They are tuning it out.

Not because AI lacks value, but because the way it is being marketed often fails to align with what buyers actually prioritize. For organizations making high-stakes, long-term technology decisions, the fundamentals still matter most: engineering quality, technical expertise, reliable delivery, and reduced risk.

The growing disconnect between what vendors emphasize and what buyers need is creating friction in the buying process and, in some cases, eroding trust.

The AI Messaging Overload

Right now, nearly every platform, service, and solution touts some form of AI integration. While this proliferation reflects genuine advancements, it has also created a crowded and often confusing landscape.

For buyers, the challenge isn’t access to innovation—it is clarity.

When every vendor claims to be “AI-driven,” true differentiation actually becomes more difficult. Messaging starts to sound the same across the board, leaving buyers unsure what genuinely matters versus what’s just baseline capability. In this crowded landscape, bold AI claims without clear context or proof points don’t signal innovation, they blend into the noise.

To break through, marketers can’t rely on generic AI language alone. They need to be more technically fluent and deeply informed about emerging technologies, so they can engage increasingly sophisticated buyers and craft messaging that rises above vague, cookie-cutter AI narratives.

What Buyers Actually Prioritize

Despite the emphasis on AI, B2B buyers consistently return to a core set of priorities when evaluating solutions:

1. Engineering Quality

Buyers want to know that a product is well-built, scalable, and designed to perform under real-world conditions. According to recent BIXA research of 480+ business buyer decision-makers, quality of engineering and technical expertise are statistically tied for the most important attributes they look for in a vendor.

2. Technical Expertise

Beyond the product itself, buyers assess the depth of knowledge behind it. Research shows that technical expertise and guidance are tied as the most important attributes buyers look for in a vendor. They prioritize teams that understand their industry, technical challenges, and implementation complexities. For instance, 97% of buyers say it is important that a vendor both understands and uses AI technologies in their own processes.

Expertise ultimately signals credibility: 41% of tech leaders who augment their existing teams with external engineers say certified AI experts make a vendor stand out—and that credibility directly reduces perceived risk.

3. Reliable Delivery

Execution matters as much as vision. Buyers need confidence that timelines will be met, deployments will go smoothly, and ongoing support will be dependable. Efficient delivery is a top-five priority for buyers, and it is the single most important factor for 17% of UK-based decision-makers. Overpromising, particularly in emerging technologies, can quickly erode confidence when delivery does not keep pace. Buyers are also pragmatic about how to build that confidence quickly — 47% value paid workshops specifically because they accelerate project momentum. Buyers also increasingly expect AI to reinforce that reliability through faster code generation and automated code reviews.

4. Risk Reduction

At its core, every B2B purchase is a risk management decision. Whether it’s financial risk, operational disruption, or reputational impact, buyers are evaluating how a solution minimizes uncertainty. A bold guarantee is the top determining factor for buyers, carrying 40% of the relative importance in their decision-making process. In fact, 88% of buyers would choose a vendor offering a 100% bug-free guarantee even if their price was 30% higher than competitors. Clear documentation, proven use cases, and transparent communication, such as through de-risking workshops favored by 34% of buyers, all contribute to lowering the #1 hesitation in the market: concerns over code quality and security.

These priorities are not new. What has changed is how easily they can be overshadowed by trend-driven messaging.

The Cost of Misalignment

When marketing narratives focus on AI but ignore these foundational concerns, a gap forms between expectation and reality. This misalignment leads to clear consequences:

• Longer sales cycles, as buyers seek additional validation and clarity
• Increased skepticism, particularly toward bold or vague claims
• Missed opportunities, when solutions fail to resonate despite strong underlying value

In some cases, the emphasis on AI can even distract from a company’s true strengths. A well-engineered product with a track record of reliable delivery may be far more compelling than a newer, AI-heavy offering that lacks maturity. But if the messaging doesn’t reflect that strength, buyers may never fully recognize it.

Reframing the Narrative

The solution isn’t to move away from AI, it’s to stop pretending the tools are the transformation.

The teams winning with AI aren’t the ones with the most tools. They’re the ones that changed how their engineers work. This is a people and process problem, not a procurement decision—and most vendors avoid saying it because it’s harder to sell.

The shift in messaging is simple but demanding: stop leading with what the technology is and start with what it takes to make it work. That means structured workflows, validated output at every stage, and a clear acknowledgment that AI without governance doesn’t reduce costs—it increases them. AI usage is not free; it is metered in tokens and accumulates quickly.

There’s also a risk that almost no transformation partner raises: internal resistance. AI champions inside a client organization pull ahead. Resistors create drag. If you don’t address adoption at the engineer level from day one, the transformation fails at the people layer, not the technology layer. Buyers should be asking their vendors how they handle this. Most can’t answer.

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Questions to challenge yourself and your teams:

• Is your AI architected well enough that the economics actually work?
• Do you have a structured methodology or just a capability?
• What happens when your engineers resist?

Building Trust Through Substance

Trust is the currency of B2B relationships. It is built through consistency, transparency, and proof. In a market saturated with AI claims, substance is the differentiator.

• That substance shows up in four ways:
• Clear, specific use cases that demonstrate real-world impact
• Technical depth that proves how solutions are built and maintained
• Evidence of reliability, backed by performance metrics and long-term customer outcomes
• Honest communication about capabilities and limitations

When buyers see that a company is willing to go beyond surface-level messaging, it signals confidence—and that confidence is often more persuasive than any single feature or capability.

The Opportunity Ahead

The current wave of AI enthusiasm is real, and so is the backlash forming underneath it. Buyers aren’t rejecting AI. They’re rejecting the version of AI that showed up late, overpromised, and left their teams holding the complexity.

The companies that will win this window aren’t the ones with the boldest AI narrative. They’re the ones who can answer the questions a sophisticated buyer will eventually ask: Is your AI architected well enough that the economics actually work? Do you have a structured methodology or just a capability? And what happens when our engineers resist?

Bad AI is expensive AI. Buyers are starting to do the math, and the vendors who can’t show their work are going to lose deals they don’t even know they’re losing.

The fundamentals haven’t changed. Proof, expertise, reliable delivery, and reduced risk. What’s changed is that AI has raised the stakes on all of them. The companies that understand that distinction – and can demonstrate it – are the ones that will eventually define this market.

About Vention

Vention is the premier global leader in software engineering, synonymous with technology designed for scale and the common denominator behind the world’s most successful tech-empowered enterprises, industry innovators, and startups.

Those distinctions will become increasingly important. Done well, regulation can protect users, foster competition and sustain innovation. Done poorly, it risks entrenching the dominance of the largest technology platforms. In many ways, those same platforms are already best positioned to shape and absorb regulatory change, potentially leaving everyone else at a disadvantage.

The sheer momentum of AI to date makes it easy to feel helpless in the face of such a technological revolution. How can any of us hope to help shape and guide the ways in which AI is to unfold?

Fortunately, the digital media industry has faced a similar inflection point before in its recent history. The journey towards cookie deprecation offers a valuable lesson, and perhaps a blueprint, for what comes next.

The Privacy Sandbox experience

When browsers began phasing out third-party cookies, it triggered a wave of uncertainty and, in some cases, outright panic across the digital ecosystem. Advertisers, publishers and ad tech vendors all faced the challenge of maintaining addressability and monetisation while ensuring user privacy. Google’s Privacy Sandbox initiative was the most notable attempt to strike that balance.

The Privacy Sandbox was not perfect, but its intent is instructive. Rather than abruptly removing a foundational technology and leaving the ecosystem to adapt overnight, it introduced a standards-based framework designed to evolve over time. It sought input from across the industry (including publishers, advertisers, developers and regulators) and aimed to create privacy-preserving alternatives that could support the economic model of the open web.

One could argue that Google could deprecate cookies, as Apple did, and introduce its own unique way of targeting users in Chrome. Instead, it opened up the discussion with the ecosystem around collaboration and iteration. This created a space, however imperfect, for broader participation and conversation, demonstrating that large-scale ecosystem change can be coordinated by consensus rather than imposed.

The cookie deprecation process made it clear that simply “switching off” a core capability at scale is not viable. Sudden changes risk destabilising publishers who rely on advertising revenue, limiting the ability of smaller tech providers to compete, and forcing advertisers into narrower, less transparent buying environments. Meaningful progress required frameworks that could be refined in real time, informed by data and shaped by those operating across the ecosystem, not only those at the top of it.

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Regulating agentic AI

Today, the digital media industry faces a parallel moment with the rise of agentic AI. These systems are increasingly acting as intermediaries between users and the digital world. It’s shaping what content is discovered, which products are surfaced, and how decisions are made. In effect, they are becoming gatekeepers to information, commerce, and attention.

As control over these systems concentrates in the hands of a few large players, questions around transparency, fairness and access become more urgent. Regulation is clearly necessary, but it must be approached with care.

A “sandbox approach” to AI regulation, at its core, means developing standards collaboratively across the industry, rather than imposing rigid rules from the top down. It also necessitates creating environments where new approaches can be tested, evaluated and iterated before being scaled. Finally, it requires that any regulation evolves alongside the technology it seeks to govern.

Large technology platforms have a critical role to play in this process. As with the Privacy Sandbox, companies like Google have the scale, data and infrastructure to help develop and test new approaches. But with that role comes responsibility. Their contribution should be to support industry-wide solutions, not to define the rules in isolation.

Collaboration, transparency and iteration

There are already signs that the stakes are rising. As AI systems become more embedded in advertising, commerce, and content discovery, brands need to collaborate effectively with chat interfaces, which act as intermediaries, and with end users. Without clear and collaborative frameworks, the risk is that regulation, however well-intentioned, ends up reinforcing the very dynamics it seeks to address.

The transition from cookies to privacy-first alternatives showed that the industry is capable of navigating complex change. It also showed that the process matters as much as the outcome. As AI becomes the primary interface for digital decision-making, those same principles must guide the next phase of regulation. Collaboration, transparency, and iteration are not just desirable; they are essential.

A sandbox approach offers a way to balance innovation with accountability, and competition with control. The window to get this right is narrow; fortunately, the blueprint already exists.

About PrimeAudience

PrimeAudience (an RTB House company) is a, AI-driven, privacy-focused adtech platform designed to boost client acquisition and enhance targeting. It uses Generative AI to create custom audiences, reducing ad costs by up to 80% and providing up to 40-60% identity resolution of website visitors without relying solely on third-party cookies

Built alongside early design partners, the Inference Engine gives AI developers unified control over performance, cost, and scale — with customers reporting up to 67% lower inference costs.

2026 State of Strategic Response Management Report reveals widening AI divide as a nine-point revenue growth gap separates leaders from novices

__________

Hi Jana, take us through your time in martech and your role at Cognitiv?

I’ve spent more than two decades at the forefront of advertising innovation. First helping lead the adoption of programmatic across EMEA during its early emergence, launching programmatic at Spotify, to now helping marketers access AI-driven solutions to scale growth. I joined Cognitiv 8 years ago, at the time we were an early player in the deep learning space, with fewer than 10 employees. Joining a start-up is always a gamble, but I felt confident in the technology and the founding team and saw it as a rare opportunity to learn about AI.

Today as SVP of Partnerships, I focus on redefining how brands and media companies leverage deep learning AI to drive performance. In a media landscape that’s more complex than ever, that means building strategic partnerships that help publishers unlock new revenue streams while enabling brands to engage consumers in more meaningful, data-driven ways. I have developed strategic partnerships with major SSPs and DSPs to bring the industry’s most advanced AI-driven curation to media buyers.

We’d love to learn more about your new enhancement, AudienceGPT. Why should marketers pay attention to it?

AudienceGPT is a fundamental shift from reactive audience targeting to predictive, intelligence-driven marketing.

Traditionally, audience segmentation was manual, time consuming, static, and relied on outdated signals like clicks or page visits that didn’t tell you much about the actual stage of the journey a consumer was in.

AudienceGPT solves this by using Cognitiv’s deep learning advertising platform to develop synthetic consumer journey profiles that can then be found programmatically. The result is a more adaptive, predictive approach to audience strategy that aligns media delivery with true consumer intent. Audiences can be activated across web, CTV, social, and audio, meeting advertisers where they are.

Modern marketers manage different types of data and workflows today. What top best practices come to mind for those looking to optimize how they clean and use data to power better outcomes and customer journeys?

During my time at Cognitiv, I’ve evaluated probably 100 data providers across contextual, attention, measurement, and audience segments, so I’ve seen a wide range in data quality and approaches.

A few best practices really stand out. First is understanding the origin of the data, whether it’s deterministic or modeled. Deterministic data, especially in its raw form, tends to be more reliable and transparent, whereas modeled data can introduce assumptions that aren’t always clear or consistent.

Second is freshness and relevance. Marketers often overlook how frequently data is refreshed. An audience labeled as a “travel intender,” for example, is only as valuable as the recency and signal behind that classification. You have to ask: what behaviors actually qualified this user, and how recent were they?

Finally, validation is critical. At Cognitiv, we’re fortunate to test data directly by running it through our models offline to see whether it actually improves predictive accuracy. That kind of rigorous testing helps separate data that sounds good in theory from data that truly drives performance.

Ultimately, the best outcomes come from combining transparency, recency, and real-world validation, rather than relying on labels or assumptions alone.

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What’s the most exciting thing about how AI is leading to a shift in marketing processes and standards as well as a shift within marketing teams in terms of how teams are structured today?

AI is reshaping marketing in a way that feels very similar to the early days of programmatic, but at a much faster pace.

From a team perspective, the traditional silos between media, data, and analytics are starting to break down. We’re seeing hybrid roles emerge, people who understand both the strategic and technical sides of marketing, and are usually proficient in deploying and working with AI.

From a process standpoint, there’s a tendency to think about AI primarily as workflow automation. And while it can help with that, the bigger opportunity lies in real-time prediction and decisioning. That’s where the biggest performance gains will come from.

Five thoughts on the future of AI and martech?

1. Audience targeting shifts towards moments of intent: The combination of contextual signals, real-time behavior, and understanding of content will outperform audience segments. This goes beyond assigning someone to a segment, to predicting their likelihood to act in that moment based on live inputs.

2. Data quality becomes the true differentiator: The future will be built on better data—deterministic where possible, transparent in methodology, and validated against outcomes.

3. AI shifts from automation to intelligence: Today, AI in marketing is primarily focused on automating execution, not redefining strategy. The next phase will move beyond efficiency gains to deliver real intelligence—powering better decisions rather than just optimizing the manual levers we’ve relied on.

4. Personalization will scale without manual effort: AI will enable truly individualized experiences without the operational complexity that used to limit scale.

5. CTV Moves from awareness to performance: CTV is a great channel for reach and scale but we’ll increasingly see it used as a medium to drive performance. The ones who win in CTV will go beyond content targeting.

Some top martech innovations and martech innovators that you’d like to shout out to in this conversation?

Two martech innovators I want to shout out are Magnite and Index Exchange – specifically Paul Zovighian, VP, Marketplaces at Index Exchange, and Zach Pucci Global, Enterprise Sales at Magnite. Both are helping push real-time curation forward in a way that’s shifting intelligence to the sell side and accelerating innovation across the ecosystem.

Real-time curation turns live data signals into actionable inputs for AI, allowing for accurate, real-time predictions. This drives improved performance for buyers in the moment, not after the fact.

Cognitiv is a leading advanced performance partner powered by deep learning. Leveraging cutting-edge AI technology and data science since 2015 to more accurately predict consumer behavior and understand nuance, Cognitiv connects brands with their customers in more precise, relevant, impactful moments at scale. Cognitiv’s Deep Learning Advertising Platform provides marketers with unprecedented flexibility, activating as a Dynamic Deal run through the DSP of your choice, as a managed service DSP, or through its industry-first ContextGPT product. Cognitiv is on a mission to bring intelligence to advertising.

About Jana Jakovljevic

Jana, SVP of Partnerships at Cognitiv, brings two decades of experience driving innovation across the advertising industry. Before joining Cognitiv, Jana was the Global Head of Programmatic Solutions at Spotify, where she successfully launched the company’s programmatic arm and pioneered the first Private Marketplace (PMP) for audio ads. At Magnite (formerly Rubicon Project), Jana held various management positions, building out international buy-side partnerships and playing a foundational role in the company’s journey from start-up to IPO. Known for landing at companies that are at the forefront of the media landscape, Jana is now focused on leveraging AI to propel the ad industry forward. Her dedication to disruption and passion for constant improvement make her a key agent of change, unafraid to break the status quo in the name of innovation.

Collecting data beyond just name and email, like phone number, birthday, and preferences, enables more personalized messages for consumers. But because the value isn’t clear, consumers are willing to volunteer only a limited subset of low-risk information upfront.

New research from Intuit Mailchimp puts hard numbers on what many marketers suspect. While 65% of brands request a phone number via opt-in popups, only 28% of consumers are willing to share it. Just 8% of marketers report opt-in conversion rates above 20%. These low conversion rates point to a broader issue: low confidence.

This is the trust gap, and it’s sitting right at the front door of the customer relationship. The good news is that closing it doesn’t require a complete overhaul. It requires a smarter approach to addressing what brands ask for, when they ask for it, and how they can use what they already have.

List quality is the real growth metric.

For years, ecommerce growth was measured in subscriber counts: The bigger the list, the bigger the revenue. Now we understand that volume isn’t the best indicator for performance. What actually drives results is a list where subscribers open and click, data is current and accurate, and every contact is deliberately acquired rather than passively accumulated over time.

A high-quality list has a direct bottom-line impact; for instance, 50,000 engaged subscribers will naturally convert at a higher rate than 500,000 disengaged subscribers. Intentional audience building reduces costs, protects deliverability, and generates engagement that translates into measurable revenue.

Building that quality starts with trust. Only 31% of consumers assume brands will handle their data responsibly, and more than half say they’re willing to engage but worried about spam. Earning a place in someone’s inbox, let alone influencing their purchasing decisions, requires establishing credibility from the start.

The ask should match the moment.

More than half (51%) of brands place opt-in popups on the homepage, and 62% use page-load popups that fire immediately upon arrival. But asking for contact information before a visitor has browsed a product or read a review can feel premature and erode trust.

The research is clear on when consumers are most receptive: Half of consumers (50%) are most likely to opt in after browsing a brand’s offerings, 33% right before making a purchase, and 24% just before leaving the site. Triggering a popup form offering a discount after visitors have viewed a product aligns with where they actually are in their journey.

In practice, marketers are required to rethink their strategy to acquisition. The brands earning quality signups have stopped asking what data they can collect and started asking what value they’re offering in exchange for it. Incentives like early access or a first-purchase discount are concrete starting points. Optimizing the opt-in moment captures useful data from the start, and surfacing what subscribers do afterward compounds that value.

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Behavioral signals fill in what form fields leave out.

Instead of asking for more than customers are ready to share upfront, marketers should consider another approach: letting customer behavior do some of the work instead.

Every visit generates data. Signals like what someone browses, how long they spend on a product page, whether they return twice in a week, or what they add to a cart and walk away from reveal intent without requiring a single form field. Consumers generate these insights naturally by engaging with your brand, creating opportunities to start building the relationship before an email address is shared.

Behavioral signals fill in the gaps, deepen over time, and make the data brands collect more actionable. The goal is to identify where someone is in their relationship with your brand and meet them there, with the right message at the right moment. Acting on that at scale, however, requires more than good instincts; it requires knowing who to target, when to send, and what to say, and then acting on those decisions when it matters most.

AI turns behavioral data into action.

Collecting behavioral data and actually acting on it are two different problems. Triggering real-time, personalized follow-up messages based on browsing behavior, purchase history, and engagement signals across email and SMS isn’t something a marketing team can do manually at scale. AI can close that operational gap.

Brands with high-quality lists are three times more likely to run fully automated programs (38% vs. 13%), indicating that the brands investing in list quality are also the ones investing in the infrastructure to act on it. Marketers now rank AI-powered optimization among the most sought-after capabilities in their tech stacks.

The practical shift shows up in the customer journey. A high-value customer who has browsed multiple times and completed transactions warrants a different sequence than a new subscriber who went quiet after the first message or someone who abandoned a cart. AI makes it possible to identify those distinctions early and route customers into the right sequence automatically, turning what used to be guesswork into something systematic.

Acting on behavioral data at scale only works within the bounds of consumer trust. Personalization that feels natural and helpful builds the relationship, while personalization that feels intrusive or misaligned breaks it. The brands getting this right are transparent about what they collect and why, which reinforces the same value exchange that drives opt-in quality in the first place.

The opt-in is just the beginning.

The brands positioned for success are treating the opt-in not as a list-building opportunity but as the first moment of a long-term relationship. Strategic timing, relevant incentives, and a clear value exchange up front set the foundation. Behavioral signals and automation build on it, surfacing the right message at the right moment.

The trust gap closes the moment brands start treating the opt-in as an invitation rather than a transaction. The result isn’t just a bigger list; it’s a more valuable one, built on trust that shows up in repeat purchases, higher engagement, and lasting loyalty.

About Intuit Mailchimp

Mailchimp is the all-in-one integrated marketing platform for small businesses.

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However, in 2026, this “less is more” strategy began to show some worrying cracks. Today, a handful of major platforms have come to dominate more than 80% of the $700 billion global ad market. As such, many brands have been encouraged to centralise their retargeting efforts within a single ecosystem in the name of “streamlining” their media spend.

Unfortunately, amid significant economic uncertainty, the decision to rely on a single retargeting partner has become a strategic risk that warrants reconsideration.

The consolidation trap

Avoiding ad fatigue, duplicated bids and wasted ad spend are obviously good ideas. However, it should be noted that the big platforms have been happy to amplify these concerns, in part, because they profit from keeping brands within the boundaries of their own ecosystem. As a result, multi-vendor retargeting strategies have been discouraged.

However, committing to a single retargeting provider comes at a cost to brands, which is often overlooked or downplayed. By placing all retargeting eggs in one supplier’s basket, brands are effectively limiting their visibility into the broader digital landscape. In a very real way, they become dependent on one system’s ability to interpret user intent, to identify an opportunity and to define a valuable target. One set of algorithms, designed and operated by an external party, ends up dictating your brand’s view of the world.

As a consequence, campaigns start to stagnate, and incremental growth becomes much harder to achieve. The audiences being targeted are drawn from the same pool, recycled within a closed loop, while potential high-value users just outside that loop remain unseen and undiscovered.

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The myth of internal competition

A persistent myth is that adding a second retargeting provider will inevitably lead to self-competition and inflated costs. This assumption has shaped media strategies for years, yet it seriously misinterprets how modern programmatic ecosystems actually operate.

In reality, a multi-vendor setup does not mean two providers blindly bidding against each other for the same impressions. Today’s advanced bidding technologies are designed to evaluate users differently, using distinct models, signals, and optimisation strategies. Each provider brings its own perspective on what constitutes a valuable impression.

Rather than duplicating effort, this creates a dynamic and competitive environment. Algorithms are forced to work harder to identify unique opportunities, refine targeting, and justify each bid based on performance potential. The result is increased efficiency and improved precision.

Moreover, overlap between providers is often overestimated, meaning that a second provider is more likely to uncover incremental audiences than to compete for the same ones. Even where overlap does occur, controlled competition can help to ensure that only the most valuable impressions are won, at the right price. Rather than inflating budgets, smart use of a multi-vendor retargeting strategy can optimise them.

Future-proofing and resilience

In 2026, market conditions are shifting constantly, consumer behaviours are evolving rapidly, and the limitations of closed ecosystems are becoming increasingly apparent. A diversified approach to retargeting ensures that brands are not overly exposed to fluctuations in one platform’s performance, pricing, or policy changes.

Advances in AI and machine learning have made it easier than ever to identify and engage users across a fragmented digital landscape. Modern retargeting technologies are capable of analysing vast datasets in real time, uncovering patterns and opportunities that would be invisible within a single ecosystem. By leveraging these capabilities across multiple providers, brands can build a more comprehensive, dynamic view of their audience and tap into new sources of incremental value as they emerge.

A multi-vendor strategy transforms retargeting from a passive exercise into a competitive system. It introduces checks and balances, encourages innovation, and drives continuous optimisation. Rather than relying on one algorithm to deliver results, brands create an environment where multiple systems compete to do so.

The dominance of large platforms has made consolidation feel like the default choice in digital advertising. While consolidation may still seem, on face value, to be an attractive option, the increasingly fragmented and unpredictable media landscape means that brand marketers should diversify their retargeting approach using the modern tech tools available to them.

By embracing a multi-vendor retargeting strategy, brands can unlock new audiences, enhance performance, and build resilience against future uncertainty.

About RTB House

RTB House is a next-generation performance demand-side platform (DSP) that uses proprietary Deep Learning AI algorithms to help brands grow. The company is a market leader in driving performance using Deep Learning across the entire purchase funnel.

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New research report series provides CMOs with a strategic framework and practical tools to orchestrate brand visibility across search and AI environments

At the heart of this evolution are the new AI-powered discovery platforms. These services collect information and give you accurate answers to your questions in a conversational style, unlike traditional search engines that give you a list of links. And that fundamentally changes the way content is consumed. Instead of having to open multiple websites, users can now rely on a single AI-generated answer to help them make decisions.

So, visibility is no longer about ranking on the first page of search results—it’s about getting into the AI’s answer. The change is transforming digital competition, compelling organizations to reconsider their martech strategies to stay discoverable in an AI-first world.

Generative AI is also transforming buyer behavior. Buyers are increasingly using AI tools to do everything from early-stage research to final decision making. They are using them to compare options, evaluate solutions and get insights. These tools function as advisors, offering tailored information according to context and intent, not just keyword hits.

This means that traditional marketing strategies that are focused on driving traffic to websites are becoming less efficient. Instead, companies should focus on influencing how AI systems interpret and display their brand. As the martech landscape continues to evolve, strategies need to change from traditional content and signals to those that are aligned with how AI models consume and prioritize information.

Simultaneously, the dominance of keyword-based SEO and link-driven navigation is slowly receding. SEO is still important, but it’s changing. Keywords alone are no longer enough to guarantee visibility, as AI systems prioritize context, relevance, and authority over simple keyword matching.

Similarly, the significance of backlinks is being redefined as AI platforms aggregate and analyze data from different sources rather than relying solely on traditional ranking factors. This progression underscores the need for more complex and flexible martech strategies that go beyond the traditional optimization playbook.

In the end, this change is a reflection of a larger shift in the way digital discovery works. The shift is from search rankings to smart recommendations, from static content to dynamic insights, from user-driven navigation to AI-led exploration. Martech strategies are no longer about optimizing for search engines, they are about optimizing for intelligence systems, and organizations need to realize that to stay competitive. AI is the future of digital discovery, and companies that adapt their martech strategies to this will be best placed to thrive in this new era.

What Are AI Discovery Engines?

With digital discovery evolving, a new class of platforms is emerging that fundamentally changes the way users access and interact with information. Central to this shift are AI discovery engines that are turning static search experiences into dynamic, conversational ones.

These engines are built to understand intent, synthesize information and give precise answers, unlike traditional systems that index and rank web pages. That’s not just a technological shift; it’s a strategic shift that forces organizations to rethink how they think about visibility and engagement. Therefore, martech strategies need to be adapted to how these systems function and how they influence user behaviour.

AI discovery engines represent a move away from navigation-based exploration toward intelligence-driven discovery. “They’re not searching across multiple sources for answers anymore, they’re using AI to aggregate and interpret on their behalf. It changes the role of content, branding and digital presence. To stay relevant, companies must evolve their martech strategies so their information is not only accessible, but also interpretable and usable by AI systems.

Definition and Concept

You can describe AI discovery engines as AI-powered platforms that synthesize information instead of just listing links. Traditional search engines are intermediaries that send users to external sources. AI discovery engines, on the other hand, are interpreters. They consume a lot of information and return one unified answer. This change removes the need for users to click through multiple pages, leading to a more efficient and intuitive discovery experience.

At the heart of these engines is that they are conversational and intent driven. They communicate with users in natural language , asking and answering complex questions in context . This kind of interaction can lead to more engagement and more accurate outcomes, as the system can improve the answers by asking subsequent questions. For businesses, this means visibility is not about being one of the many options, it’s about being part of the final answer. Hence, martech strategies must be geared to creating content that is aligned to conversational queries and intent-based discovery.

These engines use large language models (LLMs) as a core building block. They are trained on huge volumes of text data, enabling them to understand context, generate coherent replies, and adapt to the user’s intent. They don’t just get information, they interpret and reframe it. This adds a new layer of complexity for marketers, as the way content is structured and presented can influence how it’s interpreted by AI. For martech strategies to thrive in this environment, they must take into account not just the content being created, but how it is interpreted by these models.

Key Characteristics

AI discovery engines have a unique set of capabilities that set them apart from traditional search engines. These characteristics allow them to move beyond simple information retrieval to intelligent, context-aware discovery. Real-time processing, personalization and advanced language understanding allow them to deliver more accurate and meaningful experiences to users. Understanding these core traits is important to adapt digital strategies to an AI-first discovery landscape.

a) Context-Aware and Intent-Driven Responses

AI discovery engines don’t just match keywords, they understand the intent of a user’s query. They look at context, phrasing and even prior interactions to determine what the user really wants to know. This means they can provide more relevant and nuanced responses. These systems don’t match exact terms, they match meaning. This means that the content must be structured around real user intent, not just keywords in isolation.

b) Multi-Source Information Aggregation

Traditional search engines don’t work this way, of course; they give a list of links from individual sources. But AI discovery engines combine info from a broad array of inputs. They pull information from articles, databases, forums and other online sources and combine it to produce a single response. This reduces fragmentation for the user, but increases competition for visibility, as brands must now build credibility across multiple channels to be represented in these aggregated outputs.

c) Real-Time and Dynamic Output Generation

AI discovery engines are designed to produce responses that evolve with new data, and new contexts. Not static web pages, but dynamic output that can display the latest information available. This capability allows for more accurate and timely insights but also means that visibility is not static. Content has to be актуальноe and ever updating to stay relevant in these systems.

d) Personalization at Scale

One of the strongest capabilities of AI discovery engines is their ability to tailor responses to individual users. The systems study behavior, preferences and context and then generate highly personalized outputs. That makes for a better user experience, but also raises expectations for relevance. “The one-size-fits-all messaging will not work and businesses should ensure that their content can adapt to different audiences and scenarios,” said the report.

e) Conversational and Interactive Interfaces

The AI discovery engines work through natural, conversational interfaces, enabling users to ask questions and refine them on the fly. The multi-turn interaction lets users explore topics further without the need to start their search again. It makes discovery a continuous conversation instead of a linear process, with each answer building on the one before. This interactivity makes the experience more intuitive, closer to the way people naturally seek information.

f) From Retrieval to Synthesis

The conventional search engines are designed to fetch information and expect the users to interpret and compare the results. In contrast, AI discovery engines distill information into short, actionable answers. They take in a few data points, spot trends and spit out conclusions. They effectively reduce the amount of work the user has to do. This shift makes it more important how information is organized and interpreted by AI systems.

g) Recommendation-Led Discovery

The AI discovery engines are about recommendations, not listings. They are more like advisors than directories. Instead of presenting a list of options, they will often give you specific suggestions that are based on relevance and context. This alters the nature of visibility – being recommended is more important than simply being listed. For businesses, this means trust, authority and contextual relevance are critical factors in influencing AI-generated recommendations.

Marketing Technology News: MarTech Interview With Fredrik Skantze, CEO and Co-founder of Funnel

How Are AI Discovery Engines Different from Traditional Search?

One of the most profound changes in the digital ecosystem is the shift from classic search engines to AI discovery engines. Search engines worked for years on a familiar model. You typed in keywords, and the algorithms returned ranked lists of links. However, AI discovery engines are fundamentally changing this paradigm with a focus on understanding, synthesis and interaction rather than simple retrieval.

This isn’t merely a technological shift, but a strategic one, forcing businesses to reconsider their approach to visibility and engagement. As this transformation accelerates, martech strategies will need to evolve to match how AI systems interpret, prioritize and display information.

AI discovery engines are intent-driven, and provide precise answers based on context, whereas traditional search is query-driven, matching queries to indexed content. This change affects everything about how people find digital things—from how users search to how brands get found. Organizations need to evolve their martech strategies to operate effectively in this new intelligence-driven environment to remain competitive.

a) From Keyword to Context – Traditional SEO vs. intent-based AI understanding

Traditional search engines are heavily dependent on keywords to match user queries with relevant content. For years, SEO strategies have focused on optimizing for specific keywords so that the content ranks higher in search results. This method is all about keyword density, backlinking, and technical optimization. This has worked well in the past, but it is becoming more and more limited in a world where users expect more nuanced and context-aware responses.

AI discovery engines move the focus from keywords to context. They don’t just match terms, they understand what a query is about, looking at things like intent, phrasing and user behaviour. This allows them to provide better and more relevant answers, even for a complex or ambiguous question. For example, if a user asks a detailed question, they will get a synthesized answer, not just a list of loosely connected links.

The change has big implications for martech strategies. Now, content needs to be created to cover a wider range of topics and user intent, not just keywords. It demands a deeper understanding of audience needs and the ability to deliver holistic information that is rich in context. Businesses need to rethink how they approach content, focusing on clarity, relevance and depth so that their content is understood properly by AI systems.

b) From Links to Responses – Search engines’ options, AI’s conclusions

One of the biggest differences between traditional search and AI discovery engines is how results are presented. What search engines give is a list of links, and users need to visit several sources to find the information they want. This takes time and effort, as users have to assess and compare different options.

AI discovery engines, on the other hand, give you the answers. They pull together information from different sources and present one, unified response. This helps users to avoid clicking through different pages and gives them a better experience and more efficiency. But it also changes the dynamics of visibility – being one of many links is no longer sufficient. The Brands need to be part of the final answer instead.

This change will have a big effect on martech strategies. The goal is no longer simply to drive traffic to a website, but to get content included in AI-generated responses. It’s about moving towards authoritative, well-structured content that AI systems can easily interpret and trust. Businesses need to build credibility and relevance, which will determine whether their information is selected and synthesized into answers.

c) Navigation to Conversation – Static browsing vs. Active query-response

Traditional search is by its nature navigational. Users enter a query and get a list of results. They then sift through different pages to find the information they want. It’s a linear process, often requiring several iterations as users refine their queries and explore different sources.

Conversational models, however, are emerging from AI discovery engines. Users can ask questions, get answers, and then ask more questions in a conversation. The interactive nature of this allows for deeper dives and more tailored answers. With each interaction the system learns and improves its understanding so it can give increasingly accurate information.

This shift necessitates a fundamental change in business martech strategies. Content has to be created for conversationality, not only for the initial question but also for the follow-up questions that could come later. That means you get ahead of the user and you stack information in a way that can be easily built upon. Brands also need to make sure their message is consistent across various contexts, because AI systems can pull from multiple sources to keep the conversation going.

This conversational discovery of AI also changes the way users engage with content. They are not just passively consuming the information but actively engaging with it making a more dynamic and personalized experience. That’s why it’s so important that martech strategies are agile and responsive.

d) Ranking to Recommendation – Visibility shifts from page ranking to AI-generated mentions

In traditional search, ranking is the main driver of visibility. Websites fight to get to the first page of search results. The better they rank, the more visible and traffic they get. We are currently focusing on SEO to improve rankings through keyword targeting, backlinks, and technical performance.

AI discovery engines disrupt this model, moving from ranking to recommendation. Rather than a ranked list of results, they focus on highlighting particular suggestions based on relevance, context and authority. Visibility is no longer about topping a list, it’s about being part of the AI’s recommendation.

This shift has important implications for martech approaches. Businesses need to invest in trust and authority across the digital ecosystem because these are the attributes that will determine if they get recommended by AI systems. It also requires a broader view of visibility, not just the owned content but also third-party mentions, reviews and other signals that add to credibility.

Furthermore, recommendations are often personalized, which means different users might receive different suggestions based on their preferences and behavior. The martech strategies are thus even more complex, as they have to take into account different audiences and contexts. Therefore, the content should be relevant and applicable to multiple scenarios, increasing the likelihood of being recommended.

The transition to AI discovery engines from traditional search is a fundamental shift in how we access and consume information. From keywords to context, links to answers, navigation to conversation, ranking to recommendation, everything about digital discovery is being redefined. These changes require businesses to re-evaluate their approach to visibility, engagement, and content creation.

In this new landscape, winning martech strategies will need to shift away from traditional SEO practices and adopt a more holistic, intelligence-driven approach. Organizations that emphasize context, authority and adaptability will be positioned to succeed in a world where discovery is powered by AI rather than search engines.

Impact on Buyer Behavior

The rise of AI discovery engines is not just a change in technology – it’s a change in how buyers think, search and decide. Buyer journeys used to be linear, starting with search engines, followed by website visits, and ending with evaluation and purchase. That journey today is getting compressed, dynamic and more and more AI-augmented.

As buyers increasingly rely on intelligent systems for information and guidance, businesses must rethink how they engage and influence decision-making. The change means that martech strategies have to be redefined – from traffic-driven models to intelligence-driven engagement.

AI discovery engines are changing purchase psychology. Instead of exploring different sources, buyers are outsourcing the discovery process to AI systems that filter, synthesize, and recommend information. This cuts down on friction but it also affects how trust is developed and how brands are viewed. Organizations must adapt their martech strategies to these changing behaviors to remain relevant and be present and credible in AI-powered interactions.

a) AI as the First Point of Research – Buyers relying on AI for initial discovery

One of the biggest changes in buyer behavior is the shift to AI as the starting point for research. Buyers no longer begin with a search engine or visit websites directly. They visit AI platforms to ask questions, explore options and find out more. The platforms provide smart helpers that offer curated answers to help in the early stages of decision-making.

This change reduces the value of traditional entry points like search engine results pages and homepage visits. AI-generated summaries are shaping first impressions of brands, as buyers are not interacting with them directly. This means martech strategies need to focus on influencing how AI systems interpret and present information about a brand.

Companies need to make sure their content is accessible, structured and authoritative from multiple sources to win here. This makes it more likely to be included in answers generated by AI. Martech strategies are shifting from traffic generation to perception management at the earliest stage of the buyer journey.

b) Reduced Website Dependency – Fewer clicks, more direct answers

AI discovery engines are dramatically reducing the amount of websites users need to visit. These platforms provide answers directly, which means that fewer clicks and visits to pages are needed. Buyers can get the information they need without leaving the AI interface, creating a more streamlined and efficient experience.

This trend puts a strain on one of the fundamental assumptions of traditional digital marketing — that success is measured by website traffic. With fewer users coming to websites page views and click-through rates become less relevant. The emphasis now is on visibility in AI-generated responses.

This means a major shift in martech strategies for organizations. It’s not just about getting users to a website, but making sure the brand is present wherever discovery happens. This includes third-party platforms, knowledge bases and other digital points of contact that AI systems refer to as sources.

This reduced dependency on websites changes the way people consume content. Information should be short, clear and easy to interpret for AI systems. Thus, martech strategies should be geared towards structured content and semantic clarity so that core messages can still be communicated effectively in other than a website environment.

c) Trust in AI Recommendations – AI as advisor, not just a tool

With AI systems becoming ever more sophisticated, they are increasingly viewed as trusted advisors rather than just tools. Buyers use these systems to filter information, compare alternatives and make recommendations. The trust shift has important implications for decision making.

Traditional models established trust through direct interactions with the brands like website content, reviews and customer experiences. The AI-driven model performs trust mediation via the AI system itself. Buyers trust the AI recommendations and do not always check the sources behind them.

This presents opportunities and challenges for businesses. The AI recommendations, on the one hand, can greatly increase credibility and influence. However, brands have little control over the way they are represented. To win this game, martech strategies need to be built around strong authority signals across the digital ecosystem.

Consistency, credibility and relevance play important roles in influencing AI recommendations. Businesses need to make sure their messaging is consistent across all channels, as AI systems draw information from a variety of sources. Good martech strategies have to think about how they can create trust indirectly through the data and signals that AI systems rely on.

d) Shortened Decision Cycles – Faster evaluation and comparison

AI discovery engines are speeding up decision making by offering instant access to information and comparisons. Buyers can compare options, learn features, and gauge value all in one interaction. This reduces the research time and allows for quicker decision cycles.

This is efficient for buyers, but it increases pressure on businesses. There is less time to garner attention, develop relationships and influence decisions. The window of opportunity is shorter, the competition is more intense.

To adapt, martech strategies need to be about clear, compelling and differentiated messaging. Buyers may not have the time for extended research, so content needs to quickly convey value and relevance. This means moving toward communication that is concise and powerful.

On top of that, with decision cycles that are faster, brands need to be present at multiple touchpoints all the time. If a brand does not show up in the first AI generated answer, it might be excluded from further consideration. This highlights the need for proactive and adaptive martech strategies that keep brands visible and engaged at all times.

Challenges To Traditional Martech

AI discovery engines present new opportunities but also massive challenges to traditional marketing approaches. Many of the existing models are based on assumptions that are not valid anymore in an AI driven environment. The challenges, including falling traffic and measurement gaps, mean that organizations need to rethink their approach and evolve their martech strategies.

a) Loss of Direct Traffic and Visibility – Declining organic traffic from search engines

The decrease in organic traffic from traditional search engines is one of the most immediate results of AI discovery engines. As people depend more on AI-generated answers, clicks to websites go down. This diminishes the effectiveness of SEO-driven traffic acquisition strategies.

This shift can have significant implications on businesses that are heavily dependent on organic traffic. Fewer chances to engage and convert because of less visibility in search engine results pages. To solve this problem, martech strategies should go beyond traditional SEO and target AI-powered visibility.

This includes optimizing content for AI-generated responses and creating a presence across multiple platforms. It’s less about driving traffic and more about discovery influence, and that takes a more holistic view of digital marketing.

b) Lack of Control Over AI Narratives – Brands not controlling how they are described

In the AI-driven discovery model, brands can’t fully control how they’re presented. The AI model creates replies based on a mix of information from different sources, including third-party content, reviews and other outside references. This can create inconsistencies and inaccuracies in how a brand is portrayed.

The lack of control is a huge challenge for martech strategies. Businesses need to find ways to influence AI narratives indirectly, by ensuring that accurate and positive information is widely available across the digital ecosystem.

Managing brand perception becomes more difficult because you have to monitor and shape multiple sources of information. Successful martech strategies include proactive content creation, reputation management, and ongoing monitoring to ensure that narratives created by AI are consistent with brand positioning.

c) Attribution and Measurement Gaps – Difficulty tracking AI-driven discovery journeys

Traditional marketing metrics are based on trackable interactions such as clicks, visits and conversions. But AI discovery engines break this model by obscuring the user journeys. It’s hard to tell how people found a brand or what influenced them to buy it when they get answers directly from AI.

Creates significant attribution and measurement gaps. But businesses may find it difficult to understand which channels are driving engagement and how to best allocate resources. Doing this well can be a challenge. To solve this challenge, martech strategies will need to evolve to include new measurement frameworks.

This might include focusing on indirect measures such as brand mentions, sentiment analysis, and AI visibility. It also calls for a shift from direct attribution to understanding influence. As the landscape evolves, martech strategies need to evolve to glean insights from less visible but no less important interactions.

d) Content Not Optimized for AI Consumption – Traditional content structures not aligned with AI parsing

Much of the content strategies out there are aimed at human readers and traditional search engines. But AI discovery engines require content that is structured, contextual and machine learning-friendly. This leads to a mismatch between traditional content formats and AI requirements.

Content that is too complex, not well structured or simply keyword focused is not likely to perform well in AI driven environments. Martech strategies focused on clarity, structure and semantic relevance remain effective.

This means using well-structured formats, clear headings and short explanations that AI systems can easily digest. It also means creating content that answers specific questions and use cases, in a manner that reflects how users engage with AI platforms.

Businesses can increase their visibility and relevance in AI-generated responses by tailoring content strategies to suit the needs of AI systems. This transition is vital to keep martech strategies relevant in an increasingly intelligent digital environment.

Changes in buyer behavior Changes in challenges of traditional marketing approaches AI discovery engines Buyers are increasingly turning to AI for research, trusting its recommendations and making decisions quicker. Businesses are seeing less traffic, losing control and facing measurement difficulties.

To navigate this transformation, organizations need to rethink their approach and evolve their martech strategies. Businesses that focus on AI-driven discovery, build authority across digital ecosystems, and adapt content for intelligent systems can position themselves for success in this new era.

How Martech Strategies Must Evolve?

The fast-paced evolution of artificial intelligence has transformed the way consumers find, assess and interact with brands. With AI systems mediating user interactions more and more, traditional digital marketing methods based on search engines, keyword rankings, and static content are no longer adequate. That means martech strategies need to change in order to continue to be effective, relevant, and competitive.

Modern AI systems don’t just retrieve information; they synthesize it, interpret it, and present it in conversational formats. That means brands aren’t simply competing for clicks anymore — they’re competing to be part of the AI-generated response. For organizations to win in this new era they need to re-think the way they structure content, build authority and disseminate their message across platforms.

Here are the top ways martech strategies will need to change to stay aligned with AI-powered discovery and engagement models.

a) AI Visibility Optimization – Ensuring presence in AI-generated responses

AI for visibility optimization is becoming a pillar of modern martech strategies. AI visibility is different to traditional SEO, which is all about ranking web pages. AI visibility is much more about getting a brand’s content mentioned, summarised or recommended by AI systems.

AI models learn from a variety of sources, ranging from websites, knowledge bases, and forums to structured data. Brands now need to make sure their content is not just accessible, but also interpretable and trustworthy. This is about making content that answers particular questions clearly, in natural language, and in accordance with user intent.

To boost AI visibility, organizations should focus on:

• Publishing authoritative, well-structured content
• Answering common industry questions directly
• Maintaining consistency across digital touchpoints
• Ensuring content is updated and relevant

While traditional search behaviors are on the decline, brands can still be found by integrating AI visibility optimization into martech strategies.

b) Structured and Contextual Content – Creating content that AI systems can easily interpret

Writing content that AI systems can easily interpret AI systems heavily depends on structure and context for understanding and generating responses. This means that structured and contextual content is a cornerstone of effective martech strategies.

Structured Content has proper headings, bullet points, schema markup and structured data formats. Instead, what contextual content offers is that information that is meaningful, relevant, and connected to larger themes or questions from the user.

When content is both structured and contextual, AI systems are able to:

• Extract key insights more accurately
• Summarize information effectively
• Present content in a conversational format

For marketers, this means moving away from keyword stuffing and towards semantic clarity. Content should be formatted to answer questions, provide value, and give context.

With structured and contextual approaches, martech strategies can dramatically improve how AI systems understand and prioritize brand content.

c) Authority and Trust Signals – Building credibility across digital ecosystems

Authority and trust have always been important in marketing, but they are now central to how AI systems judge and pick content. AI models seek reliable and credible sources, so martech strategies should hone in on authority signals.

These signals are:

• High-quality backlinks from reputable sources
• Consistent brand mentions across platforms
• Verified authorship and expertise
• Positive user engagement and reviews

AI systems are designed to fight misinformation, so they prefer content from trusted entities. Brands that don’t build credibility risk getting shut out of AI-generated responses.

“Thought leadership, original research, and a consistent digital presence are all key to building authority,” he adds. Over time, these efforts build stronger trust signals that increase visibility.

Building authority into martech strategies guarantees brands are not just seen but are also credible in AI environments.

d) Multi-Channel Content Distribution – Expanding beyond websites to multiple content sources

Those days of only using websites for visibility are gone. AI systems source data from many places, so multi-channel distribution is a critical element of today’s martech strategies.

Brands should expand their reach across:

• Social media platforms
• Video content channels
• Industry forums and communities
• Knowledge-sharing platforms
• Podcasts and webinars

The more digital footprint, the more chances that the AI systems will come across and refer to the brand, with each channel playing a part. Also, different formats like videos, infographics, and interactive content provide more opportunities for engagement and visibility. AI systems are increasingly combining multimodal data, making it possible to interpret and use different types of information.

Martech strategies can improve overall discoverability, diversify content exposure and maximize reach through a multi-channel strategy.

e) Narrative and Positioning Strategy – Shaping how AI interprets and represents brands

In an AI world, it’s not just about where a brand shows up, but how it’s described. The narrative and positioning strategy is crucial to guide how AI systems process and articulate brand information.

AI models generate answers from patterns and associations in data. This means that consistent messaging across platforms reinforces a clear and accurate brand identity.

Successful narrative strategies include:

• Defining a clear brand voice and tone
• Maintaining consistent messaging across channels
• Highlighting unique value propositions
• Aligning content with core brand themes

When stories are not coherent or cohesive, AI systems can create inaccurate or watered-down versions of the brand. A strong cohesive story, on the other hand, is when the AI-generated responses reflect the positioning you want. By integrating narrative development into martech strategies, brands can shape their perceived identity and recommendations by AI.

Benefits of AI-Optimized Martech

As organizations adapt their tactics, the advantages of AI-optimized marketing become more apparent. By embracing AI-enabled discovery processes, businesses can unlock new levels of visibility, engagement and performance.

Here are some of the benefits that prove why investing in AI-aligned martech strategies is not just beneficial but an absolute necessity.

a) Increased Discoverability in AI Platforms – Visibility where modern buyers search

Today’s buyers are increasingly looking to AI-powered tools for information, recommendations and decision-making. It’s not enough to rely on traditional search visibility with this move.

Optimized martech strategies with AI make sure that brands are where users are looking for answers. Increased discoverability means more opportunities to interact, whether through conversational AI, voice assistants or recommendation engines.

Brands that leverage AI visibility, structured content and multi-channel distribution will be at the forefront of this new discovery landscape.

b) Higher-Quality Leads – Better alignment with user intent

One of the biggest benefits of AI-driven marketing is the capacity to better match user intent. AI systems are built to understand context, preferences and behavior, resulting in more accurate matching between users and content.

Optimized for AI, martech strategies naturally appeal to users who are:

• Specifically looking for solutions
• Later in the decision-making process
• Probable to turn

This leads to better quality leads and improved conversion rates. AI-optimized strategies focus on precision and relevance over broad targeting.

c) Stronger Brand Authority – Consistent positioning across AI systems

The secret to building authority is consistency, and AI systems reward brands with a clear and unified voice. Martech strategies can help to strengthen brand authority across multiple platforms via alignment of messaging, content and distribution.

AI systems that are repeatedly shown consistent and credible information are more likely to:

• Reference the brand in responses
• Recommend it as a trusted source
• Associate it with specific topics or expertise

This, in turn, builds brand recognition and influence over time. It’s not just about perception anymore—it’s about being seen and validated by AI systems.

d) Competitive Differentiation – Early adoption advantage

As with any technology shift, there’s a big advantage to being an early adopter. Companies that adapt their martech strategies early to fit AI trends can differentiate themselves from competitors that cling to the old ways.

This differentiation is realized in a number of ways:

• Greater transparency in AI-generated responses
• More engagement with today’s audiences
• More credibility and confidence
• Enhanced marketing efficiency

Many businesses are still scrambling to catch up, but those that adopt AI optimization can set themselves up as leaders in their respective industries.

The integration of AI into digital ecosystems is not a passing fad. It is a fundamental shift in how information is accessed and consumed. As AI systems become the dominant way users will be interacting with content, companies will have to change their marketing.

This new reality isn’t about making small tweaks to martech strategies. It requires a holistic shift to AI visibility optimization, structured content creation, authority building, multi-channel distribution, and narrative consistency.

The benefits of this transformation are dramatic – better discoverability, higher quality leads, greater brand authority, and competitive differentiation. Organizations that embrace these changes will not only survive, but will thrive in the rapidly evolving digital landscape.

The future of marketing ultimately goes to those who know how AI works—and, far more importantly, how to work with AI.

The Future of AI Discovery in MarTech

The world of digital discovery is undergoing a dramatic change. Search engines used to determine how users found information, but artificial intelligence is now becoming the main interface between users and content. This is not a marginal change – it’s a fundamental change. As AI systems become more advanced, conversational and context-aware, they are transforming how brands are discovered, evaluated and trusted.

This evolution requires organizations to rethink how marketing works at its core. Old ranking based, keyword and static content approaches are being replaced with dynamic, intelligent systems that focus on relevance, context and authority first. Martech tactics need to adapt to the way AI systems interpret and deliver information in this new environment.

The future of AI discovery is not just about seeing, but about being present in the moments that matter when decisions are made. Brands need to learn to adapt to new interfaces, new expectations and new rules of engagement.

a) AI as the Primary Discovery Layer – Shift from search engines to AI interfaces

One of the biggest changes in digital behavior is the shift away from traditional search engines to AI-powered interfaces. Conversational AI tools are increasingly being used by users to ask questions, explore options and make decisions. They want answers, not a bunch of links to sift through.

This change fundamentally alters how discovery works. AI systems do more than rank content, they interpret, summarize and recommend it. So, martech strategies need to be focused on being included in AI-generated outputs, not just being present in search results.

This change also changes what users expect. People expect now:

• Immediate, accurate responses
• Context-aware suggestion
• Personalized insights

Brands will need to produce content that not only informs but is also interpretable by AI systems to meet these expectations. This includes clear structure, semantic relevance and authoritative positioning.

AI is the new discovery layer, so martech strategies must focus on visibility in AI environments that make their content discoverable and impactful in shaping responses.

b) Continuous Optimization of AI Systems – Adaptive and responsive strategies

Where traditional SEO might have been based on periodic updates and long-term ranking strategies, AI-driven discovery requires constant optimization. AI systems are constantly learning, updating and improving their output from new data and user interactions.

That means martech strategies have to be more dynamic and adaptive. Static content is no longer enough. Brands need to be continually improving their messaging, refreshing their information and responding to changing trends.

Continuous optimization consists of:

• Regularly updating content to stay relevant
• Discover how AI systems interpret and reference brand information
• Moving to new formats and data structures
• Experimenting with various formats of content

This iterative approach helps brands stay in step with changing AI models and user expectations. And feedback loops are important, too. By understanding how content behaves in AI environments, marketers can spot gaps, adjust strategies, and boost results. “You have to be this agile to remain visible and competitive.”

In this context, Martech strategies need to move from reactive to proactive, anticipating changes and continuously optimizing for AI-driven discovery.

c) Rise of AI-Native Marketing Strategies – Marketing built specifically for AI ecosystems

With AI at the center of discovery, a new category of marketing is emerging: AI-native marketing. They are not digital marketing strategies that have been retrofitted to AI, they are strategies built for AI ecosystems.

AI-native martech strategies are all about building content and experiences that are as optimized for machine interpretation as they are for human consumption. This includes:

• Structuring data for easy parsing
• Conversational matching question in natural language
• Clear, simple, direct answers to common questions
• Creating interconnected content ecosystems

This shift also changes how success is measured. Instead of focusing solely on metrics like page views or rankings, marketers must consider:

• Inclusion in AI-generated responses
• Frequency of brand mentions in AI outputs
• Accuracy of brand representation
• Engagement within AI-driven interactions

By adopting AI-native methods, organizations can position themselves at the forefront of innovation. These martech strategies allow brands to play effectively in AI ecosystems, ensuring they are not only visible but also relevant and influential.

d) Integration with Voice and Multi-modal Interfaces – Going beyond text-based discovery

The future of AI discovery is not just text. Voice assistants, visual search and multimodal interfaces are rapidly gaining ground, and offer new ways for users to interact with information.

Specifically, voice interactions are changing how queries are formulated. Instead of typing keywords, users speak in natural language and ask complex, conversational questions. This means martech strategies need to be evolving into more subtle, more context-rich queries.

Multimodal interfaces combine text, voice, images and even video to create richer and more interactive experiences. Brands will need to diversify their content, as AI systems can now analyze and synthesize information across formats.

Organizations need to: to be successful in this environment:

• Optimize content for voice search and conversational queries
• Incorporate visual and multimedia elements
• Ensure consistency across different formats
• Leverage structured data for better interpretation

These advances open the field of discovery and new possibilities for engagement. But they also add complexity and require more sophisticated and integrated approaches. Martech strategies can reach more users and be seen and felt more by deploying multimodal capabilities to engage users at more touchpoints.

Conclusion

Digital discovery has been a defining moment in how brands engage with their audiences. Traditional search engines set the rules of engagement for years and keyword rankings were the main measure of visibility. But the advent of artificial intelligence has changed this dynamic in a fundamental way. Discovery has evolved from browsing lists of links to receiving curated, context-aware answers from intelligent systems. This shift necessitates a total re-evaluation of marketing operations, with martech strategies at the heart of this transformation.

This change is due to the transition from keyword-based SEO to AI-driven discovery. Back in the day, it was all about finding the right keywords, optimizing pages, and fighting for the top spots. These tactics are not entirely obsolete, but they no longer cut the mustard on their own. AI systems care about meaning, not matching; context, not repetition; authority, not volume. Hence, martech strategies should change to semantic relevance, structured content, and credibility. The focus is moving from getting pages to rank to systems that provide answers.

In addition, this also represents the gradual death of traditional SEO. And with AI-powered tools, users are finding ways to circumvent search engines, which no longer have a monopoly on information access. These tools give you direct answers, eliminating the need to click through to several sources. For marketers, that means visibility is no longer just about rankings. Instead, it’s dictated by whether a brand appears in the outputs generated by AI. Martech strategies will have to evolve with this reality to stay competitive, making sure content is accessible, interpretable and trustworthy in AI ecosystems.

Another important aspect of this shift is the increasing importance of intelligence in marketing systems. Modern martech strategies should not just be about content creation and distribution. It should also be about data, insights and continuous optimization. AI systems are not static, they are learning and evolving constantly, based on new information and user behavior. Marketers have to keep up. And they have to be as dynamic. They have to be adjusting their strategies in real time as trends arise.” It requires a change in mindset, from static campaigns to adaptive ecosystems that can evolve alongside AI technologies.

Moreover, the function of visibility itself is changing. In an AI-driven landscape, brands need more than just visibility; they need to be accurately represented. The AI systems are the intermediaries, shaping how the information is framed and understood. So consistency, clarity and authority are more important than ever. Good martech strategies take into account brand narratives and ensure they are consistent across all platforms so that AI systems can understand and communicate them correctly. This kind of control over representation is essential for building trust and a strong market position.

Ultimately, the future of digital discovery is about recommendations, not rankings. AI systems are becoming the primary decision-making interface that directs users to specific solutions, products and services. This puts a lot of responsibility on marketers to align their strategies around how these systems work. Martech strategies need to move from trying to be visible in search results to trying to be seen in AI-generated recommendations. This calls for a more nuanced understanding of how AI evaluates content and a commitment to building user-centric, value-driven experiences.

Hence, the move to AI-driven discovery is not just a technology shift, it is a strategic imperative. Organizations that embrace this shift and adapt their martech strategies to it will be ready to thrive in the new digital landscape. They risk becoming invisible in a world that’s run by AI deciding what’s seen, trusted and chosen if they don’t evolve. So what’s next? It’s obvious: get on the AI train, focus on intelligence, and re-imagine marketing for a future where recommendations, not rankings, rule the roost.

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Senior leadership team including newly appointed VP Americas, Oshri Raz, head to Fontainebleau Miami Beach for one-on-one meetings, providing more information on the platform driving up to 300% revenue growth for partners

Limelight Inc.’s senior leadership team will attend POSSIBLE 2026, taking place April 27–29 at the iconic Fontainebleau Miami Beach & Eden Roc in Miami, Florida.

The event also marks the first major industry appearance by Oshri Raz, who recently joined Limelight as VP, Americas Strategic Alliances, a senior appointment that highlights the company’s ambition to expand into the US market. With extensive experience in global ad tech, programmatic monetisation and revenue infrastructure leadership across the US, EMEA, LATAM and APAC, Raz’s focus at the company is to position Limelight as the primary independent programmatic infrastructure partner across the region.

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Raz will be joined at POSSIBLE by other senior members of the Limelight team including:

• David Nelson, CEO
• Oshri Raz, VP Americas
• Savina Parvanova, Global Head of Marketing
• Daniel Nelson, Director of Client Success
• Andrew Macdonald, Director of Client Success
• Uriah Goldstein, Global Head of New Business

A key focus for the Limelight team at POSSIBLE will be ARC, the automation-based toolkit designed to streamline programmatic operations and unlock significant performance gains for partners. Since its launch, ARC has delivered measurable results across Limelight’s partner network, including:

• Revenue growth of up to 300%
• A 4x improvement in auction success rate
• A 10x improvement in fill rate
• A significant reduction in manual workload

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“The team is looking forward to attending its first POSSIBLE Miami event. The USA is still the largest global trading market in programmatic advertising, and as a result, we have developed a strong presence in terms of both partners and people in the region. Oshri represents a senior, key, exciting hire for us, reinforcing our commitment to excellence and to sharing our values in the region. Our technology and model is designed to support independent, transparent and cutting-edge 360-degree programmatic activity.”

— James Macdonald, CRO, Limelight Inc.

Attendees at POSSIBLE are invited to schedule one-on-one meetings with Limelight’s experts to discuss how ARC and Limelight’s broader suite of programmatic solutions can help drive performance, efficiency and profitability.

Limelight Inc. – the world’s fastest growing white label platform, helps companies in the ad tech ecosystem to easily navigate the complex programmatic landscape, blending cutting-edge technology with best-in-class expertise and human support. Hundreds of ad networks and publishers use Limelight’s programmatic oRTB solution to build bespoke, white-labelled trading environments, drive profitability and performance at scale and unlock incremental revenues – immediately. Limelight is more than a service provider; our ethos is firmly centred on human support and strong partnerships for the global Limelight community.