Stargazer's Guide to Generative AI and the Force Within Your Data: A New Hope for Creativity

Humans existed without the internet in what appears to be a distant galaxy. In just a few decades, the internet went from being a science fiction concept to a popular tool with over 5 billion users worldwide. Recent technological breakthroughs have made generative AI more accessible to the general public, propelling it to greater popularity than the World Wide Web. 65 percent of business leaders believe the AI they use is becoming more natural and human-like, and that it will only improve. As more businesses implement generative AI to improve customer service and employee experience, it is critical to understand how to fully leverage the power of this novel AI technology.

Our comprehensive guide explains the different types, applications, examples, benefits, and challenges of generative AI, allowing you to improve your overall customer experience now and in the future.

Guide to Generative AI Table of contents:

• What is generative AI?

• How does generative AI work?

• Who uses generative AI?

• Traditional AI vs. generative AI: What’s the difference?

• Types of generative AI models

• What are some popular generative AI applications?

• Benefits of generative AI

• Challenges with generative AI

• How to use generative AI for customer service

• Frequently asked questions

Guide to Generative AI: What is generative AI?

Forrester provides a more technical definition of generative AI as "a set of technologies and techniques that leverage a very large corpus of data, including large language models (LLMs) like GPT-3, to generate new content."

Generative AI analyses large datasets using machine learning (ML) algorithms. That is, you can feed artificial intelligence a wealth of existing information on a topic, allowing it to learn and discover patterns and structures. Generative AI can generate new, original content based on the information it gathers from this dataset.

For example, you can request that ChatGPT, OpenAI's popular text-to-text generative AI chatbot, write a Shakespearean iambic pentameter sonnet about how Luke Skywalker feels about Darth Vader being his father. ChatGPT will quickly search the internet for relevant information about the Star Wars saga and William Shakespeare's sonnets, and then generate new content based on your requests. And yes, we actually asked ChatGPT this question. Here are the results:

“Upon the starry canvas of my fate,A revelation dark did cast its shade,In shadows deep, a truth I did berate,My father, once a Jedi, now betrayed.

In crimson mask, he sought to rule the skies,With heavy breath, he walked a twisted path,Yet in his heart, a shred of good beliefs,A conflict fierce, an inner war’s cruel math.

O father, kin by blood, but not by deed,Thy presence haunts my dreams, my waking days,A legacy of darkness, dark indeed,But hope persists, a light in Sith’s malaise.

Though Vader’s grip on me is strong and tight,My love, my faith, may yet redeem the knight.”

Guide to Generative AI: Who uses generative AI?

Businesses of all sizes—startups, small businesses, medium-sized businesses, and enterprises—use generative AI in various ways. Businesses can use it to improve customer service, sales, marketing, IT, development, HR, and training teams. Some examples of generative AI applications include:

1. AI-powered assistance improves the existing abilities of customer support agents.

2. Analysing large amounts of data to improve lead scoring and sales forecasting for sales teams

3. Personalising marketing communications.

4. Optimising data centre operations for IT departments.

5. Generate code for software developers

6. Creating and updating internal content and documents for HR departments.

7. Streamlining onboarding and agent training.

These generative AI examples are only the tip of the iceberg. As generative AI becomes more mainstream, businesses will discover new and better ways to implement the technology.

Traditional AI vs. generative AI: What’s the difference?

	Traditional AI	Generative AI
Objective	Task-specific and rule-based	Content generation
Learning	Uses predefined programming	Identifies patterns from large datasets
Output	Task-specific	New content or data samples

Traditional AI and generative AI differ in that traditional AI performs specific tasks using machine learning, predefined rules, and programmed logic, whereas generative AI creates human-like content by learning from large datasets. For example:

Traditional AI can improve ticketing systems by detecting customer sentiment, intent, and language in service requests and automatically routing them to the appropriate agent based on predetermined criteria (such as expertise, capabilities, and availability).

Generative AI increases agent productivity by providing intelligent writing tools, allowing teams to address requests more efficiently and consistently.

Businesses can use both traditional and generative AI to analyse data. While traditional AI can make educated predictions based on data, generative AI can create new data from existing datasets. Generative AI can also adapt to the context and generate unique, creative content.

Generative AI versus machine learning.

Machine learning differs from generative AI in that it is not limited to generative tasks. Both types of AI learn from patterns found in large datasets and interactions, but machine learning produces predictions or classifications rather than new content.

Generative AI has various use cases, meaning there are many different types of generative models. Here are some of the most common types of generative AI models.

Generative adversarial networks

Generative adversarial networks (GANs) function by training two different learning computers (neural networks) on the same datasets to produce increasingly realistic content over time.

The two networks, known as the "generator" and the "discriminator," compete with one another, pushing each other to constantly produce better content. When the GAN receives the same information, it generates a data sample (such as an image or text) based on the training data. The discriminator then examines what the generator produced and determines whether it is real or generated data.

GANs function similarly to two players competing in a game. Let's use the Star Wars droids R2-D2 and C-3PO as competitors.

The game features R2-D2 (the generator) creating images of Ewoks, the Millennium Falcon, and other Star Wars characters. C-3PO (the discriminator) examines these images and determines whether they appear real or fake, much like a Jedi inspecting a lightsaber to see if it is genuine.

Based on C3PO's feedback, R2-D2 boosts his ability to make images more realistic as they progress through the game.

Transformers

Transformer-based generative AI models are neural networks that use deep learning architecture (algorithms for detecting patterns in large amounts of data) to predict new text from sequential data. Transformers can learn context and "transform" one type of input into another, producing human-like text and answering questions.

Consider the auto-suggest feature on messaging apps. Assume Han Solo wants to send Princess Leia a text message. As he begins to type, generative AI predicts the next word in his typing sequence and provides macros (suggested text) for him to quickly select, saving him from having to type out each word.

For example, Han could type "May the" and the generative AI would suggest "force be with you."

Variational autoencoders

Variational autoencoders (VAEs) are generative models that encode input data, simplify and optimise it, and then store it in a hidden storage area known as latent space. When prompted, it extracts data from the latent space and reconstructs it to its original form. VAEs frequently produce generative AI images and text.

Consider Yoda, a powerful Jedi master who can use the Force to transform images into scrolls of encrypted text, transport them instantly to a locked chest on the remote planet of Dagobah, and then transform the scrolls back into the original image as needed.

Say you give Yoda a picture of Chewbacca. Yoda can transform it into a scroll and store it safely in his chest on Dagobah. A few days later, you ask Yoda for the photo. He once again channels the Force to access the scroll and return it to its original form.

Flow-based models.

Flow-based models take complex data distributions and transform them into simple distributions. This type of model is commonly used in image generation.

Assume that young Anakin Skywalker has a set of building blocks, each of which is a different colour. If Anakin wants to arrange the blocks to form a pattern, he can do so in any position as long as the number of blocks in the pattern remains constant. A flow-based model allows Anakin to create new patterns or refine existing ones while keeping the Force (or number of blocks) in balance.

Recurrent Neural Networks

Recurrent neural networks (RNNs) process and generate sequential data. Training an RNN on data sequences produces new sequences that are similar to previously learned data. RNNs predict the next step in a sequence based on what has happened in previous sequences. RNNs are the generative AI model that powers Siri and Google Voice search.

Imagine Princess Leia and Wicket the Ewok are playing catch with a ball in the Endor forest. Wicket always catches the ball when Leia throws it. Wicket consistently catches the ball because he has learned to anticipate the ball's path and where it will land based on previous throws (sequences).

What are some of the most popular generative AI applications?

As we learn more about the benefits of advanced AI in customer service, new generative AI applications emerge. These popular generative AI apps, like the Skywalker lineage, are the pinnacle of artificial intelligence software.

1. ChatGPT.

2. Dall-e

3. Bard

4. Bing AI.

Guide to Generative AI: Benefits of generative AI

Enhanced customer experience.

Your customer support teams can provide a better customer experience by leveraging generative AI. Manage high volumes of requests during peak hours by providing instant, automated responses to customer inquiries through generative replies, messaging tools, and chatbot software.

Generative AI allows for more natural, personalised conversations with accurate information, resulting in a better customer experience, higher customer satisfaction (CSAT) scores, and customer loyalty. Additionally, generative AI provides multilingual support, recognising and adapting to languages for 24/7 global customer service.

Increased agent productivity and efficiency.

Using generative AI tools, you can streamline workflows and make it easier for agents. Generative AI can handle simple tasks, allowing agents to focus on more complex problems. Here are some ways to use generative AI to improve agent productivity and efficiency:

Ticket summaries: Create a brief summary of the ticket content so that agents can understand the problem and respond quickly.

Advanced bots: Use data-driven suggestions to deflect tickets and provide instant, conversational support.

Content creation: Automate and streamline the content creation process to relieve content owners of the burden.

Reduced support costs

AI in the workplace enables your customer support team to do more with less. Generative AI saves time and money by deflecting tickets, streamlining workflows, and automating repetitive tasks. This means ticket queues are manageable, and agents can focus on more complex issues while serving the same or more customers.

Generative AI can also assist management teams in gaining more meaningful insights into the types of customer issues and questions that may require automation. GenAI can quickly determine which automation gaps exist and which are most beneficial to agents and business operations. For example, it can detect if a large number of customers are contacting support about resetting their passwords or tracking their orders, allowing support teams to redirect these types of inquiries to a bot. Administrators can then create these automations sooner rather than later, saving businesses both time and money.

Guide to Generative AI: Challenges with Generative AI

Generative AI can provide numerous advantages and assist businesses in navigating challenging times. However, as with any new technology, there may be unexpected twists and turns. Here are some things to think about when implementing generative AI.

Biassed, outdated, or untrustworthy information

Generative AI systems generate content based on the data they have been trained on, which may be biassed, outdated, or unreliable. It is critical to vet and validate data sources to ensure that your generative AI application is gathering reliable information. Create processes and guidelines for tracking and removing biassed data from your datasets, as well as regularly monitoring and reviewing content outputs to ensure factual and unbiased information.

Generative AI hallucinations.

Generative AI applications are designed to provide the most consistent responses to user commands. However, generative AI tools can occasionally produce clearly incorrect information or inaccurate results known as "hallucinations."

A hallucination occurs when a generative AI application provides incorrect or irrelevant information unrelated to the dataset from which it was trained. Simply put, the AI model created new content based on facts while adding its own creative interpretation, resulting in distorted information. These incidents are rare, but they have the potential to spread misinformation or insensitive content.

Concerns over human replacement

Though the goal of generative AI technology is to increase productivity and skills, employees may be concerned that implementing it will lead to their replacement. While generative AI can help automate tasks, genuine human connection is impossible to replicate and is an essential component of customer service.

When customers have problems or questions, they still want to speak with a human. According to a recent poll, 81 percent of consumers believe that access to a live agent is critical to maintaining trust with a business when they have trouble with AI-powered customer support.

Guide to Generative AI: The Future of Generative AI

It's easy to get caught up in the excitement surrounding generative AI. However, having a game plan is critical for maximising the benefits of generative AI both now and in the future.

Our Generative AI guide will teach you how to harness the power of AI. Implementing generative AI now can put you in control of your own exciting journey. We will be the Chewbacca to your Han Solo. Join us on the Millennium Falcon and soar into hyperspace.