Generative AI  has an authentic “Midas touch” when it comes to content – augmenting, accelerating, and generating new material, which is, in many ways,  revolutionizing the marketing industry – 30% of outbound marketing communications from large organizations will be generated digitally by 2025.

Generative AI gathers 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, narratives, presentations, and videos, among other content artifacts. It can also produce wholly unique products or augment existing ones, whether generated within the same paradigm (image to image) or across categories (image to text).

In other words, generative AI brings immense creative potential for marketers, with multiple cross-functional opportunities for harnessing its power.

What is Generative AI?

Generative AI is built on algorithms that can produce 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 come onto the scene, and image AI generation tools, such as Midjourney, are also growing in popularity.

This technology is founded on the tenets of natural language processing and machine learning. The software can comprehend the user’s prompt (request) and generate credible, authentic, and natural-sounding text on various topics.

The user guides the AI program when it refines and fine-tunes the text, made possible by its conversational format and interface. The next-generation AI tool then recognizes and assimilates the user’s query to deliver more precise and reliable outcomes.

Investors and business executives have taken an irrational – and highly optimistic – interest in generative AI to the point that IBM forecasts a fourfold increase in Gen-AI investments between 2023 and 2025. As marketing is expected to be a highly engaged early adopter, chief marketing officers (CMOs) are in an excellent position to demonstrate the tech’s value to their organizations.

Generative AI in Content Creation

GenAI lets marketers generate content more rapidly, in various ways, and of superior quality. These tools enable content creators to create prototypes, investigate concepts, pursue unique combinations, and discover alternative methods that inspire creativity rather than substituting or restricting it.

Generative AI tools in marketing can:

• Boost content output as well as effectiveness dramatically.
• Generate manuscripts of superior quality that need minimal editing.
• Produce content in various formats, including emails, blog posts, and social media captions.
• Research time can be conserved with AI summarized sources.

Generative AI tools, natural language processing (NLP), machine learning algorithms, and customer data analysis can create customized 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. Content creation using generative AI becomes even more effective when companies hire prompt engineers and/or invest in enterprise-ready tools that can ingest brand guidelines and CRM data.

Personalization at Scale with Generative AI

The continuous advancement of generative AI introduces fresh possibilities for delivering personalized and highly targeted advertising, both for text and visual marketing. 

For example, Facebook users in Utah could be presented with AI-generated graphics showing cyclists navigating the arid canyons of Utah. In contrast, users in New York City could be bombarded with images of cyclists traveling across the illustrious and uber-popular Central Park. By customizing the ad’s text according to the age and preferences of the viewer, large-scale personalization is now easily achievable.

For instance, craft retailer Michaels Stores incorporates generative AI using personalized interactions in its consumer engagement strategy. By leveraging generative AI, Michaels has increased the degree of personalization in its email campaigns from 20% to 95%.

This has resulted in a 41% increase in the click-through rate for SMS messaging campaigns and a 25% increase for email campaigns.

Companies like Meta are already building tools to make gen AI-led personalization available to businesses worldwide. A Meta Advantage+ catalog ad, for instance, changes the ad’s format and content based on what users are most likely to respond to.

Enhancing Customer Experience with AI

Generative AI is used in customer experience to design interactions that continuously elicit a positive response from the customer, thereby transforming ordinary encounters into moments of precise and intimate rapport. Gartner cites 38% of decision-makers as interested in generative AI to enhance their CX as one of the most prominent use cases for the technology.

Generative AI-powered conversational tools can facilitate customer self-service. This enhances customer satisfaction and decreases resolution times by guaranteeing case-specific context and tone of voice. While empowering bots, Generative AI helps agents respond more effectively across platforms, tailoring their answers to what best suits a particular CX channel.

IVR systems are transformed by the voice generation capabilities of Generative AI, generating speech that seems convincingly human-like. It can augment client data sets in the background with enrichment processes to inform better customer experiences in the future. Indeed, as we embrace new and innovative touchpoints for customer interactions, from voice search to Web 3.0, gen AI is the key to scaling and adapting CX quickly.

Overcoming Creative Limitations

AI models like GPT-4 enable a continuous stream of innovative ideas, which in turn advance the creative process, whether for formulating unique narrative shifts, envisioning artistic vistas, or creating an idea ready for productization.

Also, Generative AI displays exceptional proficiency in repetitive, labor-intensive tasks that frequently exhaust imaginative vitality.  Performing administrative duties like generating reports on data analysis, social media content, and design templates allows artists and designers to redirect their time to more substantive artistic pursuits.

These impacts are felt across creative formats and industry domains; here are a few examples:

• Art: Generative adversarial networks (GANs) help artificial intelligence produce astounding artwork, illustrations, and three-dimensional designs. Often, AI-generated works act as fountains of inspiration or creative starting points for designers and artists.
• Music: AI generates entire compositions or encourages musicians and composers to create music across different styles and genres, creating unique symphonies, freewheeling, and experimental samples.
• Medicine: By studying molecular structures, generative AI can identify potential drug candidates in the pharmaceutical industry, thereby expediting drug development and possibly developing life-saving medications more quickly.

Another area of possible application for generative AI is in the field of collaborative co-creation involving both humans and machines. Artists can employ AI technologies to create initial designs, conceptual artwork, or pieces of music, which can then undergo refinement. When using this tool to unleash one’s creative potential, the sky’s the limit, with generative AI technology and techniques evolving daily.

What Next? Challenges and Considerations for Marketers in the Gen AI Era

As marketing leaders gear up to unlock the full potential of generative AI in the creative sphere, it is also important to remember its drawbacks and limitations.

To begin with,  artificial intelligence models trained on massive datasets may generate skewed or inappropriate content. Maintaining ethical integrity and averting the dissemination of harmful material is an urgent matter of concern. Further, generative AI can rapidly generate enormous amounts of content, but its quality can vary dramatically. Also, users risk developing an excessive reliance on generative AI, which might stunt human innovation and creativity.

Increased regulatory oversight is necessary as this discipline enters its mature and developmental stages. Right now, ascertaining ownership and intellectual property rights on AI-generated content is highly intricate. It prompts inquiries regarding content rights ownership and questions on how businesses can safeguard their work against unauthorized usage.

Marketing leaders must watch for the lowest-hanging fruits in gen AI implementation and new, accessible tools penetrating the market. The barriers to entry are diminishing as cloud-based generative AI tools and embedded gen AI become increasingly more commonplace.