Popularity of AI, machine learning and data science over the past few years

AI is becoming more integrated into our lives with more AI use cases emerging. This leads to increased interest in AI, its subdomains and related fields such as machine learning and data science as seen above.

According to a recent Gartner survey, 37% of organizations are still looking to define their AI strategies. To integrate AI into your own business, you need to identify how AI can serve your business, possible use cases of AI in your business. This article gathers the most common use cases covering marketing, sales, customer services, security, data, technology, and other processes:


Companies can combine customer data and AI-powered tools to anticipate their customers’ next move and improve their journey. To do that, they can use AI to understand the market better, create unique contents and perform personalized marketing campaigns. With customer data, AI can provide accurate insights and suggest smart marketing solutions that would directly reflect on profits.

Below is our initial framework explaining impact of AI in marketing. We have developed that over time and you can see our latest view below the framework.

Shows how AI can support the marketing function


Marketing analytics

AI systems learn from, analyze, and measure marketing efforts. These solutions track media activity and provide insights into PR efforts to highlight what is driving engagement, traffic, and revenue. As a result, companies can provide better and more accurate marketing services to their customers.

Besides PR efforts, AI can make use of marketing data to predict future demand and discover ways to improve customer demand. As an example, Dell has increased its page visits by 22% by introducing Persado’s AI-powered marketing tools.

Lastly, AI-powered marketing analytics can lead companies to identify their customer groups more accurately. By discovering their loyal customers, companies can develop accurate marketing strategies and also retarget customers who have expressed interest in products or services before. 

Feel free to read more about marketing analytics with AI from this article.

Personalized Marketing

The more companies understand their customers, the better they serve them. AI can assist companies in this task and support them to give personalized experiences for customers.

As an example, suppose you visited an online store and looked at a product but didn’t buy it. Afterward, you see that exact product in digital ads. More than that, companies can send personalized emails or special offers and recommend new products that go along with customers’ tastes.

All of these are designed to lead the consumer more reliably towards a sale and support companies to increase their revenues. In a case study by Liftigniter, an e-commerce company Liftia has improved its conversion rate by 50% after it introduced the AI-based recommendation system into its website.

Social media monitoring

Companies can leverage machine learning to optimize the channel, target audience, message and timing of your social media posts, as well. For example, The Economist has achieved a 290% increase in clicks per tweet by allowing SocialFlow to reach the audience at the right time by analyzing its online habits.

Improved engagement

Every time you execute a search on Google, your reaction is recorded. If you click the top result and stay, it would be a successful search. On the other hand, when you click to the second page of results, or type in a new search string without clicking any of the results, it is likely that search results can be improved. Such feedback loops can be built on many apps and websites, improving the user experience.

Content generation

For content creators, it is always a challenge to come up with a matchless marketing strategy. In this field, AI can provide creative solutions. From the topics chosen for content marketing, AI algorithms can create unique content and deliver creative suggestions. You can read more on content generation by reading our article.

You can check out our complete guide on the topic as well.


Unlike marketing, the sales function has always been numbers-driven. With the explosion of sales data and computational power, AI is set to further increase how data driven the sales function is. AI-powered sales use cases can lead to conversion rates, reduced costs, and more accurate sales predictions.

Sales Forecasting

Companies can automatically forecast sales accurately based on all customer contacts and previous sales outcomes with the support of AI tools. With the insights from these tools, companies decrease their contact time with customers and give their sales personnel more sales time while increasing forecast accuracy. Hewlett Packard Enterprise indicates that it has experienced a 5x increase in forecast simplicity, speed, and accuracy with Clari’s sales forecasting tools.

Sales Analytics

As Gartner discusses, sales analytic systems provide functionality that supports discovery, diagnostic and predictive exercises that enable the manipulation of parameters, measures, dimensions or figures as part of an analytic or planning exercise. AI algorithms can automate the data collection process and present solutions to improve sales performance. To have more detailed information, you can read our article about sales analytics.

Customer Service

Customer satisfaction is a vital metric for companies, and they need to understand customer processes well to keep them satisfied. By learning from customer experiences, AI tools can offer companies a wide range of solutions and help them to provide a better customer experience in the future.


Chatbots can understand more complicated queries as AI algorithms improve. Thus, businesses understand their customers better since chatbots collect information from customers while interacting with them and spot their weaknesses. There are other benefits like 24/7 availability and reduced costs, as bots can handle more tasks as they learn more. All these benefits significantly improve the customer satisfaction of businesses.

The automotive industry is one of the areas that use chatbots. While a significant portion of leads to car dealers come from online channels, high conversion rates are vital for these companies. For example, Kia observes three times more conversions through its chatbot Kian, compared to its website. Kian’s availability to answer complex questions is a dominant factor for achieving high conversion rates.

If you want to have more insights on chatbots, you can find more in our article on the topic.

Call routing

Intelligent call routing systems identify the caller and the reason for the call to assign her to the right agent. Though we mentioned phone call routing in the definition, intelligent routing can take place regardless of where the customer contacts the company. With AI, skill-based routing systems use agents’ track record, their training, and skills to ensure that the caller is routed to the most capable agent. Feel free to read our article about intelligent call routing if you want to learn more.

Call analytics

AI can be used for advanced analytics on call data to uncover insights to improve customer satisfaction and increase efficiency. These insights would be helpful to discover any problems during calls and suggest new ways to improve call quality. Sestek indicates that ING Bank observed a 15% increase in sales quality score and a 3% decrease in overall silence rates after they integrated AI into their call systems.

Survey / Review analytics

Companies usually make surveys and ask for reviews to improve customer satisfaction. AI can assist to analyze text fields in surveys and reviews to discover new insights.

Protobrand states that they used to do review analytics manually through hand-coding of the data but now it automates much of the analytical work with Gavagai. This helps the company to collect larger quantitative volumes of qualitative data and still complete the analytical work in a timely and efficient manner. You can read more about survey analytics from our related article.


Since most AI solutions rely on data, data is critical for the success of a company’s AI initiatives.

Data Visualization

Data visualization is a manual data exploration technique and enables companies to have better analytics and decision making. Our blog has more information about data visualization if you are interested.

Data Cleaning

AI can identify and replace the incomplete, inaccurate, irrelevant, or otherwise problematic data and records. While “dirty” data can lead companies to reach incorrect conclusions or irrelevant customer targeting, AI-powered data cleaning tools can automate data cleaning processes with certain rules and provide better decision-making tools for companies. Feel free to read our in-depth guide about data cleaning if you want to have more information.

Data Integration

To achieve a unified view of data that is sourced from different locations and formats, it is necessary to have an established data integration solution. Data integration is the process of taking data from many disparate sources and making it usable. AI-powered systems can significantly automate these processes and integrate different types of data rapidly. This may reduce human-based errors and process time while improving productivity.

You can also take a look at our in-depth guide about data integration if you are interested.

Synthetic Data

Computers can artificially create synthetic data to perform certain operations. The synthetic data is usually used to test new products and tools, validate models, and satisfy AI needs. Companies can simulate not yet encountered conditions and take precautions accordingly with the help of synthetic data. They also overcome the privacy limitations as it doesn’t expose any real data. Thus, synthetic data is a smart AI solution for companies to simulate future events and consider future possibilities.

You can have more information on synthetic data from our related article.



Invoicing is a highly repetitive process that many companies perform manually. This causes human errors in invoicing and high costs in terms of time, especially when a high volume of documents needs to be processed. Thus, companies can handle these repetitive tasks with AI, automate invoicing procedure and save significant time while reducing invoicing errors. The company avoids re-invoicing costs with AI tools, as well.

Elekta has reduced its costs and increased its number of processed invoices from 50,000 to 120,000 with the same number of staff by automating its invoicing procedure with MediusFlow. You can also read our article about invoice automation.


Millions of cyber attacks are executed every day. Though it may be impossible to 100% prevent them all, companies can certainly learn from them to help develop better ways to stay protected. By recognizing certain patterns, AI algorithms can introduce IT systems with their weaknesses and ways to prevent losses in the future.

Data Security

Malware is a growing problem for companies. In 2014, Kaspersky Lab said it had detected 325,000 new malware files every day. AI algorithms can look for patterns in how data in the cloud is accessed, and report anomalies that could predict security breaches. By detecting any cyber-attacks or abnormal activities, AI becomes a prominent solution to protect companies from any data leakages.

Fraud Detection

AI improves itself to spot potential cases of scams across different fields. As an example, PayPal uses AI algorithms do detect fraudulent actions. The company has tools that compare millions of transactions and can distinguish between legitimate and fraudulent transactions. As AI algorithms improve, Hui Wang, senior director of global risk and data sciences, said PayPal has cut its false-alarm rate in half.

You can also read our related article to have more detailed information.


Strategy always strived to be data driven but until 2010s, the ability to deal with big data only existed in the tech function of most organizations. Today, strategy can be better informed with data as strategy teams leverage big data, 3rd party data and machine learning algorithms to uncover insights and predict the future.


AI constitutes an essential part of the tech that companies use. Many automation and robotics applications are powered by AI algorithms. Companies can advance higher efficiency rates and improve their performances by integrating AI tools into their tech.

Process Mining

Process mining tools use AI algorithms to discover actual process models out of the raw event logs. These actual processes reflect the actual performance of the business processes by a layer of software which sits on top of the company’s IT systems. As companies understand their actual processes better, they can improve their performance more effectively.

While companies can mine their processes in a variety of fields, EY has reduced its audit preparation time by 50% by automating its insights into audit risks and compliance breaches with process mining. You can read more about process mining through our article.

Robotic Process Automation (RPA)

RPA is a generic AI-powered tool using screen scraping and other technologies to create specialized agents that can automate clerical tasks. It simply replicates employee actions like opening files, inputting data, copy-pasting fields in an automated way. This way, RPA reduces errors and increases the speed of the processes.

In a McKinsey report, RPA becomes a promising new development in business automation that offers a potential ROI of 30–200 percent—in the first year. To learn more, feel free to read our article about RPA.

Internet of Things (IoT)

Although IoT has its field, using AI agents to improve IoT efficiency becomes popular lately. Gartner defines IoT as “the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment.” AI agents aim to make these networks “intelligent” and self-operating.

For example, Google Nest “learns” its users’ regular temperature preferences and adapt their working schedule. As a result, Nest states that users save 10-12% of energy for heating and 15% for cooling.

Image recognition

The main goal of image recognition is to make computers understand and be able to implement human visual perception. As AI algorithms improve, computers can also detect faces in a photo and understand our emotions by analyzing our faces. The use cases in businesses include tumor detection in healthcare, tracking UAVs in the military, emotion detection, and car plate recognition. Unlocking our phones with face recognition is another daily-life example.

Speech recognition

This use case enables computers to understand spoken human language. A speech recognition software separates the signal into small segments to match these segments to known phonemes in the related language. This technology can support to automate certain tasks.

A popular example is YouTube. It uses speech recognition to automatically generate subtitles for the videos. When you upload a video that includes speeches or talks, YouTube detects it and provides a transcription. You can also have the minute-by-minute text of the transcribed speech.

Feel free to read our voice recognition for businesses article to gain deeper insights.

Natural Language Processing (NLP)

NLP is being used in all sorts of exciting applications across disciplines. Machine learning algorithms with natural language can stand in for customer service agents and more quickly route customers to the information they need. One interesting use area of NLP is to help attorneys for sorting through large volumes of information to prepare for a case.

You can also take a look at our AI in business article to read about AI applications by industry.

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