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.

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

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

With a single prompt, anyone can now produce polished creative in seconds. Ad creation has been fully democratized. But execution is where the real story is, and where most brands are underestimating the risk.

The issue isn’t the creative. It’s everything that comes after.

As AI-generated ads flood the ecosystem, performance isn’t breaking down at the creative level. It’s breaking down across compliance, targeting precision, and delivery – areas where automation alone isn’t enough.

Creative Has Been Standardized

AI has raised the baseline for creative quality across the board. It’s easier than ever to produce something that looks sharp, reads well, and meets modern expectations.

The tradeoff is subtle but significant. When the same tools power everyone’s output, differentiation starts to erode. Messaging becomes more uniform, tone converges, and campaigns begin to feel interchangeable.

More importantly, AI lacks a true understanding of context. It doesn’t inherently recognize regulatory nuance, platform-specific constraints, or the difference between messaging that resonates and messaging that creates risk. That limitation directly impacts whether campaigns run at all.

An ad can be perfectly written and visually compelling, but still fail in-market if it doesn’t align with how platforms interpret policy or how regulations are applied in practice. In many cases, the difference between a high-performing campaign and one that gets rejected, throttled, or flagged comes down to details that AI isn’t equipped to account for.

It’s about understanding the environment an ad enters, such as how it will be reviewed, where it will appear, and how it will be interpreted by both systems and people. That layer of judgment remains difficult to automate, and increasingly critical as constraints tighten. In categories where precision matters – including healthcare, finance, and politics – that gap becomes impossible to ignore.

Execution Is Where Performance Breaks Down

When campaigns underperform today, the cause is rarely the creative itself – it’s how that creative is executed across a fragmented and increasingly constrained ecosystem.

Compliance is one of the most immediate pressure points. Every platform has its own policies layered on top of broader regulations, from FDA oversight in healthcare to strict financial advertising guidelines, and those standards are constantly evolving. Campaigns can be rejected, limited, or deprioritized without warning if those nuances aren’t accounted for upfront, especially in healthcare.

At the same time, targeting has become more complex. With signal loss and privacy changes reshaping the landscape, reaching the right audience depends less on deterministic identifiers and more on understanding intent. That requires interpreting what people are engaging with in real time and translating it into scalable strategies – something automation alone doesn’t consistently get right.

Even when those pieces align, delivery introduces another layer of complexity. Not every channel supports every category, and not every inventory source is equally accessible. Getting campaigns live – and keeping them performing – requires a level of operational fluency that goes beyond automated workflows.

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

Optimization Needs Human Governance

AI is highly effective at optimizing toward measurable outcomes. It can process signals, adjust in real time, and improve efficiency at scale. What it doesn’t do well is judge context.

In practice, that means optimization can push campaigns into environments or audiences that technically drive performance metrics but undermine broader objectives. In more sensitive categories, that can introduce compliance risks or create misalignment with brand standards.

Human oversight plays a structural role here. It ensures that optimization is grounded in strategy, not just performance signals, and that campaigns remain aligned with both regulatory expectations and brand intent as they scale.

That becomes especially critical in healthcare, for example, where the margin for error is significantly smaller. Messaging, targeting, and placement all operate under heightened scrutiny, and even well-intentioned optimization can create risk if it isn’t properly governed. A campaign that shifts toward higher engagement could inadvertently move into sensitive territory – whether that’s how conditions are framed, who is being reached, or where the message appears.

In these environments, performance can’t be separated from compliance. The two have to be managed in tandem, which makes human judgment a necessary part of the optimization process, not a secondary check.

The Next Phase Belongs to Hybrid Execution

The industry is moving toward a model where AI and human expertise operate in tandem. AI will continue to accelerate production and uncover patterns at scale. It will make campaigns faster to build and easier to iterate.

But execution – how campaigns are structured, governed, and adapted across channels – will remain a human-led discipline. It requires judgment, experience, and an understanding of systems that don’t operate uniformly.

This is where the gap is widening. Creative has become widely accessible. Effective execution has not.

Perhaps most important, execution today isn’t about limiting ambition, it’s what enables it. When campaigns account for regulatory nuance, platform dynamics, and data constraints upfront, they move faster, scale more effectively, and avoid the disruptions that stall performance.

The brands that outperform will be the ones that recognize that distinction early. They’ll invest less in producing more ads, and more in ensuring those ads actually run, reach the right audiences, and sustain performance once they’re live.

Because reaching the audience is only part of the equation. Maintaining compliant, effective execution long enough to drive impact is what ultimately determines results.

About Fyllo

Fyllo is a data and advertising partner purpose-built for regulated industries. The company helps brands and agencies in politics & public affairs, healthcare & pharma, financial services, CPG, retail, hospitality, and travel reach high-value audiences that others can’t — compliantly, effectively, and efficiently

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.

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

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.

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

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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Win-price optimization addresses this problem directly. Instead of bidding high to avoid missing impressions, win-price algorithms estimate what an impression is likely to clear for and bid just above that level. The shift is subtle but meaningful – efficiency is no longer just about impacting bidding power, but about improving pricing accuracy.

Why does this distinction matter? Because in a first-price auction, the bid is the price. When a bidding model overestimates an impression’s value, even slightly, the buyer pays the difference. Across thousands of auctions a day, that overpayment compounds –campaigns may appear healthy on the surface while efficiency steadily erodes underneath, with no obvious signal that anything is wrong.

Consider a buyer planning a broad video campaign with a $10 target CPM, for instance. Under conventional bidding logic, the model may routinely bid near that ceiling to secure wins, even when similar impressions frequently clear for far less. In a first-price auction, those inflated bids become the final price. Over time, the campaign wins roughly the same volume of impressions it would have otherwise, but consistently pays more than the market requires.

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

With win-price optimization in place, this logic changes. The bidding model looks at how comparable impressions have cleared historically, how competitive the current supply path is and how urgently the campaign needs to spend. If those signals suggest an impression typically clears closer to $4 or $5, the bid reflects that reality and still wins. The audience doesn’t change, nor does the inventory. The alignment between the bid and the true clearing price, however, is transformed for the better, surfacing savings as incremental reach, longer flight time or additional flexibility.

Gaps like these are more common than many buyers realize, though they’re rarely the result of poor planning. More often, they stem from incomplete information. When a demand-side platform, or DSP, operates with a more limited set of signals, it compensates by bidding defensively. Without the appropriate context, the safest assumption is that an impression is valuable and worth paying a premium.

Models with broader visibility behave differently. When supply path dynamics, historical clearing prices, competition intensity and real-time pacing are taken into consideration, the bidding model develops a clearer sense of when aggressive bidding is warranted and when it isn’t. Two DSPs can bid on the same impression and arrive at very different prices, not because one values quality more, but because one has a more complete understanding of price.

There’s also a practical effect. Many buyers still spend time monitoring pacing, reconciling reports and making manual bid adjustments to keep campaigns aligned. As pricing becomes more accurate, much of that reactive work falls away – the bidding model recalibrates continuously, allowing buyers to focus more on strategy and less on maintenance.

Programmatic teams have spent years optimizing who they reach and how outcomes are measured. Pricing – despite shaping the cost and effectiveness of every impression – has received far less scrutiny. Win-price optimization brings that missing dimension back into focus.

For many advertisers, the inputs required to bid more accurately already exist; the opportunity now is to use them deliberately. Because in first-price auctions, overpaying isn’t a rounding error – it’s a strategy flaw. The next phase of programmatic efficiency won’t be defined by who can target more precisely, but by who can price most intelligently.

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Agentic commerce is coming to the Asia-Pacific region. The retailers who win will be the ones whose loyalty infrastructure is fast enough for machines to find

Picture this: A loyal customer asks their AI assistant to restock the household essentials they buy every fortnight. The agent checks inventory, compares prices, and looks for loyalty benefits it can apply. Your competitor’s loyalty platform responds in under 250 milliseconds with a personalised offer. Yours times out.

The agent completes the purchase with the competitor.

That scenario isn’t hypothetical for much longer. Google’s Universal Commerce Protocol (UCP) is creating the standardised infrastructure for AI agents to discover products, check stock, apply loyalty benefits, and complete purchases on behalf of consumers, all without the shopper visiting a website or opening an app. The transaction happens inside the conversation.

For more than a decade, the retail industry has treated chatbots and personalisation engines as the headline AI story. They’re finally decent. But the more consequential shift is happening at the transaction layer, and in my conversations with retailers across Asia-Pacific, very few are thinking about it yet.

Agentic commerce to remove the shopfront entirely

It’s worth being precise about what agentic commerce actually changes, because the instinct is to file it under “better e-commerce.” It isn’t.

Traditional e-commerce moved the shopfront online but kept the same structure: browse, select, add to cart, enter details, pay. Agentic commerce removes the shopfront entirely. The AI agent becomes the interface. The “store” becomes a set of machine-readable data and APIs that the agent queries on the customer’s behalf.

This matters for two reasons. First, it eliminates the handoff that kills conversion. When a customer moves from a search result to a retailer’s mobile checkout, roughly 6-in-10 drop off. Under UCP, the customer stays in the conversation, uses saved credentials, and completes the transaction. Industry estimates suggest this could lift conversion rates meaningfully, some early projections point to double-digit improvements.

Second, it changes data ownership. On aggregator platforms, and aggregator-led e-commerce dominates our region, the platform captures the customer relationship. Under UCP, the merchant remains the legal seller and retains all customer data.

Pricing, fulfilment, and the customer relationship stay with the retailer. For Asia-Pacific retailers who’ve spent years competing on someone else’s marketplace, that’s a fundamentally different commercial model.

Loyalty infrastructure will offer competitive edge

This is where I think most commentary on agentic commerce is missing the point. The discussion tends to focus on payments and product discovery. But loyalty infrastructure is going to be the differentiator, the thing that determines whether an AI agent routes a transaction to you or to your competitor.

UCP supports identity linking, meaning a shopper’s loyalty credentials can be connected to their AI agent. When that shopper searches for a product, the agent can call out to a loyalty platform in real time, check point balances, access personalised offers, apply member pricing, all within the conversation and before checkout completes.

Think about what that means for the promotional model. Mass offers applied uniformly across a customer base have always been an imprecise tool: they attract price-sensitive shoppers and discount purchases that would have happened at full price.

In an agentic context, the agent already knows the shopper’s intent, preferences, and purchase history. A targeted offer served at that exact moment converts at higher rates with less margin erosion.

But here’s the catch: the loyalty platform has to be fast enough. We’re talking sub-250-millisecond response times at scale, across thousands of concurrent transactions. If your system can’t issue and redeem a personalised offer in the time it takes an AI agent to assemble a cart, the agent moves on. It’s not personal. It’s architecture.

Payments and loyalty are converging inside the agent

The other piece falling into place is payment infrastructure. This is sometimes discussed separately, but in an agentic transaction, payments and loyalty are resolved in the same interaction. In other words, they’re converging.

Mastercard has already completed live authenticated agentic transactions in Singapore through DBS Bank and UOB, using Agentic Tokens and Payment Passkeys. Visa is expanding its Intelligent Commerce framework across Asia-Pacific with pilot programs underway, partnering with Ant International, Tencent, and others. Singapore is emerging as the testing ground for both networks.

For retailers, the implication is that the payment authorisation and the loyalty redemption will happen in the same sub-second window. If your loyalty platform and your payment stack can’t talk to each other at that speed, you’re creating friction that an AI agent will route around.

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Physical store is advantageous

There’s a common assumption in global commentary that agentic commerce is a purely digital play. I think that’s a misread of our region.

Roughly 90 per cent of retail in Southeast Asia still happens in physical stores. That’s not a lag, rather a feature. And, it’s exactly why agentic commerce could be more transformative here than in markets where e-commerce already dominates.

UCP enables real-time local inventory queries, buy-online-pick-up-in-store transactions, and location-specific knowledge. An AI agent can confirm whether a product is available at a shopper’s preferred store, reserve it, and arrange for collection, all within a single conversation.

For a region where proximity and convenience drive purchasing decisions, this connects digital intelligence to physical operations in a way that pure e-commerce never could.

FairPrice’s “Store of Tomorrow” concept points in this direction. Digital agents help customers navigate physical aisles. Smart carts use conversational AI for in-store assistance. The checkout process integrates digital loyalty and payment without requiring traditional point-of-sale interaction. It’s not replacing the physical store, it’s making it smarter.

The consumer appetite is there. The region’s digital infrastructure is mature, mobile payment adoption is high, super-apps are embedded in daily life, and a significant majority of APAC shoppers say they want AI-powered shopping features.

Lazada has deployed multiple AI agents, Shopee is integrating AI across its buyer and seller experiences, and both card networks are running live pilots in our markets.

Three questions for your next leadership meeting

I’ve been road-testing a set of questions with the retail teams I work with across the region. They’re useful as a self-assessment for agentic readiness:

First: Can your loyalty platform issue and redeem personalised offers in under 250 milliseconds during peak traffic? Not in a demo environment, in production, at scale, across all channels including in-store.

Second: Is your product and inventory data structured in a way that AI agents can query in real time? If your catalogue lives in PDFs or behind login walls, it’s invisible to the agent economy.

Third: When an AI agent evaluates your loyalty program against a competitor’s, side by side, in milliseconds, with no human intervention, will yours be visible and fast enough to win the transaction?

If the answer to any of these is no, the agent will route your customer to a retailer who can say yes. Not because the customer chose to leave, but because the machine did.

About Eagle Eye

Eagle Eye is a leading SaaS and AI company, enabling retail, travel and hospitality brands to earn lasting customer loyalty through harnessing the power of real-time, omnichannel and personalized marketing. Our powerful technology combines the world’s most flexible and scalable loyalty and promotions capability with cutting edge, built-for-purpose AI to deliver 1:1 personalization at scale for enterprise businesses, globally.

Our growing customer base includes Loblaws, Southeastern Grocers, Giant Eagle, Asda, Tesco, Morrisons, JD Sports, E.Leclerc, Carrefour, the Woolworths Group and many more. Each week, more than 1 billion personalized offers are seamlessly executed via our platform, and over 500 million loyalty member wallets are managed worldwide.

AI-powered, API-based and cloud-native, Eagle Eye’s enterprise-grade technology is fully certified by the MACH Alliance and has received recognition from leading industry bodies, including Gartner, Forrester, IDC and QKS. To find out more visit: https://eagleeye.com/.

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Long before modern marketing existed, humans communicated solely through spoken language before adopting an early form of localization using images and symbols to communicate stories across tribes and cultures. Centuries later, innovations such as the printing press made it possible to distribute knowledge globally at scale, with works such as the Gutenberg Bible becoming some of the first widely translated texts. More recently the internet ushered in a similar exponential leap in global communication.

And now we are seeing new means of brand information dissemination that will likely have a similar impact on how we share information. Websites are falling away as the primary destination for both information and transactions, as increasingly discovery is happening through conversational interfaces, voice assistants, and AI-driven platforms where users have come to expect ultra-fast, highly contextual answers.

With AI as the new default user interface, marketers are changing their approach to content localization. The new imperative for marketers is semantically rich, intelligently structured content that machines can interpret and surface wherever discovery occurs. With generative engine optimization (GEO) and conversational search, localization is no longer just about language that resonates with local buyers but also building content that is inherently discoverable across markets, channels, and technologies.

The challenge is that global brands are rolling out AI‑generated content at scale without understanding how models interpret meaning, tone, or cultural nuance across markets. The result: off‑brand messaging, embarrassing mistranslations, and poor customer experiences. Marketers are discovering that “multilingual AI” isn’t actually delivering the necessary cultural relevance.

AI-Driven Discovery Changes Everything

For years, marketing technology stacks have been built around keyword optimization, campaign automation, and performance analytics. But as AI-driven discovery reshapes how buyers research brands and solutions, traditional SEO tactics are no longer enough. Modern search systems evaluate content based on semantic understanding — whether it demonstrates a clear grasp of buyer intent, not just keyword relevance.

AI-powered discovery engines prioritize questions over isolated terms, concepts over fragmented phrases, and contextual meaning over traffic volume. Increasingly, they evaluate whether content clearly communicates the problem a company solves, the audience it serves, and how it differentiates from competitors within specific buying scenarios. Relevance is dynamic, shifting across industries, geographies, and regulatory environments — and AI systems are designed to favor these nuances.

This means semantically aligned content attracts more qualified audiences, improves engagement, and accelerates pipeline readiness.

Global Martech Strategies Need a Semantic Foundation

Global marketing organizations have invested heavily in martech platforms to accelerate content delivery, automate workflows, and scale campaign execution. Yet international performance often lags behind expectations.

Direct translation preserves wording but often loses the contextual signals that influence conversion. Traditional transcreation can address this, but differences in local search behavior, industry terminology, regulatory requirements, and cultural framing shape how buyers evaluate solutions. For example, compliance-related searches may differ significantly between markets, while terminology used to describe risk, security, or operational efficiency can vary widely across regions.

When these nuances are lost, content may be linguistically accurate but commercially invisible — particularly to AI systems trained to evaluate authority and relevance. The result is weaker engagement, inconsistent campaign performance, and underutilized martech investments.

With a semantic approach, products, services, and value propositions are clearly defined using language aligned to real buyer challenges. Problem–solution narratives reflect real-world use cases, and content answers high-intent questions in natural language. Consistent terminology and entity clarity are maintained globally while contextual examples are adapted locally.

For revenue teams, this approach results in higher-quality organic traffic and improved conversion rates across regions.

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

Building a Semantic Framework in the Martech Stack

AI-powered content and SEO tools have become essential components of the modern martech ecosystem. Topic modeling can reveal high-intent content gaps, entity extraction can sharpen positioning, and structured data can strengthen relevance signals. AI-assisted content expansion can accelerate authority building in priority segments.

However, automation without a defined semantic framework often leads to fragmentation across markets and channels.

The foundation should begin with a semantic core defined in the source language. This includes standardized descriptions of solutions, industries, use cases, and differentiators. Establishing this foundation determines which elements must remain globally consistent to maintain brand clarity and which should adapt to local buyer behavior.

Once defined, this semantic strategy should be embedded into marketing operations — including localization workflows, governance processes, and performance measurement. This is where SEO, marketing operations, and localization maturity intersect, turning content from a production task into a structured growth asset.

The Future of Global Demand Generation

The future of global demand generation will not be defined by producing more campaigns or increasing content velocity. Instead, success will depend on ensuring that content is clearly understood by both buyers and machines across every target market.

Semantically structured global content improves discoverability in AI-driven search environments while strengthening alignment across marketing, product, and revenue teams. It increases traffic quality, accelerates pipeline contribution, and supports scalable international growth.

In an AI-driven buyer landscape, being “found” is no longer enough. Being clearly understood is what drives measurable results — and for martech leaders focused on predictable growth, semantic clarity is quickly becoming a core competitive advantage.

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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.

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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.

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The Foundation Was Already Broken

Here’s something I’ve seen time and again: AI doesn’t fix broken systems. It runs faster inside them. When CRMs hold conflicting records, AI produces conflicting outputs. When sales and marketing can’t agree on what a qualified lead looks like, AI accelerates that disagreement. When workflows were patched together through years of reactive decisions, AI speeds up the chaos. The vast majority of enterprise AI initiatives fail to deliver a measurable return, and the reasons have nothing to do with the technology itself. Poor strategy, misaligned teams, and disconnected systems and data are driving those failures.

Revenue leaders feel this directly. They’ve invested in CRMs, tech stacks, and sales enablement tools. None of them talk to each other the way they should. Teams spend more time pulling reports than acting on them. And now there’s pressure to layer AI on top of it all. Adding AI to a broken operating model is a liability, not a strategy.

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Data Quality Is a Revenue Problem

Before any AI initiative can deliver, the data underneath it has to be clean and connected. Most organizations aren’t there yet. Data quality consistently ranks among the top operational challenges for senior leaders, and the financial cost of bad data is significant. Organizations that ignore it aren’t just dealing with reporting headaches. They’re leaving real revenue on the table.

The fallout extends directly into AI deployments. Some companies are already reporting negative results from their AI investments, and the most common barrier isn’t budget or technology access. It’s on the people side. Teams that don’t have the skills or the clean inputs to work effectively with the tools they’ve been handed. Many data and analytics leaders will say their data strategy needs a serious overhaul before their AI ambitions can work. Companies are regularly drawing wrong conclusions from data that lacks business context, and AI accelerates that problem rather than correcting it.

The Martech Stack Isn’t the Answer

The tools were supposed to solve this. They haven’t. McKinsey’s martech research found that 47% of martech decision-makers say stack complexity and integration problems are the main reasons they can’t get value from their tools. Not one of the 50+ senior marketing leaders interviewed at Fortune 500 companies could clearly explain the ROI of their martech investment.

The organizations winning with AI in 2026 built the foundation before they built the model. They started with infrastructure. They treated data governance as a revenue priority, not an IT task. They rebuilt workflows to operate with AI. And they aligned their sales, marketing, and customer success teams around shared data and shared definitions before adding any new technology layer. The pattern is consistent across high-performing organizations: workflow redesign comes before model deployment. The operating system built around the model is what defines leaders, not the sophistication of the model itself.

What Comes Next

The organizations that will pull ahead in 2026 are the ones that got the basics right before deploying advanced AI. The ones still chasing model sophistication are building on unstable ground. Start with one clean source of truth. Sales, marketing, and customer success data all in one place, no exceptions. Align your teams around shared KPIs before you even think about deploying tools that depend on that alignment to work. Treat workflow redesign like the business priority it actually is, not something you get to when things slow down.

And be honest with yourself: AI will move you faster in whatever direction your operations are already pointed. If the foundation is broken, AI just accelerates the break. Get the foundation right, and AI becomes a real growth driver. Skip it, and you’re adding speed to a system that was already headed in the wrong direction.

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And marketing is only the beginning of the fallout. The damage doesn’t stop at demand generation. It moves downstream into the core of the go-to-market engine, affecting sales execution, pipeline integrity, forecasting accuracy, and ultimately revenue. Bad data isn’t confined to marketing dashboards. It is a sales problem, and it is costing companies far more than they realize.

For revenue leaders and frontline sellers, the failure rarely appears labeled “data quality.” Instead, it shows up as another “high-intent” lead that never replies. Another outbound sequence that stalls. Another quarter that closes nowhere near what the dashboards predicted. A rep follows up on what looked like a hot account and gets ghosted again. With each dead end, trust in the system erodes.

When the Funnel Distorts Reality

The moment flawed data enters the pipeline, credibility fractures. Lead-to-account mapping struggles under the weight of outdated records, constant job changes, and enrichment platforms that disagree on basic firmographics. A global enterprise may be flagged as surging in intent, yet no one can determine which region, division, or stakeholder actually demonstrated interest.

Hesitation creeps in before outreach even begins.

As the motion continues, each handoff becomes more fragile. Sequences reach contacts who lack buying authority, prospects who have already made a decision, or individuals only loosely connected to the opportunity. Sales development representatives are not simply being ignored. They are chasing ghost signals: inflated intent spikes, mismatched personas, and timing misaligned with real buying cycles.

Over time, the human response is predictable. Reps stop trusting routed leads. They build their own prospect lists. They circumvent automated workflows. They rely on personal networks rather than the GTM infrastructure meant to support them. Marketing feels sidelined. Sales feels unsupported. What started as a data issue becomes a breakdown in cross-functional trust.

The Revenue Impact No One Sees at First

The cost of bad data compounds quietly. Advertising spend and outbound energy are directed at the wrong buyers at the wrong time. Reps devote hours to opportunities that never had genuine potential. Meanwhile, legitimate high-intent accounts slip past unnoticed.

Conversion rates begin to decline. Sales cycles lengthen. CRM dashboards still show healthy pipeline coverage, yet closed-won results trail projections. Quotas are not missed solely because deals fall apart. They are missed because the funnel itself was never aligned with authentic buying behavior. Forecasts drift further from reality each quarter.

Eventually, accountability unravels. Marketing defends campaign volume. Sales questions lead quality. Leadership struggles to determine which metrics still deserve confidence. Yet many organizations remain locked in this cycle because they have already invested heavily in platforms, people, and political capital. Abandoning the motion feels like conceding failure. So budgets continue flowing into a system that amplifies flawed inputs rather than correcting them.

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Why Traditional Intent Signals Fall Short

Much of today’s third-party intent infrastructure was built for a different internet, one where human buyers performed most searches, clicks, and downloads. That environment no longer exists. Bots, crawlers, and synthetic traffic now generate a meaningful portion of online activity. Many of the “intent spikes” lighting up dashboards originate from machines, not buyers.

Outreach fueled by those artifacts sends sellers into conversations that were never real to begin with. Each failed interaction further weakens confidence in the pipeline.

At the same time, authentic buyers have migrated into harder-to-track environments. Research happens inside large language models. Peer recommendations unfold in Slack communities, private group chats, events, podcasts, and dark social spaces, not through repetitive website visits or form fills. Legacy intent systems largely miss these signals while continuing to overweight superficial digital activity.

This is not a minor calibration issue. It is structural. No incremental scoring adjustment can fix a model built on signals that no longer reflect how people buy or how modern sales teams should allocate their time.

Rebuilding the GTM Engine with Agentic Intelligence

The answer is not squeezing marginal improvements from broken intent data. It requires rethinking the architecture of the go-to-market engine itself.

Agentic marketing offers that shift. In this model, autonomous AI systems operate on real, current, buyer-level intelligence to execute the tactical work of marketing. Instead of relying on isolated, noisy signals, trustworthy insight emerges from synthesizing data across the full GTM ecosystem.

Cross-platform intelligence becomes critical. Teams can see how accounts engage across channels and prioritize outreach based on verified patterns of behavior rather than inferred clicks.

With this AI layer in place, marketers are no longer stuck patching flawed signals or chasing phantom demand. They can return to strategic fundamentals such as brand, positioning, and deep customer understanding, while automation handles execution grounded in validated data. Sales receives what it actually needs: signals it can trust, orchestrated intelligently and rooted in reality rather than noise.

In the next installment of this series, we will examine how to redesign the GTM engine around agentic intelligence, building a system capable of delivering genuine opportunity instead of misleading signals.

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