humans have to carry out dangerous and overly tough work. As robots replace humans and perform repetitive and necessary activities, the number of accidents will decrease. 

Therefore, instead of asking, “Will automation take our jobs?”. We should ask, “How can I prepare myself for the automation revolution?” as it is inevitable.

Leave A Comment

Share this story to your favorite platform!