Artificial Intelligence (AI) Use Case

An Artificial Intelligence (AI) Use Case is an system use case model that involves the application of artificial intelligence technologies.

• Context:
  • It can (typically) reference AI Applications and AI Systems.
  • It can (often) improve efficiency and accuracy in: Data Analysis, Decision-making processes, and Routine administrative functions.
  • It can (often) involve data privacy, ethical use, and bias mitigation.
  • It can (often) evolve with advancements in machine learning, natural language processing, and computer vision.
  • ...
• Example(s):
  • a Predictive Maintenance AI Use Case: Using AI for predictive maintenance in manufacturing to reduce downtime and maintenance costs.
  • a Customer Service AI Use Case: Using AI-powered chatbots in customer service to provide 24/7 support and personalized interactions.
  • a Healthcare AI Use Case: Using AI in healthcare for advanced diagnostics and personalized treatment plans.
  • a Conversational Agent Interaction AI Use Case.
  • a Fraud Detection AI Use Case: Using AI for fraud detection in the finance sector to identify and prevent fraudulent transactions.
  • an Agriculture AI Use Case: Using AI in agriculture for crop monitoring and predictive analysis to optimize yields and reduce waste.
  • an Urban Planning AI Use Case: Using AI with urban planning to enhance traffic management and public safety through predictive modeling.
  • an Education AI Use Case: Using AI in education for personalized learning experiences and automation of administrative tasks.
  • an Energy Management AI Use Case: Using AI in energy management for optimizing energy consumption and predictive maintenance of infrastructure.
  • a Retail AI Use Case: Using AI into retail for inventory management, customer behavior analysis, and personalized marketing strategies.
  • a Legal AI Use Case: Using AI into legal sector systems for streamlining document review, enhancing due diligence, automating contract analysis, and providing litigation support.
  • ...
• Counter-Example(s):
  • A Traditional Software Use Case such as a simple CRUD (Create, Read, Update, Delete) application.
  • A Manual Processes, like traditional customer service methods.
• See: AI-Supported Task, Interaction Model, System User, Designed System, Data Privacy, Ethical Use, Bias Mitigation, Machine Learning, Natural Language Processing, Computer Vision.

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References

2024

• https://www.economist.com/business/2024/02/29/how-businesses-are-actually-using-generative-ai
  • NOTES:
    • The concept of "AI Use Case" refers to specific applications or scenarios where artificial intelligence technology is employed to solve problems, enhance processes, or create new opportunities across various industries. Here is a summary focusing on this topic:
    • It involves the practical application of artificial intelligence technologies to address real-world challenges and opportunities, demonstrating AI's versatility across different sectors.
    • It spans a wide range of industries, including healthcare, finance, legal services, manufacturing, and customer service, highlighting the technology's adaptability to diverse operational needs.
    • It can significantly improve efficiency and accuracy in tasks such as data analysis, decision-making processes, and routine administrative functions, showcasing AI's potential to enhance productivity.
    • It plays a crucial role in developing innovative solutions, such as personalized customer experiences, predictive maintenance in manufacturing, and advanced diagnostic tools in healthcare, indicating AI's capacity for driving innovation.
    • It often requires careful integration with existing systems and workflows, underscoring the importance of strategic planning and expertise in the successful deployment of AI technologies.
    • It raises important considerations around data privacy, ethical use, and bias mitigation, emphasizing the need for responsible AI deployment practices.
    • It continues to evolve as advancements in machine learning, natural language processing, and computer vision open new possibilities for application, suggesting a dynamic and expanding field of exploration.
    • Here are some of the use cases mentioned:
      • Corporate Giants and AI:
        • JPMorgan Chase, with more than 300 AI use cases in production.
        • Capgemini, planning to utilize Google Cloud's generative AI for over 500 industry use cases.
        • Bayer, claiming over 700 use cases for generative AI.
      • Specific AI Applications:
        • Presto introducing a generative AI assistant for drive-through orders.
        • Spotify's AI disc-jockey for music selection and banter.
        • Instacart removing an AI tool for generating food photos due to unappealing results.
        • Amazon launching Rufus, an AI-powered shopping assistant.
        • Google adding AI to Maps for a more immersive experience.
      • AI in Customer Service and Administration:
        • Amdocs and Sprinklr reporting significant improvements in customer service handling times and scores, respectively, using generative AI.
        • Bayer's use cases for routine tasks like data extraction from Excel and drafting documents in Word.
        • Nasdaq using AI for faster evidence gathering in financial crime investigations.
      • AI for High-Skill Workers:
        • Allen & Overy's collaboration with an AI startup for legal analysis.
        • Bank of New York Mellon using AI to assist analysts with data processing.
        • Sanofi employing an AI app for real-time operational information.
      • AI in Software Development:
        • Microsoft's GitHub Copilot and its competitors from Amazon and Google.
        • Fortive reporting a significant acceleration in software development time using generative AI.
      • AI in Search and Advertising:
        • Pinterest improving search result relevance with generative AI.
        • L’Oréal utilizing AI for advertising and promotion analytics, reporting productivity increases.

2023

• https://www.walkme.com/blog/enterprise-generative-ai/
  • 
  • NOTES:
    • Enterprise Generative AI is revolutionizing the landscape of work by automating activities and enhancing the capabilities of individual workers, as highlighted by McKinsey.
    • Enterprise Generative AI transcends theoretical discussions, finding concrete applications in businesses to innovate, operate, and compete more effectively.
    • Enterprise Generative AI is utilized across diverse sectors such as media, advertising, logistics, retail, and healthcare, automating content creation, optimizing supply chains, generating personalized user experiences, and accelerating drug discovery.
    • Enterprise Generative AI employs advanced algorithms like Generative Adversarial Networks (GANs) to create new, original content ranging from text to complex design models, showcasing its versatility.
    • Enterprise Generative AI serves as a strategic partner in the corporate environment, generating lifelike images, animations, and audio for marketing, streamlining administrative tasks, and enhancing operational management.
    • Enterprise Generative AI drives increased efficiency, creativity, and scalability in the enterprise landscape, making it an indispensable tool in the modern digital transformation era.
    • Looking forward, It is expected to significantly impact digital transformation across business models, processes, domains, and cultural transformation, with businesses needing to adopt a culture of learning and adaptability to fully leverage this technology.
    • Enterprise Generative AI can automate content generation, creating diverse and engaging content for various digital platforms using natural language processing (NLP) and machine learning to generate unique, SEO-friendly content such as product descriptions, blog posts, and social media updates.
    • Enterprise Generative AI enables automatic report writing, using advanced algorithms to analyze raw data, identify key trends, and generate comprehensive reports in an easily digestible format, saving time and ensuring consistency and accuracy.
    • Enterprise Generative AI assists in email drafting by understanding the context, generating appropriate responses, and suggesting improvements for efficient and effective communication.
    • Enterprise Generative AI enhances customer support by providing instant, accurate support using NLP and machine learning to understand customer queries and generate accurate responses, ensuring 24/7 support.
    • Enterprise Generative AI revolutionizes product design through generative design algorithms, exploring all possible permutations of a solution and optimizing the design based on specified parameters.
    • Enterprise Generative AI transforms data analysis, using machine learning and statistical models to analyze data, identify patterns, make predictions, and generate actionable recommendations for data-driven decisions.
    • Enterprise Generative AI personalizes advertising, analyzing user data with machine learning to identify trends and generate tailored advertisements to improve engagement and conversion rates.

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