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Since OpenAI first introduced ChatGPT back in November of 2022, consumers and advertisers alike have wondered how AI-powered chatbots will change their lives.
In the months that followed, ChatGPT grew to 100 million monthly users faster than any other consumer application in history, Microsoft introduced its own GPT 4-powered app—Copilot—while also integrating it into Microsoft 365, and Google moved swiftly to roll out its own AI-powered chatbot, Gemini.
Amidst this frantic flurry of innovation, it’s growing increasingly likely that generative AI will soon revolutionize the search landscape. In fact, Gartner has predicted that by 2026, traditional search engine volume will drop by 25% as a result of chatbot-like applications.
But given the uncertainty around exactly how search will transform in the coming years, what can advertisers do now to prepare? Leaders can get ahead of the coming changes by adopting test and learn strategies that set their teams up for success and give them a competitive edge going into the AI-driven future.
Though generative AI seems to be everywhere just a year-plus after its public debut, the good news for search advertisers is that they will likely have a bit of time to prepare before any drastic changes to their media planning takes hold. Even as more consumers adopt tools like ChatGPT, Copilot, and Google’s generative AI-powered SGE over the next few years, they’ll mostly use those solutions for synthesizing complex information, while Google Search will remain the method of choice for simpler searches.
In the longer-term future, consumer adoption of generative AI-powered chatbots for search will increase, with competition increasing as more companies develop AI tools. We’ve seen this a bit already: After rolling out Copilot last year, Microsoft’s US search ad revenues were forecast to grow over three times faster than Google’s YoY, and the company’s share of the nonretail search market is projected to reach almost 9% by 2025. There are also a number of startups in the space, and if OpenAI or Apple create their own search engines—or partner with another company to create one—that could significantly disrupt market shares. With this increased competition, consumer use of AI for search will likely grow increasingly fragmented.
No matter what the competitive landscape looks like a few years down the line, we are likely to see lower volume across traditional search engines. Instead of going to Google Search, consumers will turn to chatbots or interact with voice-powered AI assistants like Amazon’s Alexa, Google Assistant, and Apple’s Siri in a way that is more search engine-like. As far as advertising opportunities, these AI-powered solutions will be backed by many of the same players who are currently in the search space, and those companies will undoubtedly integrate opportunities for marketing engagement within the tools.
The big takeaway? While we’ll eventually see a decline in the use of traditional search engines, we’ll likely also see a net positive engagement with generative AI-powered search engine-like queries.
This shift to chatbot-driven search may also lead to a meaningful decline in organic website traffic. One recent forecast estimated that Google’s Search Generative Experience (SGE) could reduce organic search traffic to publishers’ sites by anywhere from 20% to 60%.
Google has already steered users in that direction over the past five-to-10 years, with developments like its Quick Answers feature (which pulls content meant to answer a searcher’s question and displays it above other search results). Quick Answers can oftentimes eliminate the need for searchers to actually click into one of those search results—which makes sense, as users want to get their questions answered promptly and accurately, and Google wants to keep people in the Google ecosphere.
Generative AI-powered search engines and chatbots may increasingly have a similar ability to answer questions within their own environment, reducing the need to drive users to a website. For instance, consumers may be able to buy products directly through a chatbot without ever needing to go to the brand or retailer’s website (similar to how shoppable ad formats on Instagram and TikTok help fuel in-platform social commerce). Or, alternatively, you might be able to make a reservation via a chatbot without ever opening the OpenTable or Resy app (similar to how those apps’ APIs let you book a table directly from a restaurant’s website).
This shift bears striking similarities to how Facebook shifted its algorithm several years ago, giving preference to organic posts from brands that were also serving paid ads on the platform. Search engines will likely end up operating in a very similar way: Services like Google and Bing will still have organic listings, but they’re going to be trumped by these conversations that consumers are having with chatbots, and it’s going to be harder to get exposure in those conversations if you aren’t leveraging advertising opportunities in those spaces.
In our work with leaders at agencies and brands, we’re seeing an equal amount of fear and excitement around how AI will change not only the search landscape, but digital advertising as a whole. There’s a lot of trepidation around AI’s impact on brand safety, but it’s been complimented by a lot of underlying excitement around what these tools can empower advertisers to do—particularly the way they allow advertisers to swiftly and efficiently produce, edit, and switch out assets based on their needs.
While some leaders are actively preparing for the coming changes, we are also seeing a lot of folks who are very hesitant to adopt any generative AI solutions. In some respects, that hesitancy makes sense, given the many unknowns around how the landscape will develop. At the same time, to best set their companies up for future success, advertisers do need to lean into these AI-driven advertising opportunities now, at least to some degree. Marketing and advertising leaders who are more quick to learn and test new AI-powered tools will develop generative AI-adjacent skill sets earlier than their AI-averse peers, giving those early adopters a competitive edge in the years that follow.
How should advertising leaders guide their teams towards the future of search advertising? Here are our recommendations:
AI-powered targeting is probably the most developed AI feature that advertisers can test and learn on right now. This involves relying on Google or Meta or Bing’s AI technologies to target certain people who are potentially in market for your product. The tactic is becoming even more important in the context of signal loss, because it offers advertisers a privacy-friendly way to reach targeted audiences. These are opportunities brands and agencies should be exploring right now—you don’t want your team to still be using manual keywords and manual CPCs right up until the point where they’re no longer an option.
Then, of course, there’s generative AI, which is the shiny new toy of the moment. It’s all very exciting, but it can also be a bit scary for teams to adopt because it means relying heavily on a machine to create the right message for your audience. The consistency, quality, and accuracy offered by these GenAI features is definitely a worthy concern. It’s important to remember that artificial intelligence tools do have biases, because they’re programmed by people, who have biases, and they learn from gigantic pools of content and data, some of which is inevitably biased. We saw some of these fears play out recently, with Google suspending its Gemini chatbot’s ability to generate images of people after an online uproar when some Gemini-generated images were historically inaccurate.
Advertisers can and should test and learn on these tools, but it’s critical to put guardrails in place. By adopting a strong quality control system, advertisers can experiment with these tools and benefit from the efficiencies they offer while protecting themselves from brand safety risks and avoiding low-quality creative.
Google’s Performance Max (PMax) is, perhaps, the most exciting AI-driven advertising opportunity on the market today. Think of it as Google’s proof of concept that AI and machine learning can drive advertising results for conversion-focused campaigns across all assets and a variety of channels, combining them all into a single campaign type.
Within PMax, Google is slowly rolling out the ability to create ads using generative AI tools. For instance, an advertiser can upload a picture of their product and tell PMax to generate an image of that product on a beach at sunset. PMax will then spit out four variations of that basic image for use in an ensuing campaign. There are some enormous time- and cost-efficiency benefits to this: Advertisers can cut thousands of dollars that would typically be spent on production and go to market much more quickly. Advertisers can even download that asset and use it on other channels for greater creative continuity.
We don’t yet know what advertising opportunities will look like on chatbot-driven search engines, so at this point, it’s all speculation, but it’s important for advertisers to stay up to date on developments in this area so that they know when those opportunities come to market.
One other way advertisers can prepare is to optimize their content for Google’s Quick Answers. A Google-powered chatbot is likely to 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 prioritize.
Once of the greatest benefits that AI offers advertisers is its ability to quickly process and analyze huge sums of data. As the technology develops, more and more data-related insights will become more widely available, and businesses will need the infrastructure and the know-how to use those insights effectively.
Data-driven cultures value and use data to inform their decisions, investing time, energy, and money into employees, processes, and tools that help them to do so effectively. For leaders, this could mean levelling up a team’s processes relating to data quality and consolidation, having them perform an audit of all the different sources from which they collect data (ex. social media, website analytics, customer surveys, etc.) and expanding upon that list, or investing in a CDP to better capitalize on first-party data in a cookieless world.
By investing in AI-powered tools, data-facing teams will be able to generate new insights, improve accuracy, and automate tasks. And because they’ll be using AI for data-related tasks, teams will likely grow increasingly comfortable with these kinds of tools, which will make it easier for organizations to leverage additional AI-powered solutions as they emerge.
Advertising leaders should also pay attention to prospective employees’ AI-related skills when hiring new team members and prioritize upskilling current team members. This could be anything (and everything) from hiring more data scientists to providing employees with free learning opportunities focused on using AI to improve SEM campaigns. Again, the more leaders invest in their team’s AI abilities now, the quicker those teams will be able to adapt as new technological developments come down the line.
Lastly, leaders should aim to stay on top of news related to how search engines are changing, keep abreast of what new AI-driven advertising opportunities are available, and pay attention to what successes and failures their peers are having with artificial intelligence tools.
In particular, stay attuned to the potential challenges and pitfalls posed by artificial intelligence. Generative AI, most of all, comes with several significant risk factors. A hallucinating AI chatbot, for example, can make up fake “facts” and generate misinformation that can be difficult for content moderation tools to spot, and the resulting content can represent a threat to brand safety. (It’s also the reason 82% of US B2C marketing execs have expressed concern over marketing their brands during this year’s presidential election cycle). 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 AI-generated content and copyright infringement—from the legality of chatbots being trained on unlicensed content, to questions around who owns AI-generated media, with the US Copyright Office ruling in 2023 that works containing materials created by AI cannot be copyrighted. In the short term, be sure to 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.
It’s a lot to keep track of, but these are the things advertisers need to be doing now to ensure they’re at the forefront of the industry and to feel confident that they’re providing their clients and stakeholders with the best possible recommendations.
The quickly developing search landscape asks a lot of marketing and advertising leaders. Advertisers will need to get comfortable with being uncomfortable in the coming years as artificial intelligence moves us towards an uncertain future. Teams that test and learn early will have a leg up on those who are more hesitant to dive into the AI-driven future of search engine marketing.
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While we’re on the topic of getting comfortable with major shifts in the advertising landscape: Want to find out how your peers are adapting to signal loss and the cookieless future? We surveyed over 200 marketing professionals across agencies, brands, and publishers to find out how prepared they feel for the loss of third-party cookies, what alternative targeting and attribution solutions they’re adopting, and more. Read all about it in Identity vs. Privacy: Digital Advertising in a Cookieless World.