AI Use Cases: Where Technology and Business Value Intersect

Content Creation

AI is increasingly being designed to generate content across industries. Generative AI use cases can write articles, generate social media posts, summarize lengthy documents, and even create music or artwork based on training data. Some sources estimate that by 2025, as much as 90% of online content could be generated by AI. This includes everything from text to images, videos, and audio. The rapid acceleration of tools like ChatGPT, DALL-E, Stable Diffusion, and MidJourney has led to a massive increase in AI-generated content (you will find some of it on our blog headers).

Currently, AI is already responsible for creating billions of images, and the volume of AI-generated text and media is expected to grow exponentially as more businesses adopt generative AI technology​.

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Text Summarization

AI-driven text summarization tools help extract key information from large documents or datasets, making it easier for individuals and organizations to digest information quickly. This is especially useful in industries that handle massive amounts of written content.

Examples can be found in the new Google AI snippets, the outputs of most LLMs, industry-specific applications in law and the healthcare industry, and emerging agent-based AI.

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Product and Content Recommendations

Another one of the best uses for AI is recommendations. AI has always been central to personalized product and content recommendations on platforms like e-commerce websites and streaming services. Collaborative filtering is a core component of the Netflix experience (what to watch next) as well as the TikTok algorithm (auto-served). Every retail website deploys some type of cosine similarity or Jaccard similarity algorithm, but the field is constantly evolving.

The key is to have very large (peta to exascale) information datasets on which to train. One reason Netflix and Amazon have been successful is that they leverage high-performance AI object storage as their data store, allowing them to achieve scales not possible by traditional SAN/NAS approaches.

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Supply and Inventory Management

A branch of product recommendations is inventory management. Currently, this is more of a traditional AI approach. By leveraging new and existing algorithms, manufacturing companies can optimize supply chains by predicting demand, tracking inventory levels, and automating restocking processes. This business intelligence ensures that brands can efficiently deploy capital while ensuring minimal risk of stockout thanks to accurate demand forecasting.

Having said that, these algorithms require human oversight as they cannot account for certain logistics and supply chain shocks, such as pandemics or worker strikes. So, automation is limited.

Being successful in this space requires scalability, often distributed storage systems that are able to deliver performance and handle data diversity. This makes them ideal for modern object stores like MinIO ↗.

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Sentiment Analysis

AI-driven sentiment analysis tools can analyze customer feedback, social media posts, and phrases used in online comments, descriptions, and reviews to gauge public sentiment about a brand, product, or service. This helps companies make informed decisions about pricing or product development based on consumer emotions.

The most common algorithms for sentiment analysis include traditional machine learning models and advanced deep learning models. The choice of algorithm depends on the complexity of the text, the available labeled data, and the need for interpretability versus accuracy.

The firehose of social media provides a rich trove of data, again highly diverse in nature (text, audio, video, images), which is generally stored in high-performance object storage. To give you a sense of the volume, YouTube processes 4.3 PB of video a day, TikTok about half that, and X.com about 700 TB a day.

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Fraud Detection

Fraud and fraud detection is effectively an arms race that continues to escalate. One of the best uses for AI is to enhance fraud detection by analyzing patterns in transaction data, identifying anomalies, and flagging potential fraudulent activities. Financial institutions, payment processors, retailers, and other companies leverage a number of different algorithms to secure their platforms and reduce losses from fraud.

Those include traditional methods like Logistic Regression and Decision Trees to more advanced techniques like Neural Networks, Autoencoders, and Anomaly Detection algorithms. The choice of algorithm depends on the dataset, the type of fraud being detected, and the need for accuracy versus interpretability. Advanced models are increasingly popular due to their ability to capture complex fraud patterns in large, real-time datasets.

Those large, real-time datasets are often kept in object storage, although if latency is a key requirement and the data is structured, a traditional SAN/NAS system may be employed.

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Targeted Advertising

Unsurprisingly, many of the biggest players in AI are also some of the biggest players in the online advertising space. AI plays a vital role in improving targeted advertising by analyzing user behavior, preferences, and past interactions to deliver personalization for advertising. This increases the effectiveness of digital marketing campaigns, drives traffic, improves user engagement, and enhances the customer experience as a whole.

Ad targeting on the internet relies on a combination of algorithms. The choice of algorithm depends on the complexity of the data, the need for real-time targeting, and the desired balance between exploration (trying new ads) and exploitation (focusing on well-performing ads). Platforms like Facebook, Google, and Amazon leverage these algorithms to optimize ad placement and increase engagement rates.

While many of the approaches can work on small datasets (millions of rows, 10s to 100s TBs of data), deep learning (neural networks) using models like Wide & Deep Learning can exceed 1 PB when dealing with global-scale digital ad systems. Those tend to be run on high-performance object stores like MinIO.

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Autonomous Vehicles

Another one of the best uses for AI is autonomous vehicles. AI is at the core of autonomous driving technology, and it all runs on an object store. In fact, to our knowledge, every major initiative in the industry is object storage-based, and most of it is MinIO.

It is not just cars and trucks; think drones, robots, ocean vessels, and more. Each draws on massive amounts of video, sensor data, telemetry, log files, and the like to make decisions in real-time.

In the automotive space, companies rely on a range of complex algorithms that integrate deep learning (CNNs, RNNs), reinforcement learning, sensor fusion, SLAM, and path planning to achieve fully autonomous navigation. Companies like Tesla, Waymo, and Cruise are constantly advancing these technologies. These systems increasingly leverage machine learning over rule-based logic.

Given the scale (exabyte plus) and nature of the data (video, log files), they are the exclusive domain of object storage.

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Customer Segmentation

One of the OG uses of AI, customer segmentation, allows businesses to group customers based on their behaviors, preferences, and buying patterns. This virtual assistant helps tailor marketing strategies to specific customer groups for more effective advertising.

While these are not massive unstructured data sets, they can grow large in some cases— particularly in retail, drug/healthcare industry, and financial services.

Customer segmentation algorithms range from simple to complex, with the choice ultimately depending on the complexity of the data, the business goals, and the need for interpretability versus accuracy.

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Image Analysis

Image analysis has exploded over the past few years, driven by the growth in satellite imagery, surveillance cameras, and other video technologies. The healthcare industry is another massive driver of AI-powered innovation, and an AI's MRI analysis is already better than a doctor's. For example, image analysis in healthcare can be used to identify diseases and help inform a patient’s medical diagnosis with speed and precision.

The algorithms used in image analysis are increasingly complex and flexible. They cover autonomous driving, medical imaging, and facial recognition, helping machines interpret and analyze visual data.

This, again, is the domain of high-performance object stores like MinIO. There are two great blogs on the subject—one on YOLO ↗ (you only look once) and another on Max Planck Institute’s ↗ use of MinIO to store MRI data.

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Threat Detection

AI-driven threat detection identifies and responds to cyber threats like malware in real-time, providing a critical layer of computer security for businesses in various sectors. MinIO is used by almost all of the major players in the space because this is effectively a massive log analytics anomaly detection exercise. The volumes are massive. One customer is producing 250TB a day. This is the definition of a performance-at-scale problem and only select object stores need apply.

The field is fairly mature, and both traditional and newer AI techniques are used. Traditional approaches include signature-based detection and rule-based systems, while more advanced machine learning, deep learning, and graph-based models are applied. Algorithms like Random Forests, Autoencoders, and LSTMs are widely used for cybersecurity, while techniques like Bayesian Networks and NLP are effective for more specialized threat detection, such as phishing or insider threats.