Five Key Tasks for Capitalizing on AI and Data Analytics

For organizations looking to capitalize on artificial intelligence and data analytics, the road forward can be daunting. From identifying use cases and data sources to developing models and sorting out the software and hardware stack, it’s easy to get lost in the complexities of a project.

At Dell Technologies, we understand these challenges firsthand — there’s an almost infinite number of ways to apply AI. We have come to recognize that like all organizations, we need to take a strategic approach to AI, one that allows the best practices identified on individual projects to be leveraged across the enterprise.

With that goal in mind, we have identified five fundamental tasks, or use cases, for implementing AI in the enterprise:

Using this strategic framework, we look for similarities between different AI tasks and opportunities to use common approaches to address challenges in projects across the business. We’ve found this to be an effective way to evaluate the work effort required for AI projects.

Let’s take a look at each of these use-case tasks, along with some examples of AI projects that we’ve successfully implemented.

Anomaly detection is the process of using AI to identify outliers in a dataset. For example, organizations use anomaly detection to identify fraudulent transactions, spot malicious network behavior and recognize manufactured parts that are out of tolerance.

At Dell Technologies, we use AI-driven anomaly detection to fight warranty service fraud, which is a serious and costly threat to manufacturers. Our research found that as much as 10 percent of warranty reserves can be taken away by fraudulent claims. So, we looked at how we could get in front of fraud by predicting it with machine learning techniques.

We quickly determined that our machine learning models were much better at this task than the manual methods we used previously. The models identified key patterns of behavior that allowed our staff resources to focus more time on actual fraud cases instead of false positives.

The results were impressive. We went from a 4x ROI with people manually investigating questionable warranty claims to a 13x ROI with machine learning models. In a single month, we recovered $1 million that otherwise would have been lost to fraudulent claims.

Natural language processing, or NLP, is a sub-field of AI focused on enabling computers to interpret and respond to language-based data.

We put NLP to work in an intelligent support tool that we developed to streamline the customer support experience. Our tool, which incorporates multiple machine learning models, gives our agents predictions for the best troubleshooting steps to suggest to customers who call in to report issues with products. The tool helps agents diagnose and solve problems quickly and accurately without having to navigate through a maze of web links, troubleshooting guides and decision trees.

Today, we have more than 3,000 agents using the tool, servicing more than 10,000 customers per day. And we’re seeing some great results. The tool has helped us achieve a 10 percent reduction in call times, along with improved customer satisfaction and a reduction in the numbers of customers who have to call back because they are still experiencing problems.

Recognition is the AI process of identifying what a given artifact is — for example, which person is in the photo, what type of car is in the video, and so on. Augmented reality, or AR, is one type of recognition application. With AR, your view in the physical world is enriched with overlaid graphical elements.