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21 September 2022


5 Examples of How AI Is Used Across the Enterprise

To say that business’s use of artificial intelligence has gained significant traction in recent years would be an understatement. McKinsey’s 2021 global survey on the state of AI reported that 56% of businesses are leveraging AI in at least one function.

However, “AI” is an extremely broad term—while some organizations are sticking to basic predictive analytics, others are experimenting with advanced ML algorithms, deep learning, and computer vision. 

The opportunities for AI adoption are virtually endless—other than resulting in significant cost savings and efficiency, enterprises who leverage AI have improved customer service, maximized marketing campaign effectiveness, bolstered their security posture, optimized their supply chain, increased employee productivity, and automated tedious processes. 

In this post, we’ll take a closer look at how AI is being used across organizations to help garner some inspiration as you develop strategies for your enterprise. 

5 Enterprise Examples of AI in Action 

What does it mean to be a successful business? 

While the answer varies for everyone, being a productivity machine that’s always one step ahead of the competition sounds ideal. By developing a viable AI strategy, enterprises can do just that. 

1. Optimizing the Supply Chain 

As a consumer-facing business, Domino’s Pizza relies on having a healthy, reliable supply chain. Knowing that each of their global locations have access to enough fresh ingredients to satisfy customer demand is of the upmost importance. 

However, they have over 4,000 franchise locations, and each location has its own unique needs. Without a way to understand when Location A’s busy time is versus if Location B is understaffed, they faced issues with lost perishable products, employee downtime, and strained vendor relations.  

Ultimately, Domino’s not only needed to optimize supply chain forecasting but also to implement better tools for strategic planning at scale. To accomplish this, the pizza chain expanded its demand forecasting models to include additional data, such as demand trends, marketing promotions, and holidays. 

These AI solutions improved the accuracy of eight-week supply chain forecasts, enabled real-time modeling, reduced food waste, and optimized the use of their labor force.  

2. Sorting Textual Data 

B2C companies are inundated with mountains of textual data from emails, customer interactions, invoices, product documentation, audio recordings, images, and more. That data is rich with insights—if you have a way to process and analyze it at scale. 

The challenge is that the data comes in a multitude of formats from various sources. Manually sifting through it requires the kind of time, effort, and resources that most organizations can’t sustain.

Additionally, textual data provides more than just information—to fully mine textual data, you must analyze other factors, such as mood and emotion, especially when dealing with consumers, third-party vendors, and investors. 

AI technologies can use natural language processing (NLP) and other solutions to gather, analyze, classify, and report on structured and unstructured data.  

By applying sentiment analysis, a facet of NLP to textual customer feedback, PayPal identified their customers’ top complaint areas so that product managers knew exactly what issues to address, in which priority. They discovered that password login problems were frequently the most complained about issue.

With that feedback, they deployed engineering fixes to their site and continued using NLP to process feedback in multiple languages and monitor complaint volume. (Spoiler alert: It went down!) 

3. Reducing Customer Churn 

When Verizon experienced high churn rates among its prepaid customers, they knew they had to change their processes—and fast. They needed a way to accurately predict churn and target customers that were likely to churn with tailored marketing communications. 

However, it wasn’t that easy. Verizon has over 30 million transactions with pre-paid customers every day across 40 different channels. They needed a strategy that could capture massive amounts of data and analyze it to produce accurate predictive models to anticipate and remediate customer churn

This wasn’t something that could be done manually—they used machine learning to create customer churn models that generated insights into the leading indicators of churn. As a result, they identified 30% of churn before they lost customers.  

By understanding who was likely to churn, Verizon could figure out potential causes and develop strategies to retain their customers. No matter the industry, customer retention and loyalty building are a priority—for many businesses, customer retention is more profitable than new customer acquisition. 

4. Automating Video Editing 

In recent years, using videos in marketing campaigns and on company websites has become a popular strategy to engage customers—as of 2022, nearly 90% of businesses are using video to reach their target audience. 

However, video editing is time-consuming, complex, and requires a specific set of skills. Say an advertising company needs to consolidate massive amounts of raw footage into short-form content for commercials, social media, and other promotional channels. Without help, this would take a human specialist countless hours of manual work. 

By using AI-based customizable video editing tools, such as audio ducking, automatic reframing, color matching, and content screening, experts can streamline the editing process. Editors can utilize AI models and build catalogs that allow ML to evaluate video footage and identify hundreds of activities, objects, and places based on predefined criteria. These methods help businesses produce a higher volume of video content with greater efficiency and lower costs.  

While AI video editing tools can’t create a finished product as well as a human expert, they can save hours of tedious work pre-polishing the videos so that human editors can focus on producing the best content possible. 

5. Detecting and Preventing Fraud 

Healthcare fraud is a massive issue in the United States, Recently, a US state auditor faced challenges detecting and fighting intelligent healthcare fraudsters—both patients and medical service providers alike. 

The auditor was using a manual process to detect fraud and randomly picking samples to test for fraudulent activity. Using this system, the auditor was inspecting less than 5% of transactions, meaning that most fraudulent transactions went undetected. 

By implementing a machine learning method, the auditor was able to identify risky behavior, use supervised learning to understand fraudulent patterns, and prevent future fraud. The algorithm could scan a high volume of data and quickly learned (and flagged!) suspicious behavioral patterns. 

The results were significant—they identified about $20 million in losses and discovered new types of fraud. The auditing agency leveraged end-to-end detection and prevention, reduced downtime caused by random inspections, and gave priority to high-risk cases. 


These five enterprise AI use cases are just scratching the surface of what data science could be doing for your organization. Not only can AI help improve the efficiency of your processes and make your enterprise more profitable, when used successfully, it can also help you gain a leg up over the competition. 

If you want more inspiration, check out 50 ways our customers are using AI to power their businesses today. 

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