CES Briefing: Agentic AI era heralds SEO overhaul, Q&A with Mastercard’s Raja Rajamannar & Dotdash Meredith’s OpenAI ad assist

Expect to hear a lot about search engine optimization in 2025. Except it won’t be called that.

“It’s no longer about search engine optimization. It’s about answer engines,” said Digitas CEO Amy Lanzi.

Instead of sorting out how to show up highly in Google’s search rankings, brands are going to need to determine how to appeal to the AI agents that are expected to handle tasks for people like booking travel itineraries. “Brands need to be the answer to the questions you’re asking in Gemini or whatever [other generative AI tool],” said Lanzi.

This notion of answer engine optimization — or agentic AI optimization, or whatever it ends up being called — has been a major topic of discussion among agency executives during this year’s Consumer Electronics Show.

“The reality of AI agents gathering information and bringing it back [to people], that is search in 2025 and beyond. Making sure you are there from an SEO perspective is absolutely vital,” said Kelly Metz, chief investment officer at OMD USA.

How marketers make sure their brands are picked when someone asks an AI agent to plan their summer vacation or handle their Christmas shopping goes way beyond traditional SEO tactics, though. 

“Search has been the answer to discoverability. Now it will be an ingredient and a different paradigm,” said Jeff Geheb, global chief experience officer and global lead for enterprise solutions at VML.

SEO tactics historically have centered around tying a brand to specific keywords by seeding content around the web that makes that connection so that Google’s search engine learns to make that association when someone types one of those keywords into a search query. Keyword-based tactics aren’t going to cut it when the large language models powering AI agents can beyond recognizing keywords to understand the context and concepts underlying language to judge for themselves what is the best [insert product type] rather than rely on publishers’ articles listing the best [insert product type].

“The reality of the AI agents is they’re going to your website to bring information to users. Navigating that is challenging for brands. It’s questions like, ‘Do I want them to go to my site? How do I leverage partnerships in media to get more out of this experience to ensure I’m showing up in the right ways with the agents,’” said Metz.

“It’s not about ‘store near me.’ It’s about ‘perfect place to make me beautiful’ because I want to win ‘best brand to buy makeup from,’” said Lanzi. “It’s a whole different way to think about winning in search. That’s why Reddit is interesting.”

With Reddit’s deal with Google to make the platform’s user data available to the latter’s LLM, what people say about brands on Reddit — in the colorful language that people use on the platform — can color AI agents’ assessments as much as, if not more than, a brand’s own content pages as well as any publisher articles. 

But Reddit is just one example of the broader challenge. As LLMs ingest content across the web and beyond, marketers will have a harder time trying to curate, let alone control, what information about their brands and products is exposed to LLMs.

At the same time, brands are likely to develop their own AI agents, which may end up interacting with the AI agents used by people and being centerpieces to whatever this new SEO is called.

“The moniker we’re moving into is M-to-M: machine to machine,” said Brian Yamada, chief innovation officer at VML. “In this next era, which will start to get scale, we’ll have our agents talk to consumer agents. Then brands will have to think about brand APIs, what datasets to make available. Agents are going to decide what the experience layer is.”

That experience layer being, well, reality.

3 Questions with Mastercard’s Raja Rajamannar

How brands pay agencies for their work seems set to change as AI tools undermine the billable hours model. What exactly the new agency compensation model(s) will be is anyone’s guess. But some guesses carry more weight than others. Like those coming from CMOs. So Digiday sat down with Mastercard’s chief marketing and communications officer Raja Rajamannar during CES to get his take on the matter.

The transcript has been edited for length and clarity.

What are your thoughts on how the agency compensation model needs to change?

I’ll give you a small anecdote, and then I’ll tell you why I’m saying what I’m saying, At Mastercard, we keep receiving RFPs from our clients, our prospects. In the past, it used to take about seven weeks from the time of receiving the RFP to write the first draft response. Seven weeks. Today it takes less than one day, including human oversight. There is no increase in manpower; it’s the brilliance of AI that is making this process ridiculously efficient without sacrificing quality.

So one of the things I am challenging my own team is saying that, if there are efficiencies in our ecosystem, whether in our own team or with our partners, which is agencies, we need to challenge the existing model. There is a significant efficiency opportunity available. And I think agencies have to reinvent the model.

How would you like to see that change? Because the whole idea of billable hours is completely different in a world where AI tools cut down the time it takes to complete client projects.

If I were to go to billable hours as the way, it’s going to be brutal for the agencies, and we should not drive agencies to extinction. There has to be a different model.

It could be a project model. For this project, to get the output I will give you so much. Or it could be a combination of [the agency] will dedicate three [full-time employees] to [the brand] and those three FTEs [the brand] will pay completely, and here is the amount of tokens that that we are using for AI. So cost-plus could be a second model.

The third [model type] is outcome-based remuneration. If I say I am trying to accomplish awareness and preference for my new service or product from X to Y, I will pay you ABC dollars for that. Now you as the agency, you figure out how the hell you’re going to make it happen, and I am willing to pay per point, say, $100,000. If you [as the agency] manage to get that increase [at a cost to the agency of] $5,000 and you pocket $95,000, God bless you. But I am looking at it from my perspective: what is each percentage worth to me?

Have you pitched this to your agencies?

Not yet. This is all a work in progress. We ourselves are discovering AI. I need to have enough amount of information and knowledge and experience to be able to say, “You know what? I know that the job you are doing will only cost you so much. I can demand that they have it reduced by 70% or 80% or whatever it is, but that’s not going to be helpful to you. I know it’ll drive you out of business. So let’s go to outcomes-based compensation.”

Dotdash Meredith’s OpenAI ad assist

Dotdash Meredith’s arrangement with OpenAI extends beyond content licensing. The IAC-owned publisher is also using the ChatGPT parent’s AI technology to enhance its contextual advertising product, D/Cipher.

But first, some background. D/Cipher effectively indexes DDM publications’ article pages by content-related topics so that an advertiser looking to reach brides can target that audience by running ads on articles of interest to brides, which can include wedding-related articles as well as stories on other topics that the brides audience over-indexes on reading. When DDM introduced D/Cipher in 2023, this indexing process used natural language processing to identify common keywords. But in the second half of last year, OpenAI’s large-language model entered the mix.

DDM is now taking advantage of OpenAI’s API to have the LLM find connections among the publisher’s article corpus in a similar way to how ChatGPT is able to process text to understand the underlying concepts of what is written, not just the words on a surface level.

“The new world of OpenAI significantly improves that because we’re not just talking about words as tokens but as concepts, as conceptual structures. It’s making connection on concepts, not just on the actual language itself,” said Jon Roberts, chief innovation officer at DDM, in an interview. 

After running DDM’s content library through OpenAI’s technology, 70% of the content connections identified by the LLM were “pretty much the same, but 30% were meaningfully different. It was obviously better,” said Roberts.

And to be clear, this isn’t theoretical. ”We have campaigns that are live where those types of insights from that level of the taxonomy are improving the results for advertisers,” said Lindsay Van Kirk, gm of D/Cipher.

Case in point: DDM ran a campaign for a large CPG advertiser that was introducing a new luxury haircare product. One of the audiences that the advertiser wanted to reach was brides. DDM ran the campaign through D/Cipher and was able to see that the campaign underperformed the client-provided core benchmark when brides were included.

“On that benchmark user engagement, brides were 30% to 40% less likely to engage than the average people looking at the ad,” said Roberts.

DDM was able to detect this specific audience drop-off because the campaign was running on wedding-related content as well as unrelated content where the only audience overlap were those people checking out the wedding-related pieces. DDM recommended removing brides from the campaign, and as a result, the campaign “outperformed [the client’s higher] stretch benchmark,” Van Kirk said.