Artificial Intelligence in Marketing: 10 Use Cases to Boost Your Growth in 2025

In 2023, Harvard Business School Professor Karim Lakhani said, “AI [won’t] replace humans, but humans with AI [will] replace humans without AI.” As MobiDev’s Marketing Team Leader, I can definitively say that Lakhani’s analysis still applies today. 

Whether you’re building a marketing product or looking to enhance marketing strategies at your company, this article will provide some ideas on how AI can help you reach your goals. Armed with MobiDev’s AI development experience since 2018, I’ll share real-life applications of AI in marketing that you can apply in your business.

Benefits and Challenges of Using AI in Marketing

In 2024, Statista reported that the top three LinkedIn profile skills with AI aptitudes were content writers, graphic designers, and marketing managers. Since the latest artificial intelligence trends have primarily been Large Language Models, it’s reasonable to assume that AI’s potential to enhance marketing is extraordinarily promising. 

What I’ve found is most important when considering AI’s potential in marketing is to go back to the beginning. The most fundamental goal of marketing is to connect your brand with your audience: human beings. So AI should help you achieve this goal instead of just automating processes. With this in mind, let’s discover the benefits and challenges of implementing artificial intelligence in marketing.

TOP 5 Benefits of Using AI in Marketing

AI’s potential to enrich the marketing industry is extraordinary. Here are just a few benefits:

1. Better Customer Management: By automating routine tasks, personalizing messages, and using data to identify at-risk customers, you can greatly increase the effectiveness of your relationships with your customers.
2. Enhanced Brand Loyalty: When AI systems make experiences more personalized for your customers, you’ll increase satisfaction and improve your chances of them becoming loyal, repeat customers.
3. Improved Return on Investment (ROI): AI has unique capabilities when it comes to optimizing channel and ad placement in addition to its other cost saving measures, like business process automation and demand forecasting. All put together, AI applications can result in greater ROI.
4. KPI Measurement and Insights: AI systems are the masters of pattern recognition and data analysis. With intelligent KPI measurement systems, you can gain valuable insights in real-time.
5. Smarter Decisions: with those same insights, AI systems can guide better decision-making processes in your business. When your decisions are data driven, you can greatly reduce risk and maximize the benefits of decisions for your brand. 

Overall, AI allows teams to operate more efficiently and bring marketing campaigns to market faster. 

TOP 4 Challenges when Implementing AI in Marketing

AI tools may bring major opportunities for marketing, but they also bring a range of technical challenges into the picture. If brands want to incorporate AI successfully, they’ll have to learn how to manage each of these obstacles:

1. Integration Complexity: Existing systems often aren’t built with AI in mind. Integrating AI applications into those systems can be a serious and costly challenge.
2. Dependency on Quality Data: You can have the best AI model in the world, but it will mean nothing if you don’t have high-quality data under the hood. Don’t ignore the time you need to gather and prepare relevant data for your AI models.
3. Legal Concerns: Ensuring that your AI system is compliant with applicable data privacy regulations is critical to avoiding legal action and fines.
4. Bias and Fairness: This isn’t just an ethical dilemma; it can have major implications for the quality of the output of your product.

While AI offers powerful tools for enhancing marketing efforts, it’s crucial to address these challenges thoughtfully. By understanding and mitigating these issues, businesses can better harness the potential of AI to achieve their marketing goals.

AI Trends in Marketing to Watch in 2025

AI may not be a magic bullet, but it’s changing and advancing our industry far beyond our expectations. Let’s explore rising AI marketing trends to understand how the industry is changing.

Trend #1: Generative AI for Content Creation

By far the most profound and in many ways one of the most controversial AI marketing trends is generative AI. Generative pre-trained transformer (GPT) models that power services like ChatGPT, Microsoft Copilot, Google Gemini, and a plethora of other models have taken the world by storm with the ability to generate large quantities of human-like text in seconds. This has been revolutionary for content marketers who need to quickly generate drafts, produce ideas, or automate certain parts of their workflows. 

Web Content Creation with AI

Generative AI has made its way into every corner of the digital content marketing scene. On Halloween 2024, crowds waited patiently on the streets of Dublin, Ireland for a Halloween parade that never came, all thanks to an AI-generated event listing on a top Google search ranking website. Throughout 2024, Google has improved its algorithms to better understand and evaluate AI-generated content and will continue to do so.

Generative AI can be useful for drafting, ideation, and automation of some processes. However, if it’s not carefully guided by humans, mistakes are bound to happen.

Images Creation with AI

It’s not just text. Stable Diffusion and other image generation models are having the same impact on the world as Photoshop’s democratization did in the early 2000s. This has made it extremely easy for brands to create images for content. 

Video Creation with AI

Beyond images, OpenAI’s Sora has opened a door that humanity may never again be able to close: AI video generation. This trend has quickly garnered a reputation for one of the most intriguing benefits of AI in marketing.

Coca Cola has utilized AI video generation tools to create its latest holiday season ad, a remake of the 1995 “Holidays are Coming” commercial. Video generation tools like Sora are ushering in a new era of content, one that needs fewer human resources to operate. Despite Coca Cola’s success in ad testing with the commercial, it quickly found itself engulfed in outrage on social media, as consumers protested the use of AI in the video. According to Adweek, PJ Pereira explained that AI won’t replace humans, and humans will always be involved in content marketing. However, if we don’t look into the future of creativity, we’ll be left behind.

What’s Generative AI Good for Then?

Generative AI is profoundly good at understanding and interpreting language. It can perform sentiment analysis, function as a thesaurus, and assist with proofreading and editing. 

For AI applications in marketing, generative AI makes it possible for sufficiently trained customer service chatbots to be a viable interface between your audience and your brand. Instead of interacting with dense FAQs and communication dashboards, natural language can be the interface instead. 

How to Get The Most Out of This Trend?

Generative AI tools like ChatGPT have been recognized by many business leaders as being an information security risk. A few years ago, Apple and Samsung banned these platforms for their employees because they didn’t want proprietary information from being used as training data for AI models. They may also unwittingly expose confidential information that could hurt the company’s reputation. 

However, there are ways to use generative AI tools for business. For one, paid business plans often come with stricter privacy policies that you should investigate. You can also consider running an open-source model on your own hardware. 

ChatGPT and other proprietary LLMs can often be used for free, but they may use your input as training data to improve model performance. However, paid business versions typically provide security policies for your organization that allow you to protect your data. Always investigate these policies before you use a model for your work.

Anastasiia Molodoria

AI Team Leader

Trend #2: AI in Predictive Marketing Analytics

With machine learning algorithms applied to marketing, teams can process large data sets to get predictions. This enables data-driven decisions that can provide more consistent and engaging experiences for a brand. 

However, to succeed, you must first collect the data. Clickstream data, social media metrics like post interactions, and transaction histories of your customers, are just a few starting points to consider when feeding training data to your predictive marketing analytics engine. 

Predictive analytics is one of the best AI marketing use cases because it allows businesses to target their audience better. Instead of guessing who your target audience is, you can directly measure the likelihood of a particular demographic or user subscribing.

How to Get The Most Out of This Trend?

A vital component of the data you collect for predictive marketing analytics training is data quality and readiness. Experienced AI engineers can make the data more effective for training with cleaning. Data quality is, in most cases, more important than the AI model you select. An older AI model with better data quality will perform better than the most innovative model powered by bad data.

Trend #3: AI Product Recommendations

AI product recommendations are responsible for the success of the largest ecommerce retailers out there. Segmenting audiences doesn’t just have to stop at identifying audiences and potential opportunities for marketing campaigns. It can identify which products each individual is most likely to purchase and tailor email campaigns and even on-page experiences for them.

This is accomplished by analyzing data from customer interactions, behaviors, and interests. Each person who visits Amazon’s website will have a distinct experience because they have different shopping behaviors. By making these marketing campaigns more personalized, they become more effective at generating leads and revenue for your business.

More importantly, businesses can develop algorithms that can continuously learn over time. The more customers and behaviors it sees, the more accurate it becomes in its recommendations. As a result, customers will get a more personalized and satisfying shopping experience the more that they shop. This personalization strategy is why AI product recommendations are one of the top AI applications in marketing.

How to Get The Most Out of This Trend?

Recommendations can be created according to different logic models and based not only on your company’s internal data but also on contextual information such as time of day, event or current trends to provide more personalized suggestions. To get started, you need a dataset with products, prices, and sales data.

Trend #4: Combining Artificial Intelligence with Augmented Reality

Augmented reality technology is an important intersection with AI in marketing. Immersive experiences are a valuable tool in a marketer’s toolbox when it comes to providing personalized and engaging content. AI-based algorithms can make virtual try-on solutions more effective and realistic. 

AI can also support AR frameworks to help identify what clothing sizes a consumer needs more accurately using body measuring techniques. 

In the near future, artificial intelligence will be able to create even more personalized experiences with natural language commands. AI will create 3D objects and scenes entirely from a spoken prompt, all generated from scratch. This can offer customers even more personalized, unique, and immersive experiences. One tool that enables AI to generate shapes and objects in 3D environments is Spline, indicating that this future may not be so far off after all.

How to Get The Most Out of This Trend?

Utilize AI algorithms to analyze user behavior and preferences, feeding this data into AR applications for personalized experiences. Also, using edge computing to process data locally helps to reduce latency in AR/AI applications. This allows for smoother interactions and quicker responses in real-time environments.

Trend #5: Smart AI Search

With natural language processing (NLP) and understanding (NLU), search intent can be better understood, leading to better results. More importantly, these technologies are making it easier for AI-based search to work in more localized environments like on websites or specialized AI chatbots.

This has serious implications for marketing — most notably, it means that the status quo of search engine optimization, a keystone of modern digital marketing, could be completely disrupted. Instead of optimizing content for Google search, brands may need to begin optimizing for AI chatbot search. In fact, SEO professionals are already starting to see a rise in zero-click searches, where users find the answers they’re looking for on SERP pages and don’t click into any of the results.

Retrieval-Augmented Generation (RAG)

The most relevant technology for smart AI search is RAG, which combines the power of Large Language Models with an external knowledge base. This might be simply a directory of documents that a chatbot has access to, but it also could be the larger Internet itself. The most important part is that the chatbot can understand when it needs to draw on that knowledge base depending on the user’s query, in a manner like this:

1. User Query: the chatbot interprets the user’s query. For example, “What are the different price tiers of your product?”
2. Information Retrieval: the chatbot’s internal training data doesn’t have a direct answer to this question. To answer the question, the next move is to retrieve more information. The chatbot uses RAG to seek out answers in the most relevant document that it has access to in the knowledge directory. It opens a document called “Pricing.xml“ or something similar and parses the contents.
3. Response Generation: having both the query and the pricing information in mind, the chatbot can now make a more accurate response for the user, explaining the different pricing tiers of the company’s product. 

RAG techniques are one of the most interesting applications of artificial intelligence in marketing because they can extend the practical uses of large language models to information that meets brand needs and guidelines.

How to Get The Most Out of This Trend?

If you’re looking for a straightforward way to engage with your website’s visitors, smart search may be a good point to start. Once you’ve got them on the page, now’s your chance to let them tell their story through their question in a search box. AI can then take them to an appropriate landing page or provide them with results from its knowledge bank to help them see that you’re the best choice for their needs. 

Best of all, on-site search solutions can use machine learning algorithms to progressively improve over time.

Trend #6: AI Demand Forecasting & Dynamic Pricing

One of the most important lessons in marketing is learning when and where to direct your resources. That’s where artificial intelligence and marketing best intersect. Demand forecasting with AI is one of the best AI marketing use cases for that very reason. By understanding when customers will be more likely to want certain products and services thanks to the help of data, you can better direct your campaigns during those times.

Predicting demand and dynamically adjusting pricing has more benefits than just revenue. It can also save you on inventory and production costs. You can dynamically respond to fluctuations in demand by only producing as much product as people need. This minimizes overstock. It can also help you focus specifically on marketing and selling those high-demand products rather than your entire lineup. 

Customer Satisfaction

Being able to effectively predict customer demand is extremely helpful for your business’s reputation as it will increase customer satisfaction. You can win over customers even more if you are personalizing your forecasts to more narrow categories or even down to the individual level. For example, if you’re running an e-commerce platform and have the capability of personalizing your online storefront for each individual, you can ensure that these results best reflect data-driven predictions in consumer behavior. 

As a simple example, let’s say your customer purchased a water filter today. The water filter is only good for one or two months. Those timeframes are perfect times to begin running personalized marketing campaigns toward that customer to get them to buy a new filter. AI demand forecasting is much more sophisticated than this and can make more complex predictions.

How to Get The Most Out of This Trend?

The key to a successful demand forecasting solution for marketing is to set up data collection methods and ensure that your algorithms have access to that information. Based on our experience as AI and data science experts, three months is typically enough to get started forecasting with machine learning if the business is not subject to seasonality.

Trend #7: AI-Powered Chatbots 

Chatbots have been a common theme in just about every trend we’ve discussed so far. They might be used at the back end to inform and support marketing operations, or they might be used on the front-end to directly interact with customers on your website. In both cases, chatbots excel at automating various processes of marketing and the customer journey. 

On the back end, chatbots might be:

• Helping marketers perform research by searching the Internet for data, trends, and other information
• Creating topic clusters and ideating page titles and meta descriptions for content marketing campaigns
• Performing editing, writing, and proofreading tasks
• Translating content into multiple languages

Meanwhile, on the front-end, chatbots might be:

• Offering discounts to first-time visitors
• Suggesting newsletter subscription signups
• Providing relevant product information and suggestions
• Assisting with setting appointments with humans
• Inviting them to join informational webinars
• Helping customers with FAQ
• Simplifying the process of lead qualification

Although many enterprise chatbots do already exist like ChatGPT, Microsoft Copilot, and Google Gemini, there is still value in having your own chatbot on your website for customers to ask questions specific to your brand. A custom chatbot can be trained with the specific data that your customers are looking for and provide them with the most relevant information.

How to Get The Most Out of This Trend?

Start your chatbot development project with AI consulting to create a solution that truly syncs your business goals with market needs and tech capabilities. Our AI consultants have over 6 years of experience in building artificial intelligence solutions and know how to open the full potential of AI for efficient chatbot development.

Trend #8: Social Media Listening & Sentiment Analysis

Large language models excel at handling and understanding language. That makes them an excellent tool to deal with sentiment analysis and social listening at scale. Natural language processing assesses tone of voice, opinions, and other heuristics to give marketers insight into how people feel about a topic.

Social Listening

These techniques can be applied to social media posts in bulk. Imagine downloading thousands of posts being made by your audience about a certain hashtag. AI can aggregate the sentiment of your audience in this dataset quickly. This can help your marketing team understand whether your brand’s messaging aligns with customer perceptions and adjust their messaging if they aren’t quite hitting the mark.

There are some challenges to this approach. For example:

1. Context: AI doesn’t understand context very well. In this situation, all it knows is the tone of the text. It may not know why they feel that way, or any other factors that might play a role in an opinion. 
2. Bias and fairness: AI is only as good as the data it’s trained on, and often our datasets are biased. To avoid unfair results, human review is always necessary.

Experienced AI engineers know how to deal with these challenges and make the most of their benefits to marketers. Below you’ll see an example of NLP sentiment analysis and social listening that our team developed for this exact purpose.

This demo allows you to get general information about the input text, the emotional tone, correct spelling, and extract important keywords. 

Trend #9: Marketing Routine Automation with AI

While humans excel as marketers, their capabilities are significantly enhanced when supported by AI tools. AI in marketing can optimize daily workflows, streamlining marketing processes to increase efficiency and productivity. This allows valuable time to be redirected towards personal wellness or other critical business areas.

AI can automate various marketing tasks, including:

• Customer research
• Customer journey mapping
• Keyword research and SEO
• Competitive analysis
• Data analysis and segmentation

On top of that, AI is particularly adept at enhancing SEO optimization, especially in the realm of technical SEO. AI tools can identify complex and nuanced website issues that might be challenging for humans to detect without a thorough examination of HTML, CSS, and JavaScript. 

How to Get The Most Out of This Trend?

At the end of the day though, AI reaches its highest potential when combined with the creativity, experience, and human touch of marketers who can discern what strategies are effective and which are not. So define specific tasks to be automated and marketing objectives to guide AI implementations, ensuring alignment with overall business strategies.

Trend #10: Programmatic Advertising

Programmatic advertising is a valuable AI marketing use case. However, a key to implementing programmatic advertising is that you should aim to solve specific problems, not just use AI for everything. AI can make campaigns better with hyper-personalization and real-time changes. This allows for quick testing and improvement. Smaller companies can compete with bigger ones using AI. 

AI enhances programmatic advertising by automating ad transactions, optimizing spending, and targeting audiences effectively. A few current uses of programmatic advertising include campaign optimization, precise segmentation, creative optimization, and real-time bidding. 

Ensuring Data Privacy When Using AI in Marketing

Ensuring data privacy when implementing AI is a serious challenge. Artificial intelligence used in marketing is most effective when dealing with consumer data, which means that they are a unique risk factor when it comes to staying compliant and maintaining the trust of your audience.

You should always get consent from users before collecting and using their data for AI-driven marketing efforts to stay compliant with GDPR and CCPA regulations. Also, make sure to collect only the data necessary for your marketing objectives to minimize privacy risks.

No matter what your goals are, if you aim to ensure that AI is used ethically and transparently in your organization, you’ll be able to maintain a better relationship and reputation with your customers.

The Future of AI in Marketing

According to Statista, the market for artificial intelligence (AI) in marketing is projected to reach more than $107.5 billion by 2028, up from $15.84 billion in 2021. At the front of the stage, generative AI will continue to advance and gain traction among marketers looking for efficient content-generation options. This is the most controversial and unpredictable segment of AI in marketing because the ethical fallout can be difficult to predict. Time will tell how the industry will shift to respond.

However, looking deeper at AI’s role in the marketing industry we see use cases that aren’t as new, but those applications become increasingly important as time goes on. For example, demand forecasting models, sentiment analysis, and marketing automation are all less visible applications that are making marketing processes more efficient and campaigns more successful. 

Innovative technology will always be volatile. As time goes on, novel technologies find their place in an industry. Experimenting with those technologies is a critical step toward finding that balance that you need to succeed and thrive in a competitive market.

How to Start Implementing Artificial Intelligence in Marketing

The benefits of AI in marketing are numerous, but how are you going to reap those benefits in your organization? Let’s briefly explore the general path of AI app development for your marketing team:

1. Where will AI help most? Identify your marketing processes where you see the most opportunity for efficiency and automation. Whether you want to optimize your marketing processes or help other companies with your AI tool, it’s important to clearly understand the value your product brings.
2. Objectives: Set clear goals for using AI. Will you aim to improve ROI? Are you trying to improve lead generation? Do you want to optimize your ad spending goals? Choose key performance indicators that align with your objectives.
3. Chart Course for Development: Get a good idea of how you want to develop the AI tools that you need to accomplish those goals. This is the best time to coordinate with an experienced AI development team so they can help you find a solution that meets your budget.
4. Test, Measure, and Scale: Start with smaller implementations and pivot as needed. AI is a constantly evolving innovation, so leave room to pivot as needed. Measure your results and scale gradually.

Whether you know exactly what kind of application you want to make or if you are still in the ideation phase, it’s never a bad idea to look to AI consulting services for help. MobiDev’s AI consultants have the experience you need to make your application a reality.

If you’re ready to meet the future with your AI product fully realized, contact us today.