AI is changing the marketing landscape, becoming key to how marketers build campaigns. Generative AI is now being used across the board to boost creative development and help teams track performance signals better.
AI advertising continues to evolve from sponsored answers into systems that can use product catalogues, recommendations, and agent-led transactions. Industry publications, such as Digiday, are already tracking the development of ChatGPT advertising, while AI-advertising company, Gravity, is reportedly working with brands like Target and Best Buy.
The future of AI in marketing may lie in how deeply integrated AI becomes in workflows, decision-making, and roles. The key is understanding how AI is changing digital marketing and advertising, and answering what the future holds.
AI moves beyond content creation
Marketing teams can now use generative AI across the campaign lifecycle, from helping develop concepts to testing ideas across channels at scale. The technology is not just being used to write copy, it is helping marketers work alongside it during creative production.
With the time and cost saved using AI, marketers can therefore test more ideas and tailor campaigns to a wider range of contexts. For instance, Google Ads can generate a host of relevant headlines and descriptions using AI for its system to test, helping “deliver more relevant ads” and ultimately finding the best combination.
This raises an important question for marketers: when AI makes creative production substantially cheaper and faster, does the competitive advantage move from making content to understanding what ideas are worth producing and scaling?
As AI systems become more capable, and there is little doubt they will, the marketer’s role leans more about deciding what ideas are worth developing and less about producing each asset manually. Teams can have more time to share brand voice and creative directions, as well as ensuring any AI-generated work is unique, relevant, and aligned with their organisation’s goals.
AI ads vs paid search
Traditional paid search differs to AI ads in a few ways, with the former typically built around the search query. In a paid search, the user will enter a term before advertisers compete to appear. The resulting ad then aims to drive a click to the advertiser’s site.
Some AI ads tend to bring a more conversational layer to this process. For instance, according to OpenAI, a ChatGPT ad “considers multiple signals, including the context and intent of the current conversation, the ad’s landing page, title, copy, advertiser-provided context hints, and, when ads personalisation is enabled, select signals from a user’s broader ChatGPT experience.”
The distinction between the two ad forms is crucial for marketers. AI advertising can use more of the conversational context around a customer’s need when determining ad relevance.
AI assistants may take on a different role within the advertising funnel. Instead of matching an advert to a query, it can use a conversation to identify what ads are most relevant. Of course, this also creates new challenges for marketers as brands may need to think beyond keywords and consider a user’s questions, needs, and existing use cases, all of which can help lead to necessary recommendations and purchases.
CPC (Cost per Click) and attribution in AI advertising
CPC is already found in AI advertising, with OpenAI offering both CPM (Cost per Mille) and CPC buying for ChatGPT ads. However, what happens before the click may become the biggest challenge for marketers. For instance, AI assistants could influence a consumer by answering queries and comparing products before they even visit an advertiser’s website.
This results in more difficult attribution. Although a user could interact with an AI assistant several times, the marketers would only see a single referral click. In some cases, they would see no click at all if the transaction occurred in the AI platform.
This raises another question for marketers: should AI platforms get credit for the click they generate, or for helping influence a decision that led to a purchase?
Product-feed preparation
The quality of a brand’s product data will become significantly more important as AI assistants are increasingly integrated in product discovery. This includes accurate information on aspects such as pricing and specifications, thus equipping AI systems with improved insights for comparing or recommending products. In the marketing world, product feeds may become more than an e-commerce requirement, evolving into an integral part of AI visibility.
Amazon has already proven how product data can feed AI-led shopping with its shopping assistant, Alexa for Shopping, originally known as Rufus. Using Amazon’s product information alongside conversational context, Alexa For Shopping can answer questions about products, compare options, and make recommendations using that information.
According to Amazon, “Rufus is an expert shopping assistant trained on Amazon’s product catalogue and information from across the web to answer customer questions on shopping needs, products, and comparisons, make recommendations based on this context, and facilitate product discovery in the same Amazon shopping experience customers use regularly.”
Startup company, Gravity, is also reportedly working with Best Buy and Target to develop an agent-to-agent advertising system where AI agents exchange product recommendations using real-time catalogues from advertisers. Gravity co-founder Zach Oldham noted that the system “helps enrich the buying decision.”
AI-generated answers may introduce brand-safety risks
It is possible that an AI assistant can misrepresent a product or provide inaccurate information alongside an ad, creating new brand-safety challenges. To combat this, marketers should not only think about where their ads appear, but how their brands and products are described by AI.
A May 2026 study of 55,393 Google searches examined over 98,000 claims made in AI overviews. In all, 11% of the claims were unsupported by their cited sources. The study also found that “source quality and claim fidelity” were largely independent, suggesting that even credible sources may not always guarantee accurate AI-generated summaries.
If there are unsupported claims within AI-generated answers, brands face another concern: having less control over how their businesses are represented.
Agent-to-agent advertising – can it be audited?
As AI agents begin to interact with other agents on behalf of businesses, marketers may have limited visibility into how recommendations are made. This raises questions and concerns around transparency and accountability.
A 2026 paper, “Auditable Agents,” looked at distinguishing accountability from auditability. The research argues that “no agent system can be accountable without auditability,” identifying five requirements, those being, “action recoverability, lifecycle coverage, policy checkability, responsibility attribution, and evidence integrity.”
If AI agents make advertising decisions, marketers must understand why a certain product was recommended, what influenced this decision, and who is ultimately responsible if something goes wrong, such as inaccurate brand information.
Search advertising has allowed marketers to target what people are looking for through keywords and queries. AI assistants are set to take this further using conversations to understand what consumers need. From there, they can recommend the relevant products or services through tailored ads.
Understanding why products are recommended and what influences those decisions introduces another key issue. If AI is given more influence over advertising decisions and the customer journey, marketers will need more detailed oversight into how those systems operate.
AI governance is therefore becoming an essential business priority, not just a technical one. Organisations that have clever frameworks and oversight in place are better positioned to scale AI and manage its associated risks.
AI governance and enterprise adoption are just two key topics discussed across the DMWF community and at upcoming DMWF events, allowing marketers and business leaders to consider how AI advertising can be adopted responsibly.
Looking ahead, marketers may need to focus less on traditional clicks and more on earning a place in the AI-led buying journey.
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