• March 30, 2023
  • By R "Ray" Wang, founder, chairman, and principal analyst, Constellation Research

Generative AI’s Use Cases Are Already Emerging

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While generative artificial intelligence has been around for some time, ChatGPT has captured the hearts and minds of the general population in highlighting the tangible possibilities of what AI can accomplish, both for consumers and in the enterprise world. Generative AI can create chat responses, designs, and other new content (which also, unfortunately, includes deepfakes and synthetic data). Neural network techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers work together to create original content based on prompts, which many users have had fun with recently.

On the language side, GPTs, or generative pretrained trans[1]formers, generate conversational text using deep learning. Transformers, a type of neural network, map the relationships among all the data sources, such as text and sentence patterns. The pretraining capability allows the AI to take the model from one machine learning task to train another model. These models are then pretrained on a large corpus of text.

For images, diffusion models allow images to be created from text prompts. Using random noise applied to a set of training images, the diffusion models allow one to remove noise and create a desired image. Common models include DALL-E (also from OpenAI), Google’s Dreambooth, Imagen, Lensa, Midjourney, and Stable Diffusion.

The more that organizations interact with these AI systems, the quicker the AI systems will improve their rate of learning. Constellation Research sees five emerging use cases for generative AI in customer experience, among an infinite permutation of possibilities:

Marketing. Diffusion models will dynamically generate content, provide translation capability, and run A/B and experimentation tests for user experiences. Personalization models will gain greater context, enabling hyper-targeting for campaigns, ad networks, and polling with ChatGPT.

Sales. Sales-specific tasks such as pipeline reviews, scheduling, installed base analysis, and forecasting will move from manual to automated. Ticklers and alerts will reach out to sales reps to remind them to follow up on actions.

Service. Crawlers inside one’s internal systems can scan knowledge bases, augment case history, and hasten issue resolution. The AI can create new case tickets, augment missing information, and predict customer satisfaction.

Commerce. Product catalog creation will speed up as diffusion models take prompts from regulatory requirements, enabling faster global rollouts of new products and services content. ChatGPT models will serve as the front-end interface for order capture.

Customer success. Generative AI will identify accounts with low adoption and automatically identify at-risk customers based on their level of interaction to increase the frequency of engagement. Expect dynamic polling to generate surveys based on parameters such as dollar value, length of relationship, past interactions, and customer satisfaction.


Organizational success requires more than large learning models or better algorithms. CX leaders will need to identify the largest corpus of data available, the customer experience questions to be answered, and which skills are required to keep up with human scale in a machine world. In core CX processes such as campaign to lead, lead to order capture, order capture to order fulfillment, order fulfillment to order completion, incident to resolution, and others, opportunities will arise for generative AI to provide missing content.

Along the way every leader must determine which CX journeys are fully automated, which involve augmenting the machine with a human, which involve augmenting a human with a machine, or which require a purely human touch (see Figure 1).

Despite the massive amounts of hype, and given today’s labor shortages and need to improve time to market, pragmatic use cases for generative AI will emerge. Those organizations that fail to build a generative AI strategy will continue to fall behind. Those who adopt early will have an opportunity to deliver on exponential growth and more meaningful customer experiences.

R “Ray” Wang is the author of the new book Everybody Wants to Rule the World: Surviving and Thriving in a World of Digital Giants (HarperCollins Leadership) and founder of Constellation Research.

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