Artificial intelligence is no longer an experimental technology—it is embedded into the daily operations of some of the world’s most recognizable companies. From predicting customer demand to improving healthcare decision-making, AI data analytics is enabling organizations to turn large, complex datasets into actionable insights.
AskEnola makes these capabilities more accessible, especially for market analysts and non-technical users who want reliable, real-time answers without navigating complicated dashboards.
Here are 10 real-world examples of how leading organizations use AI to enhance analytics and decision-making.
1. Walmart: AI-Driven Demand Forecasting
Walmart leverages artificial intelligence to analyze historical sales trends, customer shopping patterns, seasonal variations, and market conditions in a given area to predict product demand. This allows Walmart to effectively manage its inventory and stock levels in thousands of stores throughout the world. AI and data analytics support its supply chain modernization and improve store-level accuracy.
2. Netflix: Personalized Viewing Recommendations
Netflix’s recommendation system is one of the most popular examples of AI in large-scale analytics. The recommendation engine uses a combination of data points, including the viewer’s average viewing time, percentage of content viewed, and the viewer’s past viewing habits, to create recommendations of movies and series they may enjoy.
3. Starbucks: Customer Personalization with DeepBrew
Starbucks uses an internal AI-based platform called “Deep Brew,” which allows the company to customize offers for each of its customers and improve operations in stores. Deep Brew uses a combination of customer application data, previous purchasing habits, and regional preferences to recommend products and assist with managing stores
4. UPS: Route Optimization with ORION
UPS has created a new route planning tool called ORION (On-Road Integrated Optimization and Navigation). This advanced AI analytics system is capable of processing a wide variety of information and factors, such as delivery routes, traffic patterns, package information, and driver constraints, to suggest optimized routes that will result in more efficient daily deliveries.
5. American Express: Real-Time Fraud Detection
American Express employs machine learning algorithms for analyzing transaction trends to identify probable incidents of fraud as they occur. Their publicly searchable systems monitor customer behavior against millions of data points to reveal atypical activity. Using these advanced AI data analytics, American Express minimizes financial risk while enhancing customer protection.
6. Spotify: AI for Playlist Curation
Spotify has recorded how its engineering group uses machine learning technology to suggest playlists, such as Discover Weekly. Spotify uses this to analyze users’ music selections, attributes of songs, and how they interact with the Spotify platform. Spotify’s use of AI technology allows it to maintain relevancy and personalized playlists for millions of listeners globally.
7. Siemens: Predictive Maintenance in Manufacturing
Industrial companies like Siemens are taking advantage of AI technologies to analyze machinery sensor data (i.e., heat, vibrations, energy usage) to predict any potential machine failures. This enables manufacturers to be prepared for potential maintenance before a machine breaks down and therefore avoid costly downtime.
8. Mayo Clinic: AI-Assisted Clinical Decision Support
Mayo Clinic collaborates with technology companies to incorporate Artificial Intelligence (AI) in clinical decision support and patient risk assessment. For example, their model analyzes structured medical data and unstructured medical data to identify a patient’s risk factors, ultimately enabling physician teams to make well-informed decisions.
9. Delta Air Lines: AI for Operational Forecasting
Delta Air Lines uses AI to enhance their operational planning by using predictive modeling to determine when to expect flight delays, how many crew members they would need to assign, and to assess what level of maintenance will be required. They also use advanced AI models to enable the airline to be more data-driven in its decision-making throughout its entire network. This makes Delta Airlines a strong example of how AI is impacting transportation analytics.
10. Target: Market Basket and Customer Behavior Analytics
Target uses AI to gain a better understanding of purchasing behavior by identifying which items are typically purchased together and identifying emerging shopping trends in its stores. They also utilize predictive analytics in order to enhance promotional efforts and merchandise offerings. This is a strong example of how AI is being utilized to assist with decision-making in the retail industry.
How Conversational Analytics Elevates These Real-World Use Cases
Enterprise organizations that use AI on their projects tend to have strong internal data resources, enabling them to leverage their data for operational improvements quickly. There are many organizations without this level of capability. Therefore, it can take longer for them to realize the potential benefits of AI in data analytics. This is where a tool such as AskEnola comes into play.
What many of these companies have in common is access to large, advanced data science teams. However, not every organization has these internal resources. This is where a conversational analytics platform like AskEnola becomes valuable.
AskEnola allows analysts, managers, and non-technical decision-makers to ask natural-language questions such as:
- “What were our top-performing regions last month?”
- “How is customer behavior trending this quarter?”
The system interprets the question and provides accurate, contextual insights instantly. This approach removes the barrier of technical queries and complex BI dashboards, encouraging broader adoption of AI data analytics across teams.
From logistics and retail to entertainment and healthcare, AI-supported analytics is helping organizations respond faster, understand customers more deeply, and make decisions grounded in reliable data. As the role of analytics continues to expand, accessible AI will become essential for staying competitive in a data-driven world.
