The impacts of AI in business operations today cannot be overstated. Why? 

First, using artificial intelligence in business is showing signs of acceleration. About three-quarters of businesses are now using AI or exploring AI uses.

Artificial intelligence (AI) is radically transforming how businesses operate, enabling them to automate repetitive tasks, analyze vast amounts of data and make informed decisions. 

By leveraging AI, businesses can streamline processes, boost productivity and gain a competitive edge.

In this blog post, you'll learn an overview of AI in business, its common uses, benefits and real-life AI examples. 

What is AI and how does it help businesses?

To understand AI in business, we must first understand AI.

What is AI (Artificial Intelligence)

Artificial intelligence can perform tasks that typically require human intelligence, such as learning, planning and problem-solving.

AI is today at the center of business growth.

So, What is AI in Business? 

Artificial intelligence in business involves the application of intelligent computer software with human-like capabilities to perform business activities without human involvement. 

How does AI help businesses?

AI enables businesses to improve customer experience, boost revenue, increase productivity, efficiency and drive business growth and transformation. How? 

Business processes today are characterized by a high level of complexity involving large amounts of data. With vast data, companies can obtain valuable insights to build strategies that radically drive growth.

Using AI in business, companies can analyze vast amounts of data quickly and in real-time and get actionable insights for better decision-making.

This enables businesses to understand and engage customers, automate business processes and improve productivity and revenue while reducing operational expenses.

How Does AI in Business Work?

AI systems work by consuming large data quantities (training data), analyzing the data for correlations and patterns, and using these patterns to predict future states. 

AI is an interdisciplinary category of computer science with multiple approaches and advancements in key technologies or AI techniques, which include the following:

Machine Learning (ML)

Machine learning (ML) is a subset of AI that involves developing algorithms and models that enable computer systems to learn and make predictions or decisions. 

It involves developing techniques that allow computers (machines) to automatically learn from data and improve their performance over time. 

ML algorithms analyze and extract patterns from large datasets to make predictions or take actions based on new inputs.

Deep Learning (DL)

Deep learning is a more advanced type of ML that works through representation but with unstructured data sets. It uses biologically inspired artificial neural network architecture.

DL is inspired by the structure and function of the human brain's neural networks. 

Deep learning algorithms have supported tasks such as image and speech recognition and NLP.

Natural Language Processing (NLP)

In AI, Natural Language Processing is a linguistic tool in computer science that enables machines to learn, read, understand, interpret, generate and respond to human language.

NLP techniques are used in various applications, such as machine translation, sentiment analysis, chatbots and voice assistants.

Computer Vision (CV)

Machine vision (computer vision) is an area in AI that enables computerized machines to understand and interpret visual information from images or videos.

Applications of machine vision techniques include object recognition, image classification, video surveillance, autonomous vehicles, medical imaging and others.

Trends of AI in Business

The evolution of artificial intelligence was slow at first, then it became suddenly rapid. Several trends are today shaping AI's application in business, including the following:

1. Generative AI

A new area of AI that has recently gained traction is called generative AI. So, what is generative AI?

Generative AI refers to the use of AI techniques and algorithms to create new and original content, such as images, videos, music or text. It used ML to process large amounts of visual or textual data, often from the internet, to create the "near equal" content as instructed. 

Generative AI has been around since ELIZA, a chatbot, in 1966. But years of AI work and greater ML technologies have led to the recent release of new generative AI systems, such as ChatGPT and more.

Generative AI applications like ChatGPT and Google Bard have applications in creative industries, content generation and simulation. 

2. Document AI to optimize processing efficiency.

Also known as intelligent document processing (IDP), document AI technology allows businesses to automate and speed up redundant data entry processes.

Document AI leverages AI with ML to facilitate data extraction from documents such as invoices, forms, claims, contracts and SOPs.  

 It does this by converting semi-structured and unstructured documents into organized information or structured data the business uses to guide decision-making. 

3. Low-code and no-code tech 

For decades, software development required knowledge and experience in the coding language. This was a barrier for many people.

Today, automation has gone beyond the IT department, and businesses can access optimized or innovative processes.

With no-code tools, businesses are leveraging AI to build and support their IT and only specialized staff. 

4. Increased data sharing 

AI is likely to pave the way for more data sharing.

Modern businesses continue to collect unprecedented amounts of data to derive valuable insights and guide decision-making. AI technologies in businesses are increasing organizations' capacity to process data and also increasingly sharing data and data insights.

The trend of AI-assisted data sharing is fueled by recent consumer privacy and protection demands. For instance, search engines like Google and Safari are banning third-party cookies. 

Now, companies share data among themselves as long as they follow the required regulations. Add data sharing with AI tools to give businesses more complete insights into their audience.

5. End-user tech that democratizes AI

There's debate about the skills gap in fields like analytics and data science that's negatively impacting digital transformation initiatives.

Data science candidates now come with a high price tag, demanding high salaries.

Fortunately, businesses are finding ways to democratize AI using low-code and no-code technology. As these technologies become more refined, businesses can use them to help close the skills gap.

6. Businesses balancing automation and augmentation

Businesses are still trying to achieve a balance between AI technologies used in automation and augmentation.

While modern AI technologies in business are designed to reduce the need for human input, they should also help businesses increase agility and maximize operational efficiency. But there's still a need for some human input, at least for now. 

For example, AI-generated content needs human intervention to streamline information.  

In the future, businesses will strive to learn to balance the two. For example, they can use AI to automate redundant processes and use strategic human augmentation to guide their directions.  

Common Uses of AI in Business

AI has become ubiquitous in various aspects of business operations. The use of AI in business operations and applications is expanding. Some of the most common use cases include:

1. Data analytics and business intelligence 

With the increasing amount of available business data, finding insights from the data can be quite challenging. This is why many businesses are adopting AI in business intelligence.

AI enables organizations to quickly analyze large data volumes and extract valuable insights. These insights help businesses identify trends, make data-driven decisions, and optimize processes.

For example, businesses can use AI data to improve marketing, understand customers better, perform segmentation and create personalized experiences.

2. Customer service, experience and support

One of the most common enterprise use cases for AI is in customer experience, service and support. Though still controversial, AI is increasingly replacing humans in customer service.

AI-powered technologies such as chatbots, virtual assistants and personalized recommendation systems enhance customer interactions. They provide instant support, tailored recommendations and personalized experiences, leading to higher customer satisfaction and loyalty.

3. Personalized and targeted marketing with predictive modeling

Targeted marketing is a key application of AI in business. 

AI algorithms can predict future outcomes based on historical data, allowing businesses to anticipate customer behavior, demand trends and potential risks. This enables proactive decision-making and resource allocation.

For example, online search providers, retailers and other internet entities use AI intelligent systems to understand users and their buying patterns.  

4. Natural language processing (NLP)

With AI, machines can now read, write and understand languages like humans.

AI-powered chatbots and virtual assistants can understand and respond to human language, providing personalized customer support and enhancing user experiences.

With advancements in NLP, businesses now offer smart digital assistant products to assist users in routine tasks.

These NLP softwares now helps businesses to generate automated business reports without human supervision. They can also conduct sentiment analysis to understand people's perception of brands from various sources such as online comments, tweets, etc.   

5. Predictive analytics and product recommendations 

To increase marketing effectiveness and customer engagement, businesses recommend products that retain customers' interest and satisfy their desires. 

Companies like Spotify, Amazon, Netflix, etc., are using AI to understand their customers' habits/behaviors to predict and recommend products.

6. Documentation and Knowledge Base Building 

With code tools like Scribe, process documentation and building company knowledge bases is now easier. 

What are the advantages of AI in business?

Implementing AI in business operations offers numerous benefits, including:

Automate routine tasks

AI can automate redundant, repetitive or time-consuming tasks. This helps free up employees' time and efforts to focus on more strategic and creative business tasks.

Improve efficiency and productivity

By streamlining processes and providing real-time insights, AI helps businesses operate more efficiently, reducing manual errors and improving productivity. This supports overall business efficiency.

Enhance decision-making

AI algorithms analyze vast amounts of data, identifying patterns and making accurate predictions. This helps businesses in making informed, effective decisions.

Improve customer experiences

AI-powered chatbots, personalized recommendations, and sentiment analysis tools enhance customer interactions, engagement and support. This leads to better customer experiences and increased customer satisfaction.

Reduce costs

Through automation and optimization, AI in businesses helps reduce operational costs, optimize resource allocation and minimize waste. Businesses can focus the saved resources on growth.

Fraud detection

AI can strengthen businesses' cybersecurity efforts by automating real-time identification and response to potential cyber threats. AI-powered security systems can detect fraud and anomalies, identify malicious behavior patterns and protect sensitive data from cyberattacks.

What are the Disadvantages of AI in Business 

While AI brings numerous benefits, it also comes with some disadvantages and challenges in implementation in business. These include:

• Cost and complexity: Implementing AI systems can be expensive and complex, with high capital investments in infrastructure, hardware, software and skilled personnel.  
• Data quality and privacy concerns: AI relies heavily on large datasets, high-quality and diverse data for training and decision-making. Businesses must ensure data accuracy, integrity and privacy to avoid biased outcomes or breaches of sensitive data.  
• Ethical considerations: AI systems can inherit biases in their training data, resulting in discriminatory or unfair outcomes. This can be unethical. Businesses must also navigate the challenge of addressing biases and ensuring fairness in AI algorithms.
• Possible workforce changes and job displacement: AI automation can lead to job displacement, as certain tasks and roles become automated and redundant or more specialized skills. 
• Security and vulnerabilities: AI systems may be vulnerable to attacks, such as adversarial attacks, where malicious actors manipulate the inputs to deceive the AI algorithms.
• Limited creativity: While AI helps generate content or make data-based predictions, it may lack the creativity and innovation of humans.

What does AI mean for Employees?

Integrating AI in business inevitably raises questions about its impact on employees. So, what does AI and the future of work look like?

AI in business is two-way:

• AI may automate certain tasks and make them redundant for human operation.
• AI in business will also create new opportunities for employees to upskill and focus on higher-value activities. 

So, rather than replacing jobs, AI can potentially augment human capabilities, enabling employees to work more effectively and creatively while improving business productivity.

While there is still more debate on what the rise of AI in business means for employees, people agree on some trends to be expected.

For example, there's likely to be a shift to more specialized skills and become technology oriented. The workforce may transform and become more specialized. Roles that can't be automated, like creativity, problem-solving and qualitative skills, may become increasingly required in job performance.

Real-life examples of AI in business

Here are examples of artificial intelligence in business examples: 

1. Recommendations on Netflix, YouTube and Spotify 

Netflix, Spotify, YouTube and other streaming services use a user's watch, listen or search history to recommend what to watch based on the user's listening or viewing history. This helps provide personalized content to improve customer experience and satisfaction. 

2. eCommerce platforms 

Amazon, Alibaba and other eCommerce platforms integrate AI into their platform to enhance customer experiences. Their recommendation engine uses ML algorithms to analyze user browsing and purchase history, providing personalized product recommendations that increase customer engagement and drive sales.

3. AI in Healthcare 

Healthcare organizations are leveraging AI for medical image analysis. Medical companies use deep learning algorithms to analyze medical images, helping radiologists detect abnormalities and diagnose diseases more accurately. This improves patient care and reduces the time and effort required for diagnosis.

4. Transportation 

There are many ways AI is being applied in transportation businesses, including the following:

• Taxi-hailing apps like Uber leverage AI to predict demand and traffic, reduce estimated arrival time and match riders and drivers.
• Maps use AI navigational systems to improve transport and navigation while providing directions and enhancing traffic detection. 
• Self-driving cars like Tesla use AI algorithms to navigate roads and make decisions.  

Leverage AI for Business Growth

Smart businesses are using AI to automate repetitive tasks, analyze customer data for better targeting, and personalize marketing campaigns at scale. Start by implementing AI chatbots for customer service, which handle 80% of routine inquiries while reducing response times. 

Use AI-powered analytics tools to identify sales patterns and predict customer behavior, enabling proactive decision-making. Automate content creation for social media and email marketing using AI writing tools, freeing your team for strategic work. Deploy AI for inventory management and demand forecasting to reduce waste and optimize cash flow.

The key is starting small with one AI solution, measuring results, then scaling successful implementations across your business operations