Reducing latency and removing data barriers as demand grows for faster, locally compliant software

Modern drone systems often depend on continuous connectivity for control and data transmission, limiting their effectiveness in real-world conditions where networks are degraded, denied, or unavailable. This constraint restricts beyond-line-of-sight operations and prevents timely delivery of mission-critical intelligence.

Through this collaboration, EVERYWHERE Communications is introducing a resilient data transport layer utilizing Iridium Satellite that allows drones to operate autonomously while continuing to communicate critical sensor data back to operators and command systems.

Marketing Technology News: MarTech Interview with Haley Trost, Group Product Marketing Manager @ Braze

The technology enables:

• Beyond-line-of-sight operations, reducing reliance on continuous pilot control
• Reliable data exfiltration, ensuring sensor data reaches decision-makers even in disrupted environments
• Autonomous mission execution, including AI-driven search and detection patterns
• Scalable coordination, supporting large numbers of drones operating simultaneously
• Efficient command and control, including low-bandwidth “burst” communication channels for mission updates

“In austere environments, connectivity is never guaranteed,” said Jake Bailey, President of EVERYWHERE Communications. “We’re enabling drones to keep operating—and keep delivering intelligence—even when the network is compromised.”

Marketing Technology News: Cross-Department Collaboration with Marketing Workflow Automation: Enhancing Alignment Between Sales, Customer Service, and Marketing Teams

As part of this effort, Parsons is delivering TAK-as-a-Service (TaaS), providing scalable, mission-ready TAK Server integration and sustainment services tailored to the operational needs of the Department of War and Federal agencies. Through secure deployment, federation, and continuous support, the company enables real-time situational awareness and seamless data interoperability across distributed mission environments globally.

“In an era where every second and every signal matters, this collaboration brings together resilient autonomy in the air and trusted mission systems on the ground to give our warfighters and intelligence professionals a decisive information advantage,” said Mike Kushin, President of Defense and Intelligence at Parsons.

The platform also supports collaborative drone operations, allowing systems to relay information across distributed networks and contribute to a shared Common Operating Picture (COP).

This partnership represents a significant step toward enabling resilient, scalable, and intelligent unmanned systems to warfighters operating at the tactical edge.

__________

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.

Marketing Technology News: MarTech Interview with Max Groth, CEO at Decentriq

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.

87% of consumers switch between screens every hour. Marketers must rethink the funnel.

Tredence, the world’s leading data science and AI solutions company, announced the launch of its suite of Agentic AI accelerators, developed in close collaboration with Google Cloud and Tredence’s global customer base. These ready-to-deploy accelerators are pre-built, industry-specific AI solutions that will help enterprises bypass lengthy development cycles, enabling them to rapidly deploy proven use cases, integrate with existing data and workflows, and move from experimentation to measurable business outcomes at scale.

The suite includes solutions that accelerate data modernization by simplifying migration from legacy systems and unifying fragmented data into a governed, AI-ready Data foundation thus reducing time, cost, and complexity.

Marketing Technology News: MarTech Interview with Kurt Donnell, CEO @ Freestar

Built on that foundation, purpose-built AI agents work alongside people across the functional areas organizations rely on most, helping teams sense, decide, and act in real time.

In supply chain, this means replacing siloed tools with a single AI-driven decision layer that delivers real-time visibility, stronger coordination, and faster response across operations.

For customer-facing functions, enterprises can anticipate needs, personalize interactions at scale, and turn every customer touchpoint into measurable business impact.

At the core, a unified intelligence layer brings these capabilities together, where AI agents operate as digital co-workers, collaborating, reasoning, and executing decisions to drive faster, scalable enterprise outcomes.

Marketing Technology News: Programmatic Ad Platforms With Unique AdTech Features

“The gap between enterprises that lead and those that lag will come down to how quickly they operationalize Agentic AI,” said Sumit Mehra, Co-Founder and Chief Technology Officer at Tredence. “With Google Cloud, we’re bringing together the power of Gemini Enterprise with solutions we’ve already tested in real-world environments. At Tredence, we don’t just build with Gemini Enterprise, we run on it. It is our trusted LLM across functions and teams, deployed at scale across our own organization. That firsthand experience is what allows our customers to move beyond pilots and actually scale AI by embedding it into day-to-day decisions and delivering outcomes they can measure.”

In FY25, Tredence delivered large-scale, measurable transformation across global retail in partnership with Google Cloud, spanning dozens of strategic accounts across four continents. From executing one of the largest cloud migrations in retail history—moving massive, complex data environments with zero disruption, to modernizing core platforms ahead of schedule, Tredence helped enterprises reduce total cost of ownership by up to 40% .

For a Fortune 500 global company, Tredence deployed Gemini Enterprise, Vertex AI, and BigQuery as the foundation of a complete Data and AI platform modernization, replacing a fragmented legacy environment with a unified, intelligent platform that powers hyper-personalized customer experiences, AI-driven product innovation, and smarter decision-making across the workforce.

Building on this foundation, Tredence accelerated enterprise AI adoption by deploying advanced AI and multi-agent systems on Vertex AI, Gemini Enterprise and Gemini Enterprise for Customer Experience, automating up to 98% of manual processes, reducing operational effort by up to 70%, and compressing deployment timelines from months to weeks.

“The impact has been global and cross-functional, from unifying supply chain intelligence across thousands of stores to rapidly launching full-scale agentic platforms, this demonstrates how the Tredence–Google Cloud partnership translates AI ambition into real, scalable business outcomes,” said Rakesh Sancheti, Chief Growth Officer at Tredence.

Unveiled at Google Cloud Next ’26, the suite represents a major milestone in the Tredence and Google Cloud partnership. This new suite of Agentic AI accelerators directly addresses the ‘Last Mile’ challenge faced by companies, by combining Tredence’s deep industry expertise with Google Cloud’s full enterprise AI stack—including Gemini Enterprise, Gemini for Customer Experience, Vertex AI and BigQuery.

_______

Hi Max, what’s a day at work like as a CEO in martech?

No two days look alike, which is both the challenge and the appeal. A lot of my time goes into conversations at the intersection of business and technology: understanding what marketing and data teams are actually struggling with, and translating that into product thinking. In martech specifically, the pace of change is relentless. New privacy regulations, shifting platform dynamics, the AI wave: you’re constantly having to update your mental model of the landscape.

I try to carve out time in the mornings for deep thinking before the meeting load kicks in. Running and skiing in the Alps help me reset. But the honest answer is that being a CEO in this space means you’re perpetually juggling the urgency of today with the strategy of tomorrow.

What’s wrong with how marketers today choose, deploy and integrate their martech stacks?

The most fundamental problem is that the customer is rarely the starting point. Martech decisions tend to be driven by internal logic (what the vendor promises, what the team already knows, what the budget cycle allows, etc.) rather than by asking: what does the person on the other end of this actually experience, and does our data infrastructure make that experience better or worse?

That sequencing problem has consequences that compound. The average enterprise today runs dozens of martech tools, each holding a fragment of the customer picture. But because those tools were chosen independently rather than as parts of a coherent whole, they rarely agree on who a customer is, what they’ve done, or what they need next. The result is a degraded customer experience. People receive irrelevant messages at the wrong moment through the wrong channel, because the system of record is too fragmented to know any better.

The deeper issue is that most stacks were built around third-party data assumptions that no longer hold. The architecture was designed for a world where you could fill gaps in your customer understanding by buying data about people from somewhere else. That world is contracting fast. What replaces it has to be built on genuine first-party relationships. Too many organizations are still patching over that gap rather than rethinking the foundation.

There’s also a governance blind spot that ultimately hurts the customer too. When tool decisions are made in marketing without legal, IT, and compliance in the room, you get a stack that looks commercially attractive but creates real risks around how customer data is handled. And these are risks that erode the trust that makes the customer relationship possible in the first place.

What martech stack optimization tips do you think more marketers need to pay closer attention to?

A few things I’d highlight:

Always start with your customer, not your tool wishlist. Before you add anything new to the stack, ask: do we have a clear, consistent picture of our customer data, or at least how we can obtain the data we need? If the answer is no, adding more tools will compound the mess.

Audit your existing stack ruthlessly. Most teams discover, when they actually sit down and map it out, that they’re paying for tools that overlap significantly or that nobody is using at full capacity. Consolidation (where it doesn’t compromise capability) almost always pays off.

Treat interoperability as a first-class requirement. When evaluating any new tool, the question shouldn’t just be “does it do what we need?” but “how cleanly does it plug into everything else?” Poor integrations are where data quality goes to die.

Invest in data quality before you invest in analytics. It sounds basic, but the signal-to-noise ratio in most marketing data environments is terrible. Better models and better campaigns both depend on better underlying data.

Finally, think carefully about where sensitive data flows, as this can present a serious business continuity problem in addition to the more obvious legal implications. Knowing where your customer data goes and who has access to it has become a core competency for modern marketing teams.

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

How can modern marketing teams create better data cleaning and data unification processes?

The first shift is cultural: data quality has to stop being treated as someone else’s problem. In too many organizations it lives in a technical backwater, handled by a small engineering team that nobody pays much attention to until something breaks. Elevating data quality as a marketing concern as well as an IT concern changes what gets prioritized and resourced.

On the process side, you need standards before you need software. That means agreeing on what a “customer” is, how identity gets resolved across channels and devices, what counts as a valid email address, and so on. These decisions sound mundane but they’re foundational. You can’t clean data if you don’t have a shared definition of what clean means.

For unification specifically, the challenge is almost always organization at its core rather than technical. Data lives in different systems because different parts of the business own different relationships with the customer. The CRM has one slice, the e-commerce platform has another, the ad platform has a third. Unifying that requires not just technical connectors but trust between teams: agreement on who can see what, under what conditions, and for what purposes. Getting that governance layer right is actually the hard part.

Identity resolution has matured significantly, and the best approaches increasingly combine deterministic and probabilistic methods depending on the context. Many teams still apply a single method rigidly where a more flexible strategy would serve them better. The key is understanding which approach fits which use case, rather than treating it as one-size-fits-all.

A few thoughts on how AI-powered martech is leading to a complete rejig in marketing?

AI is accelerating a shift that was already underway: from campaigns built around broad segments to experiences shaped around individuals. That personalization around scale changes the fundamental unit of marketing strategy.

Here’s the thing that doesn’t get said enough: AI doesn’t create competitive advantage on its own. It multiplies what already exists. If your data is siloed or poorly governed, AI will only amplify the issue. The organizations seeing the best results from AI-powered martech aren’t necessarily those with the most sophisticated models. They’re the ones with the most solid, best connected first-party data foundations.

That’s driving a fundamental rethink of data strategy. For a long time, the dominant instinct was to stockpile and ring-fence proprietary datasets. Today’s marketers are realising that intelligence compounds when data is connected via secure networks. No single brand has a complete view of the customer journey. But through privacy-respecting collaboration across brands, retailers, and publishers, marketers can feed AI richer and more diverse signals without ever exposing raw data. That network effect is where the real AI advantage lives.

Five martech thoughts to leave us with before we wrap up?

1. First-party data is not optional. Every strategy that still depends significantly on third-party data has a shelf life, and that shelf is getting shorter. The teams who’ve invested in owning their customer relationships directly will have a structural advantage that compounds over time.
2. Less stack, more depth. The arms race of adding tools has to end somewhere. The best-performing marketing teams I see are the ones who’ve made fewer, better choices when it comes to their tools and actually mastered what they have.
3. Collaboration between data owners is the next competitive frontier. Some of the most interesting marketing use cases require combining data across organizations — retailer and advertiser, publisher and brand, etc. — without either party giving up control. This kind of privacy-respecting data collaboration is still early, but the teams that figure it out will unlock insights their competitors simply can’t access.
4. Treat compliance as a design constraint, not an afterthought. Privacy regulations aren’t slowing down, and neither is enforcement. The organizations building data practices around compliance from the start will spend far less time and money fixing things later.
5. The AI opportunity in martech is real, but it has to be earned. You don’t get the benefits of AI by adopting AI tools. You get them by doing the unglamorous work of building clean, unified, well-governed data foundations and then letting AI do what it’s actually good at on top of that.

Marketing Technology News: The Death of Third-Party Cookies Was Just the Start. Are You Ready for Consent Orchestration?

The company specializes in secure data collaboration and offers a platform for data clean rooms, as well as the Collaborative Audience Platform: a unified layer that adds CDP- and DMP-style capabilities to the clean room for real-time segmentation, identity, activation, and shared audience products.  Decentriq has secured significant funding, acquired international customers, and established partnerships with major technology companies such as Microsoft.

About Maximilian Groth

Maximilian Groth is co-founder and CEO of Decentriq, a technology company founded in Switzerland.

As enterprises move from single‑prompt chatbots to complex agentic systems, they face three compounding risks: uncontrolled agent sprawl, opaque decision‑making, and fragmented governance across data, models, and tools. Google’s ADK and Vertex AI Agent Builder provide a powerful foundation to build multi‑agent applications with Gemini and other models, connect agents to 100+ systems via connectors and MCP, and orchestrate autonomous workflows at scale.

However, without a unified trust layer, enterprises still struggle to ensure that every agent action respects data policies, regulatory obligations, and business logic across clouds and applications.

Marketing Technology News: MarTech Interview with Miguel Lopes, CPO @ TrafficGuard

What Trust3 AI adds

Trust3 AI delivers a unified platform that governs both data and AI, powered by automated “Trust Agents” that continuously monitor and control how agents access data, call tools, and take actions. Trust3 AI already provides agentic governance for modern data platforms such as Snowflake, Databricks, and Starburst with features like a unified catalog, intent‑based PBAC, semantic enrichment, and end‑to‑end auditability.

“Agentic systems are only as valuable as they are trustworthy,” said Christophe Hassaine, VP of Alliance at Trust3 AI. “By integrating natively with Google’s agentic AI stack, we make it possible for enterprises to go from POC to production with Gemini and multi‑agent applications – without sacrificing governance, compliance, or control.”

With this new integration, Trust3 AI becomes a native trust layer for agentic applications built on Google Cloud – wrapping Gemini‑based agents, ADK workflows, and Vertex AI Agent Builder experiences with policy‑aware controls, observability, and compliance.

Policy-Aware Agents for Smarter AI Governance

Trust3 AI integrates seamlessly with ADK-based agents and Vertex AI Agent Builder workloads, acting as a critical policy decision and enforcement point. By evaluating every agent request against natural-language policies, intent-based PBAC (Policy-Based Access Control) rules, and regulatory frameworks like GDPR, HIPAA, and the EU AI Act, Trust3 AI ensures secure and compliant execution of actions.

Unified Governance Across the AI Ecosystem

With its unified catalog, Trust3 AI bridges the gap between raw data sources, governed datasets, and AI applications. This transparency allows enterprises to trace agent activity, while identifying which data was accessed, under what policies, and for what business purposes. Leveraging Google’s MCP support and pre-built connectors, Trust3 AI extends governance from data platforms into downstream SaaS applications and custom tools used by ADK or Vertex agents.

Marketing Technology News: Is the Traditional CDP Already Out of Date?

Trust Agents: Enhancing Agentic AI Reliability

To govern multi-agent topologies, Trust3 AI deploys specialized Trust Agents that monitor how agents delegate tasks, share session states, and orchestrate actions across Google’s agentic architecture. These Trust Agents proactively detect risky behaviors, hallucination-prone patterns, and policy violations in real time. They can intervene automatically, reroute tasks to safer agents, or escalate for human approval, ensuring trust and reliability in agentic AI systems.

Building Trust Across Agent Ecosystems

Google’s Agent2Agent (A2A) protocol enables interoperability between agents built on different frameworks and vendors. Trust3 AI enhances this capability by adding a consistent trust and audit layer to cross-vendor interactions. Enterprises can securely connect internal ADK agents with third-party or partner agents while maintaining end-to-end visibility, robust guardrails, and tamper-proof audit trails.

Lifecycle Governance and Observability for AI Workloads

Trust3 AI captures rich telemetry from agent prompts, tool calls, decisions, and outcomes, providing architecture and risk teams with a unified view of Gemini-based and multi-model agentic workloads on Google Cloud. This comprehensive observability allows teams to experiment, refine policies, and continuously optimize agents while meeting audit and reporting requirements – all without slowing down innovation.

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.

Marketing Technology News: The Death of Third-Party Cookies Was Just the Start. Are You Ready for Consent Orchestration?

That is not loyalty. That is a receipt with extra steps.

Customers figured this out before most brands did. They sign up, collect a little, and quietly disengage when nothing feels worth their attention. The program runs in the background while retention numbers slide. Algorithmic Loyalty Platforms are built specifically for this problem. They read what each customer actually responds to and shift the program around that, automatically, without anyone on your team reconfiguring rules every quarter.

What Makes Algorithmic Loyalty Different From What You Have Now

Your current program probably runs on rules your team set during initial setup. Spend this much, earn that many points, redeem from this fixed list. Those rules do not know that one customer only buys when there is a sense of exclusivity, while another responds immediately to a surprise discount.

Algorithmic loyalty learns the difference. It watches how each person interacts with your brand over time and builds a picture of what actually drives their next purchase. Algorithmic Loyalty Platforms then use that picture to decide what reward to offer, when to deliver it, and through which channel, without waiting for a human to make that call.

You stop managing a program and start running something that manages itself around real people.

How Does Machine Learning Predict the Right Reward?

The engine behind your loyalty program learns from real interactions and gets sharper over time. Here is what that looks like in practice:

• Purchase history, browsing behavior, and past redemptions feed into an individual profile that updates with every new interaction.
• The system tests which reward types drive purchases within similar customer groups and applies those findings going forward.
• Each customer receives a score for different reward categories before any offer is sent out to them.
• Behavior shifts trigger automatic profile updates, so a customer who changes habits in month two gets a different offer than they did in month one.
• Delivery timing adjusts based on when each person historically takes action, rather than when your campaign calendar says to send.

Why Should Reward Values Change Based on Your Business Data?

Setting reward values once and leaving them alone feels efficient. In practice, it means your program runs the same incentive structure whether margins are healthy or under pressure, whether stock is moving or sitting.

• Inventory Alignment:

Slower-moving products can become reward targets, clearing stock without a public sale that trains customers to wait for discounts.

• Margin Protection:

Your program automatically applies richer rewards to products where you have room and lighter incentives where margins are tight.

• CLV Weighting:

A customer with strong long-term potential receives more investment from your program than a low-engagement buyer at the same spend level.

• Seasonal Adjustment:

Reward generosity rises and falls with your actual business cycle rather than holding flat while everything around it shifts.

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

How Do You Move From Points to Experiences and Access?

Points feel like currency. Customers calculate their value, find it underwhelming, and mentally check out. Experiences and access are harder to put a number on, which makes them feel more meaningful even when the cost to you is lower.

Algorithmic Loyalty Platforms track which customers have engaged with brand events, early launches, or exclusive content in the past. Those signals identify who will respond to access-based rewards versus who still needs a price-driven incentive. The platform assigns each customer to the right reward category without anyone on your team manually building and maintaining those segments.

Where Does Gamification Fit Into an Algorithmic Loyalty Program?

Generic gamification adds noise. Personalized gamification changes behavior. Your platform can match game mechanics to individual profiles rather than rolling the same challenge out to your entire base:

• Frequent buyers respond well to streak mechanics that reward them for maintaining visit patterns they already have.
• Mid-tier customers near a behavioral threshold move faster when they can see a progress bar showing exactly how close they are.
• Customers showing early signs of disengagement re-engage more reliably when a surprise reward appears without them earning it in the traditional sense.
• Competitive mechanics work for a specific personality type and should only reach customers whose behavioral data actually supports that motivation.

What Data Infrastructure Powers All of This?

Algorithmic Loyalty Platforms depend on data that is clean, current, and connected across every channel your customer touches. Without that foundation, the models make decisions based on an incomplete picture, and the personalization shows it.

You need one unified customer profile that pulls from purchases, browsing sessions, support history, and channel preferences. These cannot live in separate systems that sync weekly. Real-time event streaming means the platform reacts to what a customer does today rather than processing it days later when the moment has passed. Your models also need regular retraining. A customer’s motivations in January look different by October, and a model that does not refresh gradually stops reflecting the people it is supposed to serve.

However, as automation evolves and data becomes more interconnected, there’s a quiet but strong shift happening. Ad Ops is transitioning from firefighting to forecasting, leaving behind reacting to what’s broken and anticipating what comes next.

Moving From Reaction to Readiness

Ad Ops teams live in constant motion, but not from lack of discipline. Manual handoffs, real-time changes, and disconnected systems make it nearly impossible to look beyond today. When every fix is urgent, strategy falls to the bottom of the list.

The process itself is creating delays, meaning even the best people spend their time just managing the workflow instead of identifying ways to improve it. Fortunately, this was the cycle purpose-built automation was created to break.

Automation: Reinforcement, Not Replacement

Even the word automation can make some Ad Ops teams nervous. The idea that technology might replace human expertise has made even the most innovation-forward individuals hesitate.

The reality is that true, purpose-built automation does the opposite. The technology can remove the mundane, time-sensitive steps that keep Ad Ops stuck in maintenance mode. With connected platforms, tracking and reconciliation can happen in the background. Workflows are routed automatically, giving Ad Ops teams the space they need to focus on higher-value work like refining campaign strategy and partnering with client success teams.

Transforming Operations into Intelligence

When powered by automation, modern Ad Ops becomes a source of intelligence. The team closest to campaign delivery has the clearest view of what drives performance when clean data flows easily across systems. Using consistent campaign pacing and having clear visibility into delivery patterns and inventory helps Ad Ops identify challenges early, including clients that require extra QA time, workflows that are creating bottlenecks, and formats that underdeliver.

Having a holistic picture transforms Ad Ops from simply a reporting function into a predictive partner backed by operational intelligence. Rather than waiting for post-campaign reports, revenue and client success leaders can partner with Ad Ops to anticipate bottlenecks, adjust capacity, and plan more effectively for the future.

Dependability as the New Differentiator

Advertisers value partners who deliver consistently. Every delay undermines confidence in workflows as well as partnerships. Even factors beyond an Ad Ops team’s control, like a missed flight, can have a ripple effect. Automation can help by minimizing those moments to create a standard path through the entire process, from IO to invoice.

Modern automation can make the mountain of Ad Ops tasks flow in a sequence with instant approvals and visibility to keep stakeholders aligned. As a result, predictability can become the new performance metric, and Ad Ops becomes the foundation of dependability across an organization.

Purpose-built automation serves clients and strengthens collaboration across the board. Finance receives cleaner billing data, sales gains confidence in inventory commitments, and IT sees significantly fewer last-minute requests.

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

When Ad Ops Lead, The Business Follows

The move to purpose-built automation is about creating better work, not just systems. Ad Ops firefighting models of the past rewarded those who could juggle the most tasks, stay calm under pressure, and still meet every deadline.

The forecasting model shifts from endurance to perspective, rewarding those who can connect insights, shape smarter workflows, and anticipate challenges before issues arise, ultimately unlocking opportunity.

When Ad Ops teams are free from reactivity, they can step into new roles. Upleveled responsibilities include optimizing performance data, advising pricing strategy, influencing revenue forecasts, and collaborating across the entire organization.

Ad Ops becomes the training ground for leadership with purpose-built automation behind them. As technology evolves, Ad Ops won’t be defined by the tools teams use, but by how they use them. It’s essential, however, not to lose sight of the fact that while purpose-built automation lays the foundation, people bring true value. Because when Ad Ops teams stop firefighting and begin forecasting, the entire business can truly skyrocket.

About Theorem

Theorem’s consultancy teams and operational expertise helps brands simplify, streamline and automate complex digital tasks. This value exchange saves clients time, reduces their costs, and increases their revenue.

Marketing Technology News: The Death of Third-Party Cookies Was Just the Start. Are You Ready for Consent Orchestration?