When considering the application of GenAI, it's essential to prioritize potential business value over merely exploring technical possibilities. The following list highlights promising use cases, organized by key value drivers, to help spark ideas.
Cost and efficiency gains
- Automate High-Volume/Repetitive Tasks: Utilize GenAI to automate repetitive tasks, with the key performance indicator (KPI) being the automation rate, measured as a percentage of tasks automated.
- Accelerate Content Development: Enable hybrid human/AI collaboration to speed up content creation. The KPI here can be an increase in hours saved compared to the current time taken to develop content.
- Content Evaluation: Reduce errors and rework by using GenAI for content evaluation. The KPI can be the decrease in the number of tickets filed to update incorrect content.
Personalize and tailor recommendations
- Generate Consistent Omnichannel Experiences: Leverage image and text generation to create uniform experiences across channels. The KPI could be an existing engagement metric, such as “time spent on a platform” or the “click-through rate" on specific sections of an application.
- Generate Contextual Cues: Improve product documentation by generating contextual cues to guide users. The KPI can be customer engagement with the documentation, measurable by page views.
- Generate Operational Insights: Enhance risk reduction by generating operational insights. The KPI could be the improvement in risk assessments.
Enhance and scale human interaction
- Provide Overview Summaries: Use GenAI to generate summaries from documents. The KPI could be an engagement metric, such as “time spent on a platform” or the click-through rate on sections of your application.
- Forecast Emerging Issues and Trends: Employ GenAI to predict emerging issues and trends. The KPI could be the application’s predictive accuracy in terms of cost savings.
- Collaborate for Innovation: Foster innovation and gain a competitive advantage through AI collaboration. The KPI could be an engagement metric like “time spent on platform” or the click-through rate on application sections.
Aligning use cases with core business priorities ensures that implementation focuses on achieving tangible results rather than deploying technology simply for its own sake. Lets take a look at comprehension and generative use cases where businesses can leverage GenAI.
Comprehension Use Cases:
Comprehension use cases utilize natural language understanding to extract insights and structured information from unstructured data. Here are some notable comprehension use cases:
Sentiment Analysis:
By leveraging natural language understanding, sentiment analysis automatically classifies the emotional tone within textual content such as customer feedback, surveys, and social media posts. This allows organizations to identify pain points and perceptions without the need for large-scale manual review. Integration strategies include sentiment API queries or batch processing analytics aggregated into reporting dashboards. This use case focuses on understanding customer perceptions and identifying opportunities for improvement.
Document Summarization:
AI-powered document summarization creates condensed snippets of text, highlighting key details within lengthy corpora like wikis, research papers, and knowledge base articles. This improves discoverability, enabling users to quickly determine the relevance of a piece of text before deciding to read the full document. It also facilitates new modes of document interaction and searchability, especially across massive repositories. This use case aims to increase productivity across the board.
Metadata Extraction:
Metadata extraction uses natural language understanding to identify and extract key information attributes from unstructured textual content. This includes entities such as people, places, companies, topics, concepts, tone, and relationships. Structured metadata makes it easier to understand documents and content at scale.
Generative Use Cases:
Generative use cases leverage GenAI's ability to create human-like outputs, tailored to specific needs and contexts. Here are some noteworthy generative use cases:
Conversational Interfaces:
Conversational interfaces allow natural dialogue between end users and intelligent assistants via chat, voice, and potentially augmented reality (AR). These fluid experiences provide answers, recommendations, and next-step suggestions, reducing the need to navigate complex apps or menus. Over time, contextual awareness of user goals and preferences enables personalized guidance.
Data Visualization:
GenAI can automatically create relevant charts, graphs, and diagrams tailored to provided datasets. Beyond basic types like histograms or pie charts, advanced visualizations including interactive infographics, animated data stories, and tailored dashboard layouts bring key trends to light. These visualizations are personalized to the consumption use case.
Report Automation:
Report automation generates personalized, dynamic summaries of the most salient business insights for specific user needs. Rather than relying on static, template-driven reports, generative capabilities allow for unique views, sending key signals from centralized data assets. Automated analysis identifies emerging issues, while customizable layouts deliver tailored briefings that different business leaders require.
Code Generation:
The recent integration of large language models (LLMs) into coding workflows presents an exciting opportunity for enhanced productivity and creative exploration. LLMs transform the way developers approach code creation by translating natural language instructions into functional code snippets. These intelligent assistants suggest alternative solutions, streamline repetitive tasks, and fill in knowledge gaps. However, LLMs are not replacements for core coding expertise. Their true potential lies in augmenting human capabilities through collaborative synergy.
Content Generation:
Leveraging the raw generative power of LLMs, organizations can automatically draft written content tailored to specific guidelines, topics, voices, and creative directions. This capability is particularly valuable for marketing, communications, and documentation needs.
Reference: Bustos, J. P., Soria, L. L., & Arsanjani, A. (2024). Generative AI application integration patterns integrate large language models into your applications. Packt Publishing, Limited.
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