Q1. The industry is clearly moving away from fragmented martech stacks. What are the main limitations you've observed with traditional setups involving DMPs, CDPs, and data clean rooms?

These tools were never designed to work together; they were built to solve different problems for different segments of the media industry at different points in time. DMPs were built mainly for publishers navigating the third-party cookie era. CDPs came along to fix the single-customer-view problem for brands internally. Data clean rooms were adopted in response to signal loss across the board by brands, publishers, and retailers alike. So you’re looking at three separate architectures, three vendor relationships, three data pipelines.

What we hear constantly from publishers and retailers is that stitching these together creates enormous operational drag. Every handoff between tools is a point of latency, a potential compliance risk, and a cost center. And because none of them were built with collaboration in mind from the start, the moment you try to do something cross-party (enrichment with a partner's data, joint measurement, audience activation beyond your own properties, etc.) you hit a wall. The stack simply wasn't designed for the collaboration era, and even less for AI.

Q2. What is driving organizations to adopt more unified and flexible data platforms today, and how urgent is this shift?

Three pressures are converging simultaneously, which is what makes this moment feel different from earlier transitions.

First, regulation has fundamentally changed what's permissible. GDPR and a growing body of case law have made clear that moving customer data freely between systems is over: organizations need technical guarantees, not just contractual ones, for hassle-free and fast collaboration. Second, the signal environment has decreased: third-party cookies are declining, and universal identity solutions have helped at the margins but haven't filled the gap. Third  and most importantly  the value of first-party data is now demonstrably tied to collaboration. Data sitting in one organisation's DMP is interesting. Connected to a brand's CDP or a retailer's transaction history, it becomes genuinely powerful.

The media players moving now are building structural advantages. Those waiting are watching legacy DMP contracts come up for renewal with no clear answer for what replaces them.

Q3. From your perspective, what does a truly "unified" data platform look like in practice, beyond just integrating multiple tools?

"Unified" gets used to mean fewer vendor logos on a slide. That's not what I mean in this case necessarily.

A truly unified platform is one where the architecture was designed from the start for collaboration and privacy with the goal of creating networks between data owners, not just optimising data within a single organisation. When a CDP or DMP adds a clean room module, the privacy guarantees are only as strong as the wrapper. Additionally, you don't necessarily inherit any network here either, meaning each partnership might have to be built from scratch.

At Decentriq, we started from the opposite direction. Our clean room uses confidential computing: hardware-level encryption where data remains protected during processing, even from us. Using that as a foundation, we built the Collaborative Audience Platform: a unified layer adding CDP- and DMP-style capabilities  segmentation, identity resolution, activation, shared audience products. In practice, a publisher can collect data, build and enrich audiences, activate to GAM or DSPs, run closed-loop measurement, and refresh automatically all in one environment, with no seams between layers. That's what genuinely unified looks like.

Q4. Many companies still rely on stitching together multiple solutions. Where do these approaches typically fall short when it comes to scalability and efficiency?

The failures tend to only become visible at scale, which is precisely when they're most painful.

The first is the identity tax. Every time data moves between tools, you make assumptions about identity resolution. If your system can only handle one ID type, you can lose a significant portion of your audience during matching. The second is engineering overhead: stitched integrations need constant maintenance, and onboarding each new partner is its own project, meaning there is a hard ceiling on how many collaborations you can run in parallel. The third, which comes up in almost every conversation with publishers replacing their DMP, is the inability to operationalize collaboration at scale. One-off clean room projects are feasible. Repeatable, automated, always-on audience collaboration with multiple partners simultaneously is a different problem (and stitched stacks weren't designed for it).

Q5. How is this shift impacting data collaboration between brands, publishers, and retailers in real-world scenarios?

The most significant change is the move from one-to-one integrations to network-based collaboration, because this changes the economics of data entirely and provides a crucial foundation for AI.

In the old model, a publisher ran a bespoke clean room project with one advertiser at a time. High cost, limited scale. A platform model enables something fundamentally different: standardised, repeatable collaborations across a growing network simultaneously. We've seen this with OneLog in Switzerland using our technology: five publishers unified under a single audience monetization platform, enabling advertisers to plan, activate, and measure across their combined audiences.

We're seeing the same dynamic for retailers. Decentriq's Collaborative Audience Platform lets them build audiences from online and offline signals and activate with brands and premium publishers (including CTV) without raw transactional data ever leaving their control. For brands, this means accessing publisher and retailer audience data through a standardized, privacy-safe workflow instead of negotiating lots of separate agreements.

Q6. Privacy and compliance remain key concerns. How do modern unified platforms address these challenges more effectively than legacy martech stacks?

Legacy stacks address privacy primarily through contracts  data processing agreements, retention policies. These are necessary but not sufficient. Contracts tell you what should happen; they don't technically prevent what shouldn't.

Decentriq uses confidential computing as the central technology for data collaboration: a hardware-level technology where data is processed inside a secure enclave inaccessible to any party, including us. The privacy guarantee is technical, not contractual. A significant recent CJEU ruling validated exactly this approach:  clarifying that pseudonymised data processed through technology where re-identification is technically impossible carries a different compliance profile than data protected only by agreement. 

For organizations navigating GDPR, this shifts the burden dramatically: instead of documenting every data flow and relying on ongoing contractual enforcement, you can demonstrate provable technical compliance. That's increasingly what regulators, legal teams, and enterprise procurement are demanding.

Q7. What role does AI and automation play in enabling more seamless and actionable data collaboration within these new ecosystems?

The critical point is where AI runs. AI operating on raw data is a privacy risk. AI operating inside a confidential computing environment  on data that is never exposed is a fundamentally different proposition.

At Decentriq, AI is embedded at several levels: lookalike modelling that extends a seed audience without either party revealing their underlying data (a luxury automotive brand saw +80% engagement and +58% conversion rate using this, for example), audience size estimation before a segment is built, and automated refresh cycles that keep audiences current across partners without manual intervention. 

Further out, the more AI is integrated into these environments, the more the collaboration network itself learns  from joint activations, measurement results, and partner interactions  rather than resetting with each new campaign. That's the direction this is heading.

Q8. Looking ahead, what key changes do you expect in how organizations approach data infrastructure and collaboration over the next 2–3 years?

Three shifts feel clear.

First, stack consolidation. Organisations running separate DMPs, CDPs, and clean rooms will consolidate around platforms that do two, if not all three three, natively. The maintenance cost, compliance complexity, and operational drag will drive that decision.

Second, the ecosystem model becomes the norm. The value of first-party data is increasingly defined not by how much you have, but by how well it connects. Publishers contributing audiences to a collaborative network unlock revenue that's unavailable to those working in isolation. Retailers whose data can activate across a premium publisher network and close the loop with sales measurement are in a completely different competitive position. That logic will only accelerate. And as AI becomes more deeply embedded in these workflows, the network itself becomes a training asset: the more data flows through a shared collaborative infrastructure, the smarter and more precise the models that power lookalike targeting, audience estimation, and measurement become. Isolated stacks simply can't compete with that.

Third, privacy-preserving infrastructure shifts from differentiator to baseline expectation. Confidential computing and hardware-level privacy guarantees are currently seen as advanced or optional. In 2–3 years, driven by regulation, enterprise procurement standards, and demonstrated risk of alternatives, they'll be standard requirements. The organisations betting on these foundations now will be ahead of that curve rather than catching up to it.

By Carey Madsen, VP and CMO, The Fletcher Group

94% of B2B buyers now use AI during the buying process, and most marketers are working hard to insert their brands into those buyer recommendations. But you’re probably making it harder than you need to.  

Here’s a scenario that plays out every day in B2B: a company earns a strong media placement in a respected trade publication. The story is sharp, well-positioned, and reaches the right audience. Then it disappears. Posted once on LinkedIn, shared internally, and forgotten. Sales never sees it. The website never references it. No one writes a follow-up post that builds on the insight. The executives who could have amplified it don’t.

This is what happens when PR and marketing operate in silos. Coverage and content don’t travel far  and in 2026, that has consequences that go beyond missed amplification. It affects how often your brand appears in AI-generated answers.

The way B2B buyers research and evaluate vendors has changed fundamentally over the past two years. Buyers no longer follow a neat funnel. They may read a trade article, which prompts a question, so they ask ChatGPT or Claude. The answer frames their next steps, which might include a visit to your website to read an FAQ or case study, an industry report, or to a competitor’s site instead.

If your messaging isn’t aligned and repeated across these channels, you haven’t made your brand known; and it’s difficult for buyers to find you, because they don’t know what you solve for. In a nutshell, vague messaging gets skipped, while consistent messages gets cited.

How Do B2B Buyers Research Vendors in 2026?

Forrester’s 2026 State of Business Buying report shows that purchasing is more collaborative, and dependent on validation from trusted sources than in previous search eras. Buyers rely on what Forrester calls a “buying network”  internal stakeholders plus analysts, peers, and earned media — to validate what they learn from any single channel, including AI tools.

The Forrester data paints a clear picture of just how early these decisions are forming:

·       92% of B2B buyers enter the process with at least one vendor in mind, and 70% of the journey happens before sales engagement 

·       9 out of 10 C-suite decision makers say they are more receptive to thought leadership than traditional marketing materials

·       94% of buyers use generative AI during the buying process, but 20% report inaccuracies—leading them to validate AI outputs against third-party sources 

Buyers use AI as a data point, then confirm what they find through media, analysts, LinkedIn, and your owned content. If your brand shows up in only one of those places, you’re missing other essential validation opportunities.

Why Do LLMs Favor Brands with Multi-Channel Presence?

This is where buyer behavior and AI visibility intersect. LLMs pull from media coverage, brand content, social conversations, and third-party validation to shape the answers buyers see. Brands that appear across more source types tend to be cited more often and with more context.

The rules of AI-fueled search are evolving in real time, but several patterns are already clear enough to act on:

·       Earned media drives the majority of AI citations. Muck Rack found that 82% of citations come from earned sources

·       Brand search volume is a stronger predictor of AI citation than traditional SEO authority like backlinks 

·       LLMs do not share the same resource pools, so, appearing on a wide range of relevant channels—owned, paid and earned—is necessary to be cited by all the most popular LLMs

In practice, this means disconnected or incomplete efforts across PR and marketing teams create visibility gaps that competitors can fill. When PR, content, and executive visibility aren’t aligned, you reduce the number of trusted signals AI systems rely on.

How Does One Asset Become Five?

The real value of integration is making one success work four times harder. This helps large companies absolutely dominate their space and lets smaller firms punch above their weight through efficient use of resources.

Here’s what that looks like in practice. Take a single starting point: your company releases original data or research on a trend that matters to your buyers.

•      Earned: The research is pitched to key trade publications and tier 1 business outlets. Stories are published, your CEO is quoted with a distinctive point of view.

•      Owned: The research becomes an un-gated blog post and report on your website, structured with clear headers, FAQ sections, and schema markup so both Google and LLMs can parse it effectively. Key data points are formatted as standalone, citable claims that start showing up in other earned media.

•      Shared: Your CEO and other executives post their own take on LinkedIn — not identical reshares, but distinct perspectives that create multiple entry points for key audiences. The company page amplifies with a summary post linking to the blog.

•      Third-Party/Paid: A LinkedIn sponsored content campaign targets decision-makers in your key verticals. An analyst briefing results in an informed industry expert that validates the narrative for media and prospect inquiries. The research serves as the foundation of a presentation or webinar at an industry event.

Does Integrated PR and Marketing Require a Large Budget?

No. In fact, smaller teams are often better positioned to do this well from day one, because they can’t afford to be spread too thin. Even some larger brands can’t activate all channels at scale, and trying to do everything at a surface level is worse than doing two things well. But whatever you do invest in, do it well, and set your campaigns up to compound across channels rather than exist in isolation. 

A single earned media placement that nobody amplifies, repurposes, or references on your website is a missed opportunity — and that’s true whether your budget is $50,000 or $500,000. A blog post that answers a question your buyers are asking but never gets shared by an executive or promoted to a targeted audience is content that only works in one way, instead of four or five.

Integration is a mindset about how assets get used, not a mandate to spend more. Start with what you have. Make each piece of content and each media win work across every channel you can reach. 

The Outcome: Consistent Presence Where Buyers Look

The B2B buyer’s journey is no longer a path you control. It is now made up of a network of sources — and increasingly, a network that AI tools reference on their behalf.

When PR, content, social, and paid efforts work together, your brand appears more consistently across those sources. That consistency builds consensus  and ultimately, trust.

1. 
   They're building a proper identity foundation. They’re not just collecting emails. They’re actually resolving those signals across web, mobile, CTV, and audio into a unified identity graph that makes the data actionable at scale.
2. They're investing in the infrastructure to enrich and activate that data in real time. They’re moving away from manual processes because the programmatic market rewards speed and precision.
3. They're putting AI agents to work. They're replacing manual processes by deploying agents to query their first-party assets, build custom audiences, and push them to activation platforms in minutes. They’re ready for the buyer agents that are already searching and evaluating their inventory.