10 Generative AI Use Cases Transforming Industries in 2025

Generative AI has emerged as one of the most transformative technologies in business. A recent report indicated that generative AI could unlock up to $4.4 trillion annually in global economic value by 2030, driven by increased productivity, accelerated innovation, and new product development. The opportunity for business leaders and technologists is clear: generative AI offers scalable solutions to longstanding challenges such as time-consuming content creation, costly R&D processes, and limited personalization at scale.

Unlike traditional AI, which primarily focuses on classification or prediction, generative AI creates new content, ranging from text and images to complex 3D models and drug molecules, based on patterns learned from vast datasets. This allows organizations to innovate faster, engage customers more deeply, and simplify operations with automation.

However, many discussions about generative AI fall into vague or overly theoretical territory without grounding the technology in practical, real-world applications. This article bridges that gap by detailing ten concrete use cases already in deployment in 2025.

Benefits and impact of generative AI

Generative AI has a tangible impact on efficiency, creativity, and accessibility. Businesses are already seeing these benefits and experiencing an impact on their workflows and work efficiency. Our 2025 Currents research report found that 79% of respondents are integrating AI within their organization. Here are some of the benefits:

Time and cost saving

Generative AI reduces repetitive tasks by automating processes and reducing the effort required. It will also boost productivity and make everyday tasks less time-consuming.

By leveraging these tools, businesses also reduce costs associated with different segments and resources. For instance, AI music generators like Soundraw are helping creatives develop background scores in minutes rather than days, and marketing teams generate custom jingles without hiring expensive studios.

Creativity at scale

AI in creative industries reduces the time required to produce innovative and compelling content. This allows businesses to create 3- 4x more content with the same team size while focusing more on quality. AI video tools like Runway let marketing teams turn product concepts into polished promotional videos, and small business owners create professional training materials without needing video production expertise.

Accessibility

Generative AI tools are now accessible through simple web browsers at costs as low as $10-20 per month, making advanced capabilities available to anyone with an internet connection. These platforms feature intuitive drag-and-drop interfaces, conversational chatbots, and real-time collaboration tools that let teams work together. What once required specialized technical knowledge can now be accomplished through natural language prompts and visual workflows, letting educators create interactive lesson plans, students produce multimedia projects, and professionals generate presentations all without coding or design expertise.

Challenges and risks

Although generative AI produces powerful results, certain limitations, challenges, and risks must be considered when integrating AI applications within your business.

Model hallucinations

AI models often experience hallucinations and can offer incorrect information, portrayed as correct information. Wyoming attorneys faced potential sanctions after their AI tool hallucinated eight fake court cases cited in official legal documents, highlighting the serious risks of AI fabricating information in professional settings. These hallucinations can be overcome with a human verification process, as specific industries, including healthcare, require precise information.

Intellectual property concerns

Various legal battles over training data usage continue to shape the landscape, with major settlements between AI companies and content creators setting precedents. For instance, Getty Images’ lawsuit against Stability AI over alleged copyright infringement in image generation training led to licensing discussions that could reshape how visual content is used in AI development. Getty Images accused Stability AI of misusing more than 12 million Getty photos to train its Stable Diffusion AI image-generation system. Creative professionals face attribution challenges when AI systems trained on their work produce derivative content. New IP licensing models are emerging specifically for AI training and output commercialization.

Deepfake

The sophistication of AI-generated media has made detecting synthetic content increasingly difficult. Organizations face reputation risks from unauthorized AI impersonation, while social media platforms struggle with the rapid spread of convincing but fabricated news stories. Regulatory frameworks are still catching up, creating uncertainty about liability and verification standards.

10 generative AI use cases

Each use case discussed below demonstrates how generative AI offers results in different businesses and industries.

1. Marketing and advertising

Most businesses spend some budget on marketing and advertising their products and services to their target audience. AI tools like Jasper, ChatGPT, and Claude can help generate engaging copy, email campaigns, articles, and visuals that help boost marketing impact.

Brands like Heinz are integrating AI tools such as OpenAI’s DALL-E for their marketing campaigns. They have created appealing visuals and different versions of their ketchup bottles.

2. Healthcare

Generative AI in 2025 is being used in the healthcare industry to speed up drug discovery and clinical trials. AI technology helps generate potential drug molecules, predict protein structures, and craft patient-specific treatment plans. It drastically reduces discovery timelines and cost overheads in pharmaceuticals.

Pharmaceutical brands like Insilico Medicine are using generative AI tools and have developed the Pharma.AI platform to create a novel fibrosis treatment that entered human trials in under 18 months, a massive leap over the typical 5–10 year pipeline. Insilico also made its drug delivery platforms using generative AI, PandaOmics, and Chemistry42, which are available for other companies as well to improve their drug discovery system, and is also working on digitizing the entire R&D for reduced errors, enhanced efficiency, reduced time, and costs for the drug discovery process.

3. Manufacturing

Generative AI is used for manufacturing, from optimizing part geometries to planning factory floor layouts. It allows manufacturing companies to reduce material waste, improve their efficiency, accelerate production and prototyping, and reduce the overall cost of production. It also helps reduce errors that occur due to manual processes.

Automotive manufacturer General Motors has integrated generative AI platforms and cloud computing into its manufacturing processes to design solutions for vehicle parts and components. This has allowed them to make lighter vehicle components, reduce production costs, and enhance their efficiency and productivity. This AI system scans all vehicle designs for issues and errors to provide an improved experience for the end-users.

4. Gaming

Professionals in the gaming industry use generative AI platforms to develop engaging, real-life stories and characters. These tools enable game developers to incorporate realistic voice-overs, visuals, storylines, and characters with a firm reference to the real world. For instance, NVIDIA ACE for Games allows developers to build digital characters that understand and respond naturally to player voice input in real time, creating immersive, voice-driven gameplay.

5. Financial services

Generative AI use cases in financial services include financial research, risk assessment, reporting, and easy customer communication for bank customers and fintech users.

Goldman Sachs released their internal generative AI assistant, GS AI assistant, for bankers, asset managers, and traders. It helps with tasks such as proofreading emails, summarizing emails and documents, and translating code to and from multiple languages.

6. Real estate documentation

Real estate professionals are adopting generative AI to create compelling property listings, virtual staging content, and personalized marketing materials. AI systems can generate detailed property descriptions, create virtual tours, and produce customized marketing content for buyer segments. Zillow Group’s acquisition of Virtual Staging AI (VSAI) in 2024 demonstrates this trend, as the technology was integrated into Zillow Showcase to create more immersive listing experiences that give buyers a realistic sense of homes before visiting. This technology enables real estate agents to scale their marketing efforts while maintaining personalization, automatically generating neighborhood guides, investment analyses, and property comparison reports.

7. Legal services

AI tools and platforms are also making their way to legal services. They simplify and facilitate the drafting of legal documents, offer contract analysis and clause drafting, summarize legal cases, help conduct due diligence, and reduce review times. These platforms enable smaller firms to compete with multinational legal teams by leveraging AI-enhanced research capabilities previously requiring dozens of associates.

One such tool, Harvey, backed by OpenAI, is a specialized legal AI tool tailored for elite law firms. It goes beyond generic language models by integrating legal databases, case law, and firm-specific templates to streamline legal work. Harvey assists in contract review, litigation strategy, and compliance by surfacing relevant clauses, precedents, and regulations, making legal workflows more efficient and reducing manual research time.

8. Supply chain optimization

Generative AI transforms supply chain operations by simulating demand scenarios, optimizing routing, and automating procurement strategies. It empowers companies to manage disruptions better and forecast inventory needs more precisely. Generative AI also improves resilience and efficiency in supply chains by reducing lead times, improving supplier matching, and optimizing distribution planning.

Unilever uses AI to analyze weather data and monitor 100,000 smart freezers globally, improving ice cream sales forecasts and reducing manufacturing waste by 10% for ingredients like vanilla and cocoa. The system helps predict demand based on temperature changes, since even a 1°C rise can significantly impact ice cream sales in seasonal markets.

9. Adaptive learning

Generative AI tailors educational content and corporate training modules based on learners’ pace, knowledge level, and preferences. It enables continuous feedback, adjusts real-time instruction, and boosts learning retention. It also provides adaptive learning experiences, acting as a tutor that responds to student queries, explains complex topics, and adjusts content to their pace and comprehension level.

Some popular tools include 360Learning, a collaborative learning platform that uses generative AI to help subject-matter experts create training courses quickly and efficiently. The platform allows admins and authors to use AI to create classes by providing instructions on the objective and intended audience or by uploading existing files.

10. Customer services

Generative AI improves customer support with bots and empathetic, context-aware interactions powered by advanced LLMs. These systems remember prior issues, personalize tone, and escalate only when necessary. AI-powered assistants are integrated into CRMs to pull customer history, automate ticket classification, and generate replies that align with brand tone. These tools also assist human agents in real time by summarizing chats, recommending responses, and flagging priority cases.

Some generative AI tools used in customer support include Zoho Desk, an AI-driven help desk software that streamlines ticket management, automates repetitive tasks, and provides insightful analytics to enhance customer support efficiency. Tidio combines live chat and chatbot functionalities, enabling businesses to engage customers in real-time and automate responses to common queries.

Generative AI use cases FAQs

What are the most impactful generative AI use cases across industries?

Generative AI is transforming industries through content creation, code generation, personalized customer experiences, and automated decision-making processes. Key applications include creating marketing materials, generating software code, developing personalized product recommendations, and automating routine business tasks that previously required human creativity and analysis.

How is generative AI changing software development and coding?

Generative AI tools like GitHub Copilot are revolutionizing software development by automatically generating code snippets, debugging existing code, and suggesting optimal programming solutions. These tools can understand natural language descriptions of desired functionality and translate them into working code, significantly accelerating development cycles and reducing the technical barrier for non-programmers.

What role does generative AI play in content marketing and creation?

Generative AI enables businesses to scale content production by automatically creating blog posts, social media content, product descriptions, and marketing copy tailored to specific audiences. The technology can maintain brand voice consistency while generating variations for A/B testing, personalizing content for different customer segments, and creating multimedia content including images and videos.

What are the key considerations for businesses implementing generative AI?

Businesses must consider data quality and training requirements, ethical implications and potential biases in AI-generated content, and integration challenges with existing systems and workflows. It’s also crucial to establish governance frameworks for AI usage, ensure compliance with industry regulations, and provide adequate training for staff to effectively utilize generative AI tools.

References

• How Multimodal Learning is Used in Generative AI

Further reading

• The economic potential of generative AI: The next productivity frontier

• ​​Getty Images lawsuit says Stability AI misused photos to train AI

• Insilico: linking target discovery and generative chemistry AI platforms for a drug discovery breakthrough

• General Motors: Driving a lighter, more efficient future of automotive part design

• Introducing NVIDIA ACE For Games- Spark Life Into Virtual Characters with Generative AI

• Goldman Sachs rolls out an AI assistant for its employees as artificial intelligence sweeps Wall Street

• Zillow Group Acquires AI company offering virtual staging

• How AI is transforming Unilever Ice Cream’s end-to-end supply chain

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