Generative AI: Practical Use Cases & Pitfalls
Explore how organizations can harness generative AI for customer service, sentiment analysis, and marketing—while addressing privacy, security, and accuracy concerns for successful, responsible adoption.
Most technology platforms have made significant investments in varying degrees of AI features, with a significant focus on Generative AI to match the hype of AI in 2023 and 2024. However, many organizations are left wondering, "What use cases will work for me, and what best practices and pitfalls should I pay attention to?"
In this blog, we'll touch on some of the most common use cases and how to best get started to maximize the value of your AI investments.
Unique Features of Generative AI
Generative AI leverages Large Language Models (LLMs) to understand natural language inputs and, more importantly, the intent of the input and respond with useful and relevant content. This could be a large, unstructured data set consisting of common text but also images, code, sound, and more. The data sets available to it are what it bases its output on, in conjunction with how LLMs will decide what to respond to an input with. In short, a "response" or "output" is generated to an input - creating tremendous productivity gains for organizations that properly set up their datasets and LLMs.
Generative AI Concerns
The main concerns of GAI are:
Vulnerabilities for Businesses
One of the…
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