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AI technology is exploding and industries are racing to adopt it as quickly as possible. Before your business plunges headfirst into a sea of confusing opportunities, it’s important to explore how Generative AI works, what businesses need to consider, and how to 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 (LLM), such as GPT-4 or BARD from Google. They are neural networks trained on large amounts of textual data from various sources such as books, websites, social media, and news articles. They learn language patterns and probabilities by guessing the next word in a sequence of words. For example, given the input “Sky is”, the model can predict “blue”, “clear”, “cloudy”, or “falling”.
By using different inputs and parameters, LLMs can generate different types of outputs such as summaries, titles, stories, essays, reviews, captions, taglines, or code. For example, given the entry, “write a catchy tagline for a new brand of toothpaste”, the template might generate “smile with confidence”, “wipe your worries away”, “the toothpaste you care about”, or “twinkle like a star”. ”
Businesses should consider red flags when using generative AI
While Generative AI can provide many advantages and opportunities for businesses, it also has some disadvantages that need to be addressed. Here are some of the red flags companies need to consider before adopting generative AI.
Public information versus private information
As employees begin experimenting with generative AI, they will create prompts, generate text, and integrate this new technology into their workflow. It is essential to have clear policies that delineate what information is permitted for public versus private or proprietary information. Submitting private information, even in an AI prompt, means the 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 inaccurate, irrelevant, or nonsensical results. These outputs are often called hallucinations or AI artifacts. They can result from various factors such as insufficient quality or quantity of data, biases or model errors or malicious manipulations. For example, a generative AI model can generate a fake news article that spreads misinformation or propaganda. Therefore, companies should be aware of the limitations and uncertainties of Generative AI models and verify their results before using them for decision-making or communication.
Using the wrong tool for the job
Generative AI models are not necessarily universal solutions capable of solving any problem or task. While some templates prioritize generalized responses and a chat-based interface, others are designed for specific purposes. In other words, some models may be more efficient in generating short texts than long texts; some may be better at generating factual texts than creative texts; some may be better at generating texts in one domain than another domain.
A lot Generative AI platforms can be more formed for a specific niche like 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. Businesses need to understand their goals and requirements and choose the right tool for the job.
Garbage inside; garbage outlet
Generative AI models are only as good as the data they are trained on. If the data is noisy, incomplete, inconsistent, or biased, the model will likely produce outputs that reflect those flaws. For example, a generative AI model trained on inappropriate or biased data can generate discriminatory text and harm your brand reputation. Therefore, companies need to ensure that they have high quality, representative, diverse and unbiased data.
How to evolve into an AI-ready business
Adopting generative AI is not a simple or straightforward process. It requires strategic vision, cultural change and technical transformation. Here are some of the steps companies need to take to evolve into an AI-ready enterprise.
Find the right tools
As stated above, Generative AI the models are neither interchangeable nor universal. They have different capabilities and limitations depending on their architecture, training data, and settings. Therefore, businesses need to find the right tools that match their needs and goals. For example, an AI platform that creates images – like DALL-E or Stable Diffusion – probably wouldn’t be the best choice for a customer support team.
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 to programming databases , medical diagnostic tools, etc. Companies 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 business should also think about control mechanisms. Where, for example, a marketing team may have historically been gatekeepers of brand messaging, they were also a bottleneck. With the ability for anyone in the organization to generate copy, it’s important to find tools that allow you to integrate your brand guidelines, messaging, audiences, and voice. Having AI that incorporates brand standards is key to eliminating the bottleneck of on-brand copy without causing chaos.
Cultivate the right skills
Generative AI templates are not magic boxes that can generate perfect texts without any human intervention or guidance. They require human skills and expertise to use them effectively and responsibly. One of the most important skills for generative AI is rapid engineering: the art and science of designing inputs and parameters that get the desired outputs from models.
Rapid engineering involves understanding the logic and behavior of models, developing clear and specific instructions, providing relevant examples and feedback, and testing and refining results. Rapid engineering is a skill that can be learned and improved over time by anyone working with generative AI.
Establish new roles and workflows
Generative AI models are not stand-alone tools that can operate in isolation or replace human workers. They are collaborative tools that can increase and improve human creativity and productivity. Therefore, companies need to establish new workflows that integrate Generative AI templates with human teams and processes.
Companies 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 release of AI models generative and solve problems when they arise. They may also need to implement new policies or protocols — such as ethical guidelines or quality standards — that can ensure accountability and transparency of generative AI models.
Generative AI is no longer on the horizon; it happened
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 various domains and industries. However, adopting generative AI is not a trivial or risk-free endeavour. It requires careful planning, preparation and execution. Companies that embrace and master generative AI will gain a competitive edge and create new opportunities for growth and innovation.
Yaniv Makover is the CEO and co-founder of any word.
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