How to implement AI in marketing (Use cases, examples, best practices)
AI is rapidly transforming marketing, with 87% of marketers now using or experimenting with AI tools. Marketers can use AI to better understand customers, streamline processes, automate and optimize decision-making, targeting, ad spend, and more—all while driving greater return on investment (ROI).
As AI becomes the new engine of high-performance marketing, the questions arise: How to use AI in marketing most effectively? Is how you're using AI going to translate to a competitive advantage in the long-term?
According to Hubspot’s 2025 State of Marketing Report, the most common ways to use AI in marketing are content creation (35%), data analysis and insights (30%), and workflow automation (20%). This article lays out how to effectively implement AI in marketing, plus how to scale AI into a competitive advantage that wins and retains customers for life.
Specifically, we cover:
Reinvent CX with AI agents
Understanding AI in marketing
AI is reshaping traditional marketing roles from manual execution to strategic oversight. By analyzing customer data in real time, AI allows marketers to automate traditionally human work and decision-making, gain customer insights, and use data in real-time to personalize experiences, optimize targeting, and maximize return on investment (ROI).
On the front end, generative AI tools like ChatGPT streamline the creation of tailored content to save time. Meanwhile, AI-driven data analytics platforms process vast datasets to enable precise targeting, accurate predictive analytics, and campaign optimization. For customers, this all translates to a more seamless, personalized experience across touchpoints, improving engagement, satisfaction, and loyalty.
Core AI technologies in marketing include:
Predictive analytics – Anticipates customer behavior and trends by analyzing historical data.
Machine learning (ML) – Continuously refines targeting and personalization by enabling machines to learn without explicit programming.
Natural language processing (NLP) – Enhances chatbots and content creation by enabling AI to understand and respond in human language.
Generative AI – Streamlines and automates the creation of content and marketing copy by predicting the most relevant text.
AI-powered marketing platforms (e.g., HubSpot) – Automates repetitive tasks like sending emails to streamline campaign management and customer interactions.
Marketing departments are awash in omnichannel customer data, and given the value of leveraging that data across the customer journey—AI can be a force multiplier.
By integrating AI with existing functions, marketers can streamline workflows and deliver the next generation of personalized, data-driven marketing experiences that drive customer loyalty, campaign ROI, and long-term success.
Automate customer service with AI agents
Examples of AI in marketing
Many companies have adopted a variety of AI into the marketing department, improving key performance metrics, efficiency, and customer satisfaction.
For example, according to Hubspot, marketers using gen AI report better-performing content (63%) with improved quality (89%) and the ability to create significantly more content (83%).
Here are the most common AI applications in marketing:
1. Content creation: Generative AI tools help marketers quickly create hyper-personalized marketing materials, reducing the time and effort required to create blogs, ad copy, or emails. This makes it easier to iterate on existing marketing strategies to improve ROI.
Example: AI tools like ChatGPT and Jasper help marketers to generate blogs, ad copy, and email campaigns quickly.
2. Customer service assistants: Gen AI chatbots enhance customer interactions by providing instant tailored support 24/7. Using NLP, they answer questions, recommend products, and deliver targeted offers across the journey—all while quickly resolving issues or routing tickets to boost customer satisfaction.
Example: Sephora’s AI-powered chatbot on Facebook Messenger gives beauty tips, answers FAQs, and even schedules in-store appointments.
3. Audience segmentation: By identifying patterns in data that humans can’t, AI algorithms can automatically divide customers into precise, relevant segments. This leads to enhanced targeting and more effective campaigns en route to better customer engagement and ROI.
Example: Amazon Personalize uses AI to analyze customer data and deliver highly personalized product recommendations.
4. Search engine optimization (SEO): AI enhances SEO efforts by automating much of keyword research, content optimization, and ranking analysis, helping to increase page rankings and brand visibility in the face of evolving standards.
Example: AI-driven SEO tools like Clearscope analyze top-ranking content and suggest keyword optimizations with AI.
5. Programmatic advertising: AI optimizes ad campaigns to help marketers get the most bang for their buck, automating ad bidding and placement to maximize ROI with real-time adjustments.
Example: AI-driven ad tools like Google Ads' Smart Bidding uses ML to automatically adjust bids in real-time based on factors like user intent, device, and location.
6. Predictive analytics: By analyzing historical data and forecasting future trends, AI helps marketers make data-driven decisions and optimize workflows. AI insights can refine marketing strategies, improve lead scoring, reduce churn, and evaluate demand for new products.
Example: AI-driven predictive tools like Salesforce Einstein AI analyze historical customer interactions to predict lead conversion likelihood so teams can prioritize high-value leads.
7. Workflow automation: AI tools automate repetitive tasks like data entry, content scheduling, and social media management so marketers can focus on strategic work, This improves efficiency, reduces costs, and ensures consistent customer engagement. The next frontier is AI agents, autonomous entities that orchestrate complex workflows across systems using APIs and real-time data, promising the next generation of marketing automation.
Example: Zapier’s AI-powered automation connects marketing tools like HubSpot, Mailchimp, and Slack, streamlining repetitive tasks like email follow-ups.
Learn more: What is agentic AI?
Leverage omnichannel AI for customer support
Benefits of AI in marketing
Integrating AI into marketing operations can translate to significant advantages for businesses and their customers.
By leveraging real-time data analysis, automation, and predictive insights, AI helps to make campaigns more effective, operations more efficient, while enhancing performance in multiple areas.
The major benefits of AI in marketing include:
1. Faster, smarter decision-making
AI algorithms and predictive analytics help marketers to identify trends, optimize campaigns, and predict customer behavior. AI agents can continuously collect, analyze, and use data in real-time to proactively optimize campaigns and strategies with the most accurate, current, and comprehensive data to better compete for customer attention and drive ROI.
Example: Ulta Beauty uses AI to drive its personalization and decision-making processes.
2. Increased efficiency and automation
AI automates repetitive tasks like content creation, email marketing, and customer segmentation, freeing up marketers for strategic work.
Example: Amarra, a formal gown distributor, uses AI to write product descriptions and marketing copy, reducing content creation time by 60%.
3. Real-time personalization at scale
AI enables hyper-personalized marketing, delivering the right message to the right customer at the right time. For example, AI agents analyze both historical data and real-time context like user behavior to deliver the most contextually relevant experience to customers at every touchpoint.
Example: Swarovski uses AI agents for customer service, enhancing sales and customer satisfaction, increasing its NPS by 45%, and resolving 50% of queries at first contact.
4. Improved conversions and ROI
AI refines audience targeting, optimizes ad buying and personalization efforts, and drives greater campaign ROI by identifying and leveraging data-driven insights. For example, ML algorithms can predict customer behavior and preferences, providing insights on how best to use resources and increase ROI.
Example: According to a recent McKinsey survey, companies using AI at scale see 5x higher conversion rates and 25% lower marketing costs.
5. Seamless omnichannel engagement and support
AI agents act as the bridge between disparate marketing tools and AI systems, using APIs and real-time data to create truly seamless omnichannel experiences. When deploy as chatbots, they help scale personalized customer engagement and support, remembering past customer interactions to maintain a single ongoing conversation with customers that spans channels. Whether offering customer support or delivering tailor promotions, AI agents help marketers to deliver consistent, tailored interactions wherever customers are to enhance engagement and loyalty.
Example: Swarovski uses AI agents for customer service, enhancing sales and customer satisfaction, resolving 50% of queries at first contact, and increasing its NPS by 45%.
6. Enhanced customer experience (CX)
By providing personalized recommendations, proactive support 24/7, and seamless omnichannel interactions, AI helps marketers increase customer satisfaction, loyalty, and advocacy with exceptional CX.
Example: Victoria’s Secret uses AI for personalized email campaigns that delight customers, driving a sizable increase in marketing metrics, customer engagement, and sales.
Steps to using AI in marketing
Ready to start using AI in marketing? As part of a comprehensive AI strategy, here are the main steps to effectively integrate AI:
Assess your business needs
Start by identifying which areas of marketing operations will benefit most from using AI. This involves evaluating your current processes, challenges, and core KPIs.
From here, you can set clear goals and objectives for what you want to achieve with AI that align with your overarching strategy. For example, you could improve conversions, return on ad spend, or customer engagement. By aligning needs with goals, you help minimize waste and maximize returns on AI investments.
Identify your use cases
Many marketers use standalone AI tools like ChatGPT for content creation, but there’s a huge gap between using AI in isolated applications and integrating it at scale. According to a recent McKinsey survey, companies that scale AI in marketing see a 5X increase in conversion rates and a 25% reduction in marketing costs.
The best AI use cases are not only valuable, actionable, and feasible in the short term, but scale into a compounding advantage in the long run. To remain competitive, marketers must go beyond one-off tools and their limited use cases and instead integrate AI into core processes to realize the greatest efficiencies, opportunities and scale AI into a competitive advantage that wins loyal customers.
Choose your AI tech stack
Next, explore the AI technologies that best suit your goals and use cases. The marketing ecosystem contains a growing variety of AI tools and systems, so consider integration capabilities and scalability when evaluating different options.
The marketing AI stack falls into four main buckets:
Machine learning: Use data to improve predictions and decision-making. Key for programmatic ad buying, real-time personalization, predictive targeting, etc.
Integrated AI: Embedded in existing platforms such as AI-driven CRM analytics
Standalone AI tools: Use-specific AI separate from core marketing platforms like AI chatbots
Task automation: Performs structured, rule-based tasks like email automation.
Develop a plan
Once you know your tools and use cases, you can create a plan to implement AI applications in the marketing department based on priority. This plan should include timelines, budget allocations, and resource requirements.
Lay out the steps to integrate these AI into existing systems, considering factors like data migration, system configuration, and potential disruptions to current workflows. According to McKinsey, 21% of top-performing AI marketing departments reported having to redesign workflows to use AI effectively.
Collect and organize data
AI relies on vast sets of clean, high-quality data to function effectively. Ensure your datasets are accurate, complete, and relevant to your use case. You may need to establish data collection and data management systems to clean and pre-process data so AI can use it to produce accurate, relevant outputs.
Invest in AI expertise
AI is complex, and can require new talent or training to use AI effectively. This involves training your current team on how to use AI tools and providing ongoing support and resources to facilitate the best possible results. It also involves hiring data scientists, machine learning engineers, and other technologies that can design, deploy, and scale AI algorithms, systems, and data architecture across the marketing department.
Integrate AI with existing solutions
Ensure your AI tools work well with your current marketing stack. This may involve API integrations, data syncing, and other technical configurations. Before full deployment, conduct pilot tests to identify, address, and resolve any integration issues.
Monitor and optimize
Once your AI marketing solutions are in place, track their performance using relevant metrics such as conversion rates, engagement, and ROI. Continuously evaluate and refine your AI marketing strategies based on performance data and customer feedback. Also, monitoring the accuracy, adaptability, and performance of AI outputs and making adjustments as needed.
Best practices for AI in marketing
Implementing AI in marketing effectively requires strategic planning, ethical considerations, and continuous optimization. Here are the best practices to ensure AI delivers maximum value:
1. Prioritize data quality
AI models are only as good as the data they process. Ensure your data is clean, structured, and up-to-date for accurate AI-driven insights.
Best practice: Implement a data governance strategy to filter out errors and inconsistencies.
2. Balance AI automation with human oversight
AI enhances efficiency, but human judgment is still essential for creativity, brand voice, and customer relationships.
Best practice: Use AI to generate content and insights, but have humans refine messaging and ensure alignment with brand values.
3. Ensure ethical AI use and compliance
AI must be transparent and comply with privacy regulations (GDPR, CCPA) to maintain customer trust.
Best practice: Communicate AI use in marketing clearly and provide customers with data control options.
4. Use AI to enhance creativity, not replace it
AI can assist in content creation but should support human creativity rather than replace it.
Best practice: Leverage AI tools like ChatGPT to generate content ideas, but have marketers the refine final output.
5. Start small and scale gradually
While its important to think long-term, it's best to pilot AI tools in specific marketing functions before full-scale adoption.
Best practice: Test AI-driven chatbots, predictive analytics, or programmatic advertising before integrating AI across all marketing channels.
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Final thoughts: Using AI in marketing
AI is revolutionizing marketing, improving key areas such as process automation, data analytics and usage, and real-time personalization to increase efficiency, conversion rates, and ROI.
Given these benefits, it’s no surprise that a recent CoSchedule survey shows that 9 in 10 marketers are planning to ramp up AI integration in 2025.
With AI becoming table stakes, marketers can start by experimenting with AI tools and scale based on ROI. However, CMOs must have a long-term plan that goes beyond one-off AI tools and integrates AI into core processes, enabling them to realize the greatest opportunities, create new efficiencies, and scale AI into a competitive advantage.
If you need help creating your AI strategy, Sendbird can help. Our team of AI experts, which includes machine learning engineers, data scientists, and others, is skilled and ready to help enterprises craft and deploy an effective AI strategy at scale.
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