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9+ Use-cases of Generative AI in Marketing

Learn how generative AI is revolutionizing marketing, as well as its benefits, use-cases, and top Gen AI tools. Discover valuable insights for using generative AI efficiently and responsibly.
13 Min Read

Table Of Contents

    Artificial intelligence and machine learning have changed the way we look at the world today. It continues to push the boundaries of human imagination in all the ways that matter. With the onset of generative artificial intelligence and tools like ChatGPT and DALL-E, our marketing approach has also changed.

    Read along to discover how generative AI in marketing can solve different marketing-related use cases and propel AI-driven marketing.

    What is generative AI?

    Generative AI refers to a subset of artificial intelligence that mainly focuses on creating content, instead of just analyzing it. This includes text, designs, music, audio, and video. Gen AI uses deep learning (foundational models) that are trained on large amounts of data and capable of performing multiple tasks in a very human-like manner.

    Unlike other AI technologies trained to perform a single task, generative AI possesses broader capabilities.

    You only have to enter a text-based prompt to generate unique content that resembles the training data. With the rising interest in generative AI, the number of industries using it has also increased, especially in marketing and advertising.

    Here are some interesting stats that show the adoption and implementation rates of Gen AI in marketing:

    There are many other AI use cases, with marketing not being the only one. Some of the others include Design (patterns, styles, and product designs), Gaming (characters, narratives, and game levels), and Entertainment (script, music, and visual effects).

    Benefits of generative AI in marketing

    A survey by Mckinsey reports that 90 percent of marketing leaders expect to see an increase in the use of generative AI tools over the next two years. So, companies that continue to use AI in their efforts will see improvements, if one were to believe the stats published.

    That said, these are the major benefits you can expect with generative AI in marketing:

    • Innovation and creativity: Inspire new concepts and designs. Marketers can do away with old content types and experiment with fresh ideas that might improve conversions.
    • Data-driven insights: Make better decisions. Companies can get valuable insights to help them along all customer touchpoints and find unique solutions that address their pain points.
    • Product development: Speed up product testing and development. Developers can automate repetitive tasks, bring in diversity, and create customized products.
    • Personalized customer experience: Eliminate the one-size-fits-all marketing. You can analyze customer data to tailor content and visuals to meet individual tastes. All at the click of a button.
    • Time and cost efficiency: Reduce creative time frames. AI systems can generate content in less than a minute and give companies the liberty to computerize simple marketing tasks.

    New: what is generative marketing?

    As the name suggests, generative marketing deals with the application of generative AI in marketing – it can be organic search, social media, or paid advertising. Gen AI models are employed to create dynamic (not static) marketing content with the help of AI tools.

    The way it works is simple: customer data from different departments is combined and then used to provide an omnichannel customer experience. For example, website visits, transaction details, and customer support tickets.

    An approach like this brings together siloed data that is otherwise isolated from each other.

    You can then use it for audience segmentation, personalization, and optimization. The aim is to create goal-based customer journeys for each prospect and provide consumer-centric marketing materials, features, or product recommendations that help them reach that goal.

    All of it is done with the help of generative AI marketing systems.

    AI algorithms not only analyze customer data but also their responses to your marketing campaigns. If done right, these AI marketing systems will have the potential to optimize your content and ad performance in real-time and improve engagement plus conversion rates.

    9 real world use-cases of generative AI in marketing

    In this section, we will address the question, “How can generative AI be used in the field of marketing?” Although there are risks involved with using generative AI in marketing, one cannot ignore the benefits. It has multiple uses, from content creation to customer segmentation and personalization.

    Applications of generative AI in marketing

    You can learn how to use generative AI models in different marketing scenarios, starting with the ten outlined below.

    #1 Content creation

    Content generation is one of the most common uses of AI and machine learning. AI content is all the hype today and is excessively utilized in content marketing. Why not? It speeds up the process by giving you new ideas along with a variety of content to work with.

    For example, AI-generated text can be used to:

    • Create new content like blog posts, emails, and social media posts.
    • Design ad copies and product descriptions.
    • Write scripts for video ads and product demos.

    AI text generators allow you to generate both short-form and long-form content at scale. This saves a lot of time and gives you the creative liberties to work with. Naturally, content quality is subpar and needs excessive edits. But paid marketing tools, like Jasper AI, solve this problem to some extent by giving you prompt templates for different types of ad copies.

    #2 Image or video production

    Tools like DALL-E, Runway, and Midjourney can generate images and videos from textual prompts. They make use of generative adversarial networks (GANs) that help them with text-to-image translation.

    This ability can help marketers do the following things:

    • Generate high-quality images, video ads, and product demos.
    • Create logos and other creative brand assets.
    • Develop images for social media posts.

    You can insert AI voiceovers and music to create engaging ad videos, which can help increase brand awareness and conversions. One prime example of a brand using generative AI in advertising is Heinz, which created the Heinz A.I. Ketchup, a short video ad to demonstrate the prominence of its signature bottle design.

    #3 Search engine optimization (SEO)

    A thorough keyword research is mandatory for a good SEO project. Experts need to analyze tons of keywords, their competitors, and user intent to build an SEO campaign that works. AI makes this process easier by sorting out keyword data and listing high-performing keywords. Furthermore, you can:

    • Discover topic clusters related to your industry.
    • Conduct keyword research by search intent.
    • Identify keyword gaps and opportunities.
    • Develop content in line with SEO parameters.

    All in all, a content marketer can learn about the topics, subjects, and words their audience searches for online and cater to the same with relevant content.

    #4 Marketing segmentation

    According to a survey by BCG, 41% of CMOs harness the power of generative AI for better targeting. Better targeting comes with proper customer segmentation. Marketing segmentation with AI involves the analysis of large amounts of customer data in short periods.

    This process can be automated and in turn aid marketers:

    • Make efficient use of resources.
    • Improve marketing and product strategy.
    • Increase return on investment (ROI).
    • Uncover new customer segments.

    Once you have a firm understanding of your target audience, you can offer tailored customer experiences.

    #5 Personalization

    Marketers can use generative AI to develop personalized marketing campaigns. With user likes and dislikes at their fingertips, they can shift the focus on the customer and give them what they want, right where they want it.

    They will further be able to:

    • Tailor content and product design.
    • Design a customer-centric marketing plan.
    • Build personalized customer journeys.
    • Give out individual recommendations.

    AI-powered autonomous marketing systems further simplify this process and help you personalize customer relationships with real time content recommendations. Given that buyers now demand personalization at every step of the buyer's journey, it becomes crucial for brands to provide it. This is the only way to ensure customer loyalty and retention.

    #6 Sentiment analysis

    Generative AI can be used to analyze customer sentiments. With machine learning technologies and deep learning models, AI can process labeled customer data (e.g., reviews, feedback, social media comments) to create synthetic textual data that reflects different sentiment polarities like positive, negative, and neutral.

    It can then be used as training data for sentiment analysis models so that they can better detect user sentiments and language variations. This in turn can help you:

    • Detect consumer sentiment in different languages.
    • Create content focused on what users like or dislike about specific products or services.
    • Develop chatbots and virtual assistants integrated with real-time sentiment analysis technologies.

    IBM’s Watson NLP and Microsoft’s Azure Text Analytics can be used to analyze large textual data sets for sentiment analysis.

    #7 Lead generation

    Some strategic generative AI tools can help with lead generation – lead capture, qualification, and scoring. You can easily rank and prioritize leads based on several factors, like demographics, online behavior, and purchase patterns with AI. Doing so enables you to classify them on a spectrum as interested or ready to buy.

    Here’s how you can accomplish this:

    • Offer valuable resources in exchange for customer information, like whitepapers, ebooks, or research papers.
    • Use chatbots and conversational AI tools to engage website visitors and capture leads.
    • Leverage predictive analytics to analyze historical data and identify prospects most likely to convert into paying customers.
    • Create targeted advertising campaigns by focusing on the right audience segments, interests, and keywords.
    • Nurture leads throughout the customer journey with personalized campaigns and messages.

    For instance, you can use AI to determine the ideal keywords and bid range for your paid search campaigns and predict the performance of future PPC campaigns.

    #8 Customer support

    Conversational AI tools can respond to and solve customer queries. AI can handle all types of inquiries via chatbots, social media, and even over the phone. It is quick, efficient, and can optimize your customer service models.

    • AI-powered chatbots offer round-the-clock assistance on multiple platforms.
    • Personalized recommendations based on browsing history and transactions.
    • Multilingual capabilities to support queries in numerous languages and localizations.
    • AI voice support to manage telephonic conversations.
    • Engage with buyers on social media channels to maintain brand presence.
    • Automate emails to promptly address common problems.

    Chatbots can enhance your overall customer experience and give your customer support teams more time to focus on other important tasks, ultimately boosting operational efficiency.

    #9 Cookieless marketing

    Cookieless marketing doesn’t rely on browser cookies for targeting users. It’s in the vogue today since many platforms (like Chrome and Safari) are limiting the use of third-party cookies.

    For those who don’t know, cookies are bits of data stored in your web browsers that track your online activity and help advertisers with ad retargeting. With them out of the picture, your only option is to use first-party data in conjunction with generative AI technologies to:

    • Analyze existing data.
    • Find patterns in user behavior.
    • Display contextual ads.

    Of course, you need to ensure that you collect data with explicit user consent and comply with existing privacy regulations.

    Bonus: Create personas with gen AI

    You must be familiar with the concept of buyer personas. They are fictional representations of your ideal customers that give you an idea about their goals, challenges, motivations, behavior, and interests.

    Customer personas have sort of revolutionized marketing, enabling marketing organizations to build targeted marketing campaigns. However, it’s hard to design them yourself unless you use automatic persona generators.

    Generative AI can help you create personas manually. ChatGPT and Bing Chat are some of the tools out there that can be employed for this purpose. With these services in place, you can:

    • Collect and analyze customer data.
    • Develop realistic customer profiles.
    • Create personas based on specific use cases.
    • Chat with buyer personas.

    Remember that initial outputs might be inaccurate since the data is random and entirely dependent on the prompts you use. For more information, refer to our article on creating buyer personas with generative AI marketing tools like ChatGPT.

    Top generative AI tools for marketing

    We have discussed some of the applications of AI in marketing. You know that you can create blogs, emails, visuals, and even produce videos for ads and product demos.

    Generative AI tools use generative adversarial networks (GANs) or variational autoencoders (VAEs) to process data and give out such results. There are a bunch of them in the market, but these are the best ones.

    Top generative AI tools for marketing

    Text (blogs, emails)

    1. ChatGPT Plus

    ChatGPT Plus is the advanced version of ChatGPT, which uses the GPT-4 model. It is apparently the strongest text generator there is, outperforming all of the others.


    • Highly creative and accurate.
    • Faster response time and connectivity.
    • Adjusts its writing style according to different use cases.


    • A limited number of prompts.
    • Too expensive for some users.
    • Extremely long responses.

    Alternatives: Bing Chat, Claude, Gemini

    2. Claude

    Claude is a chatbot developed by Anthropic AI, designed to offer better (more human-like) responses as compared to ChatGPT. It can be used for content creation, summarization, and optimization.


    • Transcribes and analyzes images, like notes, graphs, or photographs.
    • Advanced translation abilities help create multi-lingual content.
    • The free version is updated more frequently than ChatGPT.


    • Takes a long time to translate certain languages.
    • The free account has a daily message limit.

    3. Google’s Gemini

    Gemini is Google’s response to OpenAI’s ChatGPT, built on the upgraded Gemini model, earlier known as Bard. As with other text-generation chatbots, you can use it for writing, brainstorming, and translation.


    • Real-time access to websites via Google Search.
    • Generates different types of creative content (blogs, emails, reports).
    • Translates content with human-level accuracy.


    • Informational; requires lengthy instructions and follow-ups for creative work.
    • Generates errors and inconsistencies as it is still under development.

    4. Jasper AI

    As mentioned before, Jasper AI is a marketing tool based on the GPT-3 model that allows users to create copy for all types of content, like blogs, social posts, and website landing pages.


    • Plagiarism-free and unbiased.
    • Grammarly integration to avoid grammatical errors.
    • Templates, content creation, and automation options.


    • Inefficient in creating overtly technical content.
    • Plagiarism check costs extra.

    Image (visuals, creatives)

    1. DALL-E2

    DALL-E is OpenAI’s image generator that creates designs based on textual descriptions. DALL-E2 is the upgraded version trained to produce better outputs.


    • Surreal artwork at high resolutions.
    • Multiple, editable versions on a single prompt.
    • Rejects improper inputs to prevent harmful content.
    • Incorporates different concepts, attributes, and styles.


    • Struggles to produce photorealistic images.
    • Only understands the English language.

    2. Midjourney

    Similar to DALL-E, Midjourney is an AI image generator based on machine learning algorithms.


    • Advanced design capabilities that focus on aesthetics and creativity.
    • Tailored pricing plans for businesses and individuals.
    • Works directly through the Discord app.


    • Does not offer a free version.
    • Users need to sign up for Discord to use it.

    3. Adobe Firefly

    Firefly is a generative AI program developed by Adobe that allows users to create and edit all types of graphic designs with text prompts.


    • Integration with other Adobe products.
    • Context-aware image generation (generative AI fill).
    • Customizable features.


    • Only trained on Adobe stock data and openly licensed work.
    • For non-commercial use only.

    Video (video ads, product demos)

    1. Runway

    Runway is a platform that has developed a text-to-video model, Gen-2, that allows users to create videos with prompts (sometimes using their images).


    • Easy to use, better detailing and quality.
    • Fast processing time.
    • A variety of editing features (masking, color correction, VFX, etc.)


    • Lower frame rates require post-production work.
    • Video clips tend to be grainy and blurry.
    • Paid plans are expensive.

    2. Synthesia

    Synthesia is another text-to-video platform that lets you create high-quality AI video content quickly.


    • User-friendly customization options.
    • Available in over 120 languages and accents.
    • Lifelike AI avatars and voiceovers.


    • Lacks API access.
    • The personal plan only allows 10 video credits per month.

    Most of these generative AI marketing tools ease up your work and guide you in the right direction. Additionally, you can use automation tools like Hubspot and Mailchimp to boost work efficiency.

    Risks of combining generative AI and marketing

    In a perfect world, using generative AI in marketing would not raise any concerns. It would give you the ability to personalize your marketing efforts, with quick and actionable results. However, integrating artificial intelligence in marketing is not as easy as it sounds.

    The potential risks far outweigh the benefits, ranging from biases and inaccuracies to issues of copyright infringement and data privacy.

    Risks of generative AI in marketing


    Everyone has tried out ChatGPT or Midjourney at least once since they were launched. You must have noticed that the output is not accurate sometimes.

    This is a major problem with AI-generated content. Although it’s vast and limitless, the chances of it being incorrect are equally high. Your AI marketing content could contain misleading information, which if put out in public could damage your credibility.

    And this is not the only issue.

    Since generative AI cannot fully understand human emotions and culture, it might produce offensive responses to certain groups of people. Funnily enough, even though it is wrong, the output is framed in a way that sounds just right. So, it becomes all the more important to thoroughly review any AI content before you approve it for use.


    We know that AI models learn from existing datasets. The same is true for generative AI. Now imagine if this data is influenced or has some cultural, social, or political biases.

    What happens then?

    AI will generate outputs that will undoubtedly contain stereotypes. If you choose to use them as part of your marketing strategy, it would be really bad for business. This is secondary to the hit your brand reputation will take if you create content that is biased and promotes homogeneity instead of diversity.

    Here’s an example. Take DALLE, OpenAI’s image generator.

    Suppose it was trained on data that assumed all doctors to be men. The next time someone asks DALL-E to generate an image of a doctor, it might create images of only men in white coats. Thus, reinforcing gender biases and ignoring the multidimensional aspects of the profession.

    Your company will need to put strict rules and policies in place when it comes to AI to stop this from happening and avoid any legal complications.


    Generative AI is transforming marketing in more ways than one. You get a ton of information right at your fingertips, with all the resources necessary for a successful marketing campaign.

    But do you know where this data comes from? Not really.

    It is also hard for customers to distinguish between human-made and AI-generated marketing content. Buyers expect authenticity and transparency from the brands they follow. Even if you do make use of AI, you have to be upfront about it with your customers because they deserve to know it.


    As stated earlier, nobody really knows where AI models get their data from. Literally everything they create, from music to videos to text, is based on existing material that belongs to someone else.

    Using it for inspiration is one thing. But directly copying the content gen AI churns out and calling it yours? Outright plagiarism. It’s no wonder that there are intellectual property and copyright infringement lawsuits against companies behind generative AI.

    Case in point, the New York Times versus OpenAI.

    Now there aren’t any federal laws in place that address this particular subject. However, users should be careful in the way they employ generative AI in marketing because even the prompts you feed into Bing Chat (or any other tool) are recycled and used to train the model.


    Leveraging AI in marketing to improve customer experience involves the analysis of large sets of data. A lot of personal and private user data raises privacy and security concerns, especially with GDPR and CCPA restrictions in place.

    Now, not all generative AI tools have permission to store sensitive customer data. Unauthorized data can pose great risks to the companies employing it, leading to severe penalties and data breaches. So before you start with AI, it's crucial to address its biases and prioritize transparency, accuracy, and privacy.

    AI should complement and not replace human creativity.

    While it is efficient and can speed up your work, generative AI lacks the empathy, emotional intelligence, and cultural nuances that should be the foundation of all your marketing activities.

    Start leveraging generative AI: Best practices and insights

    There are a million ways to use generative AI but you need to know the proper way to do it. You cannot just haphazardly integrate it into your marketing workflow and jeopardize your marketing campaign. Here's a simplified procedure to follow before you get started:

    Best practices and insights

    Step 1. Identify opportunities: Start by building a cross-functional team to spot areas where generative AI can be used, like content creation or data analysis. Mainly focus on repetitive and time-consuming tasks that can be automated.

    Step 2. Define business objectives: You should clearly define the business objectives you want to achieve with generative AI. It will help you choose appropriate tools and craft prompts that align with your goals.

    Step 3. Set up a test environment: Establishing a test environment is necessary to check out the way AI functions and find errors, if any, before deploying it. You should also constantly test your AI models to ensure that they give accurate results over time.

    Step 4. Establish governance frameworks: It is a crucial step to maintaining privacy, security, and cost-effectiveness. Put proper AI regulations in place to prevent the distribution of harmful content and input of sensitive customer data into AI tools.

    Step 5. Train marketing teams: Your employees should know the way AI operates so that they feel confident when it comes to using it. Conduct workshops to educate them on the basics of generative AI and its potential applications.

    Companies using generative AI for marketing success

    Many companies have joined the generative AI phenomenon. While some have started using it to streamline customer interactions, others have utilized it to create striking visual content. Atlassian, Coca-Cola, and Duolingo are examples of brands using generative AI in marketing.

    Atlassian: An AI virtual assistant

    Atlassian is a software company known for its collaborative solutions that help developers and project managers efficiently work with each other.

    It has recently introduced Atlassian Intelligence, an AI virtual assistant. Built with OpenAI LLMs, the AI assistant can:

    • Compose customer responses
    • Draft content based on product specifications
    • Automate support interactions within Slack and Teams
    • Extract information from knowledge-base articles
    • Summarize documents and meeting transcripts for newly assigned agents
    • Translate natural language queries into Jira Query Language

    Coca-Cola: Create real magic

    We cannot discuss generative AI without mentioning creative ad campaigns. ‘Create Real Magic’ is one such movement by Coca-Cola that combines AI with art and customer engagement.

    Coca Cola's create real magic campaign

    The campaign makes use of GPT-4, DALL-E, and Coca-Cola brand assets to promote creators from diverse markets.

    People can visit createrealmagic.com and develop art with Coca-Cola assets. If they make something extraordinary, their artwork will get featured on billboards in places like NYC and London.

    Being all-inclusive, ‘Create Real Magic’ helps the brand achieve the following objectives:

    • Democratize brand iconography and advertising assets
    • Fostering human connection and experiences
    • Demonstrate commitment to using AI for creative purposes

    Duolingo: An AI-powered practice partner

    Duolingo is one of the most famous language-learning apps out there. It has partnered with OpenAI to incorporate GPT-4 into its services and personalized learning in a way not seen before.

    Leveraging data provided by the 500 million students who use the platform, the integration is used to power two new features.

    Explain My Answer: Users get a thorough explanation as to why their answers are right/wrong with examples, similar to human tutors.

    Role Playing: Users interact with AI personas to engage in unique language-based tasks, practicing language in various scenarios.

    Wrapping up

    Generative AI is poised to disrupt the world but in a good way. As is evident from generative AI marketing use-cases in design, content, and messaging, it will surely be a game-changer in years to come. While its short-term impact is slightly overestimated, it won’t hurt to be fully prepared.

    After all, human creativity enhanced by AI tools can give results that marketers could have only imagined in the past.

    Frequently Asked Questions (FAQ)

    How can generative ai be used in the field of marketing?

    Generative AI can be used to perform a wide range of tasks in marketing. It can be employed in:

    1. Content creation

    2. Image or video generation

    3. Search engine optimization (SEO)

    4. Marketing segmentation

    5. Personalization

    6. Customer support

    7. Cookieless marketing

    How are brands using generative AI?

    How are brands using generative AI?

    Brands like Coca-Cola, Atlassian, and Duolingo are extensively using generative AI in their product and marketing strategies. Here’s how:

    Atlassian: Uses an AI virtual assistant to simplify teamwork and boost productivity

    Coca-Cola: Creates real magic by combining AI with creative advertising

    Duolingo: Introduced an AI powered practice partner to enhance users’ learning experience

    Which generative AI tools can you use in marketing?

    These are some of the generative AI tools that you can use in marketing:

    Content creation: GPT-4, Jasper AI, and Wordtune

    Image generation: Midjourney, DALL-E2, and Adobe Firefly

    Video production: Runway and Synthesia

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