Thought Leadership

Generative AI Use Cases and Applications

Generative artificial intelligence (AI) refers to large language learning models (LLMs) that can generate new content and output based on patterns learned from training data, rather than simply classifying or summarizing existing information. As generative AI continues to advance, it has the potential to transform many business processes by automating time-consuming creative and analytical tasks.

This white paper examines common use cases for applying generative AI across various content types within an enterprise context. It outlines categories of usage—classifying information, summarizing insights, answering questions, enhancing existing content and creating new content—and provides examples for how generative AI could be applied for text, images, tabular data and programming code.

Key Applications by Content Type
Text

Generative text models can help companies analyze textual content like customer reviews, legal contracts, chat logs and more to drive efficiencies in areas ranging from fraud detection to customer service. Key use cases include:

Summarization: Quickly distill key information from lengthy documentation like legal policies, research publications, health records or support transcripts using natural language generation techniques. This improves human review and discovery of critical details.

Question answering: Build chatbots and virtual assistants powered by generative AI to automatically answer customer support, HR, medical, enterprise search and other domain-specific questions – reducing the workload for human agents.

Content creation: Generate new text content tailored to specific contexts like personalized marketing emails and social media captions, formatted reports, product descriptions optimized for keywords and style and more to assist human writers.

Images & Video

Computer vision models allow generative AI systems to classify, summarize, enhance and generate image and video content to drive efficiencies in areas like inventory and product management. Example applications include:

Classification and summarization: Analyze images from merchandise, insurance claims, social media feeds, microscopy scans, and more to categorize and identify key objects, events, styles, emotions, and other salient details – helping improve downstream analysis.

Enhancement: Boost the quality, clarity, aesthetic and details in product images, medical scans, autonomous vehicle vision feeds and more which can be important for analytics and diagnosis.

Content creation: Generate new images depicting product concepts, build 3D renderings based on layouts, develop animated scenarios and more to bring ideas to life – better assisting designers and planners.

Tabular Data & Code

Generative techniques can also be applied to structured data like tables and programming code for the purposes of analysis and optimization, and testing and the generation of new artifacts. This builds efficiencies into software development lifecycles. Real-world applications include:

Classification and metrics: Categorize code elements, predict defects, identify security risks, measure complexity and quality to more strategically focus developer time/effort.

Documentation and maintenance: Generate natural language summaries of code execution traces, produce API documentation lookup references, simplify/refine existing code with error handling and other updates to assist programmers and testers.

Content creation: Output new boilerplate code, data integration scripts, cloud configuration templates and more based on contextual prompts and constraints – quickly synthesizing routine yet complex code.

Implementation Considerations

A key benefit of generative AI is enhancing existing human workflows rather than fully automating them outright. As such, integration with surrounding infrastructure is vital for enterprise adoption. Key considerations include:

Compatibility with content repositories, data formats, and classification taxonomies.

User-friendly interfaces embedded into existing tools like IDEs and business intelligence dashboards.

Secure and compliant handling of regulated content like personal data.

Accuracy metrics and narratable audit trails to instill appropriate levels of confidence and trust.

By properly integrating generative models into legacy environments with transparent oversight, companies can transform workflows across all structured and unstructured data types. Workers are empowered to be more productive, focusing on tasks that require high levels of human intervention and skill.

Conclusion

This framework summarizes many promising applications of leveraging cutting-edge generative AI techniques across text, visuals, data and code to drive greater efficiencies in information-intensive business processes. As the underlying machine learning capabilities continue maturing in the coming years, generative AI adoption will accelerate across enterprise IT landscapes and core vertical domains like healthcare, finance, marketing, design and engineering. Solution architecture will shift from merely reactive systems to more proactive recommendations and completions within existing interfaces. This will amplify and build upon uniquely human creativity to continue to unlock new sources of value.

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