63% of marketing executives, according to Gartner, intend to invest in generative AI within the next 24 months. So, what is generative AI, and why is it a top priority? Generative AI, a category of artificial intelligence, can create a wide range of content, such as synthetic data, text, visuals, and audio, from previous training datasets, one or more AI algorithms, and a new input called a “prompt.” It has the potential to transform creative and business processes for organizations completely.

How Generative AI Works: 3 Model Variants

Generative AI models produce fresh and original content using neural networks to recognize structures and patterns inside existing data. These models can be of various kinds, and you can combine two or more to create powerful generative AI apps. Some of the examples include:

1. Variational autoencoders (VAEs)

Two neural networks, which are commonly denoted as the encoder and decoder, constitute VAEs. An encoder changes an input into a more compact and concentrated data version. The compacted representation effectively retains the data the decoder needs while eliminating extraneous information. The encoder and decoder work together to identify an easy and efficient way of data representation.

2. Diffusion models

During training, these models carry out a dual-step technique involving forward and reverse diffusion. Forward diffusion involves the gradual introduction of random noise into the training data. Moving forward, the noise is progressively eliminated to reassemble the data.

The model initiates the reverse denoising method to produce fresh data using entirely random noise. This two-step process facilitates the training of hundreds or possibly infinite layers.

3. Generative adversarial networks (GANs)

Introduced in 2014, GANs involve a contest between two neural networks. The generator creates fresh examples, while the discriminator determines if the generated content is authentic or fabricated.

Both models are simultaneously trained. As the discriminator improves its ability to identify the generated content and the generator produces higher-quality content, both become more intelligent. This repeated process encourages both parties to consistently enhance the produced material until it becomes indistinguishable from the preexisting content.

An advancement in generative AI models is their capacity to use various learning methodologies, such as unsupervised or semi-supervised, during training.

As a result, organizations can leverage vast quantities of unlabeled information to develop foundation models with more speed and simplicity. Foundation models, as their name suggests, can serve as the underpinning for AI systems capable of executing various tasks.

Applications of Generative AI

As algorithmic models become more sophisticated, generative AI examples and use cases are spread across different industries and verticals.

1. In art and design

By employing generative models for image creation and style transfers, artists are empowered to create unique and aesthetically compelling works of art. An alternative approach is text-to-image generation, in which generative models turn textual descriptions into visual representations that match them.

Also, the technology can generate 3D models or animations and transform doodles/sketches into realistic images. DeepDream Generator from Google’s AI arm, Midjourney, and WOMBO Dream (a non-fungible token or NFT creation tool) are all generative AI examples of this use case.

2. In content creation

By automating multiple aspects of content creation, generative AI can enable marketers to save time and resources to achieve faster time-to-market. AI models can produce prototype content for email campaigns and social media postings, among other tasks. Human marketers can then tweak and personalize this content.

For example, Writesonic, Jasper, and Copy.ai are AI writing tools that can assist marketers in rapidly generating high-quality copy. Gen AI can even help in visual content marketing, a truly disruptive way to use AI.

Another generative AI example is the process of modifying preexisting content. By examining trends in data and user feedback, the AI can deliver insightful recommendations and ideas for refinement. It can pinpoint areas for enhanced outcomes in advertising copy and customer communications – for example, using a tool like Phrasee.

3. In business and innovation

One of the most formidable challenges for marketers and business leaders is the arduous task of consistently coming up with new game-changing ideas.

Generative AI models can enhance the productivity of ideation sessions with innovative recommendations and different points of view. These AI-generated concepts can act as a sounding board or kickstarter for fresh and clutter-breaking ideas, eventually developing unique new strategies.

Indeed, according to a forecast by PwC, 45% of overall economic gains will be attributed to AI-driven product enhancements that are slated to boost consumer demand by 2030 massively.

This is because, as the years progress, AI will expand product range and inventory coupled with enhanced personalization, appeal, and affordability.

The Benefits of Generative AI

By understanding what generative AI is and incorporating it bolding into your business strategy, it is possible to:

1. Enhance creativity and collaborative innovation

Businesses are constantly trying new ways to make product development more collaborative. Two of the most common are idea competitions, such as hackathons and crowdsourcing. However, organizations need help to implement the multitude of ideas generated.

They may need a systematic approach to evaluating the concepts. Or, it could be difficult for contributors to provide the necessary details to make their ideas viable. Integrating disparate concepts is a further impediment. This can be circumvented with the help of generative AI, which processes and analyzes vast quantities of diverse data types.

It can help generate groundbreaking ideas – by consumers or employees – by stimulating their creativity. Moreover, it could boost the quality of undeveloped concepts, making innovation more democratized.

2. Streamline content creation processes

Conventional approaches to content development typically include protracted production cycles that enlist numerous stakeholders and teams. Generative AI reduces production time and expenses by automating content creation, expediting the process.

Natural language processing (NLP) has enabled organizations to produce outstanding content, such as product descriptions, blog entries, and social media posts, in a substantially reduced timeframe compared to independent human creators.

Marketers estimate that generative AI will reduce their workload by over five hours per week, equivalent to over one month of work per year – according to Salesforce research.

3. Personalize and customize customer experiences

Numerous instances of generative AI demonstrate how its algorithms can help customize and individualize customer experiences.

Consider, for example, a scenario wherein product descriptions evoke a solid personal response. This is achieved through generative AI, which modifies descriptions to fit precisely segmented audiences – according to their demographics, geographical location, browsing history, and user classification. Further, this technology will allow marketers to launch personalized email campaigns on a massive scale, highlighting different product attributes for different segments.

Plus, generative AI chatbots facilitate personalization through contextual reasoning. It analyzes consumer inquiries to offer responses that are not only pertinent but also highly individualized.

Finally, it could improve the search experience on a brand’s website. It boosts the capacity of the search bar to interpret inputted images, spoken queries, and brief video clips in addition to text.

Ethical Considerations: What Are the Challenges of Generative AI?

Although generative AI shows considerable potential in content creation, it has limitations. AI can also produce objectionable or inconsequential material – stemming from its limited comprehension of ethical considerations, cultural subtleties, or contextual factors. This could lead to the prevalence of biases in the output, an outcome of the training data.

Additionally, the generated content might vary in quality, occasionally yielding illogical or erroneous conclusions.  This phenomenon is known as AI hallucination, and a notable generative AI example of hallucination is this one:

The statement by Google’s Bard chatbot – that the James Webb Space Telescope had collected preliminary visuals of a planet beyond our solar system — was erroneous.

Moreover, ownership of work generated by artificial intelligence is debatable and may differ from nation to nation. For example, copyright laws in the United States state that “an image generated by artificial intelligence doesn’t have the ‘human authorship’ necessary for protection.”

Another possible problem that marketers must confront to guarantee the legality of using AI in content creation is plagiarism.  Finally, organizations must address fears of job loss when integrating gen AI into their workflows.

Generative AI Opportunities for Business Leaders

Generative Al holds enormous potential for businesses and their creative workflows and can enhance customer engagement by facilitating individualized self-service.

It automates duties requiring a high volume of work, such as software development and tax claims processing. Moreover, Gen AI and NLP help your teams manage, go through, and ultimately understand the importance of various subsets of important unstructured data, such as contracts, bills, customer feedback, regulations, and performance assessments.

By appreciating the true impact of generative AI and where it fits into your technology stack, you can unlock maximum returns from this breakthrough technology of our times.