Generative AI has an authentic “Midas touch” when it comes to content – augmenting, accelerating and generating new material, which in many ways is revolutionizing the marketing industry – 30% of outbound marketing communications from large organizations will be generated digitally by 2025.
Generative AI brings together knowledge from pre-existing artifacts to produce new and authentic content that faithfully replicates the characteristics of the training data.. It can create training modules, product designs, stories, presentations, and videos, among other content artifacts. It can also produce completely unique products or augment existing products, whether generated within the same paradigm (image to image) or across categories (image to text).
In other words, generative AI brings immense creative potential to marketers, with multiple cross-functional opportunities to harness its power.
What is Generative AI?
Generative AI is built on algorithms capable of producing new and authentic content, spanning text, images, audio and video. Text generation, like ChatGPT, is currently the most popular form; However, other generative AI text programs, such as Bard (Google) and Claude (Anthropic), have also entered the scene, and AI image generation tools, such as Midjourney, are also gaining traction. popularity.
This technology is based on the principles of natural language processing and machine learning. The software can understand the user’s prompt (request) and generate credible, authentic and natural text on various topics.
The user guides the AI program as it refines and refines the text, made possible by its conversational format and interface. The next-generation AI tool then recognizes and assimilates the user’s query to provide more accurate and reliable results.
Investors and business executives have shown irrational – and very optimistic – interest in generative AI, so much so that IBM is planning a multiplication by four of investment in AI generation between 2023 and 2025. With marketing expected to be a highly engaged early adopter, chief marketing officers (CMOs) are in an excellent position to demonstrate the value of the technology to their organization.
Generative AI in content creation
GenAI enables marketers to generate content faster, in different ways, and at higher quality. These tools allow content creators to create prototypes, explore concepts, research unique combinations, and discover alternative methods that inspire creativity rather than substituting or restricting it.
Generative AI tools in marketing can:
- Dramatically increase content production and efficiency.
- Generate high-quality manuscripts requiring minimal editing.
- Produce content in a variety of formats, including emails, blog posts, and social media captions.
- Search time can be saved with AI-summarized sources.
Generative AI tools, natural language processing (NLP), machine learning algorithms, and customer data analysis can create personalized content for a given audience.
By analyzing consumer behavior and identifying patterns in their interactions with a specific platform or brand, a unique content strategy can be formulated to increase customer engagement. Creating content using generative AI becomes even more effective when companies hire fast engineers and/or invest in out-of-the-box tools that can ingest brand guidelines and guidelines. CRM data.
Personalization at scale with generative AI
Continued advances in generative AI introduce new possibilities for delivering personalized, highly targeted ads, both in text and visual marketing.
For example, Facebook users in Utah could be presented with AI-generated graphics showing cyclists navigating Utah’s barren canyons. In contrast, users in New York might be bombarded with images of cyclists traversing the famous and popular Central Park. By personalizing ad text based on viewer age and preferences, large-scale personalization is now easily achievable.
For example, craft retailer Michaels Stores is integrating generative AI using personalized interactions into its consumer engagement strategy. By leveraging generative AI, Michaels increased the degree of personalization of its email campaigns by 20% at 95%.
This resulted in a 41% increase in click-through rate for SMS campaigns and 25% for email campaigns.
Companies like Meta are already creating tools to make AI-powered personalization available to businesses around the world. A Meta Advantage+ catalog ad, for example, changes the format and content of the ad based on what users are most likely to respond to.
Improving customer experience with AI
Generative AI is used in customer experience to design interactions that consistently elicit a positive response from the customer, transforming ordinary encounters into moments of precise, intimate connection. Gartner cites 38% of decision makers interested in generative AI to improve their CX as one of the most important use cases for this technology.
AI-powered generative conversational tools can facilitate customer self-service. This improves customer satisfaction and reduces resolution times by ensuring case-specific context and tone of voice. While empowering bots, generative AI helps agents respond more effectively across platforms, tailoring their responses to what is best for a particular CX channel.
IVR systems are transformed by Generative AI’s voice generation capabilities, generating speech that sounds surprisingly human. It can augment customer data sets in the background through enrichment processes to improve customer experiences in the future. Indeed, as we embrace innovative new touchpoints for customer interactions, from voice search to Web 3.0, AI generation is the key to rapidly evolving and adapting CX.
Overcoming creative limitations
AI models like GPT-4 enable a continuous flow of innovative ideas, which in turn advance the creative process, whether formulating unique narrative shifts, considering artistic perspectives, or creating an idea ready to be produced.
Additionally, generative AI displays exceptional mastery of repetitive, labor-intensive tasks that often deplete imaginative vitality. Performing administrative tasks such as reporting on data analysis, social media content, and design templates allows artists and designers to redirect their time toward more substantive artistic pursuits.
These impacts are felt in all creative formats and in industrial fields; Here are some examples:
- Art: Generative adversarial networks (GANs) help artificial intelligence produce stunning artwork, illustrations, and three-dimensional designs. Often, AI-generated works serve as sources of inspiration or creative starting points for designers and artists.
- Music: AI generates entire compositions or encourages musicians and composers to create music in different styles and genres, creating unique symphonies, free and experimental samples.
- Medicine: By studying molecular structures, generative AI can identify potential drug candidates in the pharmaceutical industry, thereby accelerating drug development and potentially developing life-saving drugs more quickly.
Another possible area of application of generative AI is that of collaborative co-creation involving both humans and machines. Artists can use AI technologies to create initial designs, concept artwork, or pieces of music, which can then be refined. When using this tool to unlock your creative potential, the sky is the limit, with generative AI technology and techniques evolving daily.
After that ? Challenges and Considerations for Marketers in the Age of AI Generation
As marketers prepare to unleash the full potential of generative AI in the creative sphere, it’s also important to keep its downsides and limitations in mind.
For starters, AI models trained on massive data sets can generate biased or inappropriate content. There is an urgent need to preserve ethical integrity and avoid the dissemination of harmful material. Additionally, generative AI can generate huge amounts of content quickly, but its quality can vary widely. Additionally, users risk developing an over-reliance on generative AI, which could hinder human innovation and creativity.
Increased regulatory oversight is necessary as this discipline enters its stages of maturity and development. Currently, verifying ownership and intellectual property rights over AI-generated content is very complex. This raises questions about ownership of content rights and questions about how companies can protect their work from unauthorized use.
Marketers should monitor the low-hanging fruit in AI generation implementation and new accessible tools entering the market. Barriers to entry are decreasing as cloud-based generative AI tools and embedded generative AI become more common.
