What is Generative AI?
Generative AI is a branch of artificial intelligence that produces new content by identifying and recombining patterns found in large datasets. Traditional AI systems usually focus on classification or prediction, such as recognizing shapes within an image or checking whether a customer’s credit score meets a certain threshold. Generative AI goes a step further by creating novel content in areas such as text, images, and audio, replicating patterns it has observed in existing material.
This approach relies heavily on Deep Learning, an advanced method that uses multi-layered neural networks to identify complex structures in data. Researchers and engineers feed massive volumes of text, images, or audio files into these networks so the model can learn patterns and relationships within that data. Over time, and with enough examples, the AI begins to generate new content that resembles the training examples but remains distinct.
Large language models
Today’s most popular systems such as ChatGPT and Gemini use Large Language Models (LLMs) to produce text. These LLMs break down language into mathematical representations, sometimes called tokens and embeddings, and learn which sequences of words typically appear together. When prompted, the model predicts the next most likely word or phrase, similarly to how a person might complete a sentence. Models with billions of parameters can display remarkable levels of fluency and nuance.
GenAI for image generation
Nowadays, Generative AI extends far beyond words. Many of the breakthroughs in image creation stem from architectures like Generative Adversarial Networks (GANs) and diffusion models. GANs use two competing neural networks: a generator and a discriminator. The generator produces synthetic images, while the discriminator evaluates their authenticity. Over many training cycles, the generator refines its capabilities to make outputs that are increasingly difficult to distinguish from genuine photos or illustrations.
Diffusion models work somewhat differently by gradually denoising it through multiple steps to form a coherent image. These models often excel at high-resolution image generation and are now key features in popular applications in marketing, design, and entertainment, including Adobe Photoshop and Canva.
The ability to create entirely new visuals, based on diverse prompts, has opened the door for many creative applications. Image generation models base their results on patterns seen in training data, yet they still produce novel and distinct characteristics. This is why Generative AI is sometimes described as “creative,” even though it follows learned mathematical rules.
Because image generation models rely on large training datasets of images and art, creators and industry stakeholders have raised questions about copyright and attribution. Artists are concerned that their work may have been used to train AI models without permission or compensation.
GenAI for voice and music
Audio generation is another prominent area where Generative AI shines. Systems such as Descript’s Overdub can replicate a person’s voice after being trained on a sample of recorded speech, which can allow content creators to seamlessly correct or modify dialogue in post-production. Similarly, some tools can compose music in specific styles based on text prompts. These advances help teams that need quick and cost-effective on-demand voice or music content, including advertisers, podcasters, and educational content creators.
GenAI for video creation and editing
Generative AI can also assist with video tasks. Models like Runway ML and Kling AI help professionals build video projects, adjusting elements like color grading or even entire scenes without labor-intensive editing. Motion can be generated, interpolated, or upscaled, which is particularly helpful for marketing teams, production studios, and e-learning platforms. Although video generation technology continues to improve, it still requires a human production step to refine important details.
Because of its powerful capabilities, Generative AI has become a topic of intense interest for businesses. GenAI outputs can be employed for faster prototyping, rapid content creation, and on-demand customization for different audiences. Keep reading for 16 ways organizations are taking advantage of GenAI to improve processes and innovate.
8 Generative AI applications in marketing
Generative AI offers practical ways for businesses to refine their marketing strategies and optimize outreach. By producing original material across different media formats, GenAI can ease common creative bottlenecks, reduce repetitive tasks, and support teams seeking faster results.
1. Personalized ad copy
Every business aims to connect with specific audience segments. One of the most common Generative AI use cases involves deploying Large Language Models (LLMs) that examine past content and performance metrics, and then generate text for a range of audiences. For instance, a brand that sells sports apparel might create unique product descriptions and campaign slogans for active consumers, fashion enthusiasts, or families shopping for their children’s soccer gear. By organizing data that captures different audience profiles, these systems can produce copy that reflects regional tastes, seasonal trends, and brand voice.
A business can start by collecting examples of marketing text—such as product pages, email campaigns, or social media posts—that generated positive engagement. Then an LLM is fine-tuned on these examples, or prompted using them as context. The model can then propose variations of ad text for each segment. A marketing team might specify the tone, length, or keywords, while the system adapts existing phrasing to suit each demographic.
This process does not eliminate a marketing team’s creativity. Instead, it offers fresh drafts and ideas that can be evaluated and polished. Businesses can reserve time for core strategic tasks, confident that their ads will stay relevant to each group. This helps expand reach without sacrificing quality since the system’s suggestions can be reviewed before final publication. As a result, teams can rapidly iterate on new ideas, test multiple copy versions, and measure how well they connect with each audience.
2. Visual content generation for marketing
Products and services often stand out through distinctive visuals, which shape a brand’s reputation. Generative AI applications such as Midjourney, DALL·E, and Stable Diffusion produce new graphics based on brief text prompts, a feature that can be especially valuable for businesses that operate at a high volume of campaigns. With this technology, marketers can request images that match specific moods or styles—bright, minimalistic, futuristic, classic—depending on what might resonate with potential buyers.
Businesses can save both time and money by using Generative AI to assist in creating initial mockups for a campaign. An in-house design team may then refine the results, ensuring adherence to brand guidelines. This two-step approach allows for quick visual prototyping and more flexibility when planning seasonal or regional promotions.
Moreover, these AI systems can produce multiple variations of an image. A fashion retailer might request an urban summer feel for one region and a formal, muted palette for another. Marketers compare results, gather feedback, and select which ones suit their immediate goals. Although human designers remain essential for final polishing and creative direction, Generative AI offers a rapid-fire resource that can help businesses iterate visuals more frequently and test new ideas.
3. Music and audio for advertising
High-quality audio is a critical part of online video spots, radio ads, or brand podcasts. Generative AI can assist by synthesizing human voices or composing background tracks that fit particular campaign themes. As GenAI models improve, businesses may soon be able to quickly generate high-quality audio in multiple languages or dialects, making it easier to reach international customers.
Some AI-powered music services enable the composition of custom instrumentals. A company running short video ads might request a relaxed jazz tune, an energetic pop beat, or a refined classical backing track. Once a prototype is generated, in-house teams can verify the style and emotional tone to make sure it will be a good fit. This saves time otherwise spent searching for suitable stock music or hiring composers for smaller projects.
4. Conversational chatbots and AI agents
Conversational AI tools powered by advanced language models can manage customer interactions across social media, websites, and various messaging apps. A well-structured chatbot might welcome visitors, describe products, or answer inquiries about ongoing promotions. The generative aspect comes into play when the system crafts responses that feel more organic than older scripted bots. These chatbots can also adapt to user mood or context if given enough data.
Businesses exploring this route should first define the knowledge base the chatbot will use. This often includes product catalogs, frequently asked questions, and brand messaging guidelines. Then, the generative model is trained or fine-tuned on these resources, equipping it to field complex user questions. Deployment can be done on the company’s website or through a messaging service. Regular reviews of conversation logs help teams refine the model’s performance, ensuring that responses remain accurate and consistent with the brand voice.
While chatbots can answer common questions and handle basic tasks, businesses can also take advantage of AI agents—autonomous systems that learn and adapt over time. These agents can break down multi-step problems, plan tasks on their own, and call on external data sources or APIs when needed. If a customer’s query involves details the agent does not have, it can search elsewhere— even consulting other AI systems—to fill information gaps. This adaptability makes the conversation flow more naturally and provides more complete answers, even when the user’s question spans multiple topics.
AI agents also remember past interactions and learn from them, refining future responses to better align with a user’s preferences or tone. In marketing, an AI agent can process product specs, brand guidelines, and recent customer conversations to offer highly tailored solutions or promotions. Instead of simply returning a link or a generic tip, it can walk a user through the steps to finish a complex transaction or troubleshoot an issue. Much like onboarding a skilled new team member, AI agents can become more attuned to a company’s style and strategic goals over time.
One advantage of chatbots and AI agents is their round-the-clock availability, which gives customers a way to get information or even place orders at any time, without having to wait for email replies. They also enable marketing teams to gather insights on what potential buyers prioritize and how they phrase questions. Businesses can then use that data to incorporate personalization, so returning customers get special offers or see relevant product suggestions.
5. A/B testing at scale
A/B testing helps businesses compare two or more versions of the same marketing asset to see which produces better engagement. Traditional workflows can be time-intensive because each variant is crafted by hand. Generative AI tools can streamline this process, allowing teams to create multiple variations of headlines, body text, or design elements based on a single prompt.
For instance, an organization might decide to test how various product descriptions resonate with different groups. A generative model, guided by past campaign data, can produce new versions that shift tone, content length, or feature emphasis. The marketing team then circulates these variants across email, display ads, and landing pages, collecting performance data. When it becomes clear which version outperforms the others, the model’s learning can be updated to favor that style of messaging in future iterations.
A/B testing at a large scale allows marketing teams to probe more creative angles, from unusual color palettes to unexpected taglines. Combined with immediate feedback from web analytics tools, Generative AI can refine each test and propose new angles quickly. This can improve brand messaging and overall campaign effectiveness.
6. Social media content creation
Maintaining a consistent social media voice can be challenging when daily posting schedules and platform-specific formats require a wide array of images, captions, and replies. Generative AI can propose text and visuals that align with a brand’s identity while adapting language style for platforms such as LinkedIn, Instagram, or TikTok. Instead of starting each post from scratch, a marketing team can enter basic prompts describing the tone or topic—be it announcing a new product or celebrating a seasonal event—and let an AI model generate initial drafts of posts.
This method offers a cycle of rapid iteration. If initial suggestions sound too formal or casual, marketers can refine prompts to guide the model in adjusting the tone. Some teams incorporate existing brand guidelines into the prompt, reinforcing any color schemes or taglines that must remain consistent. With image-generation tools, it’s possible to produce platform-ready visuals that match new campaign themes or revamp older posts with refreshed artwork.
Social media managers still need to review and finalize these assets, ensuring that posts are accurate and match ongoing brand campaigns. However, Generative AI gives staff more time to analyze engagement metrics, interact with followers in real-time, or plan special content for upcoming events.
7. Sentiment analysis
Customer reviews, social media posts, and online forums often contain feedback that can guide our approach to improving products and messaging. Generative AI models, particularly LLMs, can assist by reviewing these texts and identifying key sentiments—whether they are positive, neutral, or negative—faster than a human team could manage manually.
Traditional sentiment analysis methods, which often relied on word frequency counts or simpler machine-learning models, were effective but couldn’t fully interpret certain nuances such as sarcasm or irony. Data scientists would use databases like AFINN or SentiStrength, containing pre-labeled words, to classify content as positive or negative. By contrast, LLMs fine-tuned for sentiment classification can analyze the context and tone as well, giving marketing departments a more complete picture of how users view their brand and products.
Businesses can compile a collection of user reviews, support ticket transcripts, or social media comments. A Generative AI model, fine-tuned on sentiment data, can then study each statement and classify overall mood or emotion. Newer GenAI models can detect subtle hints of irony or sarcasm, and can also pinpoint recurring themes, such as frustration with a product’s price or enthusiasm for a particular feature. Businesses can then use these insights to prioritize improvements and develop targeted strategies.
8. Intent-based targeting
Recent advances in Generative AI have opened new doors to audience insights—especially when it comes to recognizing real intent based on how users interact with online content. One of the most cutting-edge innovations in this space is IntentGPT, a GenAI-powered component of RTB House’s Deep Learning stack, specifically designed to maximize engagement through precise contextual targeting.
Unlike traditional methods that rely on broad keyword lists or general page categories, IntentGPT uses advanced semantic analysis to understand the true meaning of articles and web pages. It can distinguish between casual browsing (like reading gossip or entertainment news) and genuine interest in specific products. This allows marketers to reach users not just when they’re reading about a topic, but when they’re truly in-market and more likely to interact.
For example, IntentGPT would deprioritize an article like “Lady Gaga’s Best Fashion Moments”, despite keywords such as "dress" or "style", because the user is likely there for celebrity news. Instead, it would prioritize pages like “How to Choose the Best Dress for a Wedding”, where real intent is evident.
At its core, IntentGPT operates through two tightly integrated elements:
Hyperspecific URLs—these are ultra-targeted placements identified via deep semantic scoring, where users show clear signs of real product interest.
Matching Offers to URL—this capability ensures that the most contextually relevant product from the advertiser’s feed is shown alongside each page.
The result? Brands serve ads at the perfect moment—when users are truly engaged and most receptive. In open web campaigns, this translates to higher CTRs, more quality visits, and better cost-efficiency—not by reducing spend, but by eliminating wasted impressions and focusing entirely on engaged users.
As cookies become scarcer, solutions like IntentGPT represent a future-ready evolution of advertising: highly scalable, privacy-first, and laser-focused on performance through genuine user intent.
8 applications of Generative AI in business, science, and education
Generative AI applications extend well beyond marketing. GenAI can reshape core operations in industries such as healthcare, finance, software development, and manufacturing. Below, we explore eight use cases that illustrate how organizations can integrate these approaches to improve their day-to-day processes, drive innovation, and handle tasks that were previously too complex or time-consuming.
1. Healthcare and life sciences
Out of all potential Generative AI applications, healthcare could be the one area where GenAI excels. Generative AI models can help streamline clinical research, accelerate drug discovery, and optimize patient care.
For drug discovery, these models analyze large sets of molecular data to propose potential compounds. Scientists can feed in information about chemical properties, known interactions, and existing drug examples, letting the AI suggest new structures that might address specific diseases. This speeds up an otherwise lengthy research cycle. A prominent example is Google DeepMind’s Alpha Fold 3, which expands on previous protein-structure breakthroughs to predict interactions between DNA, RNA, and small molecules with unprecedented accuracy, helping research teams identify promising drug targets.
In medical imaging, AI tools can convert low-resolution images into clearer scans. Radiologists can use these enhanced scans for deeper analysis, especially when dealing with conditions that require detailed imaging.
Meanwhile, synthetic patient data allows research teams to study disease patterns without exposing sensitive information. The AI generates plausible medical records, preserving aggregate trends without tying them to real patients.
Although human expertise remains essential, these innovations can help healthcare providers deliver a higher standard of care. With proper oversight, these systems can lead to faster breakthroughs in how we study, diagnose, and treat diseases.
2. Finance and banking
In finance, Generative AI can tackle tasks that require advanced pattern recognition and real-time responses. Banks and research firms can use these systems to produce concise summaries of large research reports, distilling key market trends and projections into brief takeaways for brokers or investment professionals. This is useful for teams that would otherwise have to manually sift through dozens of analyst forecasts and client memos every day.
Fraud detection is another area where Generative AI makes a difference. Although older rule-based models flag suspicious behavior by matching it to known profiles, generative systems can adapt more quickly to emerging tactics. When analyzing transaction patterns, these models can identify subtle anomalies that older methods might miss. If they spot irregularities in new data the system can alert investigators to examine further.
Personalized investment advice is also becoming more accessible with AI-driven solutions that review a client’s risk preferences, long-term goals, and transaction history. After synthesizing this information, the model can recommend products that match the client’s situation. These insights serve as a starting point for financial advisors, who can then add their own guidance. In an industry that depends on real-time data and informed analysis, Generative AI shortens research time and supports more precise decision-making.
3. Software development
Generative AI is rapidly changing how we approach coding, testing, and debugging. By examining large repositories of open-source projects and known bug fixes, models can generate code snippets that fit a specific set of requirements. For instance, if we want to implement a user authentication flow, the system can provide a starting template based on best practices. Developers then customize or refine this template to align with internal standards.
Debugging can also get a boost from AI models trained to detect typical programming errors. Instead of manually scanning hundreds of lines of code, a developer might paste relevant sections into a chat interface powered by a Large Language Model, receiving suggestions about likely issues. This can save hours of investigative work, particularly in large-scale applications with complex dependencies.
The creation of synthetic test data is another benefit. Before launching new features, teams need realistic data to make sure everything works in different scenarios, including edge cases or unusual user behaviors. Generative AI can craft these data sets, simulating thousands of variations, which testers can use to verify performance. This way, software is examined more rigorously before it ships, lowering the risk of unexpected failures in production.
4. Manufacturing and supply chain
Generative AI can help manufacturers optimize resources, create novel designs, and improve maintenance processes. Predictive maintenance systems—enhanced with generative components—can use sensor data to forecast machine breakdowns. Instead of waiting for an issue to cause downtime, the system recommends targeted service tasks in advance. This helps lower repair costs and keeps production lines running smoothly.
In supply chain planning Generative AI applications, models can analyze a blend of sales data, inventory records, and external information (such as market trends or weather patterns) to recommend restocking schedules. They can even suggest alternative trucking routes if disruptions appear likely. With the ability to process inputs from multiple sources, the AI system adds agility to logistics, leading to fewer stockouts or overstocks.
Generative design is another standout feature in manufacturing. The AI can propose updated versions of parts or product structures, aiming to reduce weight or material use while maintaining strength. Engineers can fine-tune these suggestions, ensuring designs meet safety guidelines. Over time, this can lead to more efficient production, less material waste, and new design approaches that might not have emerged through purely human brainstorming.
5. Entertainment and media
Generative AI tools can give media producers new ways to assist in developing scripts and creating storyboards. Writers can use AI-driven text generation to outline story ideas or propose dialogue variations, building a framework that can then be refined and finalized. This approach saves time in the brainstorming phase and helps teams test alternative plot points.
Content creators can also use AI-assisted storyboard generation tools, where image-focused models generate quick snapshots of scenes based on text descriptions. A director might type a brief overview of a character’s setting or a camera angle, and the system returns visuals for evaluation. While these images are not typically polished enough for final production, they can provide creative inspiration.
Deepfake detection is another area of importance in media. Generative algorithms can transform video and audio in ways that appear authentic, which poses reputational and security risks. To address this, companies like Sensity AI have trained models that can spot deepfake videos and images with a 98% accuracy rate. This helps protect both brands and the public at large from misleading content or impersonations.
6. Ecommerce
Generative AI has transformed online retail by speeding up product content creation and personalizing the customer journey. For example, many ecommerce brands need accurate and engaging product descriptions that highlight features, dimensions, and usage tips. With AI-generated text, large batches of these descriptions can be drafted in a fraction of the time it would take a team of copywriters. The brand’s writers then revise and approve the content, ensuring it meets style and quality standards.
In parallel, recommendation engines built on Generative AI can suggest items that reflect a shopper’s browsing or purchase history. Unlike older systems that simply rely on similar product tags, these models identify subtle connections across different categories. If a customer is interested in a particular style of smartwatch, the AI might suggest matching watchbands or fitness accessories. This process goes beyond basic “people also bought” widgets, giving shoppers curated suggestions that can lead to higher average order volume.
7. HR and recruiting
Generative AI can be a strategic tool for HR teams looking to streamline candidate evaluations and improve hiring outcomes. Rather than sifting through large volumes of resumes by hand, AI models can produce screening summaries that highlight a candidate’s qualifications, years of experience, or relevant achievements. Recruiters receive concise profiles, which helps them focus on key indicators for the role at hand.
At the same time, job seekers can harness Generative AI tools to refine their resumes so that they are a better fit for specific job descriptions. GenAI tools can scan postings, highlight important keywords, and recommend how best to present relevant experience and achievements. By tailoring their applications more strategically, candidates can stand out from a larger pool of applicants.
Teams can also use AI to propose questions for phone or video interviews. By analyzing job descriptions and established best practices, the system can generate detailed scripts that hiring managers can tailor to individual roles. Questions may cover technical expertise, behavioral competencies, or cultural alignment. This saves time, especially for companies managing large-scale recruiting drives or seasonal hiring cycles.
For candidate assessment, generative tools can design tests or case studies that mirror real workplace scenarios. When applicants complete these exercises, AI systems from companies like HireVue and Sapia.ai can evaluate the quality and relevance of their answers. Though final assessments rest with human decision-makers, this approach reduces time-intensive manual grading while identifying top performers. Over time, these methods can be refined as the system observes which traits predict success within the company. With the right oversight, Generative AI can help HR leaders focus on meaningful interactions that strengthen the hiring process.
8. Education and training
Generative AI in education can change how lessons are created, delivered, and tailored for learners. One approach involves adaptive learning platforms that assess individual student progress in real-time and present them with the right materials at the right moment. If a student struggles with a concept in algebra, the AI system can adjust subsequent lessons accordingly, offering more exercises or clarifications.
In more advanced scenarios, these platforms could generate custom content—quizzes, reading assignments, and interactive modules—that challenge students at their current levels. This stands in contrast to a one-size-fits-all approach where everyone moves at the same pace. Teachers gain more time to engage students individually, while the AI handles repetitive tasks and generates creative problem sets.
Some training programs also employ generative models to simulate real-world practice sessions. In language learning, for instance, an AI chatbot can mimic everyday scenarios, giving learners instant feedback on grammar or pronunciation. In professional settings, Generative AI might create virtual role-play exercises that evaluate decision-making or communication skills. By adjusting the difficulty to match a given user's current level, the system offers a supportive environment that still pushes learners to grow. With human guidance and consistent monitoring, Generative AI can help education stakeholders create personalized experiences that lead to stronger outcomes.
Implementing Generative AI at an organization
Implementing Generative AI requires clear goals, proper oversight, and a strategy for data management and organizational readiness. Below is a five-step approach for decision-makers:
1. Identify key use cases
Start by pinpointing the departments or teams that can benefit immediately. Some organizations begin in marketing, where AI can create ad copy or design visuals. Others may focus on product teams seeking faster development cycles. Gathering input from across finance, customer support, and operations helps prioritize tasks or projects that are most likely to deliver positive outcomes. Once potential use cases are mapped, they can be ranked based on anticipated gains, cost efficiency, and ease of implementation
2. Assess data readiness
High-quality data is essential for training an AI model. Organizations planning to generate content or analyze complex scenarios need representative, accurately labeled data in accessible formats. It is also important to ensure compliance with relevant privacy guidelines and industry regulations, especially when handling sensitive data. Existing data repositories can be audited to confirm they meet these standards. If they do not, investing in data cleansing or procurement is a wise step before launching an AI initiative.
3. Run small-scale pilots
Rather than deploying a large-scale AI project at once, many companies gain experience through a focused pilot. For instance, a pilot might use an AI-driven text generator for a subset of marketing campaigns or a prototype that designs parts for one product line. This approach allows teams to track performance metrics, such as user engagement or cost savings, while gathering feedback from involved stakeholders. If early results are less impressive than expected, adjustments can be made to data inputs, parameters, or project scope. When a pilot demonstrates promising gains, results can be used to justify further investment or expansion.
4. Ensure oversight and governance
Generative AI can occasionally produce content that conflicts with brand guidelines or ethical standards, so setting up a governance framework from the outset is crucial. This process includes defining responsible use policies, clarifying who can update models or input sensitive data, and establishing review mechanisms for high-stakes outputs. On the regulatory side, teams must stay alert to developments in data privacy and intellectual property laws. Monitoring tools and dedicated ethics committees help ensure the system remains aligned with organizational values and legal obligations.
5. Scale up
Once a pilot yields strong, consistent results, expansion can follow in other departments. Training sessions for non-technical staff can clarify how to use AI tools effectively. Evaluations at each phase will confirm whether the technology is delivering the anticipated benefits—whether those benefits are cost reduction, shorter production timelines, or improved customer engagement. Scaling may also involve integrating external AI platforms if internal resources are limited. Keeping performance benchmarks in place ensures that each expansion stays focused on measurable outcomes, such as increased revenue or enhanced user satisfaction.
The right partner for your Generative AI future
Generative AI applications have become real-world drivers of improvement in marketing, research, and day-to-day operations. Businesses can now take advantage of these advanced solutions to increase productivity and drive real growth. As the examples in this piece demonstrate, applications of Generative AI range from producing personalized ads to refining medical diagnostics—often with far-reaching benefits.
Would you like to learn more about how your company can take advantage of the power of Generative AI and Deep Learning in online advertising? Reach out to RTB House to find out how our personalized ad technology can increase your conversions.
