Opportunities And Challenges Of Generative Artificial Intelligence

Bonisiwe Shabane

-Dec 28, 2025, 2:00 PM

opportunities and challenges of generative artificial intelligence

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. Authors: Daswin de Silva; Okyay Kaynak; Mona El-Ayoubi; Nishan Mills; Damminda Alahakoon; Milos Manic Generative artificial intelligence (Generative AI) is transforming the way we live and work. Following several decades of artificial narrow intelligence, Generative AI is signaling a paradigm shift in the intelligence of machines, an increased generalization capability with increased accessibility and equity for nontechnical users. Large language models (LLMs) are leading this charge, specifically conversational interfaces, such as ChatGPT, Gemini, Claude, and Llama (large language model meta AI).

Besides language and text, robust and effective Generative AI models have emerged for all other modalities of digital data, image, video, audio, code, and combinations thereof. This article presents the opportunities and challenges of Generative AI in advancing industrial systems and technologies. The article begins with an introduction to Generative AI, which includes its rapid progression to state-of-the-art, the deep learning algorithms, large training datasets, and computing infrastructure used to build Generative AI models, as well... The contribution, value, and utility of Generative AI is presented in terms of its four capabilities of accelerating academic research, augmenting the learning and teaching experience, supporting industry practice, and increasing social impact. The article concludes with an expeditious message to the academic research and industry practitioner communities to invest time and effort in the training, adoption, and application of Generative AI, with consideration for AI literacy... You have full access to this open access article

Generative Artificial Intelligence (Gen-AI) is a new advancement that has revolutionized the concepts of Natural Language Processing (NLP) and Large Language Model (LLM). This change impacts various aspects of life, stimulating industry, education, and healthcare progression. This survey presents the potential applications of Gen-AI across various sectors, highlighting the risks and opportunities. Some of the most pressing challenges include ethical consideration, the rise of disinformation (including deepfakes), concerns over Intellectual Property (IP) rights, cybersecurity risks, bias and discrimination. The survey also covers the fundamental models of Gen-AI, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers. These frameworks are extremely important in various sectors, including medical imaging, drug discovery, and personalized medicine, and offer valuable insights into the future of technological advancements in the scientific community.

The study contributes substantially by exploring positive elements and addressing the challenges of adequately deploying Gen-AI models. Using these insights, we hope to provide a comprehensive knowledge of the potential challenges and complexities associated with the widespread implementation of artificial intelligence technologies. Avoid common mistakes on your manuscript. Artificial Intelligence (AI) [1, 2] is a rapidly expanding domain of computer science that deals with all aspects of emulating cognitive functions to solve problems in the real world and develop computers that can... Being considered the oldest field of computer research , it is commonly referred to as machine intelligence [4] to differentiate it from human intelligence [5]. According to Tenenbaum et al.

[6], the field centered on cognitive and computer science. AI is currently receiving great attention because of the achievements made in Machine Learning (ML). Throughout the history of AI, there has always been a solid connection to explainability. In 1958, McCarthy’s Advice Taker, described it is a “program with common sense” [7]. Common sense reasoning abilities were possibly being proposed for the first time as the cornerstone of AI. Rather than only focusing on solving pattern recognition problems, artificial intelligence systems should be able to construct causal models of the world that assist explanation and comprehension, according to recent research [8].

The consistent update of AI technology has led to the introduction of new advanced LLMs models such as GPT, PaLM, and Llama [2]. These models fall under the category of Gen-AI, showcasing significant progress in NLP capabilities. These models employ the neural capabilities required for processing labeled, unlabeled, or semi-supervised data via different learning methods. Adopting advanced transformer architectures characterized by encoder-decoder structures empowers LLMs to process different data modalities, including text, visual, and audio information. This versatility highlights how LLMs are key contributors to the ongoing wave of digital transformation [9]. Posted by Isobel Bartlett, James Gikas, Ngo Suet Hon, Irakli Kupatadze and Mariana Shchotkina | Aug 20, 2025 | Computer Science | 0

Generative Artificial Intelligence (GenAI) is increasingly reshaping a wide range of sectors, including business, healthcare and education, through its ability to generate personalised content and support complex tasks. This paper provides an overview of GenAI’s development from early neural networks to advanced transformer-based models, highlighting its rapid adoption following the release of ChatGPT in 2022. While the benefits of GenAI are substantial – enhancing efficiency, creativity and innovation – its accelerated deployment also raises pressing ethical, social and environmental concerns. These include high energy consumption, electronic waste, privacy breaches, algorithmic bias and the spread of misinformation. Psychological impacts, such as artificial intimacy and overreliance on AI for mental health support, further complicate its use. The paper also considers GenAI’s potential to transform the future of work and support sustainability goals.

Ultimately, it calls for a balanced approach to GenAI development – one that fosters innovation while ensuring transparency, fairness and long-term sustainability. Generative Artificial Intelligence (GenAI) represents a transformative breakthrough within the world of technology. GenAI focuses on generating new creative content – such as text, images, audio, code or video – something that extends far beyond the previous capabilities of existing AIs (Stryker et al., 2025). Unlike traditional AI models, which attempt to distinguish or predict categories within data, generative AI models learn the patterns and relationships within massive datasets and use this knowledge to produce original content in response... The emergence of GenAI has enabled people to train these models to learn complex subjects, including human language, programming, art and biochemistry, and apply this understanding to craft innovative outputs that mimic human creativity. The most prevalent types of GenAI models include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and modern language models such as Generative Pre-Trained Transformers (GPT) (Lawton, 2025).

These models rely on machine learning, utilising neural networks to encode observed data structures in order to generate new, similar content (Njoroge, 2025). While today’s strong interest in GenAI, shared by both consumers and businesses alike, was sparked by the rise of ChatGPT in 2022 (Marr, 2023), the technology used by OpenAI to develop the GPT models... In the late 1980s to the 1990s, the AI field advanced with the development of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks (Bernard, 2023). These networks were able to process sequential data, making them suitable for tasks like speech and language modelling (Marr, 2023). In 2014, this was enhanced with the advent of GANs, pitting two networks – a generator and a discriminator – against each other to generate more high-quality images and video (Stryker et al., 2025). Three years later, the transformer architecture was published, leading to more sophisticated developments in natural language processing, including OpenAI publishing their prototype for the GPT model (ibid.).

Machine learning models capable of generating new content, i.e., text, images, audio and video, and not just classifying or analyzing existing content, is generative artificial intelligence. Recently, there has been a great development of generative AI due to many deep learning techniques like generative adversarial networks (GANs) and transformer models. Generative AI is at an inflection point. Examples like DALL E 2, which is able to create a photograph and art from text prompts, and GPT 4, which is capable of producing similar human text, suggest a vast number of artistic... Some of the difficulties with generative AI are centered around bias and misinformation in the form of some of these predictions, as well as concerns about job displacement. What are the potential opportunities to explore with generative AI as it improves?

What risks should society be aware of, and what should they fear? In this article, we introduce you to generative AI, provide an overview of some key opportunities—including use cases in business and environmental sectors—and examine critical challenges related to controls, potential bias, and legal issues. Additionally, it will explore how organizations can develop a robust generative AI strategy to maximize benefits while mitigating risks. Generative AI is the class of machine learning systems that generate new text as output and not classification or clustering of existing text. It can generate text, images, audio, video and more. Generative AI models under the hood, in general, learn patterns and relationships in huge sets of existing content in order to produce then fresh examples made up of that understanding.

It is the opposite of what most AI is currently doing, which is to analyze existing data. arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Opportunities And Challenges Of Generative Artificial Intelligence

People Also Search

A Not-for-profit Organization, IEEE Is The World's Largest Technical Professional

Besides Language And Text, Robust And Effective Generative AI Models

Generative Artificial Intelligence (Gen-AI) Is A New Advancement That Has

The Study Contributes Substantially By Exploring Positive Elements And Addressing

[6], The Field Centered On Cognitive And Computer Science. AI