AI Use Cases Are Everywhere—But How Do You Build One That Works?

What is an AI use case? It depends on when you ask the question.

AI’s trajectory follows the same brutal pattern as the dot-com boom: savvy companies that want to survive the competition must have sharp strategies and sustainable business models. 

Just as Amazon emerged from a 90% stock plunge to dominate e-commerce and cloud computing, today's AI landscape will likely see a similar consolidation of power among companies that can translate artificial intelligence into tangible business results. But the game is constantly changing. According to PwC’s Matt Wood, AI is headed toward a watershed moment of innovation and growth: "2025 will bring significant advancements in quality, accuracy, capability, and automation that will continue to compound on each other, accelerating toward a period of exponential growth."

How do companies compete in a shifting landscape? Through extensive research here on Enterprise AI Today, we’ve analyzed AI implementations across industries, revealing patterns and strategies organizations can consider when developing their AI initiatives. Eight key approaches emerge that can be leveraged for new applications, ranging from Morgan Stanley's AI-powered financial advisory system to Duke Energy's breakthrough in energy grid management. While each organization's journey with AI will be unique, these lessons and use cases provide valuable guidance.

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Morgan Stanley's AI @ Morgan Stanley Assistant enhances fundamental wealth management. The financial giant faced a critical challenge: their advisors needed to quickly process and utilize insights from over 70,000 research reports produced annually. The AI assistant, built on GPT-4 and trained exclusively on Morgan Stanley's proprietary content, achieved 98% adoption among financial advisors by focusing specifically on streamlining research access and analysis. This targeted approach enabled advisors to provide faster, more informed responses to client queries while maintaining high-touch personal service.

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FedEx's Package Fingerprint technology targets a core logistics operation. The company identified package tracking—a cornerstone of their business—where AI could create immediate value. The system creates unique digital signatures for each package by analyzing comprehensive data about size, shape, weight, and movement through their network. With 18 million daily shipments across 220 countries, this technology transformed FedEx's core tracking capabilities while integrating seamlessly with existing operations. The system's success stemmed from its laser focus on enhancing a fundamental business function rather than trying to create entirely new operational capabilities.

2. Build Comprehensive Data Integration Systems

Duke Energy transformed grid management through AI. The utility provider faced significant challenges in predicting and managing power outages across its vast infrastructure, particularly during severe weather events. Traditional outage prediction and response methods were largely reactive, leading to extended downtime and inefficient resource allocation. In partnership with Accenture, Duke Energy developed an AI-powered Outage Prediction Model that integrates multiple data streams: real-time weather forecasts, historical outage patterns, geographical data, and grid performance metrics. This comprehensive system enables rapid detection and response to potential grid issues, allowing for proactive deployment of repair crews and equipment to areas most likely to experience disruptions.

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Bayer's FieldView platform is a breakthrough in comprehensive data integration for agricultural technology. The system processes information from 220 million acres across more than 20 countries, analyzing over 250 layers of high-definition data from satellites, sensors, and farm equipment. This integration enables sophisticated analysis of soil conditions, weather patterns, crop health, and yield predictions. The platform's ability to combine drone-based field scouting (MagicScout Air), digital pest identification (MagicTrap), and plant-level precision spraying (MagicSprayer) demonstrates how integrated data systems can transform complex operations. The system can identify and document more than 140 weed types, diseases, and insect species across multiple countries in seconds, enabling precise, targeted interventions that were impossible with traditional farming methods.

3. Scale Modularly

L'Oréal's beauty tech ecosystem took a modular approach to AI in a complex consumer market. The company faced the challenge of maintaining leadership in the beauty industry while adapting to increasingly digital consumer expectations. Instead of building a single, monolithic AI system, L'Oréal developed distinct but interconnected modules. Their SkinConsult AI diagnostic tool, trained on 6,000 clinical images and 4,500 selfies, can detect seven aging signs and provide personalized skincare routines. The CREAITECH AI Content Lab focuses on creating beauty images, producing 1,000 unique images in eight months. Meanwhile, their Beauty Genius virtual assistant handles personalized product recommendations. This modular approach allows L'Oréal to test and refine each component independently while maintaining a cohesive customer experience.

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Spotify's three-part AI system is another sophisticated example. Serving over 433 million active users, Spotify built its recommendation engine using three distinct but integrated technologies: collaborative filtering, natural language processing, and audio models. This modular approach enables them to analyze various data points, including song preferences, search behavior, playlist data, geographic location, and device usage patterns. The success of this strategy is evident in their metrics: 30% of all Spotify streams come from AI-recommended songs, and users who regularly engage with AI-generated playlists like Discover Weekly spend twice as much time on the platform as other users.

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‍4. Prioritize Technical Infrastructure

American Airlines' cloud migration illustrates the critical importance of infrastructure optimization. The airline faced significant operational challenges in managing its complex network of flights, resources, and customer experiences across hundreds of destinations. Their partnership with Microsoft Azure centralized data warehousing and legacy applications into a single operations hub. Their Intelligent Gating Program at Dallas Fort Worth International Airport (DFW), with its 136 gates, demonstrates the impact of proper infrastructure: the system saves up to 10 hours of taxi time per day, leading to significant reductions in fuel usage and CO2 emissions. This success relied on establishing robust cloud infrastructure before scaling their AI applications.

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Google's data center optimization through DeepMind represents a landmark achievement. Facing mounting challenges in managing energy consumption across its massive data center network, where cooling systems represented a significant operational cost and environmental impact, Google implemented an AI-powered recommendation system that analyzes data from thousands of sensors every five minutes. The system uses deep neural networks to predict how different actions affect future energy consumption. By 2018, this evolved into an autonomous control system operating across multiple data centers, incorporating eight different safety mechanisms to ensure reliable operation. The implementation reduced cooling energy consumption by 40%, while the subsequent autonomous system consistently delivers 30% energy savings on average.

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5. Invest in Specialized AI Applications

Danske Bank faced a monumental challenge in fraud detection, processing approximately 1,200 potential fraud cases daily, with 99.5% of these alerts being false positives. Rather than implementing a general-purpose AI solution, Danske Bank developed its in-house AI system specifically for fraud detection. The system performs real-time transaction analysis, recognizes patterns across vast datasets, and adapts to stay ahead of new fraud techniques. This specialized focus enabled them to achieve a 60% reduction in false positives while substantially improving their ability to detect actual fraud cases.

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Maintaining consistent product quality and optimizing manufacturing processes was difficult for PepsiCo, mainly when producing Cheetos. Through generative AI and deep reinforcement learning, PepsiCo developed a sophisticated "machine brain" that autonomously controls extruder settings, adjusting critical parameters like temperature and moisture to maintain desired product quality. This specialized system has improved product consistency and led to a 15% increase in market penetration. Integrating AI into their marketing operations has reduced campaign development cycles from 6-9 months to 3-4 months, showing how specialized applications can create value across multiple business functions.

6. Incorporate Real-Time, Adaptive Capabilities

Alaska Airlines implemented an innovative route optimization system that analyzes multiple factors simultaneously to determine the most efficient flight paths. The system processes vast amounts of data, including real-time weather conditions, aircraft weight variations, and existing route patterns, to make intelligent adjustments that save time and fuel. During a six-month trial period, the Flyways program achieved remarkable results: fuel savings of 480,000 gallons and reduced carbon emissions by 4,600 tons. This success stems from the system's ability to adapt to changing conditions and optimize routes in real time.

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Another aviation example: United Airlines' Connection Saver system showcases real-time adaptation in passenger service management. The system analyzes flight data, passenger itineraries, and airport conditions to make intelligent decisions about flight departures and passenger connections. United has significantly improved connection management by replacing manual processes with AI-driven real-time decision-making. The system automatically notifies passengers about gate changes and estimates walking time between gates during connections. In 2022 alone, United Connection Saver reduced missed connections by 650,000, demonstrating the significant impact of real-time adaptation capabilities.

7. Design for Environmental Sustainability

Salesforce's approach to sustainable AI demonstrates how environmental considerations can be integrated into core technology strategies. The company had difficulty managing AI's environmental impact, particularly regarding energy consumption and carbon emissions from data centers. Their response was comprehensive: they implemented a strategy centered on low-carbon data centers and precise emissions measurement. By training AI models in facilities powered by electricity that emit 68.8% less carbon than the global average, Salesforce achieved significant environmental improvements. Their implementation of Predictive Autoscaling technology alone was projected to reduce approximately 1,128 tons of carbon emissions in 2024.

8. Tailor Your Solution

Intuit's implementation of AI in tax services is an expert intelligence strategy. The company recognized that the U.S. tax code's complexity—which has grown from 400 pages in 1913 to over 4 million words in 2023—required more than just general-purpose AI. Their approach combines AI capabilities with expert knowledge through a comprehensive platform strategy. This implementation has generated $1.4 billion in revenue from expert-based services alone, representing 30% of their total revenue. The success stems from training their AI systems with tax experts rather than relying solely on general-purpose models, enabling more accurate and context-aware tax processing.

Conclusion

These case studies demonstrate the diverse approaches organizations can take to AI implementation. From Morgan Stanley's focus on core business processes to Salesforce's emphasis on sustainable computing, each implementation offers valuable insights for organizations developing their AI strategies. As artificial intelligence evolves, organizations that thoughtfully adapt these lessons to their specific contexts will be better positioned to realize AI's full potential.

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