AI's Impact on Marketers
By Catherine Seda and Jenny Halasz
Date: Feb 25, 2026
Sample Chapter is provided courtesy of Pearson.
AI is a game-changer for marketers. It’s time to level up.
For years, we marketers have been tasked with doing more and producing more. It’s exhausting. Even in leadership roles where we focus on marketing strategy, we’re often busy in meetings or fighting fires, including broken tracking code, budget reconciliations, and data discrepancies. We rarely get resources to help… until now.
Executives are drooling over artificial intelligence (AI). It’s so shiny, with its promise of massive cost-savings and productivity. Some executives are thinking, “AI can spit out 100 blog articles for SEO? Great. Let’s also use AI to send mass emails, create all our social media comments, design our brand campaign assets—let AI scale marketing!”
Not so fast.
AI is not a magic button. You can’t press it and forget it. It’s a tool. Yes, it’s a powerful tool for marketers that absolutely helps with content marketing and search engine optimization (SEO). When properly wielded, AI drives efficiencies. However, before we explain how marketers can integrate it effectively into their work to improve productivity without damaging consequences, let’s see how the AI revolution is impacting content and SEO, and what this means for marketers.
Content Marketing and SEO Transformation
When done correctly, content marketing is effective but can be painful. The process is manual and slow and requires many resources. Think about the steps and time it takes at a corporate brand to develop and publish a piece of content, like a blog article, for example.
At LL Flooring, where we both previously worked, when a blog article was delivered by our partner agency, it then passed through five internal teams—creative, brand, content, SEO, and e-commerce—though not in a linear order. Additionally, the installation or merchandising team would review the article if their subject matter expertise was needed. An article could take weeks to move through this workstream, and occasionally it got lost or stuck within a team. Sound familiar? Although we streamlined the blog workstream across teams, the manual processes of writing, editing, optimizing, fact-checking, and publishing a blog article were still cumbersome.
AI-powered content marketing is faster, smarter, and more data-driven.
Here are a few use cases and examples of AI content marketing in action:
Faster: Repurpose one webinar into a transcript, blog articles, social media posts, and an email newsletter within minutes or hours instead of days.
Smarter: Automatically adjust website content, like the homepage banner with a relevant call to action, in real time based on the user’s previous interactions or geographic location.
Data-driven: Predict which website pages are more likely to get increased traffic and generate content briefs with SEO in mind significantly faster, so the team can prioritize optimizing those pages.
AI helps brands save time and resources while delivering better experiences to their audiences. AI empowers marketers to improve their productivity and results, not just content marketers but SEOs (search engine optimizers) too.
AI-powered SEO tools and AI features within long-standing SEO platforms are now available. Thanks to AI’s ability to analyze huge amounts of data, it can recommend keyword strategies better and predict user trends. This saves SEOs weeks of time wading through data looking for patterns and opportunities. AI is not an SEO strategist. With the insights it can provide, SEO leaders can invest more time in building effective strategies and processes. And they’ll need to spend more time on SEO strategies, because AI is changing the search experience too.
Chances are you’ve noticed something different about Google lately. When you search on a topic, you may see an AI-generated snippet of information displayed at the top of the organic search results. These snippets are meant to provide helpful information without requiring you to click through to visit a website or multiple websites. Unfortunately, these snippets aren’t always reliable. That’s challenge number one. Another challenge is that now SEOs and content marketers need to focus on optimizing their website content to increase the chances of appearing in these snippets, as these are receiving priority placement over organic search engine listings. We’ll dig into the reasons behind why AI-generated search results have varying levels of usefulness in Chapter 3, “Search, Personalization, and SEO,” as well as what you can do to address the issues.
Now that you understand the high-level impact of AI on content marketing and SEO, you may be wondering what this means for your role and your future career. We pulled together research to help content marketers and SEOs understand the job market and what next-level skills are becoming increasingly important.
The Changing Job Market
At a global level, the overall job market looks promising. According to the World Economic Forum’s “Future of Jobs Report 2025,”1 by 2030, AI and other technologies will displace 92 million existing jobs but will create 170 million jobs. An increase of 78 million new jobs is great news.
Then why does the job market feel so difficult for marketers right now?
Economic pressures and AI advancements have been fueling a volatile job market the past few years.
According to LinkedIn’s “2024 Global Marketing Jobs Outlook” report,2 widespread layoffs impacted many marketers, and “job postings fell 42 percent in 2023 compared to 2022.” Although LinkedIn reported a 76 percent increase in marketing job postings from 2023 to 2024, it’s important to note that these reflect job listings and not necessarily roles that were actually filled. A percentage of postings are never intended to be filled, or may be completely fake, but they are created to collect résumés or the illusion of company growth.
So if your career ride has had more ups and downs than a rollercoaster these past few years, you’re definitely not alone. We’ve been there too, along with colleagues across marketing roles—from copywriters to chief marketing officers (CMOs).
According to a 2025 survey by Superpath, a career resource for content marketers, the job market is still tough. In Superpath’s “Content Marketing Salary Report” updated for 2025, more than 40 percent of respondents believe the current content marketing job market is “weak” or “very weak.”3 Although only 13 percent of content marketers surveyed reported they were laid off in 2024, as opposed to 16 percent in 2023, content marketers are dealing with layoffs. AI is a high contributor.
As marketers, we get anxious hearing stories about AI replacing human jobs. Klarna, a Swedish financial services company, reported that AI has saved the company about $10 million in annual savings, partly through using AI tools to replace human efforts for graphic design, social media, translation, and production.5 Klarna’s CEO, Sebastian Siemiatkowski, said that “AI may help the company reduce headcount from 4000 to 2000 employees” by heavily using AI in marketing and customer service.6 Wow, that’s concerning.
Our goal in sharing AI’s potential impact on marketing jobs isn’t to cause panic, but to encourage awareness and preparation. This book will help you become an AI marketing strategist so you can level up in your career. To help you understand what the future of content marketing could look like and how you can better prepare, let’s review salaries, valuable skills, and freelance opportunities.
Content Marketing Salaries
Keep in mind that salaries vary depending on multiple factors, such as geographical location, years of experience, job title, and market conditions. You can use sites like LinkedIn or Indeed to research open job postings and see current salary ranges for various content marketing positions. We think knowing industry averages helps with personal benchmarking and salary negotiations; however, before asking for a higher salary in a job offer or promotion, we highly recommend doing your own research. That said, the Superpath salary report shows the average annual income of the content marketers surveyed by regional location (Figure 1.1) and by job title (Figure 1.2). The data in these figures gives you a starting point to determine the salary range you can expect.
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FIGURE 1.1 Income by location, “Content Marketing Salary Report,” by Superpath
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FIGURE 1.2 Income by job title, “Content Marketing Salary Report,” by Superpath
For our U.S. audience, we want to clarify that the average salary of $132K for content marketers seems a bit high. Superpath’s income data includes people who supplement their income with freelance work, and it also includes executive roles; that’s why we’ve included average annual income broken down by job title.
For our non-U.S. audience, be aware that 61 percent of the content marketers surveyed in Superpath’s report are based in the U.S., which means the average income by job title is more heavily influenced by U.S. salary data.
We think income by job skill is interesting too (Figure 1.3). Marketers have become more specialized over the years. Many companies are looking for specific disciplines or expertise.
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FIGURE 1.3 Income by skill, “Content Marketing Salary Report,” by Superpath
At a small company or startup, a content marketing generalist or individual contributor wears many hats. A generalist often writes and publishes content on the company’s blog, social media, email newsletter, and other marketing channels.
In larger companies, there can be a head of content marketing who directs the strategy and brand storytelling and collaborates with leaders of other teams, such as brand, creative, SEO, email, paid media, and analytics. In corporations with multiple brands, there may be a dedicated content marketing leader for each brand or group of brands. Content marketers, who may report to the content leader or a channel leader, are responsible for producing or publishing content for channels like social media or email. These marketers need to become fluent in AI tools that help them create or execute more of that content faster. Your value as a content marketer, however, isn’t solely in knowing how to use AI tools. AI demands a complementary set of skills from content marketers—regardless of your job title or channel focus.
Next-Level Skills
Certainly, understanding AI strategy in content marketing is essential. However, as you’ll learn in Chapter 7, “Marketing Operations and Automation,” AI falls short in some areas. Human oversight is necessary. Now with the technological advancements of AI, it’s time to level up your soft skills.
In “TheFuture of Jobs Report 2025,” the World Economic Forum lists the core skills employers consider essential for their workforce, and a majority of the top 10 emphasize the importance of critical thinking, communication, and adaptability.7 This makes sense in the age of AI, when humans should be using their cognitive abilities to complement and enhance the capabilities of AI. These soft skills become even more valuable as AI takes over routine tasks, highlighting the need for marketers to focus on areas that require complex decision-making and human insight.
Employers may offer courses or stipends for educational training. Reach out to your human resources or learning and development team to learn about your options. Some employers may provide reimbursement to learning platforms like Coursera, LinkedIn Learning, or Udemy. Or you can explore these on your own.
Unfortunately, most companies have not caught up on AI training for employees. According to the Marketing AI Institute’s “2025 State of AI Report,” 62 percent of marketing and business professionals surveyed said the lack of education and training is the top barrier to adopting AI in their marketing.
It’s up to us, as marketing employees or contractors, to educate ourselves. In addition to reading books, consider AI marketing certifications for a hands-on learning experience (we’ve listed several in Appendix A, “Tools and Resources,” and you’ll find a plethora of options online). Academic institutions are starting to offer AI marketing certifications to professionals and integrate AI courses into marketing degree curricula. We suggest searching for job listings for your specialty area (SEO, social media, email) or role function (copywriter, graphic designer, ecommerce manager) to see which AI tools employers mention most often. Some offer certifications specific to their tool, which can help you in a competitive job market.
We see another way for marketers to develop an AI education: freelance.
Freelance Opportunities
Whether a side gig or a stopover on the way to full-time work, try freelance work. It’s not a bad idea to have a side hustle that requires AI, especially if your current employer doesn’t have an AI policy that prohibits it. This is one way to get hands-on AI experience. We’ve spoken with several colleagues who are employed but aren’t allowed to use AI at work. They worry about having an AI skills gap if they are laid off or have to start looking for their next job. It’s a valid concern. Stay ahead of the curve by preparing now.
If you want to freelance, first review your employer agreement for any restrictions. Then check out the marketing services other content marketers offer in Superpath’s chart, which breaks down income by contractor and full-time employee (FTE) classifications (Figure 1.4).
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FIGURE 1.4 Income by responsibility, “Content Marketing Salary Report,” by Superpath
We’re happy to see writing as an opportunity for freelance work because we personally know talented writers who’ve been hit hard in this job market, particularly with the rise of AI writing tools, and writers are usually on the lower end of the content marketing pay scale. The same can be said for creative marketers such as graphic designers, photographers, and video producers, who aren’t included in Superpath’s chart.
The freelance market is competitive, as noted by Superpath CEO Jimmy Daly, who explains this market is typically top-heavy, meaning only a small group of individuals make a lot of money (seven out of 74 freelancers reported making more than $200,000 in the Superpath report). But at least freelancing can be a good way to supplement your income. We’re not recommending you quit your day job without savings, but freelancing can be a valuable way to earn extra income while developing your AI skills. It could even be a launchpad to becoming a solopreneur or starting your own marketing agency.
It’s a compelling time to freelance or launch a small business now that AI can assist with so many business tasks, giving solopreneurs the kind of support that used to require a team. We won’t dive into business opportunities in this book, but researching the current labor shortages in your area may be worth the time investment. For example, there’s a growing demand in many places for health, education, or home services like landscaping or cleaning. Or you could start a personal project that you’re excited about. You’ll benefit by using your AI content marketing skills in your own business or a fun hobby.
A Future-Proof Framework
The hype of the AI revolution has already passed. Many companies are actively integrating AI into their marketing operations. In the age of AI, marketers and brands need a future-proof framework to get ahead and stay ahead.
For Marketers
Certainly, the importance of having a firm grasp on AI tools relevant to your content marketing role goes without saying. You may be using AI content marketing platforms like Copy.ai, Jasper, or Writer; or conversational AI tools like ChatGPT, Claude, Copilot, Gemini, or Perplexity. Another possibility is that you’re using channel-specific tools like SEMrush or Ahrefs that have integrated AI capabilities into their SEO platforms.
We believe the core pillar to your success as an AI content marketer is strategy. Content creation is one thing. It’s quite another to understand the opportunities and liabilities of AI and how to best integrate it into your marketing process to maximize efficiency with minimal risks to your employer’s brand or business. As much as every brand must prioritize customer experience, marketers are the ones on the front lines, ensuring that content aligns with customer needs and delivers value. As you’ll read in Chapter 4, “Authenticity and Authority,” creativity plays a big role. It’s up to you to find ways to keep the brand’s voice and values in content instead of letting AI kill it.
A common complaint from content marketers is feeling undervalued. That’s likely because content marketing return on investment (ROI) is often tied to long-term results like brand awareness and reputation, so it’s hard to measure results in real time. Although later in the book we highlight key content marketing metrics and how AI is changing analytics, we encourage all content marketers to improve their data analytics knowledge. You don’t need to become certified, although that wouldn’t hurt. Content marketing leaders should collaborate with analytics leadership on key performance indicators (KPIs) for content marketing. Team members should ask leaders what those KPIs are, and how to align their work to them. Although job security is never guaranteed, demonstrating content marketing performance improves your chances of staying and thriving within the company. Understanding analytics is an important steppingstone toward a leadership role.
For Brands
Our top recommendation for corporate brands is to develop a company-wide AI strategy, along with department-specific strategies. Establishing an AI framework first will help your teams avoid costly mistakes later. This framework should include goals for AI development and use. It should also include AI policy (rules) and governance (operations). Chapter 6, “Bias, Ethics, and Legal Risks,” includes guidance for creating a policy and governance strategy. You should also consult with legal counsel experienced in advising on AI use in the workplace.
To support your teams in the effective integration and use of AI technology, we highly recommend investing in AI training for employees. Ask your human resources team to explore AI workplace training services that may be provided by the employee learning or team management platform your company uses. Be sure your training covers your AI policy, what should be included in an AI policy, and who should be involved in developing it. We will explain this in more detail in Chapter 6. We also strongly encourage you to update your employee security training to include AI-related issues and safety best practices.
Companies should not solely focus employee training on policy and security. Each team using AI should receive ongoing departmental training and participate in routine audits to identify effective use cases, surface questions or concerns, and identify potential risks as AI use evolves within the organization. Employee AI education is valuable in future-proofing your company against changing technology and regulations. It will also significantly boost productivity.
We stress the importance of human supervision of AI in content marketing and SEO throughout this book. Yes, you can automate many tasks to cut costs and gain efficiency. But keeping human oversight is crucial.
Finally, every company must prioritize the customer experience when using AI. For small businesses looking to build a brand, a unique voice stands out. For larger companies, irresponsible use of AI in content marketing can damage the brand, trigger SEO issues, and become an expensive legal liability.
Introduction to AI Definitions and Concepts
People can mean different things when they talk about AI. So before we jump into discussing how it can be applied to content marketing and SEO, we think it’s essential to define the terms you will use. Although many other sources have a comprehensive glossary of AI terms, we’ve included a few important definitions that we’ll refer to throughout this book. If you’d like to read a bigger glossary, check out the “AI Key Terminology” web page on the U.S. General Services Administration website.8
Artificial intelligence (AI) refers (at a high level) to machines that can simulate human intelligence by using feedback loops of data to “learn” with minimum human intervention. AI is generally used to process volumes of data that are beyond the scale of what a human can analyze. AI-powered tools can help content and SEO marketers with their work.
Algorithms in very simple terms are any processes or sets of rules used to solve a problem or complete a calculation. Although an algorithm may run repeatedly with new information each time, or multiple algorithms may run together, they are finite, meaning they will inevitably reach an endpoint.
Machine learning (ML) is a broad concept that refers to computer systems drawing inferences from data. Machine learning uses one or more algorithms and statistical models to analyze data. Machine learning without natural language processing (NLP) is algorithmic AI. Machine learning with NLP is generative AI (GenAI). GenAI is what most of this book is about, so we cover it and explain NLP in more detail in the “Generative AI Tools, Assistants, and Agents” section in this chapter.
Algorithmic artificial intelligence (AAI) refers to mathematical AI systems that go beyond fixed rules, essentially “talking” to other computers to analyze information and make decisions. AAI is used in scientific and medical applications, such as detecting early signs of breast cancer that may be missed by doctors by comparing a patient’s images against large training datasets of previously diagnosed medical images. Google has been using AAI since 20019 in various aspects of their services.
Generative artificial intelligence (GenAI) is what most of us now think about when we think about AI. It’s a type of deep machine learning that seeks to generate new data (text, images, or other content) based on a set of training data. Chat GPT (text) and DALL-E (images) are popular tools based on GenAI, which is what most marketers or SEOs use when they use AI to help them in their work. We cover this in more detail in the “Generative AI Tools, Assistants, and Agents” section.
Natural language processing (NLP) is a subfield of AI that uses machine learning, and in some cases deep learning, to communicate with users in natural human language. NLP is tricky to understand because it can appear that the AI is “learning.” What the AI is actually doing is taking in new information. This can happen through supervised or unsupervised “learning,” as we discuss in the following sections. Although NLP can be used in any AI implementation, it is most commonly used in GenAI.
Neural networks in humans are the interconnected nodes and neurons in the brain that allow the brain to absorb, assimilate, and process new information. Artificial neural networks are modeled after the human brain but (currently) lack the ability to apply ethics, morality, or other non-defined decision structures to decision-making. Artificial neural networks use multiple nodes in layers to accomplish complex calculations and arrive at a decision. More than three layers of nodes is classified as deep learning.
Deep learning (DL) is a form of machine learning that uses multiple neural networks to simulate the processes that occur in the human brain. The computer can assimilate new information but cannot “learn” the way a human can. Deep learning is what makes it possible for GenAI to produce relevant, high-quality content for SEOs and content marketers.
Large language models (LLMs) are a type of deep learning model that are pre-trained on very large sets of data. LLMs are used for NLP to create text or responses that seem like they were written by a human. ChatGPT is a popular GenAI tool based on LLM technology. If you’ve used it, you may know that the accuracy of its responses is limited based on the data it was trained on and when that training data was last updated.
Supervised learning happens when information is intentionally introduced to an AI system using labeled data, meaning that input data is paired with corresponding output data. Clear instructions are given to train the AI system. However, supervised learning can still be risky if the training dataset accidentally includes personally identifiable information (PII) or the input data is incomplete. This is of particular concern in the health, education, and government uses of AI, as we discuss in Chapter 11, “AI in Regulated Industries.”
Unsupervised learning occurs when the AI system is provided with information that is not clearly labeled with input–output pairs and the AI is expected to make its own conclusions about how to connect the information. This commonly occurs when LLMs are updated with more recent crawls of content. ChatGPT is an excellent example of an AI that “learns” unsupervised. This opens potential legal and ethical issues that we go over in Chapter 6.
Hallucinations occur when an AI delivers an inaccurate or incorrect conclusion. This happens because the data it’s working with is limited, incomplete, or incorrect. This can commonly occur with unsupervised learning. Hallucinations are a nightmare for marketers and brands if AI generates content that misleads consumers, makes harmful claims, or violates regulations. We’ll cover ways to reduce the impact of hallucinations throughout this book.
If you’re already overwhelmed with these glossary terms, that’s OK. The graphic in Figure 1.5 may help. Now that you have an overall understanding of several important concepts in AI, let’s talk in more detail about generative AI, which is particularly relevant for content marketing and SEO.
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FIGURE 1.5 How AI works, simplified for marketers
Generative AI Tools, Assistants, and Agents
The next evolution of AI is already happening as we write this book. AI agents are the new buzzword. To fully understand AI agents, we need to explain GenAI a little more.
Generative AI (GenAI) Tools
Generative AI is what makes AI tools accessible to the non-technical person. It takes computer language and “translates” it into human language using LLMs. GenAI tools create new content, such as text, images, audio, video, or code, based on an instruction (“prompt”) given to them and an expected output style (text, image, code, and so on). Examples of these that you may already use include ChatGPT, Claude, DALL-E, Sora, and Suno. Most of the information we cover in this book pertains to GenAI tools.
AI Assistants
AI assistants like Apple Siri or Google Assistant are popular tools that use GenAI. They also use personal data about you and your habits to help you complete tasks. You can ask your assistant to tell you your schedule or make a dinner reservation. Here’s an example assistant prompt:
“Hey Siri, make a reservation for dinner at 7 p.m. today at Corner Bistro.”
The AI assistant may use your OpenTable app to make the reservation.
AI Agents
AI agents are the next evolution in AI capabilities because they can use multiple GenAI tools together to perform a task or make a decision. The key difference between an assistant and an agent is that you must tell an assistant what you want. An agent can use other information to determine the best response.
In the dinner reservation example, you can use an AI agent to complete a more esoteric task.
“Hey agent, I want to go to dinner on Friday.”
The agent can use multiple tools or even other agents to autonomously make you a reservation at Corner Bistro and respond with something like
“I made you a reservation at Corner Bistro because it has great reviews and is near your office. The best available time was 7:15 p.m.”
The key is that if you ask an agent a question, it has more than just the defined ways it can respond. For example, if you ask the agent to define a complicated process, it may choose to respond with an image of a flowchart of the process. Agents can also “learn” to start tasks on their own, to work with other agents to complete multi-step processes, and to take proactive measures without being told. Table 1.1 shows a comparison of an AI assistant with an AI agent.
TABLE 1.1 AI Assistants vs. AI Agents
AI ASSISTANTS |
AI AGENTS |
|---|---|
Reactive |
Proactive |
Respond to a specific prompt |
Determine how to achieve a provided goal |
Complete simple tasks |
Make decisions, complete complex processes |
With a diverse range of business applications, it’s no wonder the market for AI agents is expected to skyrocket. The Boston Consulting Group reports that AI agents are becoming more prolific across technology applications, growing with a 45 percent compound annual growth rate over five years.11
You may be familiar with AI agents for social media bots like Sprout Social’s Smart Inbox or business process agents like NVIDIA’s Eureka or Microsoft’s Copilot Studio.
What’s truly remarkable is that tools like these can autonomously interact with users, make real-time decisions, and improve their responses to accomplish tasks or goals more effectively. However, because AI tools and agents rely on other datasets rather than on novel ideas, they can still make costly mistakes for a brand, which we’ll explore later in this book.