Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds.
The technology, it should be noted, is not brand-new. Generative AI was introduced in the 1960s in chatbots. But it was not until 2014, with the introduction of generative adversarial networks, or GANs - a type of machine learning algorithm that generative AI could create convincingly authentic images, videos and audio of real people.
On the one hand, this newfound capability has opened up opportunities that include better movie dubbing and rich educational content. It also unlocked concerns about digitally forged images or videos and harmful cybersecurity attacks on businesses, including nefarious requests that realistically mimic an employee's boss.
Gartner has tracked generative AI on its Hype Cycle™ for Artificial Intelligence since 2020 (also, generative AI was among our Top Strategic Technology Trends for 2022), and the technology has moved from the Innovation Trigger phase to the Peak of Inflated Expectations. But generative AI only hit mainstream headlines in late 2022 with the launch of ChatGPT, a chatbot capable of very human-seeming interactions.
ChatGPT, launched by OpenAI, became wildly popular overnight and galvanized public attention. (OpenAI’s DALL·E 2 tool similarly generates images from text in a related generative AI innovation.)
Gartner sees generative AI becoming a general-purpose technology with an impact similar to that of the steam engine, electricity and the internet. The hype will subside as the reality of implementation sets in, but the impact of generative AI will grow as people and enterprises discover more innovative applications for the technology in daily work and life
Benefits and Applications
Foundation models, including generative pretrained transformers (which drives ChatGPT), are among the AI architecture innovations that can be used to automate, augment humans or machines, and autonomously execute business and IT processes.
The benefits of generative AI include faster product development, enhanced customer experience and improved employee productivity, but the specifics depend on the use case. End users should be realistic about the value they are looking to achieve, especially when using a service as is, which has major limitations. Generative AI creates artifacts that can be inaccurate or biased, making human validation essential and potentially limiting the time it saves workers. Gartner recommends connecting use cases to KPIs to ensure that any project either improves operational efficiency or creates net new revenue or better experiences.
In a recent Gartner webinar poll of more than 2,500 executives, 38% indicated that customer experience and retention is the primary purpose of their generative AI investments. This was followed by revenue growth (26%), cost optimization (17%) and business continuity (7%).
Risks of generative AI
The risks associated with generative AI are significant and rapidly evolving. A wide array of threat actors have already used the technology to create “deep fakes” or copies of products, and generate artifacts to support increasingly complex scams.
ChatGPT and other tools like it are trained on large amounts of publicly available data. They are not designed to be compliant with General Data Protection Regulation (GDPR) and other copyright laws, so it’s imperative to pay close attention to your enterprises’ uses of the platforms.
Oversight risks to monitor include:
- Lack of transparency. Generative AI and ChatGPT models are unpredictable, and not even the companies behind them always understand everything about how they work.
- Accuracy. Generative AI systems sometimes produce inaccurate and fabricated answers. Assess all outputs for accuracy, appropriateness and actual usefulness before relying on or publicly distributing information.
- Bias. You need policies or controls in place to detect biased outputs and deal with them in a manner consistent with company policy and any relevant legal requirements.
- Intellectual property (IP) and copyright. 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 ChatGPT and its competitors will become public information, and we advise enterprises to put in place controls to avoid inadvertently exposing IP.
- Cybersecurity and fraud. Enterprises must prepare for malicious actors’ use of generative AI systems for cyber and fraud attacks, such as those that use deep fakes for social engineering of personnel, and ensure mitigating controls are put in place. Confer with your cyber-insurance provider to verify the degree to which your existing policy covers AI-related breaches.
- Sustainability. Generative AI uses significant amounts of electricity. Choose vendors that reduce power consumption and leverage high-quality renewable energy to mitigate the impact on your sustainability goals.
- Written content augmentation and creation: Producing a “draft” output of text in a desired style and length
- Question answering and discovery: Enabling users to locate answers to input, based on data and prompt information
- Tone: Text manipulation, to soften language or professionalize text
- Summarization: Offering shortened versions of conversations, articles, emails and webpages
- Simplification: Breaking down titles, creating outlines and extracting key content
- Classification of content for specific use cases: Sorting by sentiment, topic, etc.
- Chatbot performance improvement: Bettering “sentity” extraction, whole-conversation sentiment classification and generation of journey flows from general descriptions
- Software coding: Code generation, translation, explanation and verification
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