What AI really is (and why it matters to you)
You’ve probably heard the buzz about AI, or artificial intelligence, in blogs, podcasts, and social feeds. But when it comes to applying it to your own marketing, it can feel a bit abstract. What is AI, really? And how is it different from the automation tools you may already use?
AI is technology that mimics human intelligence to perform cognitive tasks like writing, reasoning, and problem-solving. At its core, AI is powered by machine learning: instead of following fixed rules like traditional automation does, AI analyzes large amounts of data to recognize patterns, make predictions, and improve over time. In other words, AI learns from data. It looks at what has happened in the past to predict what’s most likely to happen next.
Watch the video below to see how AI is evolving, why it’s not just a passing trend, and how it’s transforming the way marketers work.
AI is no longer a distant idea or a passing trend. It’s a practical tool that helps marketers scale their creativity, save time, and make smarter decisions. The sooner you start exploring how it fits into your workflow, the faster you’ll uncover new ways to grow your business with confidence.
Understand the main categories of AI
AI isn’t one single tool or feature. It’s a broad field that includes different types of technologies, each built to solve a specific kind of problem. As a marketer, understanding these categories helps you identify where AI can bring the most value to your day-to-day work. Explore the gallery below to learn how each category works and see how real brands are using AI to transform their marketing.
Generative AI
How it works:
Generative AI creates new content such as text, images, or videos by learning from large collections of existing data. It predicts what words, visuals, or sounds should come next based on the patterns it has already seen.
Marketing applications:
- Write marketing copy such as emails, product descriptions, or blog posts
- Design creative assets for campaigns or ads
- Generate product photos and other visual content
Real-world example:
CarMax uses AI to create website content, such as customer review summaries and car comparisons. This allows its editorial team to focus on longer-form, strategic pieces that require deeper insight.
Predictive AI
How it works:
Predictive AI uses data to anticipate what customers will do next. It looks for patterns in behavior, such as browsing, purchasing, or engagement activity, to forecast future outcomes.
Marketing applications:
- Deliver personalized product recommendations
- Predict when a customer is ready to buy again
- Identify high-value segments or customers at risk of churn
- Optimize cross-sell and upsell opportunities
Real-world example:
Dr. Hydrate uses Klaviyo’s predictive analytics to anticipate when customers are likely to make their next purchase. This helps the brand send timely, personalized reminders that drive repeat orders.
Autonomous AI
How it works:
Agentic AI systems, also referred to as autonomous AI, are systems that can act independently across multiple datasets and platforms. They learn from interactions, plan multi-step actions, and complete tasks with minimal human input.
Marketing applications:
- Onsite chatbots that respond to customer questions and guide shoppers through product discovery
- Internal marketing agents that assist with campaign planning, execution, and optimization
Real-world example:
H&M offers a virtual assistant that supports customers throughout their shopping experience. It provides real-time answers to questions about product availability, order tracking, and store locations.
How to start experimenting with AI
So how can you start experimenting with AI? There are two main ways to access it in your work:
- Large language models (LLMs): Tools like ChatGPT or Gemini are trained on massive datasets to understand and generate human-like text. They’re flexible and can support a wide range of marketing tasks, from writing copy to analyzing data.
- Native AI features: These are built directly into the platforms you already use, such as predictive analytics in Klaviyo or automated campaign optimization in advertising platforms like Meta or Google ads. They’re often more specialized and tailored to your business use cases.
Most marketers use a mix of both, combining the versatility of LLMs with the precision of built-in AI tools.
Recognize the limitations of AI
AI can be a powerful tool for marketers, but it’s not perfect. Like any technology, it has limits that you need to understand in order to use it responsibly. Being aware of these challenges helps you make AI work for you, not against you.
Explore the dropdowns below to learn about common limitations to keep in mind when using AI tools in your marketing.
Accuracy
AI tools sometimes generate inaccurate or made-up information, known as “hallucinations.” This can happen when a model fills gaps in its data or misinterprets your prompt. Always fact-check AI-generated outputs and verify claims, especially for anything public-facing like campaigns, blog posts, or reports.
Bias
AI systems learn from existing data, which means they can also learn and repeat the biases in that data. This might show up in the language, tone, or representation of certain audiences. Review AI-generated content carefully to ensure it aligns with your brand values and treats all customers fairly.
Copyright and compliance
Some AI models are trained on publicly available content, which may include copyrighted material. AI-generated marketing content also may not account for regulations like GDPR or CAN-SPAM. Always review AI-generated text, images, or designs carefully, and treat them as starting points rather than final outputs. Add your own creative input to make sure your content is original, on-brand, and compliant with all applicable laws.
Data privacy and transparency
When using AI tools, consider what customer data they can access and how that data is handled. Avoid sharing sensitive or personally identifiable information with third-party tools. Always review platform privacy policies and communicate transparently with customers about how their data is used.
Environmental impact
Training and running AI models requires significant computing power, which consumes energy and contributes to carbon emissions. You can minimize your impact by using AI intentionally:
- Only use AI when it truly adds value.
- Choose tools and platforms that invest in sustainable practices.
Even as AI becomes more capable, human judgment remains essential. Use AI to accelerate your work, but always review and refine what it produces. Think of it as a collaboration: AI can generate ideas, automate tasks, and analyze data, but you provide the context, strategy, and creativity.
In other words, trust the process, but verify the output. Keeping humans in the loop ensures your marketing stays accurate, ethical, and uniquely human.