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Generative artificial intelligence (AI) is a branch of machine learning (ML) focused on creating systems that can generate content or data that is like what a human might produce. Unlike traditional AI models that rely on explicit programming and rules, generative models learn from vast amounts of data to understand and replicate patterns, allowing them to create original content in various forms, such as text, images, audio, and more.
In this article, we’ll explore how generative AI works, how it differs from ML, and the countless use cases for it. We’ll also look at some examples of generative AI, its benefits, and how it will affect industries and business organizations.
You’d be hard-pressed to find an enterprise that isn’t at least interested—if not invested—in artificial intelligence (AI). In fact, worldwide AI spending totaled $118 billion in 2022 and is expected to record an impressive annual growth rate of 26.3% through 2026.
Although all branches of AI are rising in popularity, none are quite so top of mind for leading organizations as generative AI. With its numerous benefits and countless use cases, this particular subset has the potential to energize workforces and stimulate long-term business growth.
What is generative AI anyways?
The term “generative AI” refers to an AI model capable of creating brand-new content, such as text, images, video, audio, and code. At a high level, generative models process a set of training data and use this information to create new work that resembles—but isn’t identical to—the original data.
Unlike traditional AI systems, generative models don't rely on predefined rules. Instead, they analyze a particular data set to learn its patterns and structures. As machine learning (ML) algorithms, this training process allows them to produce a similar end result—hence the name “generative.”
How does generative AI work?
Generative AI models aim to predict the next piece of data in a sequence. For years, this was limited specifically to numerical data. However, models have since amplified their generative capabilities and are now able to predict the next word in a sentence, next pixel in an image, and so on. In turn, generative models can be used to summarize text, create content, write music, and much more.
Deep learning is the primary mechanism that makes this possible. In short, deep learning—also called a deep neural network—is an AI function that mimics the human brain’s ability to process information, create patterns, and make decisions. Generative AI leverages this learning approach throughout the data-gathering and training process.
Once trained, generative models are ready to produce new content in response to a given prompt, such as an image, video, or text.
What’s the difference between generative AI and large language models?
Generative AI and large language models (LLMs) are often associated with one another, but there’s actually a notable difference.
Both involve the use of deep learning and neural networks to create new content. However, generative AI is a much broader concept that can encompass a much larger range of functions beyond language generation, such as image creation or music composition. On the other hand, LLMs are specific applications of generative AI that concentrate only on natural language processing (NLP) tasks.
Examples of generative AI applications
As popularity surges, a swarm of generative AI systems are hitting the market. Some of the most popular include:
- ChatGPT. OpenAI’s Chat Generative Pre-trained Transformer (GPT) is perhaps the most famous example. Although its core function is to mimic human conversation, this LLM-based chatbot also writes code, composes music, and more. Its ability to answer test questions has even allowed it to ace the bar exam, scoring in the 90th percentile.
- Bard. Released by Google in 2023, Bard is a conversational generative chatbot. Aside from its language-based functions, Bard can also interact with other Google services, including YouTube, Gmail, and Drive, to deliver personalized results. This model also understands over 40 languages.
- DALL-E. As another of OpenAI’s generative models, DALL-E produces digital images based on natural language prompts. Users simply input plain text descriptions and the algorithm generates a corresponding image. It can do so in multiple styles, including photorealism, paintings, and more.
What are the benefits of generative AI?
Given its rapidly advancing capabilities, many organizations are excited by the potential advantages generative AI brings to the table. Among the many, its top benefits include:
- Increased efficiency. Productivity gains are arguably generative AI’s most compelling business advantage. Not only does it automate time-consuming tasks, but it also frees employees up to focus on more important, value-driving work.
- Improved decision-making. Modern enterprises are storing an abundance of data, but this sprawled environment is difficult to rein in. Generative AI can rapidly synthesize, analyze, and make use of structured and unstructured data, helping businesses make better strategic decisions.
- Greater creativity. Generative AI augments human creativity, assisting with everything from content ideation to production.
- Better customer experiences. Advanced algorithms help organizations understand and analyze customer data, resulting in better service outcomes. In fact, 38% of enterprises say customer experience and retention is the primary purpose of their generative AI investments.
Ultimately, specific benefits vary depending on the use case—but fortunately, there are plenty of notable applications.
What are the top use cases for generative AI?
McKinsey estimates generative AI is most likely to deliver its greatest impact in banking, tech, and life sciences. For example, this technology could help banking institutions create up to $340 billion in additional value each year.
However, regardless of industry, businesses that implement generative AI are most likely to see value across four primary use cases:
1. Customer operations
Service experiences can make or break a customer relationship. In the spirit of this, generative AI has the power to greatly improve and accelerate customer-agent interactions.
For example, AI-powered chatbots enable self-service, delivering personalized and immediate answers to particular questions and concerns. As generative models develop, they’ll be able to handle more complex tasks, affording agents more time to focus on incoming calls and other inbound requests.
Likewise, generative AI can improve first-contact resolution by empowering agents with faster data retrieval. All told, applying generative AI to customer service could increase productivity at a value ranging between 30% to 45% of customer care costs.
2. Marketing and sales
Generative AI technology also enables marketing teams to craft hyper-personalized messaging tailored to each customer’s unique needs and preferences. By analyzing customer data, such as buying patterns or behavioral analytics, models help inform campaign decision-making.
Moreover, generative algorithms utilize these insights to streamline content ideation and creation, saving time and effort in the drafting process. There’s also the potential for teams to integrate AI into marketing automation tools, enabling them to significantly enhance personalization aimed at different target audiences.
In terms of sales, generative models will be able to help salespeople identify and prioritize leads using consumer profiles. Sales teams often have a large amount of structured and unstructured data with little way of putting it into action. Generative AI models analyze these datasets and provide more actionable insights and information about customer preferences, potentially improving sales rates.
3. Software engineering
Software development can be a time-consuming process. However, engineers who leverage generative AI will be able to amplify their programming capabilities at scale. Potentially, organizations can even train models to generate code based on a natural language description of its intended function.
Importantly, these capabilities will greatly speed up development. In fact, a 2023 study found that engineers using Microsoft’s GitHub Copilot algorithm completed tasks 56% faster than those who didn’t.
4. Research and development
AI’s role in product innovation and delivery isn’t to be underestimated. Take pharmaceuticals, for instance. By 2025, more than 30% of new drugs and materials will be systematically discovered using generative AI techniques—up from zero in 2023.
In short, generative design models help product engineers reduce costs by using and selecting resources more efficiently. Plus, leveraging generative AI will help companies achieve a greater quality product by improving the research and design process.
Maximize your AI opportunities
Almost 9 in 10 executives understand the potential benefits of generative AI. However, just 30% of enterprises are ready to adopt this new technology.
That’s where Teradata VantageCloud comes into play. With our platform, you can unlock the power of data and prepare your organization for the AI-driven future. Alongside the ClearScape Analytics™ engine, you’ll gain all the tools you need to uncover opportunities and operationalize generative AI.
Connect with us to learn more about Teradata VantageCloud and how we can help your organization tap into the power of artificial intelligence.