Artificial intelligence (AI) technology has exploded in recent years, with industries racing to adopt it as fast as possible. One of the most common and powerful techniques for generative AI is large language models (LLMs), such as GPT-4 or Google’s BARD. LLMs are neural networks trained on vast amounts of text data from various sources such as books, websites, social media, and news articles. They learn the patterns and probabilities of language by guessing the next word in a sequence of words.
By using different inputs and parameters, LLMs can generate different types of outputs such as summaries, headlines, stories, essays, reviews, captions, slogans, or code.
Red Flags for Enterprises
While generative AI can offer many benefits and opportunities for enterprises, it also comes with some drawbacks that must be addressed. Here are some of the red flags that enterprises need to consider before adopting generative AI:
1. Private Information: As employees experiment with generative AI, it is essential to have clear policies that delineate information that is cleared for the public versus private or proprietary information. Submitting private information, even in an AI prompt, means that information is no longer private.
2. Inaccurate Outputs: Generative AI models may sometimes produce outputs that are inaccurate, irrelevant, or nonsensical. These outputs are often referred to as AI hallucinations or artifacts and may result from various factors such as insufficient data quality or quantity, model bias or errors, or malicious manipulation.
3. One-Size-Fits-All Solutions: Generative AI models are not necessarily one-size-fits-all solutions that can solve any problem or task. Enterprises need to understand their goals and requirements and choose the right tool for the job.
Considerations for Enterprises
Adopting generative AI is not a simple or straightforward process. It requires a strategic vision, a cultural shift, and a technical transformation. Here are some of the considerations for enterprises:
1. Finding the Right Tools: Enterprises need to find the right tools that match their needs and objectives. AI platforms are emerging that specialize their interface for specific roles: copywriting platforms optimized for marketing results, chatbots optimized for general tasks and problem-solving, developer-specific tools that connect with programming databases, medical diagnosis tools, and more.
2. Incorporating Brand Standards: It’s important to find tools that allow you to build in your brand guidelines, messaging, audiences, and brand voice. Having AI that incorporates brand standards is essential to remove the bottleneck for on-brand copy without inviting chaos.
3. Prompt Engineering: The art and science of designing inputs and parameters that elicit the desired outputs from the models is prompt engineering. Prompt engineering involves understanding the logic and behavior of the models, crafting clear and specific instructions, providing relevant examples and feedback, and testing and refining the outputs.
4. New Workflows: Enterprises need to establish new workflows that integrate generative AI models with human teams and processes. They may need to create entirely new roles or functions, such as AI ombudsman or AI-QA specialist, who can oversee and monitor the use and output of generative AI models and address problems when they arise.
Generative AI is one of the most exciting and disruptive technologies of our time. It has the potential to transform how we create and consume content in various domains and industries. However, adopting generative AI is not a trivial or risk-free endeavor. Enterprises that embrace and master generative AI will gain a competitive edge and create new opportunities for growth and innovation.
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