May 8 2023
Robert Kurtz

Artificial Intelligence and the Future of Search Engine Marketing


Here’s a question: Why even read an article about how artificial intelligence will change the landscape of search engine marketing (SEM) when you could just...ask an AI? For example:

Robert Kurtz: In 50 words or less, tell me the most important things about how artificial intelligence is set to change search engine marketing.

ChatGPT: AI will transform search engine marketing by enabling more sophisticated targeting and personalization, improving search relevance and user experience, automating ad creation and optimization, and enhancing data analysis and decision-making capabilities. It will also lead to the emergence of new search paradigms, such as voice and visual search.

Well, there you have it—my work here is done (kidding!).

In all seriousness, ChatGPT isn’t wrong: AI will have those impacts on SEM. What ChatGPT failed to mention, however, is that AI has already changed SEM in many of those ways. In fact, Google has used a machine learning tool called RankBrain to better understand the intent behind search queries since way back in 2015. So while the hype around  tools like ChatGPT and Bard can make it seem like artificial intelligence is a brand-new toy, it’s actually impacted the world of search engine marketing for a while now.

To help you understand how AI may shape the future of SEM and what marketers can do now to prepare for those changes, let’s first explore a couple of things you may have not even thought to ask about, but are nevertheless critical to understanding how SEM will evolve—namely, how AI currently plays into the world of search engine marketing. 

How Is AI Used in Search Engine Marketing?

We can categorize the ways artificial intelligence is used in the SEM ecosystem into three main buckets:

The first, analytical AI, has been around for the longest, and is already well integrated into all the main search engines as well as their ad offerings. Analytical AI can quickly process and generate insights from large amounts of data.

The second, which we’ll call “action-oriented AI,” describes AI processes that automatically take an action, often based on the insights generated by a paired analytical AI process.

Lastly, there’s generative AI, which describes AI tools that can generate text or images in response to a prompt—think ChatGPT, Bard, or DALL-E.

Let’s take a quick look at how advertisers are already using each of these three types of AI in their search engine marketing:

Analytical AI

Search engines have been utilizing analytical artificial intelligence to improve their products for years. Google’s RankBrain, for instance, analyzes historical search data to predict what someone is most likely to click on—even if their query is one that RankBrain has yet to encounter. In this way, search engine algorithms are already powered by artificial intelligence programs that save engineers time on manual processes like sifting through tons of data and pulling insights.

Search engine marketers are already using analytical AI as well—say, when leveraging ad offerings like Google’s Maximize conversions tool, which employs machine learning to analyze user behavior and determine which users are most likely to convert based on a given KPI. It’s just one of the many ways AI can analyze and pull insights from large amounts of data, with other examples including:

  • Identifying and eliminating fraudulent clicks
  • Predicting which ads are most likely to convert
  • Identifying trends and patterns to improve SEM targeting

Assessing all the variables that go into SERP ranking and providing recommendations on how to improve

Pretty neat, huh? Next, let’s take a look at how action-oriented AI can take the insights pulled by analytical AI and use them to carry out different campaign tasks.

Action-Oriented AI

Action-oriented AI functions are often paired with analytical AI functions to further automate aspects of the campaign process. Once the analytical AI functions have pulled insights based on data, these action-oriented functions can actually execute on those insights, further saving marketers' time.

Let’s return to Google’s Maximize conversions smart bidding strategy as an example. It identifies the specific users searching the keywords you’re targeting who are most likely to convert based on your KPIs, then automatically throttles the dollar amount you’re willing to bid based on that likelihood. In doing so, the Maximize conversions tool uses both analytical AI (to analyze users’ past behaviors and select users who are most likely to convert based on those behaviors) and action-oriented AI (to throttle money to bid on those users) to maximize advertisers’ dollars and save them time.

Google’s Performance Max campaign type is another good example of how analytical AI and action-based AI can work together to automate campaign processes for advertisers. Performance Max combines a variety of action-based AI functions by leveraging AI-generated analytical insights, including ad personalization and the optimization and automation of bids. It also helps advertisers running campaigns on Google Search discover (and target ads to) additional prospective customers who are likely to convert across complementary Google-owned channels like YouTube, display, search, Gmail, and Maps.

Generative AI

Now, on to the AI category of the moment: generative AI! The cutting-edge consumer-facing AI technology can generate media—such as copy, images, videos, and even music—in response to user prompts. Headline-grabbing tools like OpenAI’s ChatGPT and DALL-E 2, Google’s Bard, and Stability AI’s Stable Diffusion (among many, many others) have already won over millions of users and helped spark a flurry of interest in the tech. Of course, if you haven’t already been playing around with any of these tools, you may still be familiar with the concept of deepfakes, i.e. fake images or videos of real people that are created with generative AI (this fake Tom Cruise TikTok account is a good, if somewhat off-putting, example).

Search engine marketers can use generative AI in a variety of ways, and like analytical and action-oriented AI systems, it can help automate time-consuming tasks. Chatbots like ChatGPT can be leveraged to perform keyword discovery and research, write ad headlines that include target keywords and comply with character limits, or help generate SEO-optimized content—from meta descriptions, to headings, to entire blog post drafts. And, since this type of AI is also the newest to emerge, SEM marketers are still discovering all the ways they can use it in real time.

One thing that’s important to note with generative AI systems is that, unlike many analytical and action-oriented AI tools, they aren’t yet at the level of “set it and forget it”. For example, according to OpenAI, ChatGPT “has limited knowledge of world and events after 2021 and may also occasionally produce harmful instructions or biased content.” And there are additional weaknesses to consider: Sure, chatbots can create blog posts that are optimized for the keywords you want to prioritize, but they can’t match the quality or accuracy of human-produced content. Content that's valuable and enjoyable for humans to consume is marked by characteristics like unique POVs, expertise based on lived experience, and relatable humor. Generative AI isn’t able to create content with these qualities (or, at least, not yet).

How Will AI Change Search Engine Marketing?

Now that we’ve covered the three main types of AI and how they’re currently used in search engine marketing, I’m willing to bet you already have some ideas around how AI will shape the future of SEM. While it’s impossible to predict exactly how these forms of AI will change the search marketing landscape, here’s what seems likely:

In general, all three buckets of AI systems will continue to improve upon what they’re currently capable of. For example, analytical AI tools should eventually be able to analyze entire websites and generate SEO strategies to improve SERP rankings, which could then be completed by action-based AI tools. And generative AI is expected to get more accurate and better at mimicking human-generated content. At the same time, search engines themselves will evolve: It's entirely possible (if not outright likely) that, in a few years, chatbot-driven search engines like Microsoft’s new Bing could be the norm.

How Can Marketers Prepare for The Future Of AI-Driven Search Engine Marketing?

So, how can paid search marketers set themselves up for success in a future shaped by artificial intelligence? Here are some top tips:

Keep Learning and Stay Up to Date on Developments

Perhaps the most important thing search engine marketers can do is stay as informed as possible on the latest developments in AI. Read the trade pubs, subscribe to a newsletter or two, and discuss the topic with your coworkers and network.

In particular, educate yourself about the potential challenges and pitfalls posed by artificial intelligence. Generative AI in particular comes with several significant concerns. AI chatbot-generated hallucinations, for example, can create disinformation that can be difficult for content moderation tools to spot and consequently presents a threat to brand safety. In fact, Gartner has predicted that by 2027, 80% of marketers will have (and need) dedicated “content authenticity teams” to combat AI-generated misinformation. There are also many unanswered questions related to copyright infringement—from the legality of chatbots being trained on unlicensed content to who owns AI-generated media, with the US Copyright Office ruling in 2023 that works containing materials created by AI cannot be copyrighted.

That bring us to our next recommendation: Keep an eye on how the regulatory landscape develops. While we can’t predict how governments will legislate the use of these technologies, it’s all but certain that they will eventually take regulatory action, and staying abreast of those developments will be critical.

Finally, marketers should jump at any chance to test and learn about new AI technologies. Working with a partner who has a Google Premiere partnership will be particularly helpful here, as you’ll be able to get in on the ground floor with any new AI-driven search capabilities they develop.

Develop a Data-Driven Culture

Next, businesses should develop a data-driven culture. As AI technology develops, more and more data-related insights will become available, and businesses will need the infrastructure and the know-how to use those insights effectively.

Data-driven cultures value and use data to make decisions, investing time, energy, and money into employees, processes, and tools that help them to do so effectively. This could mean levelling up your processes relating to data quality and consolidation, performing an audit of all the different sources from which you collect data (social media, website analytics, customer surveys, etc.) and expanding upon that list, or investing in a CDP to better capitalize on first-party data. By investing in AI-powered tools, your data-facing teams will be able to generate new insights, improve accuracy, and automate tasks. Additionally, because they’ll be using AI technology in that area of the business, they’ll likely grow increasingly comfortable with these kinds of tools, which will make it easier for your organization to leverage new AI opportunities as they develop.

Businesses should also pay attention to AI-related skills when hiring new team members and upskilling current team members. For instance, you may want to hire more data scientists, or provide your employees free learning opportunities focused on using AI to improve SEM campaigns. Again, the more you invest in your team’s AI abilities now, the quicker they’ll be able to adapt as new technological developments come down the line.

Plan for Chatbot-Driven Search Engines

A common question among search engine marketers today is, “How will SEO evolve as search engines move to chatbot-driven models?” With Bing having already released a chatbot-based search offering and Google racing to catch up, it’s clear that the way people search—and the way marketers are able to connect with those searchers—is on the brink of changing. Whatever the outputs of these new search engines end up looking like, marketers must ensure they can capitalize on coinciding advertising opportunities.

Part of the work here will be keeping abreast of any new ad products that come to market and subsequently testing and learning those as much as possible. Another hot tip: Optimize for Google’s quick answers. My guess is that, if Google was a chatbot, it would likely pull content from the same places it’s pulling those answers, so from an early adopter standpoint, this is a great area for SEM marketers to focus on.

Wrapping Up: Artificial Intelligence And SEM

While there’s no predicting exactly how artificial intelligence will shape the future of SEM, there are a few things we know for sure:

Analytical AI, action-oriented AI, and generative AI will continue to develop and grow more sophisticated, impacting search engines themselves as well as the ways marketers can advertise via paid search.

Businesses can brace for these changes by staying up to date on AI news, developing data-driven cultures, and planning for chatbot-driven search engines.

In addition to investing in AI-driven tools and technologies to adapt to this wave of change, businesses also need employees with expertise on how to best leverage those tools and technologies effectively.

Keep all that in mind, and you’ll be well-prepared to meet the changes coming to paid search.

Advanced digital media platforms like Basis rely upon artificial intelligence to help streamline the entire campaign process. Basis Technologies’ media strategy and activation teams are well-acquainted with these cutting-edge AI tools and can help any business make the most of current AI opportunities in SEM and stay informed on the latest developments.

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