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Like other forms of AI, generative AI can affect ethical issues and risks pertaining to data privacy, security, energy usage, political impact and workforces. GenAI technology can also potentially introduce a series of new business risks, such as misinformation and hallucinations, plagiarism, copyright infringements and harmful content. Lack of transparency and the potential for worker displacement are additional issues that enterprises might need to address.
“Many of the risks posed by generative AI … are enhanced and more concerning than those [associated with other types of AI],” said Tad Roselund, managing director and senior partner at consultancy BCG. Those risks require a comprehensive approach, including a clearly defined strategy, good governance and a commitment to responsible AI.
Corporate cultures that use GenAI should consider the following 11 issues:
Generative AI systems can create content automatically based on text prompts by humans. “These systems can generate enormous productivity improvements, but they can also be used for harm, either intentional or unintentional,” explained Bret Greenstein, partner and generative AI leader at professional services consultancy PwC. An AI-generated email sent on behalf of the company, for example, could inadvertently contain offensive language or issue harmful guidance to employees. GenAI should be used to augment but not replace humans or processes, Greenstein advised, to ensure content meets the company’s ethical expectations and supports its brand values.
Popular generative AI tools are trained on massive image and text databases from multiple sources, including the internet. When these tools create images or generate lines of code, the data’s source could be unknown, which might be problematic for a bank handling financial transactions or a pharmaceutical company relying on a formula for a complex molecule in a drug. Reputational and financial risks could also be massive if one company’s product is based on another company’s intellectual property. “Companies must look to validate outputs from the models,” Roselund advised, “until legal precedents provide clarity around IP and copyright challenges.”
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Generative AI large language models (LLMs) are trained on data sets that might include personally identifiable information (PII) about individuals. This data can sometimes be elicited with a simple text prompt.
Moreover, compared with traditional search engines, it can be more difficult for consumers to locate and request removal of the information. Companies that build or fine-tune LLMs must ensure that PII isn’t embedded in the language models and that it’s easy to remove PII from these models in compliance with privacy laws.
GenAI is democratizing AI capabilities and making them more accessible. This combination of democratization and accessibility, Roselund said, could potentially lead to a medical researcher inadvertently disclosing sensitive patient information or a consumer brand unwittingly exposing its product strategy to a third party. The consequences of unintended incidents like these could irrevocably breach patient or customer trust and carry legal ramifications. Roselund recommended that companies institute clear guidelines, governance and effective communication from the top down, emphasizing shared responsibility for safeguarding sensitive information, protected data and IP.
Generative AI can potentially amplify existing bias. For example, there can be bias in the data used for training LLMs, which can be outside the control of companies that use these language models for specific applications. It’s critically important for companies working on AI to have diverse leaders and subject matter experts to help identify bias in data and models, Greenstein said.
AI is being trained to do more of the daily tasks that knowledge workers do, including writing, coding, content creation, summarization and analysis, Greenstein said. Although worker displacement and replacement have been ongoing since the first AI and automation tools were deployed, the pace has accelerated as a result of the innovations in generative AI technologies. “The future of work itself is changing,” Greenstein added, “and the most ethical companies are investing in this [change].”
Ethical responses have included investments in preparing certain parts of the workforce for the new roles created by generative AI applications. Businesses, for example, will need to help employees develop generative AI skills such as prompt engineering. “The truly existential ethical challenge for adoption of generative AI is its impact on organizational design, work and ultimately on individual workers,” said Nick Kramer, vice president of applied solutions at consultancy SSA & Company. “This will not only minimize the negative impacts, but it will also prepare the companies for growth.”
GenAI systems consume tremendous volumes of data that could be inadequately governed, of questionable origin, used without consent or biased. Additional levels of inaccuracy could be amplified by social influencers or the AI systems themselves.
“The accuracy of a generative AI system depends on the corpus of data it uses and its provenance,” explained Scott Zoldi, chief analytics officer at credit scoring services company FICO. “ChatGPT-4 is mining the internet for data, and a lot of it is truly garbage, presenting a basic accuracy problem on answers to questions to which we don’t know the answer.” FICO, according to Zoldi, has been using generative AI for more than a decade to simulate edge cases in training fraud detection algorithms. The generated data is always labeled as synthetic data, so Zoldi’s team knows where the data is allowed to be used. “We treat it as walled-off data for the purposes of test and simulation only,” he said. “Synthetic data produced by generative AI does not inform the model going forward in the future. We contain this generative asset and do not allow it ‘out in the wild.'”
Many generative AI systems group facts together probabilistically, going back to the way AI has learned to associate data elements with one another, Zoldi explained. But these details aren’t always revealed when using applications like ChatGPT. Consequently, data trustworthiness is called into question.
When interrogating GenAI, analysts expect to arrive at a causal explanation for outcomes. But machine learning models and generative AI search for correlations, not causality. “That’s where we humans need to insist on model interpretability — the reason why the model gave the answer it did,” Zoldi said. “And truly understand if an answer is a plausible explanation versus taking the outcome at face value.”
Until that level of trustworthiness can be achieved, GenAI systems should not be relied upon to provide answers that could significantly affect lives and livelihoods.
Generative AI techniques all use various combinations of algorithms, including autoregressive models, autoencoders and other machine learning algorithms, to distill patterns and generate content. As good as these models are at identifying new patterns, they sometimes struggle with teasing out important distinctions relevant to human use cases.
This can include creating authoritative-sounding but inaccurate prose or producing pictures with realistic-looking imagery but misshapen representations of humans that contain extra fingers or eyes. With language models, these errors can show up as chatbots inaccurately representing corporate policies, such as in the case of an Air Canada chatbot that misrepresented corporate policies regarding bereavement benefits. Lawyers using these tools have also been fined for filing briefs that cited nonexistent court cases.
Newer techniques like retrieval augmented generation and agentic AI frameworks can help reduce these issues. However, it’s important to keep humans in the loop to verify the accuracy of generative AI information to avoid customer backlash, sanctions or other problems.
Many AI vendors argue that bigger AI models can deliver better results. This is partly true, but it can often involve considerably more data center resources, either for training new AI models or running AI inference processes in production. The issue is hardly clear-cut. As some argue, improving an AI model that has the potential to reduce the carbon footprint of an employee traveling to work or the efficiency of a product could be a good thing. Conversely, developing that model could also exacerbate global warming or other environmental problems
The political impact of GenAI technologies is a fraught topic. On the one hand, better GenAI tools have the potential to make the world a better place. At the same time, they could also enable various political actors — voters, politicians, authoritarians — to make communities worse. One example of generative AI’s negative impact on politics can be found in social media platforms that algorithmically promote or create divisive comments as a strategy for increasing engagement (and profits) for their owners over comments that find common ground but might not have the same click-through and sharing numbers.
These issues will continue to be thorny for years to come as societies sort out which GenAI use cases serve the public good and whether that should be the end goal.
Editor’s note: This article was updated in 2025 to include additional ethical issues and concerns stemming from the use of generative AI.
George Lawton is a journalist based in London. Over the last 30 years, he has written more than 3,000 stories about computers, communications, knowledge management, business, health and other areas that interest him.
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If you’ve ever struggled with finding information in your overflowing inbox, you’re not alone. That’s why Gmail is rolling out a smarter search feature powered by AI to show you the most relevant results, faster.
Instead of just showing emails in chronological order based on keywords, Gmail search results now factor in elements like recency, most-clicked emails and frequent contacts. With this update, the emails you’re looking for are far more likely to be at the top of your search results — saving you valuable time and helping you find important information more easily.
“Most relevant” search results are rolling out globally for users with personal Google accounts, and can be accessed on the web and in the official Gmail app for Android and iOS. Once available in your account, you can toggle between “most relevant” and “most recent” results. We’ll expand this to business users in the future.
A search view before and after. Now, search results are sorted by relevance instead of in chronological order to help you find your intended result, faster.
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Generative AI tools now span various types that can produce text, images, audio, videos and code. These are some of the most popular generators revolutionizing content creation.
Generative AI tools — which rely on a form of artificial intelligence that makes original text, images, videos, audio and code — has transformed daily life, enhancing both creativity and productivity.
Looking ahead, generative AI tools are expected to become as essential as smartphones, the cloud and the internet itself, promising both exciting opportunities and some serious risks.
 
Generative AI tools are software programs designed to create new content using advanced AI models. Typically built on neural networks, these models can identify structures and patterns within massive amounts of annotated data. Then, given a prompt or input, the AI is able to draw upon what it has learned to generate relevant, original works — often in real time.
Below are some of the most popular generative AI tools available today. Some specialize in a single type of content, and others can handle multiple mediums at once. Either way, these tools are shaking up a variety of industries, from the creative arts to software development.
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ChatGPT is designed to understand and generate human-like text based on the input it receives, meaning it is capable of answering questions, providing explanations, writing poems and completing lots of other text-based tasks. It can also understand and produce images, video and audio. ChatGPT’s versatility and conversational abilities make the chatbot a valuable tool across all sorts of industries, from customer service to creative writing. 
Claude generates natural written responses to both text and image-based user inputs. With broad context and reasoning capabilities, Claude can edit large documents, carry on lengthy conversations and create a variety of original content. It is also trained using a method called “constitutional AI,” where ethical principles guide its behavior. This approach aims to reduce biases and inaccuracies in Claude’s responses, setting it apart from other chatbots, according to its maker, Anthropic.
Character.ai is a platform that lets users design personalized AI assistants. Users can tailor traits like an assistant’s personality and avatar to fit their preferences, and the platform also has assistants for specific contexts readily available. For more common applications, users can search for assistants based on categories like writing, learning and gaming. 
Midjourney generates images based on natural language prompts. The tool is accessible either through its website or a Discord bot, which can be prompted to create an image using the “/imagine” command. Since its launch in 2022, Midjourney has become a popular (yet controversial) tool for publications, authors, journalists and other creatives. It even became the first platform of its kind to produce an image that won an actual art competition, sparking both wonder and widespread debate.
DALL-E 3 is a text-to-image generator developed by OpenAI. The tool is built natively on ChatGPT, enabling users to more easily produce and tweak their creations using natural language prompts. Once an image has been generated, users can quickly edit them by either conversing with ChatGPT or interacting with the image directly. To avoid producing deceptive, derivative or otherwise harmful content, DALL-E 3 will not generate images of public figures by name and will not copy the style of another living artist’s work, according to OpenAI.
Adobe Firefly is a multimodal AI tool that can input and generate text, images, audio and videos. As a result, Firefly users can pair soundtracks with visuals, produce videos from images and generate images from text prompts, among other capabilities. The tool is part of Adobe’s Creative Cloud suite and is intended to work in tandem with other Adobe applications like Photoshop, Illustrator and Premiere Pro. 
Gemini is a generative AI tool developed by Google. Powered by a family of multimodal models in various sizes, Gemini can handle a wide range of tasks. It can engage in text-based conversations, transcribe audio, create artwork, analyze videos and much more. Gemini models are being incorporated into several other Google products, including Gmail, Docs and its search engine.
Imagen 3 is Google DeepMind’s latest image generator that can process naturally written prompts, so users don’t need to be skilled in prompt engineering. In addition, Imagen 3 is built to understand and capture finer details like textures, camera angles and lighting, enabling users to produce images in a broader range of styles. Out of caution, the DeepMind team used red teaming and thorough data labeling techniques to ensure Imagen 3 meets the company’s fairness, bias and safety standards.
Suno is a music-creation program that can generate realistic instrumentals and vocals from a single text prompt. Users can play around with their prompts to craft a song about a particular topic or genre — an emotional synthpop song about rainy mornings, for example, or a rockabilly song about being in love. While Suno has admitted to training its AI model on copyrighted songs, it argues this follows the fair-use doctrine. 
Developed by former Google DeepMind researchers, Udio produces both vocals and instrumentals. Its musical creations are based on user text inputs, which can include genre, story direction and similar artists from which to draw inspiration. Once it has been prompted, Udio generates two 30-second songs to choose from, which can be extended and edited with more prompting. Like Suno, Udio has been a target for copyright infringement claims, but it also cites the fair-use doctrine. 
Soundraw generates royalty-free instrumentals and beats. The platform caters to a wide range of creators, from vocalists seeking backing tracks to marketers in need of mood-setting music for their social media posts. All users have to do is choose their preferred genre, mood, tempo and song length (up to five minutes). Once the song has been generated, users can customize the music before freely distributing and monetizing their creations on platforms like YouTube, TikTok and Instagram without any copyright concerns.
Synthesia creates AI-generated videos, complete with voiceovers and realistic-looking avatars that represent various demographics and moods. Users upload their script, choose their avatar and customize their video’s layout. From there, the platform uses natural language processing and deep learning techniques to generate footage that shows the avatar reading the script, along with additional voiceovers and supplemental text. Users can choose from more than 230 stock avatars or create their own.
Runway creates AI-generated images, animations and 3D models using relative motion analysis to generate realistic motion graphics. Its underlying model — trained on both images and videos — powers both its text-to-video and image-to-video capabilities, offering precise control over style, structure and camera movement. Used in movies like Everything Everywhere All At Once, as well as music videos for artists like A$AP Rocky and Kanye West, Runway is designed for professionals in filmmaking, post-production, advertising, editing and visual effects.
Dream Machine makes high-quality, realistic videos from both text and image inputs. Created by Luma AI, the tool was built on a scalable, multimodal transformer architecture. It can generate clips up to five seconds long, complete with realistic physics, smooth cinematography and even drama. In addition, Dream Machine lets users repurpose and edit content, allowing them to experiment with different versions.
ChatPDF is described as being “like ChatGPT but for PDFs.” Users can upload a PDF of any document they want ChatPDF to analyze, and they can then ask the tool questions about the document. Students can use the AI app to locate key points quickly, develop study questions and prepare for exams. ChatPDF is also helpful for reviewing academic pieces, legal contracts and other dense documents. 
Elicit is a platform that allows users to navigate a database of more than 125 million research papers by submitting requests and questions in natural conversation. Not only can users receive single-sentence summaries of a piece, but they can also rely on Elicit to locate other relevant research papers and organize their findings into tables. Users can even upload their own PDFs and ask Elicit questions for further analysis. 
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GitHub Copilot is a code-completion tool created by GitHub and OpenAI. Designed for both individual developers and businesses, it generates new code from natural language prompts. For example, if a user writes “design a landing page for a website,” the tool will produce the appropriate code. It is also equipped with a chatbot powered by the GPT-4 language model, allowing users to converse with Copilot in real time and ask questions about their code.
Cohere Generate is an AI text generator that can produce copy for product descriptions, landing pages, marketing emails, blog posts and other materials. Users simply need to submit a prompt or example to guide Generate’s response. Generate is powered by Cohere’s family of Command language models, which are designed to deliver high accuracy while adapting to fit an organization’s particular brand voice. 
Copy.ai is a text generator designed for sales and marketing teams. Built on top of OpenAI’s GPT-4 LLM, it can produce all kinds of content, including articles, blogs, social media posts and product descriptions — all of which can be written in a customized brand voice, ensuring each piece is consistent with a company’s identity and personality. The platform also provides an infobase, where users can teach Copy.ai the ins and outs of their products and services so that it gets the details correct in its outputs.
While it positions itself as an all-in-one marketing app, Jasper is a popular text generator, offering a suite of tools to help users write, optimize and rank their content. The tool can generate content in a variety of brand voices and lengths, whether it’s a social media post, long-form article or press release. Jasper also comes with a chat feature, a language translation tool (trained on more than 80 languages) and an art generator, which produces royalty-free images that can be used in ads, blogs and social media posts.
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Consensus is an AI search engine that focuses on academic research. Students and researchers alike can access a database of more than 200 million academic papers, refining their searches with prompt instructions, filters and quality indicators that signal the authority of each source. Researchers and clinicians can narrow their searches even further based on factors like methodology, study design and sample size.    
Amazon Q is a generative AI assistant that connects to an organization’s data, so it can help locate data, answer questions and simplify workflows. Paired with other tools like Amazon QuickSight, Amazon Q can help with writing code, guiding customer conversations and monitoring supply chains, among other applications. Amazon Q also understands data permissions, giving access to authorized users only.    
Microsoft Copilot is an AI assistant that can operate in Edge and Windows and as part of the Microsoft 365 suite. As a web browser tool, Copilot accesses Bing’s database to address user queries and improve the search experience. As a tool in Microsoft 365, Copilot can connect with an organization’s data, allowing it to retrieve relevant company data, automate business processes and generate summaries of meetings, among other capabilities. 
Generative AI by Getty Images was trained on the website’s stock images, enabling users to create fully licensed images with comprehensive usage rights. Users enter a text prompt to generate four unique images, which can be customized by adjusting color, mood, lens type, and more. These images can be downloaded and licensed, with each including legal indemnification of up to $50,000. Getty ensures that its AI-generated images do not feature recognizable characters, logos or other intellectual property. And users’ creations are not available for others to license without permission.
Colossyan helps companies create training, marketing and corporate communication videos by generating human-like AI avatars that deliver material with realistic lip-syncing. The platform offers hundreds of diverse avatars, voices and customizable backdrops, and even enables scenarios where multiple avatars can interact with each other. It can automatically translate into more than 70 languages, and includes features like conversation modes and multiple-choice quizzes for assessing viewer engagement.
Tabnine offers code completion services in more than two dozen languages and integrated development environments (IDEs). Not only can it generate code, but it can also convert natural language into code (and vice versa), test code and fix bugs. The tool can also learn from users’ individual coding patterns and styles, enabling more accurate and personalized suggestions over time. Available both online via the cloud and offline with a local AI mode, Tabnine was trained exclusively on open-source data, ensuring that the code it generates is not copyrighted and can be freely used by other developers.
Generative AI tools are software programs that can create original content (text, images, videos, audio and code) using advanced AI models.
Five of the top generative AI tools include text generators ChatGPT and Claude, image generators DALL-E 3 and Midjourney and music generator Suno. 
Some popular examples of generative AI tools include text generator ChatGPT, image generator DALL-E 3, code generator GitHub Copilot, music generator Suno, voice generator ElevenLabs and video generator Synthesia.
Yes, many generative AI tools come with free plans that offer basic features. These include ChatGPT, Claude, Character.ai, Imagen 3 and GitHub Copilot.

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How to Use Perplexity AI for Business: Simple Tricks  Analytics Insight
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