Published: 26 July 2023
ChatGPT arrived with a jolt, and suddenly, GenAI captivated the public in a manner seen only with the most transformational innovations. This Resource Center for executive leaders focuses on ChatGPT and GenAI, the underlying technology, and Gartner’s view of their impact and potential.
The Generative AI Resource Center features a research collection focused on ChatGPT and GenAI, the underlying technology, and Gartner’s view of their impact and potential.
Topics in this initiative include:
Current GenAI Landscape: Prioritize revenue growth and customer experience over cost savings, and push for a relatively quick time to production.
Future Direction for GenAI: Evaluate potential use cases for value and feasibility to determine priority.
Strategize, Plan and Communicate: Minimize risks and tap into business value by evaluating the opportunities, benefits and pitfalls that AI models present.
Manage Security, Risk and Governance: Manually review all outputs, and set clear organizational policies for use until enterprise-grade security and risk controls are implemented.
Evaluate, Design, Test and Implement Technology: Beyond the big platform players, watch out for many hundreds of specialty providers funded by ample venture capital.
Develop Talent and Skills: Define core AI and ML roles, skills and responsibilities to align the right skills to the required roles in advanced analytics initiatives.
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ChatGPT has galvanized public attention since the end of 2022. Suddenly, everyone from CEOs to chefs, marketing executives to musicians, board directors to barbers, was talking about artificial intelligence. Then they began playing with ChatGPT, interacting with a humanlike chatbot that seemed to understand their intentions. It created poetry. It explained complex topics clearly enough for 7-year-olds to understand. It mistakenly wrote believable obituaries for people still alive. It posed an immediate threat to information businesses, and it seemed full of opportunity. It is all of that and more.
Rarely has Gartner seen such an explosion of public interest and market activity. The market, of course, will settle as players gain market share, combine through acquisitions or disappear altogether. But, unlike previous technology trends, it is easy for everyone in an enterprise to imagine how this technology could be used, as people can experience it directly. This will make generative AI (GenAI) a durable trend, along the lines of the internet or smartphones. Sure, the hype will burn off as the reality of implementation sets in. But Gartner believes GenAI is here to stay and its impact will continue to grow as people discover new applications for the technology in their daily work and lives.
This document captures Gartner’s foundational research and advice that will help you learn about GenAI, set and communicate policy, prioritize use cases, and implement pilots. Because this space is evolving so quickly, we will update this document regularly to reflect our most current advice.
This describes the coverage of the Generative AI Resource Center, which offers explanations of ChatGPT and GenAI, the underlying technology, and Gartner’s view of their impact and potential.
Our research in this area addresses the following topics:
ChatGPT is an application that sits atop a large language model (LLM) called generative pretrained transformer (GPT). LLMs are trained on billions of words and trillions of parameters. Complex math and enormous compute are required to create these trained models, but they are, in essence, prediction algorithms. In the case of GPT and other LLMs, they predict the order of words and generate language that makes sense in response to a question, called a “prompt.”
This generation is characteristic of GenAI and is not limited to text. GenAI can create code, images, video and audio, too. The more human the reinforcement that is applied during model training is, the more accurate the generated responses are. The humanlike nature of ChatGPT interactions is mesmerizing, and has people camped out in the uncanny valley.
Why should I care about GenAI at all?
What are the most beneficial use cases currently implemented in my function or industry?
As a leader, what do I need to know about GenAI?
Visit the Generative AI Resource Center frequently to see Gartner’s latest research.
Potential use cases must be examined for value, risk and feasibility, and then prioritized. Many enterprises are building pilots to support those use cases, testing the accuracy of response, architectural and data requirements, and potential value. Enterprises that aren’t piloting will face tough questions from their investors.
Gartner is helping clients assess and prioritize use cases in conjunction with our consultants and executive partners. We are building out research to examine use cases as they relate to industries, business functions, roles and markets — most notably through our Use-Case Prisms. So more should be expected in this space very soon.
General use cases include:
Written content augmentation and creation: ChatGPT can produce in many ways a “draft” output of text, which the user then reviews. ChatGPT can produce the length and style of text desired.
Question answering and discovery: It enables users to locate answers to input, based on data and prompt information.
Tone adjustment: Text manipulation can soften language or professionalize text, for example.
Summarization: It offers shortened summaries of conversations, articles, emails and webpages, and the length of the summary can be specified. It can convert to and from bullet points.
Simplification: It can create titles and outlines and extract key content.
Classification of content for specific use cases: It can classify by, for example, sentiment or topic.
Chatbot performance improvement: Multiple methods can be improved, such as entity extraction, whole-conversation sentiment classification and generation of journey flows from general descriptions. These methods can also be used to improve the performance of other applications involving user interactions.
Software code generation, translation, explanation and verification.
Growing familiarity with ChatGPT and GenAI is revealing many and varied use cases. The breadth and depth of these use cases are surprising, wonderful and disconcerting. It is by no means obvious how the underlying LLMs will change applications, impact workers, transform organizations and influence society.
Exploratory investigations done by end-user organizations and vendors reveal use cases in the user experience, as well as use cases deep within the workings of applications and their programmatic interfaces. Moreover, the ability to marshal multiple models, working in concert, points to LLMs gaining a foundational role in applications, with myriad use cases arising. Examples include:
Augmented authoring: It generates or drafts copy, reformats text, changes the style and tone (such as professional, informal and simplified tone), simplifies, and so on.
Augmented reading: It summarizes, extracts facts, answers questions and so on.
Translation: It translates languages, programming languages, data formats and so on.
Knowledge management. It retrieves and synthesizes information, classifies content and data assets, and so on.
Programming: It generates code.
What should we watch out for on the horizon with GenAI, and how might it impact my team, function and organization?
How and when should we prepare for the future direction of GenAI, including scaling GenAI solutions?
What are the smartest companies doing to get ahead with GenAI?
Visit the Generative AI Resource Center frequently to see Gartner’s latest research.
GenAI’s underlying technologies include the LLMs themselves (of which there are several), natural language processing and massive computing using GPUs. Of course, the model requires an enormous amount of digitized content, which comes from the public internet, books and other information sources. Human trends have also converged: our willingness to talk to computers to accomplish tasks, the desire for a better user experience in information retrieval, and increased productivity with less burnout.
Yet, people also fail to distinguish fact from fiction, thanks in part to social media and confirmation bias. This is a problem because these chatbots sometimes make things up. They confabulate or hallucinate, and can reflect the biases inherent in the foundation model. Maybe this makes them more like humans than we realize?
What should I do today about GenAI?
What GenAI questions are my leadership and the board of directors asking right now?
What is, or should be, the impact of GenAI on our business, AI and other strategies?
Visit the Generative AI Resource Center frequently to see Gartner’s latest research.
In the OpenAI version that launched in November 2022, anything put in a user query was preserved and available to anyone using the interface. So, if a user put proprietary intellectual property (IP) or personal information into a prompt, that information was open to the world. To be clear, we mean the underlying LLM is locked and immutable. The data used in the prompt is retained in a secondary data construct that is used to inform and perfect future queries.
Things have evolved since then to partly address this issue (such as chat history being turned off). For example, Microsoft’s implementation of OpenAI using Azure services promises to protect enterprise data and keep the prompt details secret and unavailable to other users. The other major cloud providers are following suit, and this market will present multiple options in record time. However, any enterprise serious about using ChatGPT still needs the familiar mix of information protection and governance techniques, such as encryption, identity and access management, and information classification. Other risks of ChatGPT include:
Transparency risks: GenAI and ChatGPT models are not explainable and are unpredictable. Even the vendors don’t understand everything about how they work internally.
Accuracy risks: GenAI systems consistently produce inaccurate and fabricated answers. Outputs generated by ChatGPT and GenAI should be assessed for accuracy, appropriateness and actual usefulness before being accepted.
Bias risks: ChatGPT’s training is based on data selectively sourced from the web and books. And web-sourced data, and its inherent biases, is weighted more than five times greater than book-sourced data. Enterprises must have in place policies or controls to detect biased outputs and deal with them in a manner consistent with company policy and any relevant legal requirements.
IP and copyright risks: There are currently no verifiable data governance and protection assurances regarding confidential enterprise information. Users should assume that any data or queries they enter into the tools will become public information. Enterprises should take steps to avoid inadvertently exposing enterprise IP. Users must also filter out copyrighted materials in ChatGPT outputs to which OpenAI does not have the rights, which can create legal risks for the user.
Cyber and fraud risks: Enterprises must prepare for the use of GenAI systems for cyberattacks and fraud, such as deepfakes of enterprise products and communications. Enterprises should confer with their cyberinsurance providers to verify the degree to which AI-related breaches are covered by their existing policies.
Sustainability risks: GenAI uses significant amounts of electricity and water. Enterprises that invest in GenAI should encourage the executive team to choose vendors that reduce power consumption and leverage high-quality renewable energy to mitigate the impact on sustainability goals.
Many enterprises are quickly coming to terms with these risks to their IP and to their business models. It is essential to create and communicate a clear usage and governance policy that determines appropriate use of GenAI tools, protection of data and acceptable use cases.
What are the security and other risks associated with GenAI and its applications?
What are the regulatory and legal implications to using GenAI, including creator copyright infringement?
What are the emerging best practices for the governance of GenAI implementations and operationalization?
Visit the Generative AI Resource Center frequently to see Gartner’s latest research.
Meanwhile, the GenAI marketplace is on fire. Beyond the big platform players that will quickly dominate enterprise-grade LLM runtime, many hundreds of specialty providers funded by ample venture capital are buzzing around the GenAI hive. The enterprise application providers are building LLM capabilities into their platforms (for example, Salesforce and SAP). The service and solution providers are ramping up capabilities and spending large amounts of money to build GenAI practices. Gartner will accelerate market coverage through the remainder of 2023 and into 2024.
What are the best practices to pilot and test the use of GenAI in my organization?
What does the GenAI market look like?
Should we buy or build GenAI solutions, and how can I start exploring vendors and solutions?
Visit the Generative AI Resource Center frequently to see Gartner’s latest research.
Many Gartner clientshave GenAI pilots underway for code generation, text generation or visual design. The pilots come in three categories, with the second being the most common:
Gartner is quickly developing a set of design patterns and a technical reference architecture to represent these three approaches. In the meantime, we provide some foundational resources.
How do we build an AI literacy program to communicate the potential of GenAI to our teams?
How do we upskill our existing team members to build or optimize the usage of GenAI solutions?
What are the new roles and skills required to take full advantage of GenAI techniques and solutions?
Visit the Generative AI Resource Center frequently to see Gartner’s latest research.
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