A guide to AI in marketing

A recent study from the management consultancy McKinsey estimates that generative AI might add as much as USD 4.4 trillion to the global economy annually.¹ And particularly in recent years, AI-powered tools became increasingly ubiquitous across the marketing landscape to augment human labor and streamline workflows.

From customized content creation to task automation and data analysis, AI has seemingly endless applications in marketing. Here are some key definitions, benefits, use cases and finally, a step-by-step guide for integrating AI into your next marketing campaign.

AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. Today, AI technologies are being used more widely to generate content, increase team efficiency, improve customer experiences and deliver more accurate results. According to the consultancy McKinsey, as of 2024, AI adoption across the global business landscape increased to 72%.²

With the increasing utility of generative AI, marketing departments use the technology to instantly create hyperpersonalized marketing materials, distill insights from customer data and iterate on existing marketing strategies. Given the vast amounts of omnichannel data processed by marketing departments, and the value of leveraging that data, AI adoption is increasingly critical for businesses that want to remain competitive. According to the IBM Institute for Business Value’s annual CEO study, over 70% of the highest performing executives that were surveyed believe that competitive advantage depends on having the most advanced generative AI. 

Marketing teams that are equipped with cutting-edge AI tools can see the impact of their marketing efforts in near real-time and adjust their tactics accordingly. AI marketing platforms can create AI marketing strategies and analyze data faster than humans that use ML algorithms and recommend actions that are informed by sentiment analysis from historical customer data.

AI marketing tools can help marketers identify actionable insights from data that is generated by a campaign in near real-time. Additionally, the same tools can help identify the right channels for a media buy and even the optimal placement of an ad based on customer behavior. Modern AI marketing solutions help stakeholders ensure that they are getting the most out of their investment in a campaign.

Digital campaigns generate more data than humans can keep up with, which can make measuring the success of marketing initiatives difficult. AI-enhanced dashboards help marketers link the success of their efforts to specific tactics they’ve deployed, helping them better understand what’s working and what might be improved.

AI technologies help marketing teams improve their customer relationship management (CRM) programs by automating routine tasks like the preparation of customer data. They can also reduce the likelihood of human error, deliver more personalized customer messages and identify at-risk customers.

Today, many marketers struggle with the sheer amount of data available to them when they’re planning a campaign. AI can help by performing predictive analytics on customer data, analyzing huge amounts in seconds by using fast, efficient machine learning (ML) algorithms. It uses the data to generate insights about future customer behavior, suggest more personalized content and spot patterns in large datasets for marketers to act on.

Marketing departments use AI tools in a wide variety of customer-facing and internal applications. Examples of AI for customer experience include optimizing social media posts, email marketing and content marketing efforts. Internally, marketers use AI to perform audience segmentation, analyze consumer data and intelligently automate routine tasks. Some examples of ways in which enterprises are increasing their use of AI to achieve marketing goals include: 

AI helps businesses intelligently and efficiently divide up their customers by various traits, interests and behaviors, leading to enhanced targeting and more effective marketing campaigns. The final result is stronger customer engagement and improved ROI.

The launch of OpenAI’s generative AI platform ChatGPT in 2022 prompted a flood of new use cases for AI. AI used for content generation can save marketing teams time and money by creating output such as blogs, marketing messages, copywriting materials, emails, subject lines, subtitles for videos, website copy and other types of content. The technology can also automatically translate content from one language to the other or create multiple cohesive iterations of the same campaign materials across platforms. 

Early AI-powered customer support tools included chatbots, which once trained might interact with customers seeking simple answers to frequently asked questions. Today, generative AI-powered assistants can interact with customers in natural language, no matter where they are on their customer journey, helping to resolve tickets quickly and increasing customer satisfaction.

AI allows businesses to improve their e-commerce programs and digital marketing capabilities by giving them a more nuanced understanding of their customer’s needs and buying habits, automating tasks and simplifying workflows. Technologies such as AI agents, which can continuously collect and analyze data, often power personalized recommendation engines on e-commerce sites by tracking user activity and generating products an individual is most likely to buy. 

Predictive analytics analyzes historical data and forecasts future trends, helping marketers make data-driven decisions and optimize their workflows. By identifying customer behavior patterns, AI predicts which products might perform well, optimizes pricing strategies and improves lead scoring. Businesses can use these insights to refine their marketing strategies, reduce customer churn and break into new markets based on consumer demand. 

Programmatic advertising is the automation of the purchasing and placement of ads on websites and applications. AI significantly enhances organizations’ abilities to conduct programmatic advertising by using customer history, preferences and context to deliver more relevant ads with higher conversion rates.

Deploying an AI solution to enhance search engine optimization (SEO) helps marketers increase page rankings and develop more sound strategies. AI can help marketers create and optimize content to meet constantly changing standards. 

Sentiment analysis uses AI to evaluate customer opinions and emotions as expressed through social media, online reviews and customer feedback. For example, an AI agent can sift through vast amounts of textual data to extract underlying attitudes. By understanding audience sentiment, businesses can adjust their messaging, manage their reputation and respond proactively to customer concerns. 

Using the power of AI, marketers can streamline repetitive and time-consuming tasks, allowing them to focus on strategic initiatives. By automating processes like data entry, transcription and simple customer interactions, businesses improve efficiency and reduce operational costs. AI-powered tools also schedule content, manage social media posts and personalize communication, helping to ensure consistent engagement with customers. 

AI-driven solutions are only as strong as the quality of the data they are trained on. Regardless of how technically advanced a tool is—if it was trained on inaccurate and non-representative data, it is unable to generate high quality and effective answers and decisions. To prepare for successful AI marketing initiatives, many businesses take the time to standardize and clean their datasets to help ensure accuracy and efficiency. 

The efficacy of AI is reliant on moving data seamlessly across systems. To maximize these tools’ efficacy, businesses typically try to ensure data integration across all platforms and systems, including CRM software, website analytics and sales platforms. Implementing robust data pipelines and cloud-based infrastructure allows for real-time data processing, enabling AI to deliver accurate insights and personalized customer experiences. 

Just like humans, AI requires significant training to learn a new task. For example, if a business needs an AI solution that talks to its customers in an engaging way, it needs to invest the time and resources necessary to teach it. To build an application like this, marketing departments often need a large amount of data about customers’ preferences and potentially, data scientists who specialize in doing this training. Increasingly, leading businesses are designing purpose-built AI tools that are trained on task-specific or company-specific datasets, increasing the technology’s efficacy. 

Because AI is trained on personal customer information, the laws surrounding what is usable must be strictly followed. Companies who deploy AI for marketing purposes are responsible for adhering to consumer data regulations or risk incurring heavy fines and reputational damage. Practicing good data governance and providing transparent explanations of how AI is built and deployed facilitates consumer trust. 

Before successfully implementing an AI integration, marketing leaders and stakeholders across an organization typically set well-defined goals. This provides a systemic process through which to evaluate an AI tool. Following deployment, these technologies must be continuously monitored to help ensure that they’re meeting benchmarks. 

Integrating AI can change the fundamental nature of an employee’s job. Laying out training programs and change management systems can help ease the transition toward AI, and help ensure that marketing departments get the most out of the technology. It will also help them know which tasks must be done by a human rather than a machine.

AI integration can be as simple as intelligently automating a marketing workflow with pre-built apps, or as complex as building a series of internal productivity tools based on company data. In either case, the following five steps can help a business successfully incorporate AI into its marketing strategy. 

The first step to integrating AI into a marketing campaign is to set out goals and expectations. During this step, business leaders identify bottlenecks and outline ways in which they hope that AI can improve marking practices over the long term. Once stakeholders align on expectations, it’s easier to choose an AI solution and set meaningful key performance metrics (KPIs) to evaluate success. 

Data scientists or engineers with a background in AI, machine learning and deep learning don’t typically sit on marketing teams, but their expertise is necessary for successful AI marketing initiatives. To address this issue, organizations can invest in hiring the data scientists and engineers they need, or go to a third-party vendor for help training and maintaining their AI marketing tool. Both approaches have their advantages and disadvantages, primarily around the level of investment an organization is willing to make.

One of the biggest challenges facing AI marketing solutions is the use of customer data for training and implementation purposes without violating privacy laws. Throughout the training process, organizations must find ways to maintain their customers’ security and privacy. This might mean investing in more infrastructure to securely store customer information.

The success of an AI marketing tool depends on the accuracy and relevancy of the data that it’s been trained on. AI tools that are trained on data that doesn’t accurately reflect customer or company intentions cannot provide useful insights into customer behavior or make useful strategic recommendations. By prioritizing the quality of their data, enterprises  help ensure that their AI solutions help them better achieve the outcomes that they seek for their marketing programs.

Organizations selecting an AI solution have many different platforms and capabilities to choose from. If they followed the first four steps carefully—laying out their goals, hiring the right talent and ensuring the quality and accuracy of their data—choosing the right tool is straightforward.

Depending on the tool, integrating AI can be as simple as inputting datasets into an off-the-shelf application, or as complex as systematically infusing AI for multiple functions across a department. In either scenario, as new workflows emerge, this step involves paying careful attention to change management as employees acclimate to new ways of working.

During this step, organizations analyze AI-powered workflows to help ensure that they’re meeting KPI targets, monitor outputs and intermittently feed AI tools new data to improve accuracy. Leaders might also track employee adoption and key productivity markers, altering strategies to continuously optimize AI-augmented workflows.