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The game-changing benefits of artificial intelligence for business

What is artificial intelligence?

There have been several attempts to define AI by intellectuals, firms, entrepreneurs, etc. Some define artificial intelligence as a machine with human intelligence, some say it is a machine trained to solve problems, and some consider AI as a computer with the ability to perform tasks.

And all these definitions have one similarity - the primary objective of AI is to solve problems.

Artificial intelligence is the development of a computer system that can perform tasks that require human intelligence. AI is the foundation of innovation in modern computing, providing value for individuals and businesses.

Types of artificial intelligence

There are many types of AI, but the main types are categorized into four types:

Reactive machine

As the name implies, Reactive AI is designed to respond to specific situations based on pre-programmed rules. It can’t predict future outcomes unless it has been fed the appropriate information. Reactive machines are good for simple tasks since they respond solely based on the present data.

This means such machines cannot use previously gained experience for present actions since they are designed to respond immediately. An example of Reactive AI is IBM Deep Blue, a machine that beat chess Grandmaster Garry Kasparov in 1997.

Another Reactive AI is Google’s AlphaGo, which has beaten top human Go experts. Amazon’s Alexa also has reactive components that enable quick responses to user inputs.

Netflix's recommendation engine is also a Reactive AI that reacts to a user's viewing history, search queries, and other data points such as time of day and device used to recommend content that the user is likely to enjoy.

An important note about the Reactive machine is that it responds to sensory input, and has no memory or experience.

Limited memory

Limited-memory, unlike Reactive AI, can store experience for a short time but isn't able to store long-term data. It can make use of past experiences to make decisions for the present. A self-driving car that collects data from its surroundings is an example of a limited-memory machine since it can use the data to make decisions about the car's speed, direction, and braking.

Machines with limited memory can recall the past and utilize that knowledge to make judgments in the present. They can only use a small number of prior experiences while making decisions. The properties of their training data were considerably better absorbed by these AI models, but more crucially, they are able to get better over time.

Limited-memory AI can also be used in fraud detection systems to examine previous transactions and spot trends that point to fraudulent conduct. AI applications such as chatbots and virtual assistants for self-driving vehicles are all driven by limited-memory AI.

Theory of mind

How would you feel about interacting with an AI that understands your thoughts and feelings?

Theory of mind is a type of AI that is capable of comprehending human feelings, opinions, and intentions. It can predict people's behavior based on their emotions and mental state.

Although this type of artificial intelligence is not yet mature, research is being conducted to improve and develop it. It plays a major role in psychology since it focuses mainly on emotional intelligence.

An example is Emotional AI, which is currently under development. It is designed to respond to human emotions by analyzing voices, images, videos, and other data.

Another example is the Kismet robot head, developed by Professor Cynthia Breazeal, which can recognize emotional signals on human faces and replicate those emotions on its face.

Self-aware

Self-aware AI is self-conscious and aware of its internal state and surroundings. It can make decisions based on its desires. It means such machines based on this technology have intelligence similar to human beings as well as needs, desires, and emotions.

There have been attempts by many people to create a self-aware machine, and one of the famous experiments is called the Chinese Room, proposed by John Searle.

A participant in this experiment is put in a room with a set of instructions for manipulating Chinese characters, but they are not truly aware of the characters' meanings. This person can respond coherently in Chinese, but he or she does not understand the language. According to Searle, this is comparable to how computers handle data - they alter symbols according to pre-established rules but do not fully comprehend the significance of the data they are processing.

This advanced AI does not yet exist and is still a hypothetical concept. It is expected to be very intelligent, self-aware, and sentient. Building such machines will be one of the milestones in the AI field.

However, some researchers have argued that it might be possible to create a conscious machine by simulating the structure and function of the human brain. This approach is known as whole-brain emulation or mind uploading and involves creating a digital replica of a human brain that can be run on a computer. While this approach is still largely theoretical, some researchers believe it could be possible in the future.

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Benefits of using artificial intelligence for business

Using artificial intelligence in business brings many benefits that work well for both big and small companies. AI tools make it easier to connect with customers, increase sales, and make more profit. Here’s how AI helps:

  • Generates data for better insight into your business
  • Facilitates decision-making
  • Improves risk management
  • Enhances customer experience
  • Makes your products smarter
  • Increases revenue

Now, let’s look more closely at each of these ways AI can make a big difference in business.

AI generates data for better insight into your business

Artificial intelligence and machine learning dominate the business world by generating massive amounts of data and insights. This data can be used to predict the outcome of a business. AI tools can analyze enormous amounts of customer reviews, surveys, social media posts, and more at speed and scale far beyond human capabilities, spotting trends and patterns and revealing problems customers face with products and services.

The importance of the data cannot be overemphasized, as it explains the reasons for high or low sales. Customer reviews about a product can also help the company make effective decisions, and without surveys or review systems, companies could easily lose customers as a result of customer dissatisfaction.

Did you know that Netflix's recommendation engine is worth $1 billion per year? The Netflix team claims that "consumer research indicates that a typical Netflix member loses interest after selecting for 60 to 90 seconds, having looked at 10 to 20 titles on one or two screens. Either a user discovers something exciting, or the likelihood that they will stop using our service increases significantly. Netflix officials estimate that if their members don't receive a suitable recommendation, they might lose at least $1 billion annually.

How to automate tasks with AI tools to gain business insights and save costs

Knowing how to automate tasks will save on labor costs and increase efficiency. Automated tasks can range from simple tasks like form filling, scheduling meetings, and file organization to reporting, routine communication, etc.

Businesses can automate processes like email and chat responses using AI-based applications to streamline customer support. Administrative tasks, such as appointment scheduling, data entry, and reminders, can be automated to save workers time for more complex duties.

Method of using AI to increase business efficiency and productivity

There are several ways in which AI can increase the efficiency of a business. This method involves making use of AI tools, such as:

Predictive Analytics

Predictive Analytics uses historical data to predict the future. The process involves using AI tools, machine learning models, and statistics to uncover patterns that could predict the future.

AI tools like TensorFlow can be used to monitor the performance of a business, Keras is an open-source library that can be used to implement deep learning to business applications, e.g Netflix uses Keras to build a recommender system to predict user preference. Scikit-learn provides a range of tools for statistical modeling and predicting fraud. Amazon SageMaker is a service managed by Amazon that provides tools for businesses to train and deploy machine learning models for predictive analytics application.

Businesses can learn more about consumer behavior, product demand, and market trends by automating this process with the use of AI tools. This can aid supply chain optimization, waste reduction, and customer satisfaction enhancement.

Natural Language Processing (NLP)

NLP is a form of AI that understands human languages. It can translate texts from one language to another. Businesses can gain insights from unstructured data sources like consumer feedback, social media, and online reviews by automating this process. They can use this to recognize new trends and consumer preferences, which can guide the creation of products and the development of marketing plans.

Some of the Natural Language Processing tools are TextBlob, Google Cloud Natural Language API, SpaCy, Stanford CoreNLP, Natural Language Toolkit (NLTK), etc.

Robotic Process Automation (RPA)

Using RPA, repetitive processes like data entry, invoice processing, and customer support questions can be automated. Businesses can cut expenses, improve productivity, and eliminate errors by automating certain processes.

Some of the RPA tools used to automate tasks and improve efficiency are UiPath, Automation Anywhere, Blue Prism, WorkFusion, Pega, etc.

Chatbots

Artificial intelligence-powered chatbots can communicate with customers and offer service 24/7. With tools like natural language processing (NLP) to understand customer inquiries and automate responses.

Examples of such chatbots are Botpress, Dialogflow, Microsoft Bot Framework, IBM Watson Assistant, Landbot, etc.

Image and Video Analysis

Image and Video Analysis are AI-powered tools that can automate large-scale visual data. They can help businesses with marketing plans, supply chain optimization, and product design.

Many image and video analysis tools are available, from basic image manipulation to complex systems for advanced computer vision tasks. Some examples are PyTorch, MATLAB, OpenCV, ImageJ, TensorFlow, Adobe Premiere Pro, Final Cut Pro, etc.

AI improves decision-making

Businesses can get a competitive edge in their sector and make more informed decisions that provide better results by utilizing AI tools and technology.

One of the biggest retailers in the world, Walmart, once had issues with inventory control, which resulted in stockouts and overstocking. Walmart built an AI-powered inventory management system to address this problem. This system employs machine learning algorithms to monitor weather patterns, sales data, and other factors to forecast demand and optimize inventory levels. Walmart has been able to improve sales by 2% while reducing stockouts by 30% as a result.

Starbucks once had trouble accurately predicting demand, which resulted in waste and overstocking. Starbucks put in place an AI-powered demand forecasting system to address this problem, which employs machine learning algorithms to examine weather patterns, sales data, and other factors to forecast demand. This has enabled the business to enhance operational efficiency and reduce waste by 30%.

How AI helps by analyzing data sets and patterns

While modern enterprises produce large amounts of data, AI tools streamline the process of analyzing this data and converting it into meaningful information. For example, if a company is experiencing low sales of a product, AI can be used to analyze past sales records and customer reviews to determine the cause of the loss and influence the company's decision.

AI programs can be taught to spot patterns in complex data sets that people might overlook. For instance, an AI system may examine a large dataset of medical records and spot trends that point to a greater risk of contracting a particular disease.

NLP algorithms can extract important insights from the data and detect patterns in the language used.

Examples of companies improved through AI

A lot of businesses have benefited from AI, and below are some of the companies that improved their businesses with AI tools:

  • Microsoft had an issue with spam and malicious emails getting through their email security filters. To spot suspicious patterns, they used AI and machine learning algorithms to evaluate the content, graphics, and other components of incoming emails. Their capacity to recognize and prevent spam and phishing emails significantly improved as a result.
  • Amazon's Product Search Algorithm: Product recommendations on Amazon's website were problematic. The recommendations given to customers were neither pertinent nor unique. To make product recommendations considerably more precise and helpful, Amazon deployed AI recommendation engines that learned from consumer browsing and purchase histories.
  • Airbnb's Discrimination Problem: Airbnb faced a problem with discrimination on its platform where hosts were more likely to reject bookings from guests with certain names or profile pictures. To address this issue, Airbnb used an AI tool to identify and remove biased language and images from their platform. After implementing the changes, Airbnb reported a decrease in discrimination and an increase in bookings from guests with traditionally discriminated-against profiles.
  • Delta Airlines: Baggage delays or losses caused by Delta Airlines' baggage handling procedure resulted in consumer complaints and a decline in customer satisfaction. Delta used an AI tool to evaluate data from their luggage handling process and identify which bags were most likely to be lost or delayed to resolve this issue. With this knowledge, Delta was able to take preventative action to avoid delays and guarantee that luggage reached consumers on time. Customer satisfaction significantly increased as a result, and the number of missing baggage claims decreased.

AI improves risk management

There are several ways AI tools and machine learning can be used for risk management by businesses. This is possible as a result of the technologies’ ability to handle and analyze large volumes of data at a fast speed with considerably lower degrees of human intervention. Here are ways in which AI can improve the risk management of a business:

Fraud detection

The invention of the internet and social media has made communication easier and encouraged online transactions between businesses. But it also brought about fraud, cybercrime, and cyberattacks, which made companies insecure about online transactions.

AI tools can analyze large amounts of data and monitor transactions to identify potential threats or fraudulent activities.

Fraud detection traditionally requires a significant amount of manual effort and resources. With AI-powered fraud detection systems, much of this manual effort can be automated, freeing up resources for other risk management activities. The benefit of AI fraud detection tools is that they reduce the number of negative reviews from customers and help save money.

Cybersecurity

AI is applied in cybersecurity to protect computers, servers, networks, and data from malicious attacks. AI can help companies manage cybersecurity risks by detecting attacks and potential threats.

AI reduces the risk of a company's data being stolen and manipulated with malicious motives. It protects a company's data from theft and damage and also prevents unnecessary costs from fraudsters.

Credit risk analysis

Credit risk analysis is simply the assessment of a customer's credibility to determine whether the customer will default on a loan. AI can analyze credit scores and payment histories of customers to prevent financial loss for a business.
The benefit of AI intervention is that it protects the reputation of the company, increases its decision-making, and reduces financial loss.

Predictive analysis

Al can be used to identify issues that pose a threat to a company by analyzing past data to identify patterns and trends. An AI system, for instance, can examine previous customer complaints to spot any trends that would point to a potential product flaw.

Examples of businesses using AI for risk management

As AI technologies advance, companies are making use of AI tools for risk management to secure their companies. Below are a few companies adopting AI-supported risk management:

  • JP Morgan: As one of the largest banks in the world, JP Morgan uses AI to prevent and detect fraud by analyzing large sets of data. This includes market data, news articles, and social media posts to identify any potential threats to the bank's investments.
    AI is used by banks to assess loan applications, monitor trading risks, and detect fraud.
  • Zurich Insurance Group: Zurich Insurance Group uses AI to enhance data and algorithms for privacy and security. The business analyzes consumer data, including credit scores, financial histories, and other details.
    By analyzing large amounts of data accurately, AI helps the company make more informed decisions about which customers to accept and which to reject. This improves the underwriting process and reduces the company's exposure to fraudulent claims.
  • Airbus: Airbus is a European multinational aerospace company that uses an AI tool called Skywise to manage supply chain risk. It also improves the quality control of its products and services through the examination of production processes, customer feedback, and maintenance records.
  • Allstate: Allstate, one of the biggest insurance companies in the United States, implemented a platform called Allstate Business Insurance (ABI), which uses machine learning to look for fraudulent activities.
    ABI marks a claim for further investigation by Allstate's fraud investigation team once a probable fraud indicator has been found.

AI improves customer experience

Through past customer reviews, behaviors, and purchase histories generated and analyzed by AI, companies can improve their products and services to satisfy their customers' needs, leading to an increase in customer loyalty and sales.

AI is used by voice assistants like Google Assistant and Amazon Alexa to handle client requests. It enhances the overall customer experience by enabling customers to interact with businesses more naturally and intuitively.

Also, based on records, AI algorithms can provide product recommendations to customers.

How AI personalizes customer interactions through a recommendation system and predictive analytics

A recommendation system uses customer data to suggest products and services. It usually uses past search history, review history, purchase history, and demographic information. The recommendation system is a machine learning model trained to understand the preferences of people and products using data gathered from interactions.

Such systems are used by major companies like Google, Instagram, Spotify, Amazon, Reddit, Netflix, etc. to increase engagement with users and the platform.

For example, Netflix uses a customer's viewing history, ratings, and other data to provide personalized recommendations. Netflix's recommendation system also uses natural language processing (NLP) algorithms to analyze customer reviews and feedback.

Predictive analytics makes use of machine learning, statistics, and AI to predict the future. This can be based on customer data, such as purchase history, browsing behavior, and demographics, to predict future customer behavior.

AI makes your products smarter

Businesses can make their products and services smarter with the right AI tools. Below are ways of making a product smarter using AI:

  • Personalization: Personalization is a process where AI suggests products for customers based on user preferences and behaviors. It is usually based on a customer's browsing history, location, and purchase history.
  • Quality Control: AI can improve the quality of a product by monitoring production data for defects. It reduces the waste of resources and improves product quality.
  • Predictive Maintenance: It is a process that uses data analysis to monitor equipment performance and predict maintenance needs during regular operations to prevent defects and the chances of a breakdown.
  • Autonomous operation: Autonomous operation is designed to allow devices like cars, robots, planes, etc. to be able to execute without guidance from humans. Devices powered by AI are now able to track customer behavior, preferences, and patterns. Autonomous operations aim to minimize manual interactions and maximize self-directed plant operations.

Using customer data to optimize business

Customer satisfaction is important for business growth. If a customer leaves a bad review on a product, it could affect the reputation of the company and also reduce sales of that product. Also, companies rely on customer data for innovation in fulfilling their customer’s needs and staying ahead in the changing market.

There are several ways to obtain customer data for business optimization and they are as follows: social media, testimonials, surveys, feedback forms, ratings, reviews, etc.

Through this data, a company can determine its customers' preferences and improve its online reputation. Amazon is known for its collection of data on customer preferences, purchase histories for product recommendations, and improvement of product management.

Increase revenue

With the use of AI tools, companies can reduce the workload on their employees, increase their productivity, and boost their revenue.

With AI tools, companies can analyze customer reviews, provide personalized recommendations to users, predict future sales based on historical data, and avoid making decisions that will affect the business negatively.

Increase sales through personalization

Personalization can be a powerful tool to increase sales for a business and customer engagement. While more than 90% of consumers claim they are more likely to purchase products from companies that remember them and make tailored offers, 80% are open to sharing their personal information to get a better customer experience.

Google makes use of deep learning in search and browsing history to personalize search results, ads, and recommendations on its applications like YouTube, Google Shopping, Google Maps, etc.

The LinkedIn machine learning model provides customized career and professional recommendations based on users' profiles and activities.

Netflix's recommendation system uses a machine-learning algorithm to provide personalized content to users based on their viewing history and ratings.

How to improve marketing through innovation

Innovation is a critical factor in the growth of a business. By constantly adopting new and creative ways to improve their products, companies can make decisions that keep them ahead of their competitors.

Below are ways to improve a business's marketing through innovation:

  • Ads and Retargeting: Companies can use ad targeting to deliver relevant products and services to their customers based on search. For example, if a user goes into an online store to buy a particular product, they get ads with links to other websites with that particular product.
  • Leverage customer data and analytics: Businesses can use AI tools on customers' data to identify customer's preferences and trends
  • Collaborate with partners and influencers: One of the fastest ways for a business to promote a product is through partners and influencers. Social media influencers can encourage their followers to use a product.
  • Content recommendation: Companies can also train an AI model to generate personalized content, product, or service recommendations for their customers.

Conclusion

AI technologies are continually advancing and exerting influence over individuals, businesses, and their everyday routines. Enterprises are integrating AI into their products to enhance their standing and meet customer satisfaction.