What Is Artificial Intelligence (AI)? Examples & Use Cases

Artificial intelligence (AI) has evolved from being a buzzword to a pivotal tool in business, fundamentally changing how companies approach marketing and customer engagement.

Recent studies indicate that nearly 90% of business leaders are planning to increase their investments in AI and machine learning (ML), particularly within their marketing strategies.

So, what’s driving this shift? The power of AI lies in its ability to analyze behaviors and precisely target customer segments. It enables businesses to make more informed decisions, tailor strategies to individual needs, and gain a deeper understanding of their customers.

In this article, we’ll delve into the multifaceted roles AI plays in enhancing key business areas, including marketing, operations, product development, human resources, customer support, and security.

AI’s capacity to streamline processes, boost efficiency, and generate actionable insights ensures that every interaction and decision is more impactful and effective.

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is the branch of computer science focused on creating systems that can perform tasks that normally require human intelligence. These tasks include understanding spoken language, recognizing images, making decisions, and even playing games.
Instead of just following a fixed set of rules, AI systems learn from the data they process. They use algorithms to analyze patterns and adjust their behavior based on experience, a process known as machine learning.

Artificial Intelligence is not a single technology but a collection of different techniques and methods. Some of these include machine learning, deep learning, natural language processing (NLP), and robotics. Each of these areas contributes to making computers smarter and more useful.

For instance, when you ask your smartphone for directions or use an online service that recommends movies, AI is working in the background to provide accurate and helpful responses. Technologies like deep learning enable these systems to handle complex tasks by responding to the way our brains work.

While the idea of AI may seem advanced, at its core, it is about enhancing our everyday tools to make life more convenient and efficient.

Also Read: Quantum Computing for AI

History of AI

Artificial Intelligence (AI) began as a bold idea in the mid-20th century and forms a key part of the foundation and history of artificial intelligence. In 1950, Alan Turing, often considered one of the inventors of artificial intelligence, proposed that machines could simulate human thought in his seminal paper, “Computing Machinery and Intelligence.” Turing introduced the Turing Test—a method to evaluate whether a machine could exhibit behavior indistinguishable from that of a human. In 1956, the Dartmouth Conference, organized by John McCarthy and colleagues, officially established AI as a research field and coined the term “Artificial Intelligence.”

During the 1960s and 1970s, researchers developed early AI programs that focused on problem-solving, language processing, and simulating basic human interactions. These pioneering efforts laid the groundwork for future advancements, although progress slowed during the 1970s and 1980s, a period often referred to as the “AI Winter” due to limited computing power and unrealistic expectations.

The resurgence began in the 1990s with the advent of machine learning, enabling systems to improve performance by learning from data. Today, breakthroughs in deep learning and big data analytics have transformed AI, driving innovations in image recognition, natural language processing, and autonomous vehicles—reshaping industries worldwide.

How does AI Work?

Understanding how AI works can be compared to understanding how a brain learns. AI systems start with data, which is then used to train models that can make predictions or decisions. Here is a simple breakdown of the key steps involved in how AI works:

• Data Collection

The first step in any AI system is gathering data. This data can come from various sources, such as customer records, sensor readings, images, or text. The quality and amount of data collected are very important because the AI’s performance depends on it.

• Data Processing and Cleaning

Raw data is often messy and incomplete. Before the AI can use it, the data must be cleaned and organized. This involves removing errors, filling in missing values, and sometimes converting data into a form that is easier for the computer to understand.

• Model Training

Once the data is ready, it is used to train an AI model. Training involves feeding data into a computer program and allowing it to learn patterns from the data. This process is similar to how a student learns from examples and practice tests. The model adjusts its internal settings until it can make accurate predictions based on the input data.

• Algorithm Selection

Different tasks require different methods. For example, recognizing faces in photos might use a type of AI called deep learning, while predicting sales trends might use traditional machine learning algorithms. The choice of algorithm is crucial as it determines how well the AI can learn from the data.

• Testing and Validation

After training, the AI model is tested on new data to see how well it performs. This step is important to ensure that the model is not just memorizing the training data but can apply what it has learned to new situations. If the model does not perform well, it may need more data or a different algorithm.

• Deployment

Once the AI model is tested and refined, it is deployed in a real-world setting. This might mean integrating the AI into a mobile app, a website, or even a piece of machinery. During deployment, the AI continues to receive data and may be updated to improve its performance over time.

• Feedback and Improvement

AI needs continuous feedback to stay effective. As new data comes in, the model can be retrained or fine-tuned, making it more accurate and useful. This process of continuous learning is what makes AI a powerful tool for solving complex problems.

Also Read: AI Ticketing System

Key Features of Artificial Intelligence

Businesses today leverage AI to transform key operations, improve decision-making, and drive innovation. Let’s break down some of the core AI functions at work:

• Machine Learning (ML)

Machine learning algorithms analyze data, spot patterns, and make predictions. These AI tools help businesses extract valuable insights from diverse data sets, enhancing decision-making processes. While ML models learn independently as they process data, they can also be customized to meet specific business needs.

• NER and Semantic Search

Named Entity Recognition (NER) identifies crucial entities within data, such as locations, brand names, or notable individuals. Semantic search enhances this by providing a contextual understanding of user queries. Together, they process and analyze large volumes of unstructured data, improving search accuracy, automating data tasks, and extracting actionable insights.

• NLP and Sentiment Analysis

Natural Language Processing (NLP) and sentiment analysis allow businesses to understand and interpret human language. These tools are vital for analyzing customer feedback, social media content, and other text data, while also streamlining report generation and automating responses.

Why is Artificial Intelligence Important?

Every year, as the technology of artificial intelligence advances significantly, it is projected to be worth $42 billion by 2023. This means that AI will become more common than ever before and will have the capability of taking over a lot of daily tasks.

The importance of artificial intelligence has been known for quite a long time now. AI is significant because it simply makes our lives easier.

As per the reports, the global revenues from AI for enterprise applications will grow from 1.62 billion in 2018 to 31.25 billion in 2025.

Types of AI

Artificial Intelligence (AI) can be categorized in various ways to better understand its development and applications. Here are the different types of AI:

• Narrow AI (Weak AI)

Narrow AI refers to systems designed to perform specific tasks without possessing consciousness or genuine understanding. These AI systems operate under a limited set of constraints and cannot perform beyond their programmed functions. For example, a language translation program can convert text from one language to another but cannot perform unrelated tasks like image recognition. Narrow AI is prevalent in many applications today, such as virtual personal assistants and recommendation systems.

• General AI (Strong AI)

General AI, also known as Strong AI, refers to systems that possess the ability to understand, learn, and apply knowledge across a broad range of tasks, similar to human intelligence. These systems can reason, solve problems, and adapt to new situations without human intervention. For instance, a General AI could learn to play a musical instrument, solve mathematical problems, and understand literature, all without being specifically programmed for each task. As of now, General AI remains a concept under research and development, with no existing systems achieving this level of intelligence.

• Superintelligent AI

Superintelligent AI refers to systems that surpass human intelligence across all domains. These AI systems would not only perform tasks more efficiently than humans but also possess superior problem-solving abilities, creativity, and emotional intelligence. The concept of Superintelligent AI raises significant ethical and existential questions, as such systems could potentially make decisions beyond human comprehension or control. Currently, Superintelligent AI is a theoretical concept and does not exist in practice.

Based on Functionalities

Reactive Machines

Reactive Machines are the most basic type of AI systems that can respond to specific inputs with predefined outputs. These systems do not have memory or the ability to learn from past experiences. They operate solely based on the current data available to them.

For example, a chess-playing AI that analyzes the current board position and selects the best possible move without considering previous games is a Reactive Machine. While limited in capability, Reactive Machines can perform their designated tasks efficiently within their specific domains.

Limited Memory

Limited Memory AI systems can use past experiences to inform current decisions. They have the capability to learn from historical data to improve their performance over time. For instance, self-driving cars utilize Limited Memory AI to observe the speed and direction of other vehicles, helping them make better driving decisions. However, the memory in these systems is not permanent; it is transient and used only for a specific task at hand.

Theory of Mind

Theory of Mind AI refers to systems that can understand emotions, beliefs, and intentions, enabling them to interact more effectively with humans. This type of AI aims to comprehend human mental states and respond accordingly, facilitating more natural and effective interactions. For example, a customer service AI with Theory of Mind capabilities could detect a user’s frustration and adjust its responses to be more empathetic. Developing such AI systems is a significant challenge and is currently an area of active research.

Self-aware AI

Self-aware AI represents the most advanced form of AI, where systems possess consciousness and self-awareness. These AI systems would have an understanding of their own existence, internal states, and the ability to introspect. They could potentially make independent decisions and exhibit behaviors driven by their own desires or goals. The development of Self-aware AI raises profound ethical and philosophical questions, and such systems remain purely theoretical at this stage.

Based on Technologies

Machine Learning

Machine Learning is a subset of AI that enables systems to learn and improve from experience without explicit programming. It involves algorithms that identify patterns in data and make decisions based on those patterns. For example, a spam email filter uses Machine Learning to recognize and block unwanted emails by learning from examples of spam and non-spam messages. Machine Learning is widely used in various applications, including recommendation systems, fraud detection, and predictive analytics.

Natural Language Processing (NLP)

Natural Language Processing solutions allow AI systems to understand, interpret, and generate human language. NLP enables interactions between humans and machines using natural language, making technology more accessible. For instance, virtual assistants like Siri or Alexa use NLP to understand voice commands and provide appropriate responses. NLP encompasses various tasks, including language translation, sentiment analysis, and speech recognition.

Deep Learning

Deep Learning is a specialized area of Machine Learning that uses neural networks with multiple layers to model complex patterns in data. It is particularly effective in tasks such as image and speech recognition. For example, Deep Learning algorithms can identify objects in images or transcribe spoken words into text with high accuracy. Deep Learning has been instrumental in advancements like autonomous vehicles and advanced image processing.

Robotics

Robotics involves the design, construction, and operation of robots that can perform tasks traditionally carried out by humans. AI enhances robotics by providing intelligence, enabling robots to perform complex tasks autonomously. For example, robotic vacuum cleaners use AI to navigate and clean homes without human intervention. Robotics has applications in manufacturing, healthcare, and space exploration.

Computer Vision

Computer Vision enables AI systems to interpret and process visual information from the world, such as images and videos. It allows machines to understand and analyze visual data, facilitating tasks like facial recognition and object detection. For instance, social media platforms use Computer Vision to automatically tag individuals in photos. This technology is also used in autonomous vehicles to recognize traffic signs and obstacles.

Expert systems

Expert systems are a branch of artificial intelligence designed to emulate the decision-making abilities of human experts. These systems utilize a comprehensive knowledge base and a set of rules to analyze information and provide solutions to complex problems within specific domains. For instance, in the medical field, an expert system can assist in diagnosing diseases by interpreting patient data and suggesting possible treatments. Similarly, in finance, expert systems can evaluate market trends to recommend investment strategies.

Role of AI in Different Industries

With its growing demand, AI technology has proved to be an asset for humans that is programmed to reduce human effort as much as possible in different areas. Some of them are:  

• AI in Medical Sciences

Artificial intelligence has made a significant impact on the medical industry. There have been various machine-learning algorithms and models working efficiently to diagnose critical diseases such as cancer or tumors based on symptoms and health records. AI in healthcare is another efficient step that facilitates providing 24/7 personalized assistance.

• AI in Air Transport

Air transport, as the most systematic mode of transport in the world, needed to optimize its operations. Artificial Intelligence helps in planning the routes, along with flight landing and take-off charts. The technology is also used in many aircraft for navigation maps and taxiing routes, along with checking the entire cockpit panel to ensure the correct working of every component.

• Artificial Intelligence in Gaming

AI has played a crucial role in the gaming industry. This is one sector where AI has made significant strides. Thanks to advancements in technology, bots are always available for gameplay, eliminating the need for a second person. The integration of AI has also enhanced the level of personalization and graphics, elevating the industry to new heights.

• AI in Banking

The role of artificial intelligence in banking has seen a leap in the last few years. It is being used in the banking industry to scale new heights in customer relationship management. The sector is implementing the technology from a ground level with a principal aim of climbing heights in customer-centric approaches, which includes communicating with them.

8 Key Roles of AI in Business

Artificial intelligence is used in a variety of ways in businesses. With an increasingly vast amount of data available today, and the evolving preferences of customers, businesses can no longer rely on traditional business methods. With the introduction of Artificial intelligence technology, a new realm of possibilities has opened up to drive business growth. The role of artificial intelligence in business simply involves intelligent computer software with human-like capabilities. The purpose of AI is to improve customer experience, boost revenue, increase productivity, and drive business growth and transformation.

• Being Relevant amid Competition

  The need to uniquely understand the needs and preferences of consumers is invaluable in today’s business world. AI applications in business help companies be relevant in the fierce competition while simultaneously maintaining growth. 

• Increase in Productivity

  Introducing AI in business results in efficiency and productivity gains. The advanced technology handles tasks at a pace and scale at a level that humans can’t match. At the same time as AI removes tedious tasks, it allows workers to move to high-value tasks. As per reports, 54% of the leaders say that artificial intelligence has increased productivity within their business. While 61% of the executives believe that AI has identified opportunities within their datasets that otherwise have been overlooked.

• Increase in Customer Engagement

  By increasing the role of artificial intelligence in business, companies uniquely understand and engage the customer, automate business processes, and improve revenue while reducing operational expenses.

• Opportunity for Business Model Expansion

  One of the significant benefits of artificial intelligence in business is that it provides a great opportunity for the expansion of business models. As data and analytics are deployed into enterprises, it opens up new opportunities for businesses to participate in different areas. For instance, autonomous vehicle companies with the help of data could identify new revenue streams related to insurance. On the other hand, an insurance company could apply AI to its vast data stores to get into fleet management.

• Improved Quality and Reduction in Human Error

  With AI applications in business, organizations can expect a reduction in errors as well as strong adherence to established standards. When AI and machine learning are integrated with a technology that automates repetitive, rules-based tasks, the combination not only speeds up the process but also reduces errors. The automated routine tasks accomplished by AI also free up the employees to focus on more creative and strategic work.

• Better AI Services to Customers

  In the current times, delivering a positive customer experience has become a price of doing business. Businesses have been trying to embody everything about the consumers, their needs, solutions, and competition in the market. However, AI can do all that and more. It facilitates more customized and personalized interactions between the organizations and each individual customer. 

• Helps in Improving Speed of Business

  As fast as businesses move in this digital age, artificial intelligence helps it in moving even faster. AI in business enables shorter development cycles. Artificial Intelligence cuts the time it takes to move from design to commercialization, and that shortened timeline delivers better. 

• Enhanced Security

  Artificial intelligence can have a profound impact on businesses. By implementing the technology, businesses can enhance their cybersecurity measures. Artificial intelligence in business will facilitate detection and response to threats more quickly and effectively.

Benefits of AI

Businesses being run on data and AI provide them a competitive edge with enhanced decision-making capabilities. It processes and analyzes complex data and gets valuable insights; this way, companies can better understand users’ needs and preferences. It is a robust innovation and helps to reduce operational costs. Here are the 10 benefits of artificial intelligence that describe what is the use of AI in businesses:

• Improved Efficiency

Artificial Intelligence significantly enhances business efficiency and productivity by automating routine tasks, reducing errors, and accelerating data processing. This automation allows employees to focus on strategic activities, leading to improved operational performance. AI’s ability to analyze large datasets enables faster decision-making, further boosting productivity.

• Improved Decision-Making

AI technology can analyze vast amounts of data to provide actionable insights, facilitating data-driven decision-making. This capability enables businesses to respond swiftly to market changes and consumer preferences, thereby enhancing their strategic planning and operational efficiency.

• Cost Reduction

By automating workflows and optimizing processes, AI helps in reducing operational costs. For example, AI can minimize wastage, reduce errors, and streamline supply chain management, leading to significant cost savings.

• Enhanced Customer Experience

AI-powered chatbots and virtual assistants provide instant, personalized responses to customer inquiries, improving customer satisfaction and engagement. These tools can handle multiple customer interactions simultaneously, ensuring prompt service and support.

• Scalability

AI solutions can handle increasing amounts of work without compromising performance, allowing businesses to scale operations efficiently. For example, Repurpose, an eco-friendly brand, utilized AI to automate financial processes, optimize marketing campaigns, and streamline employee evaluations, enabling the company to scale and compete with larger brands.

• Risk Management

AI can predict potential risks by analyzing patterns and anomalies in data, enabling proactive measures to mitigate these risks. This is particularly beneficial in sectors like finance, where AI can detect fraudulent activities and ensure compliance with regulations.

• Talent Management

AI assists in identifying skill gaps, predicting employee performance, and enhancing recruitment processes by analyzing candidate data, thereby improving talent acquisition and retention strategies.

• Real-Time Analytics

AI’s ability to process massive amounts of data and perform real-time data analysis allows companies to make critical decisions and act much faster, ensuring that the company’s competitive position remains strong.

• Driving Innovation

AI enables businesses to create new products and services, giving them a competitive edge. By analyzing market trends and customer behavior, AI helps companies develop offerings that meet evolving demands.

• Enhanced Cybersecurity

AI can detect and respond to security threats in real time, protecting businesses from cyber-attacks and data breaches. By analyzing patterns and anomalies, AI systems can identify vulnerabilities and prevent potential security incidents.

Risks of AI in Business

Businesses have constantly been looking for ways to improve their performance, productivity, and business results with the help of AI services. However, it is important to consider certain risks of artificial intelligence in business and to figure out how to mitigate their impact. 

• Strategy Risk

A mistake while crafting an AI strategy for the business can set the stage for other downstream problems. Unfortunately, the strategy is often in the hands of those who don’t have a thorough understanding of AI capabilities. It includes the risk of uncoordinated policies, lack of executive support, and overestimation of AI potential. In this, the most obvious mitigation strategy is to have an alignment between AI leadership and the executive team on the strategy and risks associated with it.

• Financial Risk

One of the most common risks of artificial intelligence in business but often talked about is the cost of model development. There is an uncertainty factor with AI work which means that in comparison with software development, the process will always be experimental and non-linear. There is a possibility that even after all the expensive development, the end result is not always positive. The financial leadership must understand this and not treat it just as another project. 

• Lack of Trust

The risk of trust emerges when the end-users of the AI-powered applications are hesitant to use or adopt the model. The reason could be the poor performance of the model under certain conditions, lack of help when questions arise, and poor user experience, among others. This leads to a difficult question of what companies should opt for model performance or its adoption by the intended users.

• Compliance and Regulatory Risk

Artificial Intelligence can cause major headaches for use cases or verticals that need to comply with risks or regulations. If no action is taken, the competitor may get too far ahead. In case the action is taken, unforeseen consequences such as fines by regulators must be taken into consideration. In this case, the risk management teams must be well aware of the AI work and its implications. 

Use Cases of Artificial Intelligence in Business

Smart assistants like Siri and Alexa or vacuum clean without your help are just a few examples of how AI is changing lives both at home and in business. From marketing to customer experience, AI is seemingly everywhere and is impacting how business is done. As different businesses are embracing this new technology, check below some common AI in business examples: 

• Helpdesk Chatbots

Chatbots are defined as a computer program that is designed to simulate human conversation. It is important to note that no actual human is behind the chatbot, but the technology mimics real-life conversation. As per the estimates, chatbots can slash operational costs by up to 30%, and 85% of human interactions will be handled without humans in the coming years.

There are three common types of chatbots that can make enterprises efficient:

1. Rule-based: The chatbots provide pre-determined answers to the user’s specific questions.
2. Intelligence: These bots use machine learning to obtain information from the user. They are trained to understand words and phrases and have the capability to improve over time as they understand more questions. 
3. Powered by Artificial Intelligence: They are the combination of the first two and can remember the context of the conversation. These AI-powered chatbots use a combination of machine learning, and natural language processing to understand customers.

• Facial Recognition Technology

In facial recognition technology, your face is captured in a photo, and facial recognition software reads the geometry of your face. The software identifies the facial landmarks and the result is a facial signature which is a mathematical formula that is compared to a database of known faces. The technology is used by many organizations for security reasons.

• Smart Assistants

This is probably one of the most prominent AI applications in business. The most popular smart assistants in the market today include Siri, Alexa, Google Assistant, and Cortana. They offer a variety of skills and services, which enable your voice to:

1. 1. Search for information online
   2. Access personal information
   3. Make phone calls and send messages
   4. Make an appointment
   5. Set up reminders
   6. Place orders for products and have them delivered

• Smart Products

From thermostats to light bulbs, there are smart products in the market that will make your life that is designed to make your life much easier and many are AI-equipped. For instance, early robot vacuums were effective but were not able to take directions from humans but now with AI, users can be more specific about what they want.

• Travel and Delivery

In present times, consumers often want their products and services to be delivered almost instantly. The introduction of AI in business is successfully helping transform the landscape of delivery and travel. For example, Walmart 2020 launched ‘Express Delivery’ allowing consumers to receive the order in two hours or less.

With this kind of delivery option, an AI system, which features resource optimization and vehicle routing, helps in optimizing routes and assigns delivery tips. It also checks the availability of customers who are eligible for two-hour delivery options.

How Artificial Intelligence Will Change the Future of Business?

The latest available data predicts a lucrative near future for the artificial intelligence market. The AI market was projected to grow by 47% in the last two years and this upward trajectory has been forecasted to continue increasing by at least 26% each year until 2025.

The future of AI in business promises simplified experiences and ends the struggle with tedious tasks. There is no doubt that artificial intelligence will alter many industries, with technology taking over jobs and a shift towards more analytic and scientific occupations.

The structure of consumerism will change with new start-ups and applications emerging to take their place. The use of artificial intelligence technology in business has the potential to revolutionize how companies engage with consumers, grow within the market, and compete with each other.

While these advances may not be fully mature in the next 5 to 10 years, they are certainly on their way. With the future of AI set to change the rules of business, companies need to ready themselves for its impact on the workforce and industry to reap its benefits.

Appventurez’s AI Solutions for Businesses (Startups & Enterprises)

AI has revolutionized business operations and will continue to do so for the foreseeable future. In a commercial setting, AI reduces the time spent on repetitive tasks, enhances customer experience, and boosts staff productivity in IT operations and sales.

Understanding how AI can drive business success is crucial, and as an AI app development company, Appventurez is here to help you achieve that. Properly implemented chatbots and AI solutions can significantly increase productivity and efficiency.

Our experienced team at Appventurez specializes in integrating AI technology into business applications. We assist businesses of all sizes in leveraging AI development services to bring their vision to life. Connect with us today to turn your ideas into reality.