AI marketing has evolved from a concept to a transformative force and is being adopted at speed by forward-thinking marketers to help them with everything from lead scoring to personalization to content creation.
“Artificial intelligence (AI) and machine learning (ML) continue to push the boundaries of what is possible in marketing and sales,” notes a report by McKinsey, adding that businesses already investing in AI are seeing “a revenue uplift of 3 to 15 percent, and a sales ROI uplift of 10 to 20 percent”. It’s clear that businesses that don’t focus on incorporating AI into their marketing strategies will be quickly outpaced by competitors.
While some marketers might have concerns that AI will replace their roles, the reality is that—in the near future at least—AI could actually make them better at their jobs. “AI itself is not going to put anyone out of work. I think marketers who know how to use AI are going to put other marketers who don't know how to use it out of work,” says Amanda Laviana, Senior Marketing Manager - Brand & Content at Plenti. “It’s going to be a matter of figuring out how to use the tools to your advantage and use it to automate the parts of your job that can be automated, that don't need that human touch, and use your energy for the creative parts, the human parts of your job.”
In this comprehensive guide to AI marketing, we’ll dig into what exactly AI marketing is and why marketers should be using it; use cases for AI in your marketing strategy; the pitfalls of using AI and how to overcome them; real-life examples of how marketers are already using AI in their day-to-day; and how to start incorporating AI into your marketing strategy today.
AI marketing is simply the use of artificial intelligence and machine learning in marketing. Marketers are using AI for content creation, analyzing data, predicting their audience’s behavior, personalizing marketing campaigns, generating chatbot responses, lead scoring, and much more.
The two types of AI most commonly used in marketing are task automation, which the Harvard Business Review defines as “applications which perform repetitive, structured tasks that require relatively low levels of intelligence. They’re designed to follow a set of rules or execute a predetermined sequence of operations based on a given input, but they can’t handle complex problems such as nuanced customer requests.”
The second type of AI that’s increasingly being used by marketers is machine learning applications such as large language models (LLMs) which the Harvard Business Review defines as “algorithms are trained using large quantities of data to make relatively complex predictions and decisions. Such models can recognize images, decipher text, segment customers, and anticipate how customers will respond to various initiatives, such as promotions.” GPT-4, which powers ChatGPT, is perhaps the most well-known example.
Many marketers are already using AI to handle what the publication refers to as “narrow tasks”, including “digital ad placement (also known as “programmatic buying”); assist with broad tasks, like enhancing the accuracy of predictions (think sales forecasts); and augment human efforts in structured tasks, such as customer service.” Popular tools like Grammarly are also AI-powered, though marketers might not think of them this way.
Why should marketers use AI?
While AI has the potential to supercharge almost any industry, marketers might just have the most to gain, argues the Harvard Business Review. “Marketing’s core activities are understanding customer needs, matching them to products and services, and persuading people to buy—capabilities that AI can dramatically enhance,” noting that an analysis of more than 400 use cases for AI by McKinsey found marketing was the area where AI could contribute the greatest value.
More recently, Hubspot’s State of AI report found that 68% of marketing leaders at the director level and above believe that if AI and automation were fully implemented in their company, their businesses would see unprecedented growth. Hubspot also found that, of the business leaders who said their companies were already investing in AI and automation tools, 71% reported positive ROI, and 72% said AI and automation made their employees more productive.
How to lay the foundations for an AI marketing strategy
As with any transformative technology, it’s important to think strategically about how you will incorporate AI into your marketing in order to reap the biggest benefits. “Crafting effective strategies begins with understanding that AI isn't a magic wand – it's a tool that amplifies your efforts that's why it is crucial to define your objectives clearly,” says Debbie Chew, an SEO manager for Dialpad, an AI-powered cloud communication platform. “Whether it's enhancing customer segmentation or optimizing ad campaigns, align AI's capabilities with your goals.”
1. Identify your needs and goals
Start by identifying specific challenges that AI can help your marketing team overcome, whether it's refining lead scoring accuracy or crafting personalized content. This initial step allows you to target areas where AI's capabilities align with your objectives and broader business goals, be it elevating revenue, enhancing customer experiences, or streamlining operational efficiency.
Marketers should be able to answer three questions before implementing AI in their strategy, says Michael Oliver, Director of Marketing at MBK: “You need to be able to say, ‘Okay, we have this device that will allow us to do x y z—here's how we will use it, here's the difference using it will make, and here's how we're going to measure that difference’,” he says.
Chew suggests writing a roadmap that starts with a small AI integration and gradually scales. “Regularly evaluate results and fine-tune your strategies. AI isn't static – it evolves alongside your business. So, embrace continuous learning. Overall, AI can revolutionize marketing, but success hinges on strategic implementation and adaptability.”
Set goals for the short, medium, and long term. “Leaders can start thinking strategically about how to invest in AI commercial excellence for the long term,” advises McKinsey. “It will be important to identify which use cases are table stakes, and which can help you differentiate your position in the market. Then prioritize based on impact and feasibility.”
2. Organize your data
The successful integration of AI into your marketing strategy begins and ends with data. “When deploying AI, data quality is paramount. Garbage in, garbage out – so ensure your data is clean, diverse, and ample,” says Chew. Marketers should also be constantly on the lookout for new sources of data, notes the Harvard Business Review, “since most AI applications, particularly machine learning, require vast amounts of high-quality data.”
Begin by analyzing your data sources and technology infrastructure to identify how AI can leverage your data effectively. When it comes to unlocking AI's real potential in a world of channel-agnostic customer journeys, APIs are essential. APIs open the gates between software systems, apps, channels, and platforms. With these "gates" open, online channels and data sets become a nervous system for AI to crawl through — analyzing and collecting information from every customer. This neural network of apps is key to consolidating data across every available data silo. It's something that humans could never manage alone — there's just too much information to process.
When AI can seamlessly access data from API-connected channels, it can organize it, streamline it, and present it in a way that reduces the cognitive load on marketing teams. The result? The management of high-volume, multi-channel messaging at scale is not only humanly possible, it's better than ever before. The best framework to facilitate this is a customer data platform (CDP) that can unify data, segment audiences, measure success, and automate marketing across the entire customer journey. This means that, instead of focusing on managing enormous datasets, human marketers can shift their focus to data-driven marketing decisions.
3. Select the right tools for the job
Familiarize yourself with key AI technologies, such as machine learning and natural language processing, to understand their potential applications and match these technologies with your specific objectives and tasks, for example, use machine learning for customer segmentation and natural language processing for content generation. “One way of thinking about it is, what are the parts of your job that are maybe a little bit mundane? The ones we don't like doing every day,” suggests Charlie Windschill, Director of Growth Marketing at Ortto. “I personally hate that first blank page moment of trying to write an outline or research a topic, so how can I use AI to help me jump over that first hurdle?”
Don’t add new tools into your processes just for the sake of it either. “I feel really strongly that you shouldn’t add anything to your tech stack that doesn’t need to be there,” says Jessica Kuipers, Marketing Manager at My Wealth Solutions. "Each tool has the possibility of adding to a really powerful and effective tech ecosystem, or not. Many tools cross over between business operations and customer-facing, which affect each other more than we sometimes realize. Tools that aren’t directly customer-facing might impact how the team understands the customer’s needs or the speed they can respond to an inquiry – even indirectly, it’s still affecting the customer.”
By aligning AI technologies with your goals and considering the impact of any new technology on the customer experience, you’ll ensure that your chosen tools are optimized for the tasks at hand, setting the stage for streamlined and efficient integration of AI into your marketing strategy.
4. Upskill your team
Empowering your marketing team with the necessary skills to embrace AI is vital to maximize its potential. By providing comprehensive training and upskilling opportunities, you equip your team to confidently navigate the AI landscape and harness its capabilities to their fullest extent.
“As a CEO, one of the things that I'm finding is that you have to encourage your team and empower them to feel like they can still contribute and be creative without the fear of being replaced by AI. You need to show them that they can use AI as a tool to be more effective,” says Alexandra Senter, CEO of the Big Smoke agency. “I think we should look at it as an opportunity to be better as opposed to something we're scared of because being fearful of it is going to slow down the ability to ramp up as an organization and use it.”
Amanda Laviana suggests “introducing the full team to the benefits that come with using AI, making it a team effort, and sharing those processes so that everyone feels equal in their understanding of what the tools are, how they work, and how they can actually help them with their job. That's a big one, because I think as soon as people understand how much easier these tools can make their jobs, most people are going to be so eager to adopt them.”
Enhance your marketing with AI: Real-world examples and best practices
5 ways to make AI a part of your marketing strategy
With 70% of leaders saying they are already using AI in their strategies, with a further 19% testing it, according to BCG, the time to make AI a part of your marketing strategy is now. BCG found the greatest area of focus so far is personalization, with roughly two-thirds of respondents pursuing efforts there, but other applications include predictive analytics, content creation and SEO, customer support, and testing and optimization.
AI's predictive analytics capabilities enable marketers to forecast future trends or behavior based on historical data. This is particularly valuable in lead scoring, where AI algorithms evaluate the likelihood of a lead becoming a paying customer. By considering factors like website behavior, demographics, and engagement patterns, AI helps prioritize leads with the highest conversion potential, ensuring sales teams focus their efforts on the most promising prospects.
“AI’s advanced algorithms can leverage patterns in customer and market data to segment and target relevant audiences. With these capabilities, businesses can efficiently analyze and identify high-quality leads, leading to more effective, tailored lead-activation campaigns,” notes McKinsey. “AI can also offer sales leadership with real-time next-step recommendations and continuous churn modeling based on usage trends and customer behavior. Additionally, dynamic customer-journey mapping can be utilized to identify critical touchpoints and drive customer engagement.”
Predictive analytics can also be used to spot opportunities humans can’t. “I think one of the questions marketers should be asking is, ‘How can I spot the next opportunity to make product enhancements or build campaigns and target particular users using AI?’” says Charlie Windschill. “I do think that machine learning and algorithms are surfacing opportunities in data."
AI's advanced algorithms analyze user data to understand preferences, behaviors, and purchase history and create highly personalized experiences across various touchpoints, from email campaigns to website interactions, that would be impossible to achieve without AI.
A few examples of AI-driven personalization include the Starbucks app, which uses AI to analyze purchase history, location data, and user preferences to offer personalized rewards and discounts; Spotify and Netflix’s curated content, which use AI algorithms to analyze users' listening habits, preferences, and even contextual information like time of day and location to provide personalized recommendations; and Salesforce, whose AI tool, Einstein, is used to analyze email conversations and provide real-time suggestions on the best next steps in the sales process.
“AI coupled with company-specific data and context has enabled consumer insights at the most granular level,” reports McKinsey, noting that “winning B2B companies go beyond account-based marketing and disproportionately use hyper-personalization in their outreach.”
3. Content creation and SEO
Content creation and SEO is perhaps the most popular use case for AI in marketing right now, thanks to the accessibility of tools like ChatGPT. Marketers can streamline their content generation process, creating high-quality and relevant material at an unprecedented speed. AI algorithms analyze vast amounts of data to identify trends, consumer preferences, and keywords, enabling marketers to tailor their content to the exact needs of their target audience. This not only enhances user engagement but also boosts SEO rankings by incorporating the right keywords and optimizing content structure.
AI also assists in automating routine tasks such as meta-tagging, content distribution, and performance tracking, allowing marketers to focus on strategic planning and creativity. As AI continues to evolve, its capacity to refine content strategies and maximize SEO outcomes is expected to become an indispensable asset for marketers aiming to stay competitive in the digital landscape.
“Content creators can use these tools to create drafts, explore ideas, seek unusual combinations, and find other ways to inspire their teams’ creativity, rather than replacing or constraining it,” adds BCG.
4. Customer support
AI-driven chatbots have revolutionized customer support by providing instant responses and assistance around the clock. These bots use natural language processing to understand customer queries and deliver relevant solutions. For routine inquiries, chatbots can provide quick answers, freeing up human agents to focus on more complex issues. This not only improves customer satisfaction but also reduces response times and enhances brand reputation.
“AI-enabled bots [...] can help marketers understand customers’ needs, increase their engagement in a search, nudge them in a desired direction (say, to a specific web page), and if needed, connect them to a human sales agent by chat, phone, video, or even “co-browsing”—allowing an agent to help the customer navigate a shared screen,” reports Harvard Business Review.
“AI-enabled service agents [...] are available 24/7 to triage customers’ requests—and are able to deal with fluctuating volumes of service requests better than human agents are. They can handle simple queries about, say, delivery time or scheduling an appointment and can escalate more complex issues to a human agent. In some cases, AI assists human reps by analyzing customers’ tone and suggesting differential responses, coaching agents about how best to satisfy customers’ needs, or suggesting intervention by a supervisor.”
5. Testing and optimization
Continuous testing and optimization is the cornerstone of any modern marketing strategy - and AI can make this process easier than ever. “I fundamentally believe AI will disrupt traditional A/B testing and many other tools that marketers use today,” says Michael Sharkey, co-founder and CEO of Ortto. He built an AI-powered subject line tester, training a neural net on every subject line Ortto had ever sent via its platform.
“We taught the AI what subject lines led to a high open rate, and which subject lines led to a low open rate. We weighed our results based on the audience, the engagement of that audience, and a number of other factors such as from name and email address,” says Sharkey. “Using the subject line AI, we could give the user an accurate open rate for the subject line they input, and suggest alternatives that may lead to a higher open rate. When these subject lines were adopted, the result would only deviate plus or minus a couple of basis points from the prediction. This accuracy meant we could almost guarantee a dramatic open rate increase for all emails sent using the subject line AI.”
This would have been impossible without AI, notes Sharkey. “AI makes what once seemed impossible, possible. And it’s these practical use cases that can lead to meaningful results that excite me about the future of AI and its contribution to the industry as a whole."
Amanda Laviana calls ChatGPT “the smartest, most well-equipped brainstorm partner ever”. While Amanda notes that ChatGPT “never provides the end result”, it certainly gives me ideas, so when I have something I need to write I do my own research first and get a rough sense of how that's going to work. Then I would take that to ChatGPT and ask it to spit out, say, five hundred words. I’d of course fact check that against what I found myself, then pull pieces out of their article - for example, if it’s communicated something more simply than I've been able to, or it’s given me an angle about the article that I haven't gotten into. It really uncovers a bit of the mystery about the thing that I'm writing about without me even thinking about it."
Laviana notes that "it’s part of the beast of our jobs to be repetitive, but ChatGPT can help us find ways to be original in ways that, before, required us to spend so much time racking our brains or just going with something generic. So I love that ChatGPT gives us back a bit of time to focus on the creativity that we have to sacrifice through the mundane tasks of our day-to-day. It can give us context, give us a bunch of ideas, and then we'll whittle that down and make something great out of it.”
For competitor research
ChatGPT offers an easy shortcut for marketers who want to compare their messaging to their competitors—and make sure they’re communicating their unique value proposition in a way that’s actually going to make them stand out from the crowd, says Sophia Firth, Growth Marketing Manager at Rungway. “Because ChatGPT pulls information from around the web, you can use it to test whether or not you’re repeating messaging that’s already being used somewhere else.”
There are two approaches you can use to test the uniqueness of your messaging. The first is simply by asking how your company compares to some of your competitors using a tool like code interpreter. “Another way you can do this is to put in your top-level positioning and ask it to come with the rest. See what it comes back with—if it’s the same as what your messaging is, then you know that it’s probably already been done somewhere else.”
For gap analysis and SEO
“ChatGPT 4 with Code Interpreter is like having a $5,000 a month data analyst at your disposal for just about anything,” says Andrew Boyd, MD at Finty.com.
Co-founder of Finty, who has been using it to make data-driven decisions about what content the business should prioritize. Andrew says a typical workflow involves exporting “as much data as possible from Ahrefs, Google Search Console, and advertising platforms”, which he then uploads to a new ChatGPT-4 chat with Code Interpreter enabled.
“Then I'll typically ask if it understands what the data is about (and it is surprisingly really good at this),” says Andrew. “One of the best things it can do is gap analysis. So I'll ask it to query the data to find the best-performing pages, then make suggestions based on its own knowledge as to what we should build next. I can feed it in more data, for example, Ahrefs report for a competitor, then ask it to suggest what to build to close the content gap with prioritization.”
Challenges of AI and how to overcome them
“While the business case for artificial intelligence is compelling, the rate of change in AI technology is astonishingly fast—and not without risk,” warns McKinsey. “When commercial leaders were asked about the greatest barriers limiting their organization’s adoption of AI technologies, internal and external risk were at the top of the list.” Data and privacy concerns, bias and fairness, hallucinations and accuracy, and the temptation to rely on AI for everything are among the most well-recognized risks.
“The balance lies in incentivizing experimentation with GenAI while mitigating the numerous risks,” notes BCG. “Using AI responsibly means developing and operating AI systems that align with organizational values and widely accepted ethical standards while also achieving transformative business impact.”
Data and privacy concerns
“Companies must keep customers’ interests top of mind. The smarter and more integrated AI applications are, the more worries customers may have about privacy, security, and data ownership,” advises Harvard Business Review. The onus will be on companies as they expand their use of AI to “ensure privacy and security controls are transparent, that customers have some say over how their data is collected and used, and that they get fair value from the firm in exchange.”
There could be significant ramifications for businesses that put their customers’ data at risk. “The way businesses exploit their data is under increasing scrutiny from consumers and regulatory entities,” says John Pennypacker, VP of Sales & Marketing at Deep Cognition. “While implementing an AI marketing plan, digital marketing teams must ensure they are using consumer data ethically and by regulations like GDPR, or they will face harsh fines and reputational damage.”
Marketing leaders should also make sure they have appropriate guidelines in place for employees using public chatbots, advises BCG. “All information typed into generative AI tools will be stored and used to continue training the model; even Microsoft, which has made significant investments in generative AI, has warned its employees not to share sensitive data with ChatGPT.”
Bias and fairness
AI models can inherit biases present in the data they're trained on, which can lead to discriminatory outcomes or misrepresentation of certain groups. Ensuring fairness and reducing bias in AI algorithms is a significant challenge and one that marketers must be aware of when they’re using these tools.
“One of the things that we noticed when using AI to produce images, is that sometimes they might not be very diverse, so you need to make sure your prompts from an image perspective - and from a content perspective - take that into account,” says Alexandra Senter. “ We talk as marketers about living and dying by the brief you give your team, right? I think that's doubly true with AI.”
Hallucinations and accuracy
Content creators need to stay alert for AI “hallucinations” in LLMs like ChatGPT, where it misrepresents (or completely makes up) facts or quotes. “Human marketers can certainly use AI-generated content as a starting point, but any and all content produced by AI needs to be fact-checked, refined, and personalized, otherwise the marketer runs the risk of publishing material that's costly because it's off-brand, communicates to a too-generalized audience, inaccurate, or simply ineffective,” says Joey Hall, Director of Operations, EVGMedia.
Amanda Laviana shares an example of a hallucination: “One example is a piece we were writing about the incentives you can get in each of the Australian states and territories for driving an electric vehicle. We already know this information, because EV loans are a major product offering for us” she says.
“If we hadn't known this ahead of time, we may have actually believed it, but we just asked ChatGPT to give us a synopsis of the incentives and it just completely made up this information that was wildly incorrect - it made up amounts of incentive amounts and certain tax benefits that can make driving EVs more affordable. But it sounded so convincing and it would be really easy to believe if we didn’t already know that it was wrong. As a fintech, we actually might be on the line from like a regulator for putting out false information, so it's just a little bit higher stakes in that way.”
Overreliance on AI
While AI can enhance efficiency, it can lack the creative spark and personal touch that’s essential to creating content that stands out and building a genuine connection with its customers. Marketers must make sure that they strike the right balance between automation and personal interaction.
“The problem is, on one side, it's easy to produce content using AI. But at the same time, it takes away a lot of the creativity required. I think it's that juxtaposition between effective content creation, but it still has to be nuanced, have that humor, and have that personality,” says Alexandra Senter.
“When you’re seeing examples of influencers on Instagram, say, using AI to generate the captions for their posts: you can tell how different it is when they use AI to promote a product compared to their other, original content that’s a lot more real, more likely to appeal to their audience. So I think we're going to see more and more people really focus on showing a more genuine part of themselves when they're writing and writers who thought for a long time they might become irrelevant are actually realizing they might become more relevant.”
Entrepreneur Matt Little agrees. “AI is meant to augment human competencies, not substitute be a substitute for them,” he says. “Keeping the human touch in marketing communications is key to building authentic customer relationships.”
The future of AI marketing
With the pace of AI development, it’s difficult to predict how it will impact marketing in a few months, let alone a few years. However, marketers are preparing for AI to have a significant impact in some areas, including search, emotion, and sentiment analysis, and the use of augmented and virtual reality.
Search and SEO
There’s no question that AI is set to dramatically change the search landscape in the coming years. “While it's still too early to predict the impacts definitively, we anticipate that [AI] may influence SEO traffic in a similar way to other 'zero-click' elements. By occupying prime real estate on the search result page, SGE could satisfy user queries directly on the search results page, potentially reducing the need for users to click through to a webpage,” notes the Marketing AI Institute.”
This could result in a decrease in top-level site traffic. However, it's likely that the traffic that does reach a site will be more targeted, potentially increasing conversion rates.”
Dave Davies, Amplification Team Lead at Weights and Biases, thinks AI could see search could move beyond web pages into new environments altogether. “In the next five years, I think we’re just going to see a move into two different sorts of landscapes. I think search will diminish a little bit and move into, I don’t want to say Metaverse, but into different environments that don’t exist right now.
Emotion and sentiment analysis
Wish you knew what your customers were thinking? “Companies spend huge amounts of time and money in efforts to get to know their customers better. But despite this hefty investment, most firms are not very good at listening to customers,” reports Harvard Business Review. “It’s not for lack of trying, though — the tools they’re using and what they’re trying to measure may just not be up to the task. Our research shows that the two most widely used measures, customer satisfaction (CSAT) and Net Promoter Scores (NPS), fail to tell companies what customers really think and feel, and can even mask serious problems.”
Sentiment analysis, however, makes understanding how your customers really feel, possible. “Sentiment analysis, also known as opinion mining or emotion artificial intelligence, is a natural language processing (NLP) technique that determines whether a piece of content is positive, negative, or neutral,” explains Microsoft. “By analyzing text and statistics, a sentiment analysis tool can understand what customers are saying, how they’re saying it, and what they really mean—both from an individual’s and the public’s perspective.”
Augmented reality (AR) and virtual reality (VR)
While the adoption of the Metaverse might have stalled, augmented reality (AR) and virtual reality (VR) are being adopted at pace by businesses everywhere. What’s the difference between the two? “Virtual reality (VR) is any software that immerses users in a three-dimensional interactive virtual environment, usually using a VR sensory device that brings real-world actions into a virtual world. Many VR experiences are 360 degrees.It’s a computer-generated simulation, and each virtual reality world allows people to fully participate in the unique world,” explains Hubspot. Virtual conferences and trade shows are examples of VR that have been increasingly taken up by businesses since Covid-19.
“Augmented reality (AR) layers virtual elements on top of a real-world scene, allowing users to exist in the space they’re physically in but benefit from the augmented elements in their experience,” continues Hubspot. An example of AR is Ikea’s feature that allows customers to view their products in their own homes.
Artificial intelligence is here to stay and it’s only become a bigger and more integral part of marketing strategies as it continues to evolve. Not too long ago, handing your customer service over to a robot was unheard of, but now it’s commonplace, and many of the biggest companies in the world embrace chatbots and voice assistants for their customer service.
It’s clear now that businesses that don’t leverage AI in their marketing will soon find themselves left behind those who are using it to transform the way they create content, personalize their messaging, optimize their marketing campaigns, and even predict their customers' behavior.
“We believe that AI will ultimately transform marketing,” writes Harvard Business Review. "The marketing function and the organizations that support it, IT in particular, will need to pay long-term attention to building AI capabilities and addressing any potential risks. We urge marketers to start developing a strategy today to take advantage of AI’s current functionality and its likely future.”
Andrea Warmington is a content strategist and writer, who has been working in content for 10+ years. She started her career as a journalist before moving into the world of content strategy, for both B2B and B2C businesses. She has a lifelong love of storytelling and believes in taking a journalistic approach to all of the content she creates. In recent years, she's developed a real passion for leading transformative content projects that establish tech businesses as thought leaders and reputable publications in their own right.