How is AI Used in Businesses? 7 Transformative Applications You Need to Know
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How is AI Used in Businesses? 7 Transformative Applications You Need to Know

Ryan Thompson
Ryan Thompson
June 4, 2024
Last updated on:

June 4, 2024


Read time: 8 mins

Artificial Intelligence (AI) has revolutionized businesses by automating tasks, enhancing decision-making, and predicting trends. Its impact spans across various sectors, from customer service to supply chain management. However, implementing AI is not simple as it entails skilled personnel, involves data privacy concerns, and requires auxiliary features to integrate into existing systems.

Businesses often struggle with data quality and accessibility, as well as the high initial investment required for AI adoption. Additionally, there may be resistance from employees apprehensive about job displacement. Despite these hurdles, the potential benefits of AI, such as increased efficiency, cost reduction, and improved customer experiences, are undeniable. The benefits incentivize businesses to overcome implementation difficulties and embrace AI-driven solutions for sustainable growth.

This blog discusses the top seven applications of how is AI used in businesses for growth, boost in sales, customer engagement, and more.

Top 7 Applications of AI in Businesses

AI has become an integral part of modern business operations, revolutionizing various industries. Here are seven key applications of AI in businesses:

1. Customer Service Chatbots

Traditional customer service involved long wait times, frustrating phone queues, and often inconsistent support experiences. However, with the advent of AI, particularly in the form of chatbots, customer service has undergone a transformative evolution. These AI-powered chatbots are transforming the way businesses interact with their customers, offering efficient, round-the-clock assistance across various channels.

Chatbots are developed with natural language processing (NLP) and machine learning (ML) algorithms that enable them to understand and answer to customer inquiries in real-time. By analyzing vast amounts of data, they provide personalized and contextually relevant support, resolving issues promptly and effectively.

Moreover, chatbots are available 24/7, offering support whenever customers need it, irrespective of time zones or business hours. This round-the-clock availability not only improves the customer experience but also helps businesses maintain a competitive edge in today's fast-paced market. According to the Tidio Survey, 69% of respondents who utilized chatbots expressed satisfaction with the experience, while 21% remained neutral, and 10% reported dissatisfaction.

2. Predictive Analytics for Sales and Marketing

In the past, sales and marketing strategies often relied heavily on intuition, past experiences, and generalized demographic data. While these methods were sometimes effective, they lacked the precision needed to truly understand customer behavior and predict future trends. This led to missed opportunities, inefficient resource allocation, and suboptimal decision-making.

However, with the advent of predictive analytics powered by AI, businesses have undergone a transformative shift in their approach to sales and marketing. By leveraging ML algorithms, businesses analyze vast amounts of data to uncover valuable insights and anticipate customer behavior with utmost accuracy.

Predictive analytics algorithms can parse a multitude of data variables, including customer demographics, browsing behavior, purchase history, social media interactions, and more. This approach enables businesses to identify patterns and trends that would have previously gone unnoticed, allowing them to design their sales and marketing efforts more effectively.

The case of how predictive analytics is used to improve sales and marketing can be seen in how Amazon utilized it to predict the festival season sales. The global e-commerce giant utilizes predictive analytics to analyze their vast database of customer behavior and purchase history. It generates insights that enable Amazon to forecast demand for various products accurately and adjust its inventory levels accordingly. For example, during major shopping events like Black Friday, Amazon employs predictive models to anticipate which products will experience a surge in demand and ensures sufficient stock availability. This strategic use of predictive analytics has contributed significantly to Amazon's dominance in the e-commerce industry, driving sales and enhancing customer satisfaction.

Impacts of Predictive Analytics on Sales Performance
Image 1 - Impacts of Predictive Analytics on Sales Performance

3. Personalized Customer Experiences

Earlier, to understand customer experience, companies collected customer information through surveys, feedback forms, and purchase history. However, analyzing this data and tailoring experiences to individual preferences was a labor-intensive task. Businesses struggled to predict customer needs and anticipate their next actions accurately. This often resulted in generic marketing campaigns and product recommendations that failed to resonate with individual customers.

With the emergence of AI technologies, businesses gained the chance to analyze vast amounts of customer data. AI algorithms analyze both structured and unstructured data in volumes from various sources, including social media, online interactions, and purchase history, to create highly detailed customer profiles in real-time. This enables companies to understand each customer's unique needs and preferences on a granular level, allowing for the delivery of personalized recommendations, product suggestions, and targeted marketing messages. According to Medallia's research, 61% of consumers are open to paying extra for personalized experiences. However, only a quarter of these experiences are currently highly personalized.

4. Product Recommendations

Before AI, businesses relied heavily on manual methods to suggest products to customers. This typically involved human analysts examining purchasing patterns, conducting surveys, and analyzing market trends to identify potential products of interest for specific demographics. These manual processes were time-consuming, labor-intensive, and often prone to errors.

However, with the rise of AI, particularly machine learning algorithms, product recommendations have undergone a significant transformation. AI-powered recommendation systems analyze customer data, including past purchases, browsing history, demographics, and even real-time behavior on websites or mobile apps. By leveraging this data, AI can generate highly personalized recommendations tailored to each customer's unique preferences and behaviors.

The use of AI in product recommendations has not only revolutionized e-commerce platforms but has also extended to various other industries such as media streaming services, online marketplaces, and even brick-and-mortar retail stores. By leveraging AI, businesses can offer personalized recommendations that resonate with customers, foster brand loyalty, and ultimately boost revenue.

The case study of Netflix exemplifies how understanding product recommendations can drive business success through personalized suggestions. In 2017, Netflix introduced a personalized recommendation system aimed at enhancing user experience. By analyzing viewing habits, Netflix predicts what a viewer might enjoy next. One notable case involved the success of their original series, "Stranger Things." Despite its unconventional premise, Netflix recommended it based on users' interest in '80s nostalgia and supernatural themes. This led to unexpected popularity, demonstrating the efficacy of their recommendation system. By understanding user preferences, Netflix achieved higher user engagement and retention.

5. Supply Chain Optimization

Manual processes once dominated supply chain management. Every step, from forecasting demand to managing inventory, was labor-intensive and prone to errors. Excel sheets were the backbone, and decisions were often made based on historical data and gut feelings rather than real-time insights.

However, with the advent of AI, the landscape has shifted dramatically. Supply chain optimization, once a complex and time-consuming endeavor, has become streamlined and efficient. AI algorithms analyze data in real-time, predicting demand patterns and even foreseeing potential disruptions before they occur.

AI-powered solutions dynamically adjust inventory levels, identify optimal transportation routes, etc. These capabilities empower businesses to enhance efficiency, reduce costs, and improve customer satisfaction. Furthermore, AI enables proactive decision-making by providing actionable insights based on real-time data. This enables businesses to adapt to continuously changing market conditions and customer preferences. According to Gartner, high-performing supply chain organizations utilize AI to optimize processes at a rate exceeding 2x that of their low-performing counterparts.

How is AI used in business supply chain management?

AI in supply chain management optimizes operations by forecasting demand, managing inventory, enhancing logistics, predicting disruptions, and improving decision-making. It enables real-time data analysis, automates processes, and increases efficiency, reducing costs and improving customer satisfaction.

Read our blog, ‘5 Strategic Applications of Artificial Intelligence in Logistics and Supply Chain Management,’ to learn the top applications of AI in supply chains.

6. Process Automation

Previously, business operations relied heavily on manual processes, often leading to errors, inefficiencies, and delays. Tasks, including data entry, document processing, and repetitive administrative duties, consumed significant time and resources. Human intervention was required at various stages, leading to bottlenecks and reduced productivity.

With the advent of AI, particularly in the realm of process automation, businesses have undergone a transformative shift. Process automation, one of the primary applications of AI in business, involves the delegation of repetitive and rule-based tasks to intelligent systems. 

These systems can perform tasks such as data entry, invoice processing, customer service inquiries, and even complex decision-making processes autonomously, freeing up human resources for more strategic and creative endeavors. For instance, AI-driven systems can automatically extract information from documents, classify data, and perform routine calculations with remarkable precision.

The case of how AI is used to improve businesses is seen in how Siemens used process automation to revolutionize manufacturing, enhancing efficiency, reliability, and competitiveness on a global scale. In 2016, Siemens, a global engineering and technology conglomerate, implemented a comprehensive process automation system to optimize its manufacturing operations. Facing challenges of inefficiency and errors in production processes, Siemens integrated advanced automation technologies across its factories worldwide. This included robotic arms for assembly, digital twins for real-time monitoring of the manufacturing process, and AI-powered analytics for predictive maintenance. By automating repetitive tasks and streamlining workflows, Siemens achieved remarkable improvements in productivity and quality control. For instance, production downtime reduced by 40%, while defect rates dropped by 30%.


Top Business Processes Suitable for Automation
Image 2 - Top Business Processes Suitable for Automation

7. Content Management

In the past, content management in businesses relied heavily on manual labor and traditional software tools. Employees spent significant time organizing, categorizing, and tagging content to make it accessible and searchable. This process was often tedious, time-consuming, and prone to human error.

The integration of AI in content management automates many of these manual tasks. Through NLP and machine learning algorithms, AI systems can now analyze vast amounts of content, extract relevant information, and automatically categorize and tag it. This not only saves time and reduces human error but also improves efficiency and accuracy.

Additionally, AI-powered content management systems can personalize content delivery based on user preferences and behavior. It analyzes text, audio, and video content. These systems can identify trends, sentiment, and key topics, enabling businesses to make data-driven decisions and tailor their content strategies accordingly. By analyzing data such as past interactions, demographics, and browsing history, AI can recommend relevant content to users, leading to better engagement and customer satisfaction. According to the 2024 State of CMS report, businesses are incorporating AI into their daily operations, utilizing it for tasks such as content creation (65%), content editing (59%), and content ideation and research (56%).


The future of AI in businesses lies in enhanced decision-making, automation, and personalized customer experiences. Companies increasingly leverage AI for predictive analytics, customer service chatbots, and predictive maintenance. Businesses implement AI by integrating advanced software, hiring skilled data scientists, or adopting AI platforms. However, many opt to outsource AI to specialized firms to reduce costs and access cutting-edge expertise. Outsourcing also allows companies to scale AI solutions quickly without significant in-house investment.

Invensis offers advanced AI-driven solutions for services like customer support, finance and accounting, order management, eCommerce support, back office BPO, and more. With 24 years of experience, we enhance customer support with AI chatbots for round-the-clock assistance, streamline finance and accounting with predictive analytics, optimize order management through automated processing, and boost eCommerce with AI-powered engines. For example, our AI chatbots improve response times and customer satisfaction, while our predictive analytics in finance reduces errors and enhances decision-making. Reach out to Invensis today for exceptional services supported by AI!

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