ML for small businesses

Top AI and Machine Learning Use Cases Across Industries

AI is changing the landscape of nearly every industry and field. From 2019 to 2022, 83% of enterprises increased their budgets for AI and machine learning year-over-year1. As businesses globally understand the importance of reinventing themselves, they are more readily adopting AI in their processes. Finding the right AI use cases for your specific needs is the primary step to take. 

However, an issue that arises from AI’s growth in popularity is that businesses are jumping straight into an AI implementation without a deep understanding of specific use cases or issues they want to solve. To get the most out of an AI/ML implementation, it is important to be prepared and to align expectations with reality. For a successful AI implementation, businesses need to identify the specific ways in which AI can serve them and add value to their core offerings. In this article, we look at 6 different industries and the most common use cases within them where AI has established itself as a game-changing implementation.

AI in banking & financial services

With every industry looking to reinvent itself with AI/ML, banking and financial institutions are no exception. It is now a need of the hour to derive insights from the heaps of data that exist in the databases to redefine customer experience and drive business use cases to mitigate financial risk and maximize return on investment.

Credit Management & Credit Risk Scoring

Train and deploy machine learning models that can quickly and easily detect suspicious and fraudulent activities in real time to combat money laundering, fraudulent transactions, etc. There are increased regulations from governments and financial institutions worldwide to prevent security breaches. Generate insights from data residing in data lakes and data pipelines to learn about fraudulent and malicious behavior and protect customers’ financial data.

Read our case study on fraud detection using machine learning.

Targeted Marketing

Analyze customer journeys to learn about customers’ tastes, preferences, and buying patterns to deliver personalized services. Take personalization to a whole new level by using sophisticated Natural language Processing (NLP) techniques such as sentiment analysis and information extraction to understand human emotions and form long-lasting customer relationships through continuous innovation.

AI in telco

If the Covid-19 pandemic has taught us one thing, it’s that connectivity matters the most. With the roll-out of 5G technology, IoT, and widespread adoption of cloud computing, telcos lie at the forefront of digital disruption. The industry as a whole has to pivot to leverage AI and machine learning to adapt to the fast-changing business landscape to drive revenue and growth.

Network Optimization

Telcos have to handle a large volume of trouble tickets each day that can negatively impact customer experience if not resolved quickly. Classification algorithms such as decision trees or random forests can help quickly establish the root cause. This will empower the NOC team to optimize resource allocation and take measures in advance to mitigate risks.

Fraud detection

Customers put their trust by sharing their personal and financial data and it is incumbent on telcos to reciprocate that trust by deploying effective models that can detect malicious behavior. Illicit activities, such as identity theft, fake authorization, and account cloning are on the rise. 80% of fraud specialists have seen AI-based platforms reduce false positives, payment fraud, & and prevent fraud attempts2. Unsupervised machine learning techniques could be leveraged to detect such anomalies in real-time so action could be taken to prevent such fraudulent activities.

Read our case study on fraud detection using machine learning.

Reduce customer churn

Telcos have a global presence with operations in different markets. Hence, it is extremely important to be able to uniquely identify the customers’ needs, preferences, and behavior in each market. By delivering personalized products and targeted campaigns, data science analytics can help telcos form deeper, meaningful, and long-lasting customer relationships that will enhance brand image and reduce churn.

Read our case study on customer churn prediction.

AI in marketing

Understanding consumer behavior has become quite complex with the advent of big data. Only those businesses will prosper who can make use of the enormous amounts of customer data that they have and convert it into actionable insights to engage with potential and existing customers on a more personalized level. Consequently, marketers need to up the ante to stay relevant and impactful.

Be sure to check out our dedicated guide to the top AI use cases for marketing, or if you're already looking for options, our guide to the top AI tools for marketers.

Explainable AI for understanding customers

It has become extremely important to understand why your customers are behaving the way they are. Gone are the days when you could just build buyer personas based on descriptive statistics. Explainable AI helps marketers understand the predictions generated by machine learning models so they can deliver a 360-degree customer experience to boost revenue.

Sentiment Analysis

This is the age of social media. Customers are sharing their feelings and emotions towards your product on social media channels. No, it is up to you to passively listen to those comments or actively mine them using sentiment analysis techniques to realign your goals and strategies that drive maximum impact and let you stay on top of the mind of your customer base.

Market Basket Analysis

Acquiring new customers is costlier than retaining existing ones. Hence, it makes sense to identify opportunities to cross-sell and up-sell to your existing customer base as it will keep costs down and profits up. Clustering and association rules which are unsupervised machine learning techniques can map customer journeys to identify the relationship between items bought by a customer so you can promote and cross-promote products to similar buyer personas.

Predicting Customer Churn

As mentioned above, it's far cheaper to keep a customer than to acquire a new one. It makes sense then, to do everything possible in order to reduce customer churn. One way to combat this problem is by predicting customer churn before it happens, giving a marketer time to come in with an offer and prevent them from jumping ship. This will improve customer lifetime value, and increase any company's bottom line.

Why not try our Templated Customer Churn Solution

AI in Manufacturing

The manufacturing industry has undergone a dramatic transformation over the decades due to increased robotization and automation. Now, Data Science presents itself as the next step in evolution as companies face increased competition from global supply chains and look for innovation to cut costs, boost revenue, and maximize productivity by harnessing the power of AI and analytics.

Product Development

With devices connected to the internet and providing a continuous stream of information, businesses can mine useful insights about product usage and be more proactive in developing features that cater to the needs of the customers. Machine Learning models can ingest the sensor data and make predictions that can help companies continuously improve their products.

Predictive Maintenance

Equipment failure or breakdown can lead to delays or even complete shutdowns, causing businesses to incur heavy losses. Build and optimize classification models that can analyze time-series data to predict equipment failures before they happen so as a company, you can plan in advance for any unforeseen delays in production to meet customer demand.

Read our use case on machine learning-powered predictive maintenance

Supply Chain Management

Leverage big data to train and deploy models to optimize production processes and accelerate product delivery to global markets. Manufacturing firms can incur huge financial losses due to delays and hence there is a need to prepare contingency plans for such delays. Techniques such as survival analysis and regression models could empower companies to predict lags and accurate delivery times.

AI Use Cases in the Public Sector

People are increasingly looking to their governments and state institutions to provide them with resources and equal opportunities to improve their standard of living. AI is at the heart of decision-making when it comes to the allocation and efficient utilization of resources to transform societies in all spheres of life through technology and innovation.


Governments can build models based on data collected from healthcare institutions to help them project the number of hospitals, clinics, rehabilitation centers, and other facilities needed to adequately address the needs of the people. Geographical data could tell which areas need special attention. With the threats posed by pandemics like COVID-19, it has become increasingly important to be able to predict the outbreak of such diseases well in advance using AI.


Law enforcement agencies can benefit hugely by analyzing crime statistics and finding correlations and hidden patterns. Deployed models can continuously learn from data and update themselves. Security and law enforcement agencies can turn these insights into actionable intelligence to be better able to respond to violent incidents, cybersecurity threats, and criminal activity.


With trillions of dollars being spent on defense by governments worldwide, it has become more important than ever to make defense institutions more efficient. AI-powered solutions can help governments monitor and track tier borders to detect infiltrations, safeguard their air and naval bases and upgrade equipment and logistics to significantly improve response time in case of a possible threat.

AI in Logistics & Supply Chain

Businesses across all industry verticals, be it retail, manufacturing, distribution, or logistics are interested in optimizing their supply chains and making them less vulnerable to the ever-changing market dynamics due to global disruption, lack of transparency, and uncertainty. Data Science holds the key to a whole new world of endless possibilities to make supply chains more resilient and efficient.

Inventory Management

Leverage machine learning and deep learning models to analyze past historical trends and build reports to uncover hidden trends and patterns to prevent over or under-stocking and always stay cash-flow positive.

Read our use case on supply and demand forecasting

Optimal Routing

Collect and analyze vast amounts of data from various sources to construct statistical models using machine learning. This will enable you to determine the most efficient and cost-effective route for delivering shipments. Additionally, you can track and monitor goods and services throughout their entire logistics journey in real-time, ensuring you stay informed about any potential damage caused by unfavorable weather or road conditions.

Predictive Maintenance

Having equipment up and running is key to ensuring a smooth supply chain. It’s possible with AI/ML - Predicting failures via advanced analytics can increase equipment uptime by up to 20%.3 With the help of AI and advanced analytics that take advantage of distributed systems to process huge volumes of data in parallel, maintenance costs can be significantly reduced by detecting in advance faults in the production lines and taking the necessary measures to rectify the equipment before it breaks down.

Read our use case on predictive maintenance




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