Top Generative AI Applications Across Industries Gen AI Applications 2025

Five useful examples of generative AI in action

For instance, in natural language processing (NLP), models like BERT or GPT are pre-trained on vast corpora of text data. Conversational AI refers to the technology that helps machines engage in a more natural way with humans. These technologies are known for their conversational abilities, assisting people through back-and-forth communication. It protects the research and development of AI technologies and can be better at specific tasks than open source counterparts but loses out on the benefits of public oversight. Big tech companies and startups are developing early-stage solutions that can partially automate functions like software development, sales, marketing, and regulatory compliance.

Its value is shown in the ways it can detect data flow anomalies and extrapolate how they will play out in the future in terms of results or behavior. ChatGPT (OpenAI) is a conversational AI built on the GPT architecture that generates human-like text and helps with tasks such as content creation, customer assistance, and education. It excels at understanding and keeping conversation context and it can be tailored to individual use cases, making it applicable to a wide range of industries.

Top 9 generative AI use cases for business

Appian helps insurance businesses streamline claims processing, minimize errors, and accelerate decision making which results in faster payouts and better client experience. With the power of generative AI, Jasper Campaigns creates cohesive and compelling content across various marketing channels. Its analytics tools measure campaign performance and give insights that help refine and optimize future marketing strategies. Like many video generation tools, Synthesia employs generative AI to create professional-looking videos from text input.

• That involves rearchitecting their initial solutions to ensure the best possible performance.
• And we recommend organizations establish guiding principles for responsible AI use that cover topics like transparency, safety, security, and fairness.
• The complexity of generative AI algorithms and their computational requirements can pose challenges in terms of processing power and infrastructure.
• Original or specialized writing might become increasingly valuable as generic, AI-generated writing proliferates on the internet, obscuring genuine human perspectives.
• It is a pioneering effort aimed at enhancing speech recognition for people with speech impairments, with an eye toward increasing their ability to communicate as well as their independence.

Flow-based models are generative models that transform a simple probability distribution into a more complex one through a series of invertible transformations. These models are used for image generation, density estimation, and data compression tasks. GANs consist of two neural networks, a generator and a discriminator, that are trained together competitively. The FinTech industry thrives on innovation, constantly seeking new ways to enhance its approach and drive profitability.

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“Many enterprises do not want their IP to be used to train or improve models or be used for anything but providing output from the model,” DeSanto said. In addition, he noted that security professionals are concerned that AI services could experience data breaches, risking their sensitive intellectual property data. There are many practical reasons enterprises might adopt one approach over the other. Open AI, while not as fast, enables greater scrutiny of underlying code, models and data and often results in improved explainability and security.

By identifying these patterns, predictive AI may conclude and forecast possible outcomes or future trends. Both generative and predictive AI use advanced algorithms to tackle complicated business and logistical challenges, yet they serve different purposes. Knowing their different goals, approaches, and techniques can help businesses understand when and how to employ them.

This enables lenders to make more accurate and informed decisions regarding loan approvals, interest rates, and credit limits, ultimately minimizing default risks and optimizing loan portfolios. The convergence of Generative AI and finance represents a cutting-edge fusion, transforming conventional financial practices through sophisticated algorithms. The use of Generative AI in finance encompasses a wide range of applications, including risk assessment, algorithmic trading, fraud detection, customer service automation, portfolio optimization, and financial forecasting.

To achieve these goals, operators must ensure uninterrupted operation and prevent unexpected downtime, keeping their machines in perfect condition. However, navigating siloed data — such as maintenance records, equipment manuals and operating procedure documentation — is complicated, time-consuming and expensive. While both approaches have merits, the decision to use open versus closed AI has become a topic of fierce debate, with the principal players tending to come down hard on one side or the other. Regulators concerned about giving political adversaries an advantage, for example, tend to oppose open AI — likewise for vendors striving to maintain a competitive moat.

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AI-powered supply chain management tools can track supplies as they make their way through the various links and partners in the supply chain. AI in supply chain management has the potential to improve demand forecasting, inventory evaluation, customer communication, operational performance and even sustainability. Adobe Photoshop’s new Generative Fill feature is one example of the way generative AI can augment the graphic design profession. The feature lets people with no photo editing experience make photorealistic edits using a text prompt. Other tools — such as Dall-E and Midjourney — also create realistic looking images and detailed artistic renderings from a text prompt. AI assistants and chatbots let users book flights, rent vehicles and find accommodations online and offer a personalized booking experience.

Led by top IBM thought leaders, the curriculum is designed to help business leaders gain the knowledge needed to prioritize the AI investments that can drive growth. Access our full catalog of over 100 online courses by purchasing an individual or multi-user subscription today, enabling you to expand your skills across a range of our products at one low price. 2016 DeepMind’s AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves). Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy.

This enhanced brand perception can set a company apart from its competitors, attracting new customers and retaining existing ones. In an increasingly crowded market, staying ahead of the curve and embracing cutting-edge technology can be a crucial differentiator. For industries such as fashion, AI can generate original designs or assist in refining patterns based on trends, making it easier for brands to innovate quickly. In media, AI-generated content personalization is increasingly used to engage diverse audiences by curating experiences based on user interests.

• Then, the platform spits out a bot, which the business can adapt and deploy in its contact center.
• This means teams can explore different options rapidly and find the best solutions without lengthy delays, leading to faster project completion and innovation.
• The tools enable people without coding skills to use visual interfaces and intuitive controls like drag-and-drop to create and modify applications quickly and efficiently while the actual code remains hidden in the background.
• Mastercard is supercharging its fraud detection capabilities by deploying generative AI, which considerably quickens the discovery of compromised payment cards.

What follows is a snapshot of today’s examples, not an exhaustive list of applications. Some are based on proofs of concept and demos that are promising but are not ready for enterprise deployment. While these examples are cross-industry, industry-specific agentic applications are also emerging. For example, some firms in the finance and insurance industries are using gen AI to assist underwriters evaluating prospective clients. Credibly, a lending platform for small businesses, uses gen AI, paired with machine learning, to evaluate loan risk and to speed up the lending process, says Ryan Rosett, co-CEO and founder of the company.

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Prior to joining Diversified Communications, Matt covered the world of baseball and other sports for over a decade. When not writing he enjoys learning about new developments in the world of technology, spending time outdoors, and reading. We start with a guest post recently contributed to Geo Week News by Usman Shuja, the CEO of Bluebeam. It’s a fascinating look at the AI adoption in the construction industry given it comes from the CEO of a company that is working to implement AI in many of their offerings, but doesn’t necessarily take a rosy view of the trend. Shuja, to be clear, is not anti-AI in construction, but lays out a case for implementing with care and with all end users in mind.

Furthermore, the organization may obtain competent individuals for the company’s development through Artificial Intelligence. NASA uses AI to analyze data from the Kepler Space Telescope, helping to discover exoplanets by identifying subtle changes in star brightness. Precision agriculture platforms use AI to analyze data from sensors and drones, helping farmers make informed irrigation, fertilization, and pest control decisions. Google Maps utilizes AI to analyze traffic conditions and provide the fastest routes, helping drivers save time and reduce fuel consumption.

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The most common foundation models today are large language models (LLMs), created for text generation applications. But there are also foundation models for image, video, sound or music generation, and multimodal foundation models that support several kinds of content. It can generate realistic visual effects and add complex graphics, which helps speed up the production process and enhance the overall media quality. For example, AI tools can quickly generate backgrounds, special effects, or even complete scenes, reducing the time and effort needed by human creators. These models then draw from the encoded patterns and relationships in their training data to understand user requests and create relevant new content that’s similar, but not identical, to the original data. Ivalua offers a unified source-to-pay platform that improves supply chain management with powerful AI capabilities.

In the finance sector, Generative AI has become a tool that financial institutions cannot afford to overlook. Its integration into financial institutions profoundly improves efficiency, decision-making, and customer engagement. By automating repetitive tasks and optimizing workflows, Generative AI streamlines operations, reduces errors, and cuts costs, ultimately enhancing businesses’ bottom lines. As AI technology progresses, the difference between generative and predictive AI becomes increasingly distinct.