Assistive technology 2: Generative AI

Generative artificial intelligence

Generative artificial intelligence, often referred to as generative AI, is a subset of artificial intelligence focused on creating new content, data, or information. Instead of simply recognising patterns or making predictions based on existing data, generative AI has the capacity to generate entirely new content, which can be in the form of text, images, audio or other media.

Generative AI has a wide range of applications, from art and content creation to natural language generation, drug discovery, and even scientific research. It has the potential to automate creative and complex tasks, but it also raises ethical and philosophical questions, particularly in terms of content authenticity and the potential for AI-generated content to deceive or mislead.

Generative AI in copywriting

Generative artificial intelligence is being used in copywriting to automate and assist in the creation of written content, such as articles, product descriptions, advertisements, and more. One of the most notable applications of generative AI in copywriting is the development of natural language generation (NLG) models, which are trained to generate human-like text. You have already seen ChatGPT or Bard; both use NLG models. These models have the potential to significantly impact the field of copywriting in the following ways:

Content generation

• Generative AI can produce large volumes of written content quickly. This is especially useful for businesses that require a high volume of content for websites, blogs, and marketing materials.

Product descriptions

• E-commerce companies can use generative AI to automatically generate product descriptions, helping them list a wide range of products more efficiently.

Social media posts

• AI-powered tools can create social media posts, including text and image captions, to maintain an active online presence and engage with audiences.

Email marketing

• AI can assist in composing personalised email marketing campaigns, tailoring content to individual subscribers’ preferences.

Data Reporting and summaries

• Generative AI can be used to automatically create reports, summaries, and insights from raw data, helping businesses quickly understand and act on their data.

News articles and blog posts

• News organisations and content platforms can use AI to generate news articles, summaries, and blog posts for topics that require rapid reporting.

Multilingual content

• Generative AI can create content in multiple languages, expanding the reach of businesses to global audiences.

Consistency and branding

• AI can ensure content consistency in terms of tone, style, and branding, maintaining a coherent voice across various channels.

The impact of generative AI in copywriting

• Efficiency: Generative AI speeds up the content creation process, enabling businesses to produce more content in less time.
• Cost savings: By automating content generation, businesses can reduce the costs associated with hiring copywriters or content creators or enabling existing staff to do more creative work.
• Scalability: Generative AI allows businesses to scale their content production easily, making it suitable for large e-commerce platforms, news organisations, and content-driven businesses.
• Personalisation: AI can generate personalised content tailored to individual user preferences and behaviours, enhancing customer engagement.
• Content availability: AI can generate content variations for A/B testing and marketing experiments to optimise messaging for different audiences. A/B testing is a technique to try out new approaches and test if the new approach is having the intended impact.
• Time savings: Content creation becomes more efficient, freeing up human copywriters to focus on more creative, strategic, and complex tasks.

Challenges of generative AI

However, it’s important to note that while generative AI can be a valuable tool in copywriting, it is not without its challenges. Ensuring content quality, avoiding issues related to misinformation, and maintaining the authenticity of the brand’s voice are all important considerations.

It’s often most effective when used in conjunction with human oversight to review and fine-tune the AI-generated content.

Now let’s move on to our final example of assistive technology, conversational AI.