Generative AI is set to revolutionize marketing and PR, paving the way for a groundbreaking era of limitless innovation and unmatched productivity. I will change how we approach content creation, communication, and storytelling.
Why this matters:
Generative AI streamlines time-consuming tasks, empowering marketing and PR teams to concentrate on strategy and creativity.
By the numbers:
- By 2025, AI-generated marketing content from large firms will grow from 2% (2022) to 30% (Source)
- By 2030, a blockbuster film will debut with 90% AI-created content, a leap from 0% in 2022 (Source)
If you work in marketing or PR, you need to pay attention to generative AI because it will reshape the core of our industry. This new technology can supercharge content creation and unlock unprecedented personalization and efficiency. However, with great power comes great responsibility. As generative AI pushes the boundaries of what’s possible, you must stay vigilant to maintain authenticity, protect your brand, and uphold ethical standards.
Ignoring generative AI is not an option; embracing it responsibly is the key to thriving in this industry. To harness its potential and navigate the challenges, you must integrate generative AI into our existing workflow, ensuring that human creativity and oversight remain central.
What is Generative AI?
Generative AI is artificial intelligence that creates new content, such as text, images, or music, by learning patterns and structures from existing data. Instead of just analyzing and processing information like traditional AI systems, generative AI can produce original output, mimicking human creativity.
I know … this sounds crazy.
These AI systems are often built using deep learning techniques, learning from vast amounts of data. The more data they’re exposed to, the better they become at generating realistic and coherent content. Common examples of generative AI include chatbots, which can converse with users, and art generators, which can create unique digital images or music compositions.
Common Concerns With Generative AI
There are much more significant concerns with generative AI relating to national security, cybersecurity, privacy, and ethics. Of course, these are not my areas of expertise, so I’ll stay in my lane. But generative AI does raise several concerns for marketers and PR pros too:
- Authenticity: As generative AI becomes more advanced, it can create content that closely mimics human writing or design. This may lead to difficulty in distinguishing between genuine and AI-generated content, potentially damaging trust in brand communications.
- Misinformation: AI-generated content might inadvertently spread false or misleading information, which can harm a brand’s reputation or lead to the spread of fake news.
- Loss of creativity: While generative AI can be a helpful tool for producing content quickly, relying too heavily on it might result in losing human creativity and originality.
- Job displacement: As generative AI becomes smarter, some worry that it might replace human roles in content creation, leading to job losses in the industry.
- Ethical considerations: Generative AI can sometimes produce inappropriate or offensive content, which could damage a brand’s reputation.
These concerns must always be top of mind and managed using filters, guidelines, and checks and balances. Doing so will ensure the responsible and effective use of generative AI and protect your business simultaneously.
I highly recommend reading this piece from Michelle Garrett about the concerns of using generative AI for marketing and public relations.
Conversational AI Versus Generative AI
Conversational AI enables software to understand and respond to natural language, allowing for more human-like interactions. Generative AI uses machine learning algorithms to generate original content, such as text, images, or audio, without being explicitly programmed.
There are similarities and differences. Here’s a comparison of the two:
Similarities
- Learning from data: Both generative AI and conversational AI use deep learning techniques to learn from large data sets. This helps them understand patterns and structures in human language, images, or other forms of content.
- Creating content: Both AI systems generate content, whether text, images, or music. In addition, they can produce new output based on the data they have been trained on.
Differences
- Purpose: Generative AI creates new content, such as text, images, or music, by understanding patterns and structures in existing data. Conversely, conversational AI primarily focuses on engaging in interactive dialogues with users, simulating human-like conversation.
- Interaction: Conversational AI is designed to respond to user inputs, typically through text or voice, to provide information, answer questions, or carry out tasks. Generative AI doesn’t necessarily involve direct user interaction; it can create content independently without specific user inputs.
- Applications: Generative AI has many applications, including art generation, music composition, and content creation for marketing, public relations, or entertainment. Conversational AI is mainly used in chatbots, voice assistants, and customer support channels, assisting users in obtaining information or completing tasks.
In summary, while generative and conversational AI share similarities in their learning processes and content creation capabilities, they differ in their primary purposes, user interactions, and specific applications. For example, Generative AI produces new content, whereas conversational AI centers on engaging in two-way conversations with users.
Generative AI tools for Marketing and PR
These are several generative AI tools available today. Each tool has unique features and capabilities, and choosing the right tool depends on what you need:
- Natural Language Generation (NLG) tools generate human-like language. Some popular NLG tools include ChatGPT, Anyword, QuillBot, and Jasper AI.
- Music Generation tools create original music compositions. Some popular music generation tools include Amper Music, Jukedeck, and AIVA.
- Image Generation tools generate images from scratch or modify existing images. Some popular image-generation tools include DALL-E, NVIDIA’s StyleGAN, and DeepArt.io.
- Video Generation tools create or modify existing videos from scratch. Some popular video generation tools include Synthesia, RunwayML, Artbreeder, and Vamify.
- Chatbot tools create virtual assistants that can interact with users in natural language. Some popular chatbot tools include Dialogflow, IBM Watson Assistant, and Botpress.
Generative AI has the potential to revolutionize the marketing and PR industry, bringing about a new era of innovation and productivity. By 2025, AI-generated marketing content from large firms is predicted to grow from 2% to 30%, while a blockbuster film with 90% AI-created content is expected to debut by 2030.
Generative AI can streamline time-consuming tasks and enable teams to focus on strategy and creativity. However, concerns about authenticity, misinformation, loss of creativity, job displacement, and ethical considerations must be addressed to ensure this technology’s responsible and effective use.
Generative AI creates new content, while conversational AI primarily focuses on engaging in interactive dialogues with users. Generative AI tools for marketing and PR include natural language generation tools, music generation tools, image generation tools, video generation tools, and chatbot tools. To harness the potential of generative AI and navigate its challenges, companies must integrate it into their existing workflow while maintaining human creativity and oversight.
They Hype of Generative AI is Real
Yes, it’s real.
You’ll notice in the below chart that the volume of mentions around OpenAI, ChatGPT, and Generative AI is steady. The spike in November was the ChatGPT launch, resulting in huge increases in social media conversations across all the major networks and media coverage.
As with most hot and trending topics like Generative AI for marketing, whenever there is a rise in media coverage or conversation on social media, there is always a correlation between search interest. Below is an embedded chart from Google Trends that proves this case.
People Also Ask
What can generative AI be used for?
Generative AI can be used for various tasks such as content creation, image and video generation, music composition, design, and drug discovery. It can help automate repetitive tasks, personalize content, and generate new, creative ideas.
What is generative AI content?
Generative AI content refers to any text, images, or other media generated by artificial intelligence systems designed to create or modify content using machine learning algorithms, like GPT-3 or DALL-E.
What are some of the features of generative AI?
Generative AI typically features deep learning capabilities, the ability to generate new content, the capacity to learn from a large dataset, creativity, adaptability, and often unsupervised or semi-supervised learning.
What are generative AI’s best examples?
Some best examples of generative AI include OpenAI’s GPT-3 for text generation, DALL-E for image generation, and Magenta by Google for music and art creation.
What is the most popular generative AI?
The most popular generative AI is OpenAI’s GPT-4, known for its remarkable text generation capabilities and wide range of applications. However, Google Bard released a public beta on March 21, 2023, and the hype is growing.
What are the challenges with generative AI?
Challenges with generative AI include data quality and bias, the need for vast computational resources, difficulties in controlling output, ethical concerns, potential misuse, and the inability to understand complex human emotions and context.
What can generative AI not do?
Generative AI cannot fully understand or replicate human emotions, consciousness, or empathy. It also struggles with generating content in a highly controlled manner, ensuring ethical usage, and avoiding replicating biased or harmful content.
What are the flaws of generative design?
Flaws in generative design include the potential for suboptimal solutions, overfitting, the possibility of generating impractical or unfeasible designs, and the need for human intervention to evaluate and refine outputs.