ChatGPT or Generative AI Is This Year’s POC And Shiny Object

While generative AI has been around for some time, ChatGPT has captured the hearts and minds of the general population in highlighting tangible possibilities of what AI can accomplish both in the consumer and enterprise world. In fact, Generative AI has the ability to create chat responses, designs, and other new content including deep fakes 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.

On the languages side, GPTs or what’s known as a generative pre-trained transformer, generate conversational text using deep learning. The pre-training capability allows the AI to take the model from one machine learning task to train another model. These models are then pre-trained on large corpus of text. Transformers, a type of neural network, maps the relationships among all the data sources such as text and sentence patterns.

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 approaches include DALL-E also from OpenAI, Dreambooth by Google, Imagen, Lensa, Midjourney, and Stable Diffusion.

The more organizations interact with these AI systems, the quicker the AI systems will improve their rate of learning.

Move Beyond The Hype And Start With Five Use Cases

Constellation Research sees five emerging use cases for generative AI in CX among an infinite permutation of possibilities:

1. 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.
2. Sales. Sales specific tasks such as pipeline reviews, scheduling meetings, install 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.
3. 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.
4. Commerce. Speed of product catalog creation will improve as diffusion models will 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.
5. 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, customer satisfaction.

Choose When to Design For Machine Scale And When To Add Human Scale

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 what 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, there will be opportunities for generative AI to provide missing content along the way.

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

Figure 1. The Four Questions Every CX Leader Will Ask In Their Journeys

Source: Constellation Research, Inc.

The Bottom Line: Generative AI Is Here To Stay

Despite the massive amounts of hype, pragmatic use cases for generative AI will emerge. Given today’s labor shortages and need to improve time to market, expect more pragmatic use cases to emerge. Those organizations who 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.