“Artificial intelligence is ushering in a new era of modern marketing and advertising — one where AI is starting to shape your image and influence how consumers perceive your brand. In this new reality, a marketing strategy designed for machines is essential. And it needs to be implemented fast, or you risk falling behind in the race for consumer preference.
Today, winning over AI means winning over the consumer.”
FEDERICO ISUANI
PARTNER AND MARKETING SOLUTIONS AMERICAS LEAD
THE NEXT MINDSET: MACHINE MARKETING
Are you ready for AI to hold more power than your own marketing plan?
Generative artificial intelligence isn't just another tool: it's the driving force behind a profound reshaping of communication and the trust consumers place in brands.
The Next Mindset invites us to shift our perspective to shift our mindset. It challenges how we act in three critical areas for any organization: influence, decision-making, and engagement. When we apply this lens to marketing, one conclusion becomes clear: AI is no longer just a technological resource. It's a stakeholder in its own right, influencing visibility, reputation, and brand credibility.
This raises uncomfortable but urgent questions:
- Is your business, communications, and marketing strategy ready to engage with AI as a decisive player?
- Are you aware of the real risk of becoming invisible if algorithms don't recommend your brand?
Algorithms are reshaping how we discover, choose, and trust brands — live and in real time. It's no longer enough to move people emotionally; you also have to convince the systems that decide what gets seen. Tools like ChatGPT, Google's AI Overviews, and voice assistants like Alexa are rewriting the rules of the game.
That's why we're taking a bold step forward with The Next Marketing: Machine Marketing, the first strategic ecosystem built to compete in this new landscape. It blends creativity, data, and technology toward a single clear goal: helping brands not only survive but also lead in the era of algorithms.
It's no longer enough to move people emotionally; you also have to convince the systems that decide what gets seen.
This report offers a disruptive, yet practical roadmap that explores the magnitude of this shift, introduces the dual-marketing model (people + algorithms), and presents real-world solutions already generating measurable impact.
The challenge isn’t looming on the horizon — it’s right here, right now. If algorithms are already deciding what information gets surfaced and which brands earn credibility, the question is simple:
What are you doing today to ensure your brand stays relevant in tomorrow's conversation?
Machines take the lead in marketing
How artificial intelligence is rewriting the rules of marketing
From click to zero-click: The age of instant answers
The "click-based internet" is fading. For years, consumers browsed links, compared sources, and made their own decisions. Today, more than 35% of global searches are answered directly by AI — no need to leave the chat interface.
Google is already there: its AI Overviews now serve up complete answers within the search page itself. In just two months, the share of queries receiving this format doubled, from 6.49% in January 2025 to 13.14% in March. And the trend shows no signs of slowing: more users are now getting a single algorithmic answer instead of a list of links.
The implication is massive: brand visibility no longer hinges on ranking high. It hinges on being the answer, the one response the machine delivers.
From human trust to algorithmic trust
For decades, trust was built through human recommendation: family, experts, media, and later, influencers. That's shifting fast. Today, over half of consumers trust AI recommendations as much or more than those from influencers. And 45% say AI is just as credible as traditional media.
The reasons are clear: speed and perceived neutrality. AI offers instant answers, comparisons, and what users view as freedom from the biases of salespeople, influencers, or search engines. When a machine recommends a brand, it's read as an objective endorsement backed by extensive data.
We're witnessing a profound cultural shift. The old "Zero Moment of Truth" (ZMOT), which happened on Google or Amazon, is evolving into the AIMOT, AI Moment of Truth: the moment a brand's first interaction with a consumer happens through a chatbot or voice assistant.
The new gatekeeper isn't human. It's a machine. The numbers back it up: by 2024, there were already 8.4 billion voice-enabled devices in the world — more than people on the planet. By the end of 2025, 85% of customer service interactions will be handled without a human.
In short: trust is no longer strictly human. The entity deciding which brands are credible doesn't have a face—it has an algorithm.
The risk of invisibility
What marketing efforts are machines missing?
The great blind spot: 70% of digital efforts go unnoticed by AI
Over the past decade, brands have built up a digital arsenal, including SEO, social media campaigns, influencer collaborations, programmatic ads, optimized websites, and apps. All aimed at one thing: visibility.
However, that logic no longer holds. Today, consumer-brand interactions don't just happen through search engines or social feeds. Instead, they are increasingly mediated by language models and generative AI. In this new ecosystem, much of the content crafted for humans goes unseen by algorithms.
In this new ecosystem, much of the content crafted for humans goes unseen by algorithms.
The numbers are telling: more than 70% of current digital visibility investments don't impact how AI understands or recommends a brand. Perfect SEO won't guarantee you a spot in an algorithmic summary. A viral Instagram campaign might be invisible to a model trained on other sources. Even a major news story might be ignored if it's not in a machine-friendly format.
The result? A growing disconnect between what brands think they're communicating and what AIs are actually "learning." The risk is clear: algorithmic invisibility. If the AI doesn't recognize your brand, it may as well not exist.
The traffic disruption: when AI becomes the final destination
For over 20 years, digital marketing ran on a simple premise: traffic is everything. More visibility meant more clicks, visits, and conversions. The click was the gold standard.
That's changing fast. With generative AI, users no longer need to click away for more info. The conversation is the destination. Conversational models built into search engines like Google now deliver instant, personalized answers. Traffic no longer flows. It stays with the algorithm.
The data backs it up: media and site clicks are down by as much as 35%. Organic traffic, once the backbone of digital strategy, is in decline. And this isn't a temporary dip. It represents a structural shift. LLMs don't redirect; they retain.
The click, once the metric that defined digital success, is becoming obsolete. And with it, the idea of visiting a website as a default behavior.
Ironically, we're producing more content than ever, but fewer people are actually seeing it on online platforms.
With generative AI, users no longer need to click away for more info. The conversation is the destination.
The pillars of machine marketing and ai visibility
How to win over humans and algorithms alike
Generative AI isn't a trend. It's a turning point. Tuning your SEO, redesigning your site, or increasing ad spend won't cut it. This is a structural shift that calls for a complete reinvention of marketing.
In this new era, brands must think in dual audiences:
The human audience, still drawn to emotion, inspiration, and trust.
The algorithmic audience, which filters, ranks, and determines which brands enter the conversation and how they're perceived.
The key is accepting that marketing is now dual by design. It's not about choosing between humans or machines, but about building strategies that work for both simultaneously.
That's the goal of LLYC's Machine Marketing ecosystem: the first integrated strategic offering that fuses creativity, data, and technology to make brands more visible, quotable, and credible in this new landscape.
Here are four strategic levers we're already putting into action:
AI Audit & Activation: uncover your algorithmic footprint
The first step to competing in the AI era is understanding what machines know about your brand. AI Audit & Activation is the next-gen strategic audit. It analyzes how AIs see your brand, how they describe it, and how you stack up against competitors.
It's more than a diagnosis. The activation phase translates insights into action: optimizing brand narratives to improve credibility, correcting misinformation, boosting presence in trusted sources, and building a plan to increase your Algorithmic Share of Voice & Quality.
In short, it's not just about observing. It's about training AI to say what your brand needs it to say.
Website Visibility: speaking machine fluently
AI models don't read like humans. People respond to short, emotional, visual messages. Machines prefer long-form, detailed, contextualized text.
Website Visibility delivers two key services: auditing how ready your website is for machine comprehension, and developing a parallel web ecosystem specifically built for AI to understand, index, and reference.
This includes content architecture, technical signals (sitemaps, hosting, robots.txt), and everything else that facilitates machine readability. The goal? Bridge your corporate narrative with the language of algorithms.
To be online is not enough. The AI must see you, understand you, and recommend you. That's a Story Model translated into algorithmic language.
Answer Engine Optimization (AEO): own the answer
SEO marked the first digital marketing era. AEO defines the next. The goal now isn't to appear in a list of links. It's to be the answer the AI delivers.
To get there, we combine technical elements (structure, schema, trusted sources) with strategic levers (narrative, reputation, tone) to increase the chances of being cited. And it pays off: clicks driven by AI-mediated search convert up to 23 times more than traditional SEO traffic.
In practice, AEO turns the AI into the new trusted recommender and your brand into the preferred choice.
Content MAGIA: human-led, AI-powered creativity
Creativity still drives marketing, but now it scales with AI. MAGIA (Machine Augmented Generative Intelligence for Advertising), enables high-volume content production with generative AI, always supervised by expert humans.
It's a hybrid formula, scalable and personalized, without losing brand consistency. From posts to videos, FAQs to infographics, MAGIA helps generate hundreds of pieces in multiple formats and languages, reviewed and refined by our specialists. It democratizes creativity and accelerates experimentation.
In a world where 90% of advertisers will be using generative AI by 2026, MAGIA places your brand at the forefront, where speed and intelligence become the new competitive edge.
Toward dual marketing
The present future: strategies that speak to people and algorithms alike
The solutions we've outlined aren't just concepts or far-off ideas. They're the starting point of a structural transformation already underway. Marketing is shifting from a one-dimensional model to a dual reality.
In this new paradigm, brands must build trust on two levels at once:
- The human level, where emotional connection, inspiration, and relevance remain essential.
- The algorithmic level, where every digital interaction helps train the AI systems that decide which brands show up and how credible they appear.
The real strategic challenge isn't choosing between these two worlds. It's learning to operate in both, simultaneously. The minimum requirement to compete will be knowing how to train AI ethically and effectively while still designing memorable experiences for people.
Those who understand this first will lead. Because the click is no longer the center of the digital experience. The new arena for reputation, influence, and business is the machine-led conversation.
LLYC's Machine Marketing model accelerates that transition. It helps companies earn a seat in the algorithmic conversation without losing their human essence and sets the path for a marketing strategy built for what's already here.
LLYC's Machine Marketing model accelerates that transition. It helps companies earn a seat in the algorithmic conversation without losing their human essence.
Take our recent AIgent project for ALDI: a conversational agent that generated over 11,000 personalized recipes in just two weeks, delivering reliable information while advancing a clearly defined social purpose: reducing food waste. Projects like this show how the brands of tomorrow will still launch campaigns, but they'll also speak through AI systems trained to amplify their narrative.
The conversation is no longer optional. Machines are listening, learning, and deciding. Is your brand ready to speak their language?
Voices anticipating the future
Machine Marketing: when brands speak to people and algorithms
Luis Manuel Nuñez Maestre
Machine Marketing is a strategic approach that integrates communication with both human audiences and algorithmic systems that process and scale information globally.
Its core lies in bidirectionality: designing messages that connect emotionally with people and are simultaneously understood and prioritized by large language models (LLMs). To succeed, brands must master four principles:
Understand the sources and weighting criteria for these models, and align content with high-quality, high-authority information.
Apply advanced semantic optimization to craft narratives that are clear, structured, and easily interpretable.
Establish ethical content governance to minimize bias and strengthen trust.
Continuously monitor and adapt strategies based on model updates and evolving user behaviors.
Ensure technological interoperability, adapting messaging across multiple platforms and environments.
In this ecosystem, a brand's competitiveness depends on its ability to speak in two languages: human and algorithmic. The challenge is twofold: to be present and relevant in both human memory and machine memory.
Leading in the AI era: new rules for Project Managers
Irati Isturitz
AI has radically transformed the role of the Project Manager (PM). Once focused on task coordination and execution, the PM is now more strategist than scheduler, guiding projects toward long-term impact.
Platforms like COR, ClickUp, Monday, and Asana have automated routine workflows, freeing time for what matters most: strategic thinking, creativity, and decision-making. AI has also brought new predictive capabilities: PMs can now anticipate risks, simulate scenarios, and test hypotheses before launch. Even A/B testing, once limited to paid media, can now be applied early on to messaging, concepts, and campaigns, cutting errors before they hit the market.
The PM's value now lies in integrating these tools without losing human control. AI doesn't think for us, but it helps us think better. Clients know this and expect faster, more tailored, more transparent projects. That raises the bar. PMs must set validation protocols, build continuous training cycles, and guard against passive dependence on technology.
Ultimately, AI doesn't eliminate the PM role. It elevates it. It gives back time, broadens vision, and demands a new kind of leadership: one that leverages machine efficiency without sacrificing human judgment and quality.
Strategic AI: data governance, innovation, and competitive edge
Patricia Charro
Talking about AI in general terms barely scratches the surface. The real challenge for brands is understanding each model's specific role, capabilities, and impact on competitive advantage. Machine Marketing isn't just about optimizing for AI-driven search. It's about designing solutions that merge corporate data, business intelligence, and strategic creativity.
Today, any digital channel can be indexed by AI models. Platforms like YouTube and Instagram already feed systems like Gemini and Google, forcing organizations to optimize every digital asset proactively. At the same time, developing proprietary AI tools trained on secure internal data unlocks levels of efficiency and differentiation that generic models can't achieve.
This evolution calls for new rules and a shift in focus. Data governance needs to be prioritized strategically and championed by leadership. Only by organizing our data and establishing clear methodologies can we transform information into genuine competitive intelligence. Cybersecurity is also non-negotiable: if purchases and bookings are now handled through AI interfaces, corporate websites must evolve into secure, conversational architectures.
The future of marketing is hybrid, blending innovative ideas with the vast capabilities of AI. The true advantage comes from integrating expert insights and human experience with technology driven by sophisticated models. After all, AI merely amplifies the input it receives. A strong, ethical, and unique strategy is essential to ensure that it ultimately fosters reputation, trust, and innovation in return.
Machine Marketing: training machines without losing emotional impact
Roberto Carreras
Marketing is undergoing a radical shift: we no longer create content only for humans, but also for machines that synthesize, recommend, and make decisions. In this context, Machine Marketing emerges as the discipline that helps brands resonate with both algorithms and people.
The key lies in two emerging areas of focus. First is Answer Engine Optimization (AEO), which positions a brand as a priority source of AI-generated responses. Second is Large Language Model Optimization (LLMO), which trains models using brand-specific data and narratives to ensure the AI-generated content accurately reflects the company's identity.
This change fundamentally reshapes the function of websites. They have evolved from mere visual portals into essential infrastructures for algorithmic training. Brands must now establish a "Conversational Identity" to ensure consistency across all automated platforms. Without this strategic framework, a brand's voice might become unclear and lose its impact.
Add to that the power of algorithmic amplification — distributing content intelligently to strengthen a brand's footprint in the memory of AI models. The more frequently a brand is cited, the more likely machines are to remember it. At the same time, AI opens new creative territories: from the resurgence of audio and transmedia video podcasts to immersive storytelling.
Ultimately, the challenge is balancing efficiency with emotion by training machines to speak with precision without losing sight of the real impact that comes from moving people.
Machine memory: the new marketing frontier
Ibo Sanz
The core principles of marketing haven't changed — it's still about leaving a mark in people's memory. What's different now is the emergence of a new kind of memory: that of generative AI. As this machine memory becomes more influential, CMOs and communications leaders must embrace Machine Marketing.
Understanding how models and tools work is essential. While models remain static between updates, platforms like ChatGPT now have browsing capabilities and can update responses in real time. This changes the rules of visibility: it's not just about showing up first, it's about being consistently found.
That's why visibility now depends on strategic audits that evaluate reputation, positioning, and performance within AI environments. These initiatives must stay focused, avoid trying to do too much, and rely on tools that provide representative samples and actionable data. They also require cross-functional coordination, breaking down silos to reinforce consistent messaging across all channels.
Success will be measured differently: less by website traffic, more by algorithmic influence; less by vanity metrics, more by meaningful conversions. As user experiences shift towards conversational interfaces, organizations must revamp their digital assets to ensure that AI interactions feel natural, coherent, and secure.
In this new landscape, marketing is no longer just about human persuasion — it's about strategically training your algorithmic spokespersons. Because AI remembers, and brands that speak to it with consistency will be the ones leading tomorrow's conversations.
Deep learning for Machine Marketing: from data to algorithmic understanding
Gonzalo Candaosa
Marketing has always aimed to persuade people, but now it must also persuade algorithms. Where traditional market research offered static snapshots of consumers, deep learning gives us dynamic narratives. And with AI, we can even anticipate "trailers" of the future by detecting patterns that haven't yet materialized. It's no longer about describing what happened, but about understanding how perceptions are formed and forecasted, both in humans and machines.
Automation has transformed our daily work. Tasks that once took hours — like classifying comments or generating reports — now take seconds. This has shifted our focus from retrospective analysis to identifying the signals that shape algorithms in real time and influence brand reputation. The challenge is shaping those signals so they're consistently interpreted by these systems.
The financial sector is a good example. When someone asks AI, "Which bank is the most trustworthy to open an account with?", the answer doesn't come from a catchy slogan, it comes from verifiable signals like customer reviews, regulatory reports, and media mentions. In Machine Marketing, what consumers perceive as narrative, algorithms decode as trust signals. If those signals aren't available, clear, and consistent, the machine will recommend a competitor.
Speaking to the algorithm means recognizing that every digital interaction becomes training data. Models don't feel emotion or parse slogans. They detect language patterns, cross-source consistency, and indicators of authority. From the deep learning perspective, our role is to map those patterns, explain how they shape Machine Marketing, and guide our understanding of AI-driven reputation.
Timing is critical. Algorithmic reputation doesn't update at the same pace as human perception. For real-time web-reading systems, changes can reflect in days or weeks. For models trained on static datasets, updates might take months — or even years. Brands must build strategies with two timelines in mind: one short-term, one structural.
Machine Marketing requires fluency in two languages: that of the consumer and that of the algorithm. In that duality lies the opportunity to create a future where human creativity and machine intelligence don't compete but complement one another. Crafting inspiring messages is no longer enough — they must be transformed into signals that models recognize as credible. Only then will Machine Marketing deliver meaningful, sustainable impact.
Creativity will continue to inspire people while also leaving a lasting impression on the memory of machines. In this emerging landscape, deep learning serves as the foundation for that machine memory, with us acting as trusted architects shaping which brands will hold significance in the future.