Despite all the recent buzz, artificial intelligence (AI) is nothing new. In fact, computer programs designed to simulate human intelligence have been around since the 1940s. From the early stages, we’ve dreamed of computers doing everyday human tasks, from filing taxes to writing poetry.
We’re seeing rapid innovation in AI technologies as they’ve finally become good enough to provide broad business and personal value. Today’s AI boom results from a string of 21st-century breakthroughs that culminated with the 2022 release of ChatGPT, the first publicly available AI content creation tool to survive the testing phase.
Tools like ChatGPT are unique for their ability to create high-quality written and visual responses, known as generative AI. Here’s what you need to know about generative AI technology—including what it is, how it works, and how business owners use it to increase efficiency, improve products and services, and reduce costs.
What is generative AI?
Generative artificial intelligence (AI) refers to any machine learning model you can use to create new content, including text, images, video, audio, or software code.
Generative AI models differ from non-generative (or “discriminative”) models in their ability to create unique content:
- Discriminative models. Discriminative models are designed to distinguish between types of information. They spot patterns that separate data classes, enabling high-quality predictions or suggestions.
- Generative models. Generative models are designed to produce new content by learning from a large existing dataset. For example, they spot patterns in written works to predict the most likely sequence of words that will generate a human-like response.
An AI tool that can write a unique blog post based on user inputs is an example of generative AI technology. By contrast, a tool that analyzes inventory and sales to predict future manufacturing needs is an example of a discriminatory AI tool.
How does generative AI work?
Generative AI systems work by processing large amounts of existing data and using that information to create new content. Essentially, developers create an algorithm, feed it human-generated content, and instruct it to identify patterns in thetraining data.The result is a collection of rules that express patterns consistent throughout human-generated content, and these rules guide the AI in creating new content.
Developers of generative AI models create these systems using a specific type of machine learning known as deep learning. Machine learning models capable of deep learing use particularly complex algorithms that extract high levels of information from source data.
Historically, AI trainers have relied on supervised learning techniques, which involve feeding a generative AI model large volumes of manually labeled data. One consequential breakthrough is the development of algorithms that can self-train using unlabeled data, a process known as unsupervised learning.
Unsupervised learning eliminates the need for developers to label their own data, allowing them to train tools on larger volumes of source information. As the size of training data sets increases, AI models become more accurate and capable.
Common applications for generative AI
- Text generation
- Image generation
- Product discovery
- Research and analysis
- Customer service
- Marketing personalization
Generative AI tools can support a range of business processes. Here are common applications:
1. Text generation
Many generative AI models support written content creation. AI writing tools like ChatGPT can generate content including emails, social media posts, press releases, video scripts, stories—and essentially any other type of written content reflected in its training data set.
Many can also generate code and synthetic data—artificially generated information that mimics real-world data but doesn’t directly come from actual events or measurements. Some tools also include transcription and speech generation functions.
2. Image generation
You can use generative AI to create visual content. Generative AI tools like Stable Diffusion and DALL-E use natural language processing (NLP) and computer vision technologies to recognize objects and generate realistic images from text or image inputs. They can also transform image styles and edit natural images—images derived from the real world, i.e., not created by algorithms or digital art techniques).
3. Product discovery
Generative AI tools can personalize the product discovery process using conversational search functions to provide intelligent recommendations based on user preferences and behaviors. Media companies like Netflix, for example, use generative AI to provide personalized content recommendations.
4. Research and analysis
Generative AI tools can help you more efficiently conduct research. They can provide concise summaries of reports and extract and consolidate raw data from multiple sources. They can also efficiently process unstructured data, report on findings, and uncover relevant trends.
5. Customer service
Generative AI chatbots can support customer service functions, handling basic customer queries autonomously and escalating more complex cases to a customer service agent. Integrating generative AI into your customer service program can extend your team’s capacity and boost customer satisfaction.
6. Marketing personalization
Generative AI tools can help you personalize marketing efforts at scale. These tools gather and analyze large amounts of audience information and use the findings to personalize the content of the marketing messages as well as when and where they go out.
Benefits of generative AI
Using generative AI can save you time and money without requiring a compromise on product or service quality. Here are four key benefits:
Increased efficiency
Generative AI tools produce work quickly. Use them to quickly complete tasks like producing creative content or conducting marketing research so you can focus on higher-level objectives.
Improved accuracy
Use generative AI tools to reduce human error in quantitative processes like running payroll or analyzing data. Use them to find and fix mistakes in written content and identify discrepancies in large data sets like budgets or financial information.
Reduced costs
Generative AI tools can extend your team’s capacity, letting you do more without increasing your payroll obligations. They can also analyze and optimize business processes, like inventory management and supply chain logistics, further decreasing business expenses.
Better intelligence
Generative AI tools can process massive amounts of complex data from a wide range of sources, helping you increase the amount of data you can analyze and improve the quality of your analysis.
Limitations and risks of generative AI
Here’s an overview of the challenges associated with implementing generative AI in a business context:
Inaccuracies
Generative AI tools can make mistakes. Failing to verify the accuracy and quality of AI information can pose a risk to your business. (ChatGPT even includes this disclaimer under its prompt bar: “ChatGPT can make mistakes. Consider checking important information.”)
Poor transparency
Generative AI tools don’t always disclose how they’ve arrived at a specific answer, making it difficult to vet responses.
Bias
AI tools can reproduce the biases in their training data. For example, one early AI-powered recruiting system penalized female applicants after interpreting the underrepresentation of women in technical fields as evidence that women are less suited to technical work.
Privacy concerns
Information you provide to a generative AI tool isn’t necessarily confidential or secure, so using AI tools to process proprietary data can represent a security risk.
Sustainability concerns
AI tools require massive computing power, translating to significant energy usage that can conflict with your company’s sustainability goals.
Generative AI FAQ
What is the difference between predictive and generative AI?
Predictive AI models recognize expressed patterns in data and use this information to predict future outcomes. Generative AI models focus on patterns related to how data is created, which allows them to replicate the generative process and produce new original content.
What type of AI is ChatGPT?
ChatGPT is a generative AI chatbot built on the large language model GPT, which is short for generative pre-trained transformer.
What is the main goal of generative AI?
Generative AI systems aim to quickly produce high-quality, original content.
