Among all company functions, marketing benefits the most from artificial intelligence (AI). It can significantly improve core activities such as understanding customer needs, matching them with products and services, and persuading them to buy your product or service.
AI marketing tools can analyze data to provide deeper insights into customer preferences and behaviors. This helps companies tailor their products and services to meet customer demands better.
AI marketing tools can also automate and optimize campaigns, making them more effective and efficient. For example, they can personalize email marketing, manage social media interactions, and create targeted advertisements, improving customer engagement and conversion rates.
Although it has already made significant inroads in marketing, its role is expected to grow even more in the coming years. It will become increasingly sophisticated as technology advances, offering more precise and impactful strategies. Companies that utilize AI early will likely see substantial benefits in terms of increased efficiency, better customer insights, and higher sales.
What exactly is AI in marketing used for?
In 2025, marketing relies heavily on data about competitors, market trends, and customer opinions. A vast amount of information is available, but manually collecting and analyzing it is challenging. AI marketing tools simplify this process, making it quick and scalable.
They use techniques like sentiment analysis to gather positive and negative product reviews. It also provides competitor intelligence, updating you on rival brand activities. These systems can also write creative content, helping meet the constant demand for SEO-optimized material. ChatGPT and other alternatives are well-known examples that people use to create drafts and generate ideas.
You’ll likely use multiple AI marketing tools, as most data manipulation software now uses machine learning and automation to deliver impressive results at scale. This makes analyzing data, tracking competitors, and generating content easier.
AI marketing in 2025
Many companies now use AI for various tasks. For example, it can handle digital ad placement (programmatic buying), improve the accuracy of predictions such as sales forecasts, and support structured functions like customer service.
AI in marketing is applied at every stage of the customer journey. When potential customers are in the “consideration” phase and researching a product, AI targets ads at them and guides their search. AI can streamline sales by using detailed data on individuals, including real-time geolocation, to create highly personalized product or service offers. Later in the journey, AI helps with upselling and cross-selling, reducing the chances of customers abandoning their online shopping carts. For instance, after a customer fills a cart, AI bots can provide a motivational message to close the sale, such as “Great choice!”
After the sale, AI-powered service agents are available 24/7 to handle customer requests. They manage fluctuating volumes of service queries more effectively than human agents, dealing with simple questions about delivery times or scheduling appointments and escalating more complex issues to human representatives. Sometimes, AI assists human reps by analyzing customer tone and suggesting appropriate responses. It can also coach agents on how to meet customer needs best or recommend supervisor intervention.
Four types of AI marketing
AI in marketing can be categorized based on two dimensions: the level of intelligence and whether it’s a stand-alone tool or part of a broader platform. Some technologies, like chatbots or recommendation engines, can fit into multiple categories depending on their specific implementation.
Source: Zaiper
Types of Intelligent techniques
The level of intelligence in applications determines what they can do and how complex the tasks they can handle are. There are two main types: task automation and machine learning.
- Task automation
These applications perform repetitive, structured tasks that require low levels of intelligence. They follow a set of rules or execute a predetermined sequence based on a given input, but they can’t handle complex problems like nuanced customer requests.
An example would be a system automatically sending a welcome email to each new customer. Simple chatbots, such as those available through Facebook Messenger and other social media providers, also fall into this category. These chatbots assist customers during basic interactions by guiding them through a defined decision tree. However, they can’t understand customer intent, offer customized responses, or learn from interactions over time.
- Machine learning
These algorithms are trained using large amounts of data to make complex predictions and decisions. These models can recognize images, decipher text, segment customers, and anticipate customer responses to various initiatives, such as promotions.
Machine learning already drives programmatic buying in online advertising, e-commerce recommendation engines, and sales propensity models in customer relationship management (CRM) systems. It’s more advanced variant, deep learning, is rapidly becoming among the most powerful tools. However, it’s important to note that existing machine-learning applications perform narrow tasks and need extensive training data.
Types of structure techniques
The other two types of AI in digital marketing fall under the structure: stand-alone and integrated applications. Let’s take a look at both of them.
- Stand-alone applications
Stand-alone AI applications are isolated programs separate from the primary channels customers use to learn about, buy, or get support for a company’s products. Employees also use different channels to market, sell, or service these offerings. Customers or employees have to go beyond their usual channels to use these AI tools.
For example, consider the color-discovery app created by Behr, the paint company. Using IBM Watson’s natural language processing and Tone Analyzer capabilities, the app delivers personalized Behr paint-color recommendations based on a space’s desired mood. Customers use the app to shortlist colors for their room, but the actual paint sale happens outside the app, although it connects to Home Depot for ordering.
- Integrated applications
Integrated AI applications are embedded within existing systems and are often less visible to customers, marketers, and salespeople who use them. For example, machine learning that makes split-second decisions about which digital ads to show users is built into platforms that handle the entire process of buying and placing ads. Spotify’s integrated machine learning has offered music recommendations for a long time. If the recommendation engine were stand-alone, users would need to go to a separate app to get suggestions.
Combining intelligence and structure
Combining the two types of intelligence (task automation and machine learning) with the two types of structure (stand-alone and integrated) yields four categories:
- Stand-alone machine-learning apps
- Integrated machine-learning apps
- Stand-alone task-automation apps
- Integrated task-automation apps
Understanding which category an application falls into can help marketers effectively plan and sequence the introduction of new tools. Companies can better integrate AI into their marketing strategies and workflows by identifying the right quadrant for each application.
Challenges for AI marketing
To drive growth marketing in 2025, success depends on understanding customer needs and responding to them with speed and precision. The rise of real-time, data-powered marketing decisions has made AI in digital marketing essential for marketers. However, integrating AI into campaigns and operations requires careful consideration by marketing teams. Since the development and utilization of AI marketing tools are still in their infancy, there are several hurdles to navigate when incorporating AI into digital marketing strategies.
- Training time and data quality
AI in marketing isn’t smart right out of the box. It needs time to learn about a company’s goals, what customers like, and what has worked in the past.
Just like humans, they need training. But they won’t make the right decisions if these tools aren’t trained with good-quality data. Bad data means bad decisions, and that’s not helpful for anyone.
- Privacy concerns
People are more concerned about their privacy these days, which means companies must be careful about using customer data for marketing. Digital marketing teams must ensure that their AI marketing strategies follow the rules, like GDPR, which protect people’s privacy. If they don’t, they could get into big trouble.
- Approvals
It’s difficult for digital marketing consultants and teams to show their bosses why investing in AI in digital marketing is a good idea. Sure, they can show numbers like ROI, which are easy to understand. However, proving that AI makes customers happier or improves the brand’s reputation is harder. That’s why they need tools that can measure these things accurately.
- Doing it right
Since AI in digital marketing is still new, there aren’t clear rules for how to do it best. Every marketing team has to consider the long-term effects, not just the short-term gains. They need to plan carefully and make sure they’re ready for whatever comes next.
- Adapting to changes
AI marketing is changing the way marketing works. Some jobs might disappear, and new ones might pop up. Studies say that many marketing jobs could soon be done by computers. That means marketers have to learn new skills to stay ahead.
Using AI for marketing in campaigns
To effectively incorporate AI into marketing campaigns and operations, starting with a comprehensive plan is crucial. This approach helps marketing teams mitigate potential challenges and maximize the benefits of their AI investment quickly and efficiently. Before deploying any strategy, consider the following key aspects:
- Establish clear objectives
Before diving into AI for marketing, defining clear goals and establishing marketing analytics is essential. Identify areas within campaigns or operations that could benefit from improvement, such as segmentation. Then, measurable Key Performance Indicators (KPIs) will be set to gauge the success of AI-enhanced marketing efforts. This is particularly important for qualitative objectives like enhancing customer experience.
- Ensure data privacy compliance
Ensure that your AI marketing platform adheres to data privacy standards to maintain consumer trust and compliance with regulations. Implement necessary measures to safeguard consumer data and prevent unauthorized use for personalized marketing purposes.
- Secure adequate data volume and sources
Successful AI marketing relies on access to a vast amount of high-quality data. Utilize data from your organization’s CRM system, past marketing campaigns, and website analytics. Additionally, consider incorporating second and third-party data sources, such as location and weather data, to enrich customer insights and enhance campaign effectiveness.
- Acquire data science expertise
Many teams lack the expertise required to use AI and analyze large datasets effectively. Collaborate with third-party organizations specializing in data science to collect, analyze, and interpret data for optimal AI performance and ongoing maintenance.
- Ensure data quality
Maintaining data quality is crucial for the accuracy and effectiveness of programs. Establish robust data cleansing and maintenance processes, focusing on key dimensions such as timeliness, completeness, consistency, relevance, transparency, accuracy, and representativeness.
- Develop an integrated strategy
Integrate AI for marketing into your existing strategy rather than using it as a standalone solution. Using AI-driven insights, identify opportunities to optimize labor-intensive tasks such as data analysis and attribution. By complementing traditional methods with AI technologies, marketers can enhance campaign performance and drive meaningful results.
Incorporating AI into marketing campaigns requires careful planning and execution. By addressing key considerations such as goal-setting, data privacy, talent acquisition, data quality, and integration, marketers can harness the power of AI to drive innovation and achieve marketing excellence.
Breaking it all down
AI marketing programs offer benefits for marketing teams of all sizes. They enhance productivity, boost ROI, and improve efficiency, especially when handling large volumes of data.
For instance, even simple AI applications, like using a machine learning program to craft email subject lines, can significantly impact digital marketing campaigns.
Using AI in marketing, regardless of scale, enables brands to stay competitive and consistently achieve revenue targets. Each step towards integrating AI into marketing strategies contributes to maintaining a competitive edge and surpassing revenue goals.
