Hearst is using AI to turn advertising RFPs into bespoke audiences in minutes with a new product that moves the company beyond targeting into strategy. AURA IQ, launched last month at Cannes, is the next evolution of AURA, the company’s proprietary first-party audience platform introduced in 2024.

The new system uses AI to translate campaign briefs into custom audience strategies at scale while continuously refining performance as campaigns run. Rather than simply matching advertisers with predefined segments, it helps identify and optimize high-value audiences throughout a campaign.

For years, publishers have viewed first-party data as a defense against third-party cookie loss, platform dependency, and declining referral traffic. Hearst’s approach points to a different future. First-party data creates competitive advantage when publishers combine trusted audience relationships with AI to turn proprietary insights into scalable advertising strategy.

From targeting tool to AI-powered platform

AURA, launched in 2024, was a first-party ad targeting tool that utilized AI-enabled technology and Hearst’s audience data to connect advertisers to engaged customers at scale. Last year, Hearst extended AURA’s reach internationally to the UK, Italy, the Netherlands and Spain, to activate its first-party data across 300 million monthly visitors and more than 50 brands globally. The same year, it expanded to programmatic buyers on Amazon DSP, and to Connected TV (CTV) with AURA TV to connect advertisers with audiences across screens.

Now, AURA IQ transforms RFPs into actionable media strategies and custom audience targets for clients.

“We went from a tool to a platform,” explains Mike Nuzzo, SVP and head of data solutions and insights at Hearst Magazines. “And, I think that leap in and of itself is significant because it actually functions more like an interactive tool than just a targeting tool.”

Hearst Magazines Global Chief Revenue Officer Lisa Ryan Howard explains that the AURA IQ platform allows the company to build recommendations in a faster, smarter, more automated fashion and to uncover real time trends, anticipate market shifts and work more strategically with marketers and advertisers.  

For Hearst, the biggest value of this new platform is in the upfront workflow. In a live demonstration, Hearst showed how AURA IQ can take a RFP and turn it into tailored audience recommendations and client-ready intelligence in minutes.

“It allows us to build bespoke audiences per brief, and it’s ethically done in minutes,” explains Nuzzo. “It uses all of our levers from the data side, so, audience contextual and real-time trends. And it sharpens every campaign (and) it gets better with time because we’re feeding data through it.”

Howard says the company tested the product in beta and analyzed how much time, speed and accuracy it saved the team. The answer, she says, was substantial. “It went from five days, roughly, to four minutes,” Howard says.

“I think for publishers that’s one of the most important things we can do to compete with the nimbleness of creators and the scale of the platforms,” she says. “We need to be faster. We need to be smarter. And so AI’s helping us do that.”

How AURA IQ changes Hearst’s sales workflows

Hearst says the value of AURA IQ is speed, but the bigger shift may be how that speed changes the workflow. Right now, AURA IQ automates the audience-building and insight-generation work that used to take the first several days of RFP response. Instead of humans manually matching briefs to Hearst’s content and audiences, the AURA IQ platform does that matching automatically and at scale.

It was important for Hearst to have humans involved in the process, all the way through, Howard says. “Our marketers and sales teams use this for RFP response,” she says. “The account strategists, the data analysts, the sellers then review it thoroughly in the platform and either go with it as is or make relevant edits based on their own knowledge.”

While AURA IQ improves timing and workflow, it doesn’t replace editorial or sales judgment. Humans still review and approve the output before it goes to a client.

The platform frees up time for marketers to solve the hard problems for advertisers, Nuzzo says. “This just allows us to give back some time to the human to think creatively and strategically about the whole business. [It takes] that one component that we know AI can help with and all the data that sits underneath it to help them just move this one piece of it much faster.”

Howard explains that the hardest work came before the AI layer even got built. It was an 18-month to two-year effort getting Hearst’s own systems, from ad server to order management to analytics, actually talking to each other. Only then did that first-party data become useful when the underlying systems were organized enough to support it.

For now, AURA IQ doesn’t build specific media plans. That is phase two for AURA IQ, Howard explains, saying, “Account strategists will also still be involved on the planning side to manage the actual media plans, but the automation of it is there.”

“The more you can simplify and create, as Mike calls it, a kind of federated system where different tools are communicating to each other interchangeably, that’s an unlock,” she says.

The infrastructure behind Hearst’s advertising AI

For digital media companies watching these developments, the biggest takeaway may be the workflow Hearst built around AURA IQ.

Ana Milicevic, co-founder of Sparrow Advisers, a firm that advises marketers and C-suite executives on building data products, says that publishers are testing a variety of AI tools that support operational efficiency.

“We’ll see many flavors of AI-powered offerings coming from publishers in the coming months,” she says. “The part that is likely replicable … is operational efficiency: AURA IQ seems to present a layer that facilitates RFP response, and that alone would be a time-saver for many publishers.”

 AURA IQ’s core function, she notes, is speeding up RFP response, which is often a bottleneck requiring an expensive, specialized staff footprint to solve manually.

Anthony Katsur, CEO of the IAB Tech Lab, says that the most realistic uses of agentic AI in publishing right now are in discovery, curation and the creation of private marketplace deals. The best use cases, he says, are the ones that solve specific workflow problems and make existing systems smarter and more efficient.

He says other publishers could build something similar, but with a caveat. “I think it’s a brilliant approach. I think other media companies can absolutely replicate what they’re doing,” he says, “(But), you need scale. You know AI thrives on a very rich, robust data set… but other media companies of that scale, absolutely.”

He pointed to DPG Media, a Belgian media group with more than 80 brands in Belgium and the Netherlands, which launched a similar platform in March. Called Audience Discovery Agent, it is based on agentic AI and enables advertisers to quickly identify the most relevant target groups for their campaigns.

“An agentic approach is making the existing programmatic workflow smarter, more effective, more efficient,” Katsur says. “That’s how I see this evolving.”

The new competitive edge for advertising

What Hearst has built with AURA IQ points to how publishers are thinking about optimizing their internal workflows. The publisher ad tech stack is being reimagined, Milicevic explained. “In practice this will likely mean that previously key components of a publisher’s ad tech stack will be replaced, resulting in a more streamlined stack,” she says.

Katsur agrees, saying that that the broader ad stack is being rethought around AI, but the value is likely to show up first in planning and activation rather than measurement. He says publishers need to move forward with purpose, asking what problem AI is meant to solve and whether it creates real efficiency or incremental revenue. He pointed to strong use cases like reconciliation, PMP management, smarter media briefs and RFP responses.

“Is applying an LLM the best use best use case to apply AI to, or is it things like reconciliation? Is it things like again creating a management of PMPs? Is it putting together smarter media briefs or responses to RFPs from agencies?” he asks, “Those, I think, are probably higher and better use cases of AI than just automating the IO.”

AURA IQ’s emergence suggests publishers are thinking about how AI fits into their businesses, and also rethinking the infrastructure that sits underneath ad sales.  The next competitive edge may come from building internal infrastructure that makes data, sales and planning work together, and lets publishers turn their data into something advertisers can use.