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AI technology is exploding and industries are racing to adopt it as soon as possible. Before your business dives headfirst into a confusing sea of opportunity, it’s important to explore how generative AI works, what red flags businesses should be aware of, and how you can evolve into an AI-ready business.
How generative AI actually works
One of the most common and powerful techniques for generative AI is large language models (LLMs), such as GPT-4 or Google’s BARD. These are neural networks trained on massive 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 series of words. For example, given the input ‘The sky is’, the model can predict ‘blue’, ‘clear’, ‘cloudy’ or ‘falling’.
By using different inputs and parameters, LLMs can generate different types of output, such as summaries, headlines, stories, essays, reviews, captions, slogans, or code. For example, given the input, “write a catchy slogan for a new brand of toothpaste,” the model could generate “smile with confidence,” “take away your worries,” “the toothpaste that cares,” or “shine like a star.” ”
Red flags that companies should consider when using generative AI
While generative AI can provide many benefits and opportunities for enterprises, it also has some drawbacks that need to be addressed. Here are some of the red flags companies should consider before adopting generative AI.
Public versus private information
As employees begin to experiment with generative AI, they will create prompts, generate text, and build this new technology into their workflow. It is essential to have clear policies delineating information released to the public versus private or proprietary information. Submitting private information, even in an AI prompt, means that information is no longer private. Start the conversation early to ensure teams can use generative AI without compromising proprietary information.
Generative AI models are not perfect and can sometimes produce output that is inaccurate, irrelevant or nonsensical. These outputs are often referred to as AI hallucinations or artifacts. They can result from various factors, such as insufficient quality or quantity of data, model bias or errors, or malicious manipulation. For example, a generative AI model can generate a fake news article that spreads misinformation or propaganda. Therefore, enterprises should be aware of the limitations and uncertainties of generative AI models and verify their output before using it for decision making or communication.
Using the wrong tool for the job
Generative AI models are not necessarily one-size-fits-all solutions that can solve every problem or task. While some models prioritize general responses and a chat-based interface, others are built for specific purposes. In other words, some models may be better at generating short texts than long texts; some may be better at generating factual lyrics than creative ones; some may be better at generating texts in one domain than the other.
Many generative AI platforms can be further trained for a specific niche, such as customer support, medical applications, marketing or software development. It’s easy to just use the most popular product, even if it’s not the right tool for the job at hand. Enterprises need to understand their goals and requirements and choose the right tool for the job.
Waste in; garbage outside
Generative AI models are only as good as the data they’re trained on. If the data is noisy, incomplete, inconsistent, or biased, the model is likely to produce results that reflect these flaws. For example, a generative AI model trained on inappropriate or biased data can generate copy that is discriminatory and could damage your brand’s reputation. Therefore, companies must ensure that they have high quality data that is representative, diverse and unbiased.
How to evolve into an AI-ready enterprise
Adopting generative AI is not a simple or straightforward process. It requires a strategic vision, a culture shift and a technical transformation. Here are some of the steps enterprises need to take to evolve into an AI-ready enterprise.
Find the right tools
As mentioned above, generative AI models are not interchangeable or universal. They have different capabilities and limitations depending on their architecture, training data, and parameters. Therefore, companies need to find the right tools that fit their needs and objectives. For example, an AI platform that creates images, such as DALL-E or Stable Diffusion, probably wouldn’t be the best choice for a customer service team.
Platforms are emerging that specialize their interfaces for specific roles: copywriting platforms optimized for marketing results, chatbots optimized for common tasks and problem solving, developer-specific tools that connect to programming databases, medical diagnosis tools, and more. Enterprises should evaluate the performance and quality of the generative AI models they use and compare them to alternative solutions or human experts.
Manage your brand
Every company also needs to think about control mechanisms. For example, where a marketing team was traditionally the gatekeepers for brand messages, they also formed a bottleneck. With the ability for anyone in the organization to generate text, it’s important to find tools 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.
Develop the right skills
Generative AI models are not magic boxes that can generate perfect texts without any human input or guidance. They require human skills and expertise to use them effectively and responsibly. One of the most important generative AI skills is prompt engineering: the art and science of designing inputs and parameters that get the desired outputs from the models.
Rapid engineering involves understanding the logic and behavior of the models, creating clear and specific instructions, providing relevant examples and feedback, and testing and refining the results. Rapid engineering is a skill that can be learned and improved over time by anyone working with generative AI.
Set up new roles and workflows
Generative AI models are not standalone tools that can operate in isolation or replace human workers. They are collaborative tools that can enhance and enhance human creativity and productivity. That’s why enterprises need to build new workflows that integrate generative AI models with human teams and processes.
Enterprises may need to create entirely new roles or positions, such as an AI ombudsman or AI QA specialist, who can monitor the use and output of generative AI models and address them as they arise. They may also need to implement new policies or protocols, such as ethical guidelines or quality standards, that can ensure the accountability and transparency of generative AI models.
Generative AI is no longer on the horizon; it has arrived
Generative AI is one of the most exciting and disruptive technologies of our time. It has the potential to transform the way we create and consume content across domains and industries. However, applying generative AI is not a trivial or risk-free endeavor. It requires careful planning, preparation and execution. Companies that embrace and master generative AI gain a competitive advantage and create new opportunities for growth and innovation.
Yaniv Makover is the CEO and co-founder of Every word.
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