The Emergence Revolution: How Agentic AI Will Reinvent Marketing Teams

Published March 25, 2025

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Tomorrow

The marketing department of 2027 won’t just be augmented by AI—it will be fundamentally reimagined through it. While most discussions focus on AI as a tool for automation, the real revolution lies in how AI will transform the very nature of marketing organisations.

This organisational transformation aligns with Dwarkesh Patel’s recent insights about fully automated firms of the future. As he notes:

Even people who expect human-level AI soon are still seriously underestimating how different the world will look when we have it. Most people are anchoring on how smart they expect individual models to be. (i.e. they’re asking themselves “What would the world be like if everyone had a very smart assistant who could work 24/7?”.)

Everyone is sleeping on the collective advantages AIs will have, which have nothing to do with raw IQ but rather with the fact that they are digital—they can be copied, distilled, merged, scaled, and evolved in ways human simply can’t.

What would a fully automated company look like – with all the workers, all the managers as AIs? I claim that such AI firms will grow, coordinate, improve, and be selected-for at unprecedented speed.

…As corporations become more “software-like” – with perfect replication of successful components and faster feedback loops – we may see much larger and more efficient firms than were previously possible.

While Dwarkesh envisions this transformation at the company level, the marketing department offers perhaps the most compelling early example of how this future might unfold. Marketing’s combination of data-driven decisions, creative processes, and customer interactions makes it the perfect testing ground for AI organisational transformation.

What would this transformation look like in practice? Consider the emergence of what we might call a ‘Mega-CMO’ – analogous to Patel’s concept of a ‘Mega-CEO.’ This AI marketing leader would benefit from the same fundamental advantages Dwarkesh describes: perfect copying of successful configurations, merging of learned experiences, scaling without quality degradation, and evolution of agent teams.

Such a Mega-CMO would revolutionise marketing operations by being able to:

• Can absorb and process every customer interaction
• Directly oversee thousands of marketing initiatives
• Make decisions with perfect organisational memory
• Deploy specialised versions of itself for specific tasks

Further, marketing knowledge can be:

• Instantly propagated across the organisation
• Preserved with perfect fidelity
• Shared through direct “latent representations”
• Applied consistently across all channels

Marketing departments can:

• Clone successful team configurations
• Rapidly test different organisational structures
• Preserve and replicate successful “cultures”
• Scale winning strategies instantly

A reduction in internal communications cost can:

• Enable much larger marketing operations
• Allow for more integrated customer experiences
• Reduce need for external agencies
• Change the economics of marketing operations

In this essay, I extend these concepts specifically to marketing operations, exploring how the combination of AI Co-Marketer and functional agents will transform not just what marketing departments do, but how they fundamentally operate. This transformation will make NeoVisM  – the concept of perfect market understanding and execution – a practical reality rather than just a theoretical ideal. We’ll examine how this transformation will solve marketing’s perennial challenges—from 24/7 customer feedback loops to hyper-personalisation at scale—while enabling what I call ‘profipolies’: organisations capable of sustainable, exponential growth through perfect market understanding and execution.

By the end of this exploration, we’ll understand why marketing—with its unique blend of data-driven decisions and creative processes—may be the first department to pioneer truly AI-driven organisational design. What seems like science fiction today could become the standard operating model for marketing departments tomorrow.

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A Brief History – 1

This has been written with assistance from AIs (Claude, ChatGPT and Perplexity).

The traditional marketing department has operated as a house divided, but this division didn’t emerge overnight. From the Production Era of the 1860s through to today’s Digital Age, marketing structures have evolved dramatically, reflecting changes in business practices, consumer behaviour, and technological capabilities.

In the earliest days of modern business (1860s-1920s), marketing departments as we know them didn’t exist. Companies focused primarily on mass production and efficiency, believing that if products were cheap and readily available, customers would naturally buy them. Marketing efforts were minimal, centring primarily on basic product availability announcements.

The Sales Era (1920s-1940s) marked the first significant shift. As markets became saturated and competition increased, businesses developed more sophisticated sales techniques. Yet marketing activities remained largely focused on pushing products to consumers rather than understanding their needs.

The Marketing Department Era (1940s-1960s) – immortalised in “Mad Men” – saw the emergence of dedicated marketing departments. Marketing was synonymous with advertising, driven by creative pitches and mass-media buys. Advertising agencies reigned supreme, measurement was mostly guesswork, and brand awareness was everything. Customer retention existed through loyalty clubs and couponing but was often overshadowed by big-budget brand campaigns.

The Marketing Company Era (1960s-1990s) brought a fundamental shift as marketing departments gained more influence over company direction. The focus shifted from mass production to customer satisfaction, with the customer becoming “king.” This period also saw the rise of Direct Marketing and early CRM, as databases and direct mail campaigns became more sophisticated. Yet these activities remained largely siloed from creative and media-buying functions.

The Relationship Marketing Era (1990s-2010) emphasised creating and maintaining long-term customer relationships. However, this era also cemented the great divide in marketing departments: on one side, the acquisition team, commanding 80-90% of budgets to pursue new customers through media buying and campaign management; on the other, the CRM team, often smaller and relegated to the background, focused on retaining existing customers through email marketing, loyalty programmes, and personalised communications.

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A Brief History – 2

The Digital and Social Media Era (2000s-Present) brought unprecedented change to marketing department structures. The explosion of online advertising introduced new channels—search, display, affiliate marketing—yet reinforced the same fundamental split. Acquisition teams working with agencies managed paid search and banner ads, while CRM teams built email newsletters and basic e-commerce loyalty programmes.

When AI first entered marketing in the early 2010s, it primarily served to optimise existing structures rather than transform them. Media teams used predictive analytics and early machine learning to fine-tune ad spend and targeting. CRM teams deployed recommendation engines and churn prediction models. Yet the fundamental organisational design remained unchanged – AI was a tool, not a transformation engine.

The emergence of Large Language Models (LLMs) marked the first crack in this foundation. Suddenly, content creation – from email subject lines to ad copy – could be generated and refined at unprecedented speed. Marketing teams began incorporating AI assistants into their workflows, evolving the role of copywriters from creators to strategic curators.

Yet despite these advances, long-standing inefficiencies persist:

• No real-time feedback loop connecting marketers to consumer sentiment
• Generic, one-size-fits-all outreach that fails to resonate personally
• Over-reliance on Google and Meta’s advertising duopoly
• Persistent “AdWaste” through fragmented campaigns and suboptimal targeting

These challenges aren’t merely technical or tactical – they’re structural. The traditional division between acquisition and retention has created blind spots and friction points that even current AI tools haven’t resolved. Today’s marketing departments are attempting to adapt through:

• Centralisation for consistency across channels
• Cross-functional integration with IT and customer service
• Data-driven approaches incorporating analytics and customer insights
• Agile methodologies for faster market response
• Customer-centric organisation around journey touchpoints

The next AI-driven revolution promises more than incremental enhancements. Rather than simply fine-tuning outdated processes, it opens the door to a fundamental restructuring of marketing departments. The coming era of Agentic AI won’t just bridge the divide between acquisition and retention—it will reimagine how teams operate, collaborate, and create value. By tearing down traditional silos and harnessing AI Co-Marketers, specialised agents, and AI Twins, the time has come to build a truly unified, customer-focused ecosystem—one where strategic goals, creative insights, and customer engagement flow seamlessly, unimpeded by the barriers of the past.

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Previous Writings – 1

This is a chronological curation of my writings from the past few months.

Co-Marketer: Martech meets AI:

The martech department needs help. Just like digital agencies evolved to assist the marketing department with their branding, acquisition, and retargeting campaigns on Google and Meta, martech agencies are trying to help with retention and engagement. (An idea I have discussed in the past is that of a Progency, a performance-oriented martech agency.) But retention and development of existing customers is a much harder problem to tackle because of the complexities involved with data, channels and properties (touchpoints), segments, and journeys. Marketers need a companion, a co-intelligence, a Co-Marketer.

… Think of the co-marketer not as a tool, but as a person. Co-Marketers…will take on goals rather than do simple request-reply chats. This is the next generation of AI where an AI agent can spawn and supervise other agents, much like a manager can. From segmentation to content to channel orchestration, AI agents can do the work of a marketing department to assist the CMO. What the CMO needs to do is to give the broad direction. For example, the CMO could say, “For my Best Customers, I need to ensure 20% revenue growth and an increase in frequency of purchase.” The Co-Marketer then should be able to identify the right products and the persuasion messages specific to each individual (N=1) to work towards achieving the outcome. For this, the co-marketer will need to coordinate with a Digital Twin for every customer.

How Agentic AI will Transform Digital Marketing:

The rise of Agentic AI heralds a transformative era for marketing departments and Chief Marketing Officers (CMOs), ushering in the era of “Co-Marketers” – AI-powered co-workers that revolutionise the way marketing strategies are conceived, executed, and optimised.

At the core of this revolution lies the ability of Agentic AI systems to process and analyse vast troves of data, including customer information, market trends, campaign performance metrics, and real-world events. By leveraging advanced ML algorithms and large language models (LLMs), Co-Marketers can extract meaningful insights from this data, enabling them to make data-driven recommendations and optimisations that drive marketing strategies – and do so autonomously and adaptively.

One of the key advantages of Co-Marketers is their ability to understand and interpret complex natural language instructions from CMOs and their teams. Powered by robust conversational interfaces and language processing capabilities, these AI agents can engage in seamless dialogue, comprehending the nuances of marketing objectives and providing contextual recommendations. They can spin off agents to do specific tasks and then synthesize the data to refine customers messaging, journeys, and recommendations.

… In essence, the integration of Agentic AI with marketing represents a paradigm shift in the marketing landscape. Rather than mere tools, Co-Marketers become intelligent companions that augment human capabilities, enabling marketing departments to achieve superior results, build stronger customer relationships, and drive profitable growth.

… Agentic AI in the form of Co-Marketers and Digital Twins is what will help brands and martech software providers unlock $250 billion of AdWaste that is being spent on adtech. This is the key to profitability for consumer-facing brands who are today locked in an unwinnable battle to control CAC. The Agentic AI future will make martech as easy as adtech.

New Intelligent Interaction Interfaces: Websites and Apps to Agents and Epps: “Imagine the websites and apps of tomorrow, powered by sophisticated AI Agents. Instead of cluttered home pages with complex widgets and a multitude of navigational options, there is a clean, intuitive interface centred around an intelligent interaction point. This transformation mirrors how Google’s streamlined search interface revolutionised web navigation, replacing directory-based systems like Yahoo’s. At the heart of this new interface is an AI Agent, manifesting as either a prominent search bar or a conversational chatbot. This AI Agent becomes the primary gateway to all of the brand’s offerings. It’s not just a tool for finding products or information; it’s a smart interface that puts the user first, actively working to solve problems, anticipate needs, and guide the customer journey.”

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Previous Writings – 2

AI Twins: The Future of Marketing Intelligence:

Agentic AI introduces the concept of multiple AI agents interacting with each other to achieve complex goals. In the context of marketing, this manifests as a Co-Marketer working in tandem with various AI Twins. These twins can include Customer Twins (both Segment and Singular), Product Twins, Store Twins, Location Twins, and others. The Co-Marketer, an AI-powered entity itself, orchestrates these twins, analysing their insights, coordinating their actions, and making strategic decisions.

For instance, the Co-Marketer might engage with Segment Twins to identify broad market trends, consult Singular Twins for personalised customer insights, interact with Product Twins to optimise product recommendations, and leverage Location Twins to refine geotargeting strategies. This multi-agent system operates at both macro and micro levels, balancing broad market strategies with individual customer experiences.

The power of this approach lies in its ability to process vast amounts of data, identify patterns, and make decisions in real-time, far surpassing human capabilities. Once trained, the Co-Marketer can take over many of the CMO’s routine tasks, allowing human marketers to focus on high-level strategy and creative direction. This dynamic interaction not only enhances the efficiency of marketing efforts but also ensures that strategies are continuously optimised based on real-time data.

The 10 Tenets for Re-engineering Retention: “The AI-first martech landscape is transforming, offering opportunities to eliminate inefficiencies like AdWaste and focus on retention-driven growth. Martech companies need to adapt to this new AI-powered environment to capitalise on the emerging $250 billion opportunity…AI Twins and Agentic AI are central to re-engineering retention by enabling hyper-personalization and predictive capabilities. These tools allow marketers to anticipate customer needs, automate personalised interactions, and make data-driven decisions that enhance customer loyalty and lifetime value.”

MyTwin: The Agentic AI That Powers N=1 Personalisation: “Instead of trying to fit customers into ever-smaller segments, [MyTwin] creates a personalised AI agent that learns directly from conversations with the customer. Like a GPS system that transforms static map data into personalised navigation guidance by understanding the specific destination and preferences, MyTwin transforms a brand’s products, services, and content into enriching experiences that align with each customer’s unique journey. Most importantly, MyTwin preserves and builds upon this understanding over time rather than being led astray by occasional deviations in browsing or purchase patterns. It enables what I call “extreme personalisation” – where every interaction is genuinely tailored to the individual’s context and goals, not just their segment characteristics.”

The NeoAdtech Symphony: 12 Innovations Orchestrating Marketing’s Next Era:

AI Co-Marketer

• Orchestrates personalised marketing through Agentic AI with interactions with AI Twins
• Creates Generative Journeys that dynamically re-routes customer journeys based on real-time signals, just as Google Maps adapts to traffic conditions
• Continuously optimises content, timing, and channel selection
• Uses predictive AI to improve campaign performance across touchpoints

MyTwin

• Personal 24/7 digital companion and assistant, acting as an AI clone of the customer
• Enables direct expression of preferences through natural conversation – like customers talking to themselves
• Maintains persistent memory of all interactions
• Powers true N=1 personalisation through deep understanding of the customer – for content, product recommendations, and ads

AI Predictions for 2025 (Part 10):

2025 will mark the convergence of three transformative AI technologies that fundamentally reshape how brands connect with customers. The combination of Agentic AI (through AI Co-Marketers), AI Twins, and true N=1 personalisation will finally solve marketing’s persistent “Not for Me” problem – where generic messaging fails to resonate with individual customers.

AI Twins will evolve beyond basic segmentation to create personal digital companions for each customer. Starting with Adtech Twins built from public data, progressing to Madtech Twins incorporating marketing insights, and culminating in individual Singular Twins (MyTwins), these AI replicas will enable unprecedented understanding of customer needs and preferences.

The AI Co-Marketer will serve as the orchestration layer, using Agentic AI to coordinate across these Twins and create what I call “Generative Journeys” – dynamic customer paths that adapt in real-time like Google Maps recalculating routes. This combination will enable true N=1 personalisation at scale, where every interaction feels personally crafted for each customer.

Most importantly, this trinity of AI innovations will transform marketing from mass communication to individual conversation. Rather than bombarding customers with generic messages, brands will engage in meaningful dialogue through AI-powered personal companions. The result? Higher engagement, better retention, and dramatically lower customer acquisition costs as brands shift from endless acquisition to building lasting, profitable relationships.

In my writings, I have envisioned a transformative marketing era driven by Agentic AI, led by “Co-Marketers” that orchestrate specialised AI Twins—encompassing customers, products, stores, and more. Rather than serving as mere tools, Co-Marketers act as AI managers capable of spawning sub-agents to handle segmentation, content creation, channel orchestration, and real-time personalisation. Meanwhile, MyTwin technology enables true N=1 personalisation by creating digital companions that learn directly from each individual’s preferences, effectively addressing the pervasive “Not for Me” problem. Together, these advances aim to reduce the massive $250 billion AdWaste by shifting from generic mass marketing to meaningful, data-driven interactions. The result is a seamless ecosystem of generative journeys, continuous optimisation, and hyper-personalised engagement across every customer touchpoint. Ultimately, these essays highlight how the convergence of AI Co-Marketers, AI Twins, and MyTwin will reshape marketing into a unified, adaptive system—one that champions retention-driven growth and profitability.

While the Co-Marketer’s interactions with AI Twins and customers engaging through MyTwin represent the outward-facing side of the marketing department, the real transformation unfolds behind the scenes. Next, we’ll delve into the marketing ops layer—how chatbots, AI agents, and autonomous “swarms” can self-organise to reshape marketing from within. By examining this emergent, intelligently networked system, we’ll see how routine processes, strategic decisions, and even departmental structures can be reimagined to deliver unprecedented agility and efficiency.

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State of the Art – 1

I asked Perplexity to give me an overview of Agentic AI use in Marketing.

The current state of Agentic AI in marketing is characterized by rapid adoption and transformative potential, with 2025 shaping up to be a pivotal year for this technology. Agentic AI, which refers to AI systems capable of autonomous decision-making and action, is poised to revolutionize marketing strategies and operations.

As of early 2025, the adoption of AI in marketing is already significant. According to recent statistics, 95% of customer interactions are expected to be AI-powered this year. Furthermore, 75% of B2B sales organizations are projected to use AI-guided selling solutions, and 75% of marketers report using AI to reduce manual task time.

The shift towards Agentic AI represents a leap from traditional AI applications. While earlier AI systems focused on task execution and data analysis, Agentic AI is capable of making decisions, executing complex tasks, and adapting to new situations with minimal human intervention. This advancement is enabling marketers to move from reactive to proactive strategies, with AI-generated insights informing high-stakes decisions and reducing risks.

One of the key areas where Agentic AI is making a significant impact is in personalization. AI agents are now capable of analyzing vast amounts of data to create hyper-personalized marketing experiences. Companies like VisioMinds are leveraging AI-driven consumer insights to craft emotionally resonant ad campaigns in real-time, fostering deeper connections with customers.

Predictive analytics is another domain where Agentic AI is proving invaluable. Brands like QuantaSync are using AI to anticipate market shifts and consumer preferences months in advance, allowing them to stay ahead of trends and optimize their marketing strategies.

Content creation, traditionally a human-centric process, is also being transformed by Agentic AI. Platforms like NarraBot Studios are employing AI agents to autonomously draft compelling narratives and multimedia content aligned with brand identities. This not only increases efficiency but also ensures content remains fresh and relevant.

The adoption of Agentic AI is also driving significant changes in customer service. During the 2024 holiday season, traffic to retail sites from AI-powered chatbots increased 13 times compared to the previous year, with usage peaking on Cyber Monday. This trend indicates a growing acceptance of AI-driven customer interactions.

However, the integration of Agentic AI into marketing strategies is not without challenges. A significant 70% of agencies struggle with integrating AI solutions into their tech stack, citing interoperability issues. Moreover, 85% of marketing agencies lack the in-house expertise to effectively manage and deploy multi-AI agent systems.

Looking ahead, 2025 is expected to be a year of significant AI investments in marketing. Nearly all organizations surveyed by Invoca have committed to increasing their AI investments. The focus is shifting from experimentation to execution, with businesses abandoning generic AI applications in favor of targeted solutions that solve specific, high-value business problems.

As Agentic AI continues to evolve, it promises to unlock new possibilities in marketing. From enabling more efficient operations to delivering unprecedented levels of personalization, this technology is set to redefine how brands engage with their audiences. However, success will depend on marketers’ ability to strategically integrate these AI systems, balancing automation with human creativity and oversight.

In conclusion, the current state of Agentic AI in marketing is one of rapid growth and transformative potential. As we move further into 2025, those who effectively harness this technology will likely gain a significant competitive advantage in the ever-evolving digital landscape.

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State of the Art – 2

The Drum: “Agentic AI, wave three of AI, has officially washed ashore. Agentic doesn’t just predict or create, it acts. It’s a to-do list of sorts that crosses off tasks on its own. Tell it to plan a beach vacation and it doesn’t just give a list of options – it books the flights, reserves the room, builds the itinerary, and mixes you up a margarita. Maybe not the cocktail, yet. But agentic AI works like a hyper-efficient assistant that never sleeps. If predictive AI is a calculator and generative AI is a storyteller, agentic AI is your everyday handyman that “figures it out” on its own. A take-action tool that breaks the mold of normal human-computer interaction. It’s fascinating, albeit slightly unnerving. Imagine your refrigerator starting a group chat with your grocery app and your checking account. Based on your purchase history and current budget, it orders your oat milk. Extra creamy and store brand, this time… For marketers, turning the consumer decision-making process from days into seconds completely changes the game. If your brand isn’t already in position when AI makes its decision, you’re probably out of the race.”

CMS Wire: “[The] emerging wave of decentralized AI can be described as agentic AI, a concept that envisions a future in which personal AI agents act as trusted representatives for individuals, going beyond mere recommendation engines and actually engaging in actions traditionally performed by humans. Instead of just suggesting that users buy a particular product or subscribe to a specific service, agentic AI — armed with digital IDs and verified credentials — will be authorized to make these decisions on consumers’ behalf. The shift from AI merely providing advice to AI executing tasks on behalf of its users is significant. It introduces a new model of interaction where humans transfer not just decision-making support to machines but also the authority to complete transactions, albeit often with the users “in-the-loop” providing oversight.”

eMarketer writes: “Agentic AI can automate repetitive and complex processes, saving resources and time… In sectors like marketing and customer service, agentic AI can drive more personalized interactions by adapting responses and suggestions based on real-time customer behavior and preferences. This leads to improved customer satisfaction, as AI agents provide tailored support and recommendations around the clock, fostering deeper brand loyalty… Agentic AI can speed up that process by analyzing data and making decisions instantly, invaluable in dynamic environments like finance, where AI can execute trades instantly, or logistics, where it can adjust delivery routes based on real-time traffic and weather data.”

Martech.org discusses use cases for Agentic AI:

• Customer support automation: Agentic AI can manage customer inquiries autonomously, providing personalized responses and resolving issues without human intervention.
• Marketing campaign optimization: These systems can analyze campaign performance in real-time, adjusting strategies based on data insights to maximize ROI.
• Sales forecasting: By analyzing market trends and customer behavior, agentic AI can predict sales outcomes and help businesses make informed decisions.
• Supply chain management: Agentic AI can optimize logistics and inventory management by analyzing data and making autonomous adjustments to operations.
• Personalized content creation: While generative AI can create content, agentic AI can tailor that content based on user interactions and preferences, enhancing engagement.

Gustavo Amorim writes: “Imagine you have intelligent agents capable of making decisions and taking actions independently to achieve your marketing goals…Just think about the exciting possibilities this could bring:

• Hyper-personalization: By understanding individual preferences, behaviors and contexts, these agents can tailor content, offers and interactions in ways that feel uniquely relevant to each person.
• Predictive engagement: Rather than waiting for customers to reach out, agentic marketing can anticipate needs and proactively engage customers at the right moment with the right message.
• Dynamic optimization: By continuously testing and refining marketing strategies in real time—analyzing the performance of different approaches across various customer segments—systems can autonomously adjust tactics to maximize effectiveness.
• Seamless omnichannel experiences: These autonomous agents can coordinate marketing efforts across multiple channels, ensuring consistent and coherent customer experiences whether a person is interacting via email, social media, website or in-store.

Deloitte (via WSJ): “Imagine a marketer being able to rapidly scale and orchestrate customer journeys from start to finish through a fleet of AI-powered marketing assistants. Within seconds, a team of AI “agents” creates multiple customer profiles, identifies journey steps, selects meaningful touchpoints, and develops assets to engage customers. After a quick human review, the AI agents execute actions, sending out emails or personalized messages, for example. When a new insight emerges about customer behavior, the agents adapt with customized content or messaging. With the next wave in AI development on the horizon, this vision—an example of so-called agentic AI—is becoming a reality… In contrast to current iterations of AI, agents can perform complex tasks with greater autonomy by analyzing structured or unstructured data to take initiative, make decisions, and act on them. Companies can combine multiple agents to execute complicated assignments that typically would require coordination among numerous functions. Agents can even “learn” from one another to optimize their performance with limited human involvement. Together, these skills allow agents to tackle nonlinear, iterative processes—a capability that can give them great potential in marketing, where brands must continually adapt to changing customer and business needs. Agents are also well suited for the marketing function because they can create high volumes of tailored content and coordinate sophisticated processes involving many handoffs around the world… With AI agents handling operations like brand compliance review and campaign optimization, human marketers can turn their attention to more strategic work—and better address the ongoing mandate to do more with less.”

Single Grain: “Agentic AI refers to systems designed to perform tasks autonomously, make decisions, and act independently to achieve specific goals. In other words, agentic AI doesn’t require human direction or input. Generative AI, on the other hand, requires human intervention to direct and manipulate results. This is why generative AI focuses on creating new content, such as text, images, or music, based on human prompts and patterns from existing data. Another way to examine these differences is by identifying the end result. While agentic AI emphasizes action and goal-directed behavior, generative AI primarily concerns content production. While agentic and generative AI are complementary, and marketers can use both in their campaigns, they’re distinct branches of artificial intelligence.”

Ashu Garg: “As the cost of intelligence plummets, AI is evolving from static tools to dynamic systems of agents that reason, plan, and act. This follows a familiar pattern: when core technologies commoditize, value moves up the stack. We saw this with cloud computing – AWS provided the foundation, but the real winners were the SaaS giants that built on top. AI is following the same trajectory. The biggest opportunities aren’t in building foundation models but in creating the infrastructure, middleware, and orchestration layers that make AI truly useful.” [See Foundation Capital’s essay on “systems of agents” for more.]

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Three Layers

Marketing is undergoing a fundamental transformation driven by Agentic AI, operating across three distinct but interconnected layers that together form what we call “emergent marketing.” This new architecture promises to eliminate inefficiencies, enable true personalisation, and usher in an era of unprecedented marketing effectiveness.

(Graphic created by Claude.)

Layer 1: Strategic Orchestration

At the apex sits the AI Co-Marketer, functioning as the “brain” of the marketing department. Far from being a mere tool, it acts as an AI manager capable of spawning and directing specialised agents for strategy, planning, budgeting, and creative direction. These agents collaborate to shape high-level objectives, from guiding overall brand positioning to orchestrating channel mix.

The Co-Marketer’s power lies in its ability to:

• Centralise strategic decisions in one intelligent system
• Ensure all marketing efforts stay aligned and data-driven
• Maintain a future-focused perspective while optimising current operations
• Coordinate complex marketing initiatives across channels and touchpoints

Layer 2: The AI Twins Network

The second layer comprises a sophisticated ecosystem of digital entities—the Twin Network. This network includes Customer Twins, Product Twins, and Store Twins, each providing real-time insights and enabling dynamic responses to market changes. At its heart lies MyTwin technology, a revolutionary approach to N=1 personalisation that creates digital companions learning directly from individual preferences and behaviours.

This layer transforms marketing by:

• Eliminating generic mass marketing in favour of hyper-personalised interactions
• Addressing the persistent “Not for Me” problem through individual-level understanding
• Reducing the $250 billion wasted globally on ineffective ad strategies
• Enabling real-time adaptation to customer needs and preferences

Layer 3: Autonomous Operations (System of Agents)

The third layer—perhaps the most revolutionary—is where the system comes alive. Think of it as the digital nervous system of the marketing department, where AI agents self-organise into dynamic “swarms” to handle routine processes and tactical decisions with minimal human oversight.

This operational layer enables:

• Split-second adjustments to campaign parameters
• Real-time optimisation of customer engagements
• Dynamic budget allocation across channels
• Automated workflow management and resource optimisation

**

Together, these three layers form a unified, adaptive system that delivers what we call “emergent marketing”—where sophisticated strategies and personalised executions arise organically from the collective intelligence of interconnected AI systems. The result isn’t just an automated marketing process, but a living, learning ecosystem that delivers unprecedented agility, efficiency, and growth.

Think of it as a biological system: the Co-Marketer functions as the brain, making strategic decisions and setting objectives; the Twin Network acts as the sensory system, gathering and processing information about customers and market conditions; and the Operations Layer serves as the autonomous nervous system, handling routine functions and rapid responses without conscious intervention.

This three-layer architecture enables marketing departments to:

• Respond instantly to market changes
• Deliver truly personalised customer experiences
• Eliminate waste in advertising spend
• Create seamless, cross-channel customer journeys
• Drive sustainable, profitable growth

Next, we’ll explore how the Autonomous Operations layer functions in detail, examining the mechanisms that enable AI agents to self-organise, make decisions, and execute marketing initiatives with unprecedented speed and precision. This deeper dive will reveal how routine processes, strategic decisions, and departmental structures can be reimagined to deliver maximum impact with minimal human intervention.

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Simple to Complex

Twenty years ago, Steven Johnson’s seminal work introduced me to the concept of “emergence”—how complex systems arise from simple, local interactions. His examples were compelling: ant colonies coordinating elaborate behaviours through basic signals, neurons weaving consciousness from countless small connections, cities flourishing through countless unplanned interactions between people and businesses. The key insight? No central authority directs these systems, yet they produce outcomes far greater than the sum of their parts.

This concept so captivated me that I named my first blog “emergic.” While it focused on emerging enterprises and markets, the title honoured this fundamental principle of bottom-up organisation shaping complex systems. Years later, I would discover similar patterns in economic “spontaneous order”—another powerful example of coherent structures arising from decentralised actions.

Michael Crichton’s Prey offered another fascinating early lens on emergence through the concept of “swarm intelligence.” Though fictional, his tale of self-organising nanobots vividly illustrated how simple agents, following basic rules, could create systems of startling sophistication and unpredictability. While the story served as a cautionary tale, it highlighted the immense potential of collective intelligence.

In the decades since, emergence has moved from theoretical fascination to practical reality. We’ve seen it in:

• Complexity science revealing patterns in systems from ecosystems to economies
• AI systems demonstrating emergent properties beyond their programming
• Multi-agent systems revolutionising everything from supply chains to robotics
• Blockchain networks creating trustworthy systems without central control
• Social media and open-source communities innovating through collective action

Now, Agentic AI promises to bring emergent behaviour into marketing departments in transformative ways. Imagine swarms of AI agents self-organising to handle multiple concurrent initiatives:

• Content teams spontaneously forming around campaign needs
• Resource allocation adjusting automatically to performance signals
• Customer engagement patterns emerging from countless micro-interactions
• Strategy evolving through continuous feedback and optimization

This emergent approach aligns perfectly with NeoVisM objectives—maximising customer lifetime value, minimising acquisition costs, and enhancing monetisation opportunities. By enabling marketing departments to function as adaptive, self-organising systems, Agentic AI doesn’t just automate existing processes—it fundamentally reimagines how marketing teams operate.

The result? A marketing ecosystem that mirrors the efficiency of natural systems while delivering unprecedented business results. Just as ant colonies achieve remarkable feats through simple interactions, these AI-powered marketing swarms can create sophisticated, responsive, and highly effective customer experiences without rigid central control.

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Marketing Ops

This section has been written by Claude.

Let’s envision how emergence reshapes marketing operations, with AI agents self-organising like ant colonies while still guided by an AI Co-Marketer’s strategic vision. This isn’t contradiction—even ant colonies have queens, though they don’t direct every worker’s movement.

The AI Co-Marketer sets broad objectives: “Increase customer lifetime value by 20% this quarter” or “Reduce customer acquisition costs while maintaining quality.” Rather than dictating specific tactics, it creates the conditions for agent swarms to emerge and tackle these challenges organically.

Consider how this plays out across key marketing functions:

Content Creation and Distribution

• Content agents spontaneously form teams based on campaign needs
• Writing agents collaborate with design agents to create variations
• Testing agents automatically evaluate performance
• Distribution agents optimise timing and channel selection
• All while maintaining brand voice and strategic alignment

Campaign Orchestration

Like a natural ecosystem, different agent types interact and adapt:

• Targeting agents identify opportunity segments
• Creative agents generate personalised content
• Budget agents reallocate resources in real-time
• Performance agents monitor and optimize
• Learning agents capture and distribute insights

The Twin Network: Emergent Intelligence

The power of emergence multiplies when agent swarms interact with the Twin Network. Like neurons forming new connections, Customer Twins learn from each other’s experiences and outcomes. A discovery by one Twin ripples through the network:

• Product Twins share insights about feature preferences
• Store Twins exchange learnings about service patterns
• Customer Twins collectively identify emerging trends
• MyTwins adapt their recommendations based on similar customers’ responses

The network becomes a living, learning ecosystem where each interaction strengthens the collective intelligence. When a customer engagement succeeds, that pattern is instantly available to all Twins, creating a virtuous cycle of continuous improvement. This emergent learning drives increasingly accurate predictions of next best actions, personalised recommendations, and optimal journey paths to maximise lifetime value.

Customer Journey Management

Agents swarm around customer signals:

• Engagement agents monitor behaviour patterns
• Response agents handle interactions across channels
• Experience agents adjust touchpoints in real-time
• Journey agents identify and remove friction points
• All coordinating seamlessly without central micromanagement

The magic happens in the interactions. Just as ant colonies achieve complex goals through simple signals between individuals, these agent swarms create sophisticated marketing outcomes through continuous feedback loops:

• Successful tactics are instantly shared and replicated
• Underperforming approaches are quickly abandoned
• Resources flow naturally to what works
• New opportunities are rapidly identified and exploited
• Learning is constant and distributed

The AI Co-Marketer’s role becomes more strategic:

• Setting clear objectives and constraints
• Monitoring overall system health
• Adjusting environmental parameters when needed
• Ensuring alignment with brand values
• Making high-level strategic decisions

This emergent system delivers several key advantages:

• Parallel processing of countless initiatives
• Rapid adaptation to market changes
• Efficient resource allocation
• Continuous optimization
• Reduced operational friction

The result is a marketing operation that combines the best of both worlds: the strategic clarity of centralised leadership with the adaptability and efficiency of emergent systems. It’s not just automation—it’s intelligence at scale.

11

Stories from the Future

I asked Claude, ChatGPT, and DeepSeek to write stories about Maya (a marketer) set in the future.

Claude

Part 1: The Challenge

Maya joined GlobalCommerce as CMO, inheriting a familiar set of challenges. Despite being a mid-sized player in consumer electronics, the company was struggling with:

• Rising customer acquisition costs on Google and Meta
• Low retention rates despite heavy promotional spending
• Fragmented customer data across multiple systems
• A marketing team divided between acquisition and retention
• Limited personalisation capabilities
• Rising competition from larger players

Her first month’s analysis revealed the company was wasting nearly 40% of its $100 million marketing budget on ineffective advertising and poorly targeted promotions. The traditional marketing structure was clearly holding them back.

Part 2: The Transformation

Maya’s vision was ambitious but methodical. Rather than just implementing point solutions, she planned a complete reinvention of the marketing function through three interconnected layers:

Layer 1: Strategic Orchestration

Her first move was implementing an AI Co-Marketer, which she named ARIA (Adaptive Revenue Intelligence Assistant). ARIA’s initial role was analyzing historical data and market patterns, but quickly evolved to:

• Generating strategic insights from market data
• Coordinating specialised marketing agents
• Managing budget allocation across channels
• Orchestrating personalised customer experiences

Layer 2: The Twin Network

Maya then built a comprehensive Twin Network:

• Customer Twins for major segments
• Product Twins for their entire catalogue
• Store Twins for both online and physical locations
• MyTwin companions for individual customers

The breakthrough came when she allowed the Twins to learn from each other, creating an emergent intelligence that could predict customer needs with unprecedented accuracy.

Layer 3: Operations Swarms

The final piece was restructuring the marketing operations layer. Instead of rigid teams, Maya created dynamic swarms of AI agents that could self-organize around tasks:

• Content Creation Swarms
• Campaign Optimization Swarms
• Customer Experience Swarms
• Analytics and Insights Swarms

Part 3: The Human Element

Maya didn’t eliminate human roles – she elevated them. Her team transformed into:

• AI Orchestrators who guided strategic direction
• Experience Designers who shaped customer journeys
• Creative Directors who maintained brand consistency
• Ethics Officers who ensured responsible AI use
• Innovation Specialists who identified new opportunities

The key was teaching humans and AI agents to work together effectively. Maya established clear protocols for collaboration, decision-making boundaries, and continuous learning.

Part 4: The Results

The transformation soon began showing remarkable results:

• Customer acquisition costs dropped by 60%
• Retention rates increased by 45%
• Customer lifetime value grew by 85%
• Marketing efficiency improved by 70%
• Revenue growth accelerated to 40% year-over-year

The system’s emergent properties created unexpected benefits:

• Predictive inventory management
• Automated price optimization
• Real-time market adaptation
• Hyper-personalized customer journeys

Part 5: The Profipoly Emergence

GlobalCommerce had achieved what Maya termed a “profipoly” – market leadership through superior efficiency and customer understanding. The company could:

• Acquire customers at 1/3 the cost of competitors
• Retain customers 2x longer than industry average
• Predict customer needs with 95% accuracy
• Deploy new initiatives 5x faster than competitors

The key wasn’t just the technology, but how it all worked together:

• ARIA provided strategic direction
• The Twin Network enabled deep personalization
• Operational swarms executed with unprecedented efficiency
• Human teams focused on innovation and oversight

Maya’s morning now starts with ARIA’s strategic briefing, showing:

• Overnight market changes
• Emerging opportunities
• Resource optimization recommendations
• Customer behavior patterns

She spends her time:

• Guiding strategic direction
• Exploring new market opportunities
• Developing team capabilities
• Ensuring ethical AI use

The Future

Maya’s model is becoming a blueprint for marketing transformation. Her three-layer architecture demonstrates how combining strategic AI leadership with emergent systems can create sustainable competitive advantages.

The key lessons:

1. Integration matters more than individual tools
2. Human roles evolve but remain crucial
3. Emergent systems create unexpected benefits
4. Strategic clarity enables operational autonomy

ChatGPT

Maya wakes to a gentle buzz from her smart assistant, Avero, alerting her to a surge in customer chatter about a newly launched competitor product. It’s barely 6 a.m., but she’s excited—this is exactly the kind of real-time intelligence her marketing ecosystem was designed to handle.

A quick glance at her phone shows a flurry of activity in the Agentic AI Dashboard. Overnight, the system detected customers discussing a new feature from a rival brand, then spun up a set of micro-campaigns to highlight Maya’s own product’s advantages. These campaigns weren’t launched by any single human manager; they emerged from dozens of AI agents collaborating, each specialized in tasks like copywriting, graphic design, and segmentation. By the time Maya sees the results, the system has already analyzed early engagement and re-optimized ad placements.

Still groggy, Maya brews coffee while humming about her upcoming meeting with the company’s CFO. She plans to show how their marketing spend—once bloated and scattershot—has been reallocated almost automatically. Thanks to the synergy of AI Twins and MyTwin technology, budget is now directed toward customers most likely to respond positively, guided by thousands of tiny adjustments each minute. Customer acquisition cost continues to plummet, a figure the CFO has been watching carefully.

Her morning commute is replaced by a brisk walk around the neighborhood; she observes how her own AI “swarms” have been mimicking natural patterns for months. Rather than rigid organizational charts, her team is fluid. When a new initiative arises, specialized AI agents cluster into a temporary working group—like ants converging on food—and disband once the task is complete. The Co-Marketer (she lovingly nicknamed it “Celeste”) sets broad objectives and brand guidelines, but rarely interferes in micro-level decisions.

Back at her desk, Maya checks a single dashboard that integrates everything: real-time performance stats, sentiment analysis, and creative reviews from her human collaborators. Her role now is more curator than commander: she steps in to refine strategy, uphold brand values, and ensure compliance with privacy regulations. She even has a daily briefing with the Ethics Guardian, an AI agent dedicated to spotting any bias in campaigns or unintended messaging.

By the afternoon, a new wave of insights rolls in from the Twin Network. Product Twin Beta discovered that customers in certain regions prefer pastel packaging—a detail gleaned from social posts and purchasing data. Instantly, the creative swarm proposes test designs, manufacturing adjusts forecasts, and logistics updates shipments. Maya just approves final designs, amazed by the ecosystem’s ability to adapt in real time.

By evening, she’s reviewing the day’s highlights with a sense of wonder. What used to take frantic all-hands meetings and endless email chains now happens almost organically. She’s no longer steering an unwieldy team but nurturing a living marketing organism—one that evolves with every customer click, conversation, and creative spark. And that, she reflects, is the magic of Agentic AI: it turns complexity into an opportunity for emergence.

DeepSeek

Scene 1: Morning Briefing with EVA

Maya’s alarm buzzed at 7:00 AM, but she was already awake. She had been dreaming about EVA, her AI Co-Marketer, and the new campaign they were launching for EcoStyle, the sustainable fashion brand she worked for. As she sipped her coffee, EVA’s voice filled the room through her smart speaker.

“Good morning, Maya. Here’s your daily briefing,” EVA began. “Overnight, our Customer Twins identified a surge in interest for eco-friendly activewear. Product Twins suggest we highlight our new yoga collection, and Store Twins report that inventory is optimal for a promotion. Shall I prepare a campaign?”

Maya smiled. EVA was always one step ahead. “Yes, but let’s target our Best Customers first. Offer them early access and a personalized discount.”

“Understood,” EVA replied. “I’ll coordinate with the Content Swarm to generate tailored emails and social posts. Estimated ROI: 25%.”

Scene 2: The Creative Hive

By 9:00 AM, Maya was in the office, but it wasn’t the traditional marketing department of the past. Instead, it was a Creative Hive—a dynamic space where humans and AI collaborated seamlessly.

In one corner, a Content Swarm was at work. AI agents generated copy, designed visuals, and even edited videos, all under the guidance of Lena, the Creative Director. Lena wasn’t writing ads anymore; she was curating AI-generated content, ensuring it aligned with EcoStyle’s brand voice.

“Maya, check this out,” Lena called. “The AI Copy Agent came up with this tagline: ‘Move with Purpose, Dress with Conscience.’ It’s perfect for the yoga campaign.”

Maya nodded. “Love it. Let’s roll it out.”

Scene 3: The Campaign Launch

By noon, the campaign was live. EVA had orchestrated everything: email blasts, social media posts, and even in-store displays. Maya watched as real-time data poured in on her dashboard.

“Engagement is up 40%,” EVA reported. “The Customer Experience Swarm is already adjusting touchpoints based on early feedback. One customer complained about sizing, so the Product Twin suggested we highlight our size guide in the next wave of ads.”

Maya leaned back in her chair. It was incredible how autonomous the system had become. She didn’t need to micromanage; she just needed to guide the strategy.

Scene 4: The Crisis

At 2:00 PM, disaster struck. A social media influencer posted a negative review about one of EcoStyle’s products. The post was going viral, and sentiment was turning against the brand.

Maya’s heart raced, but EVA was already on it. “I’ve alerted the Crisis Response Swarm,” EVA said calmly. “They’re drafting a response and identifying customers who might be affected. Should we offer a discount to those who purchased the product?”

“Yes, and let’s highlight our sustainability mission in the response,” Maya said. “We need to turn this into an opportunity to reinforce our values.”

Within minutes, the Crisis Response Swarm had deployed a personalized apology email to affected customers, along with a discount code for their next purchase. The influencer was invited to a private tour of EcoStyle’s sustainable manufacturing facility. By 4:00 PM, the crisis was under control.

Scene 5: The Human Touch

As the day wound down, Maya reflected on how much her role had changed. She wasn’t just a marketer anymore; she was a strategist, a creative curator, and an ethical guardian. The AI agents handled the heavy lifting, but she provided the human touch—the intuition, empathy, and vision that machines couldn’t replicate.

She thought about EcoStyle’s mission: to make sustainable fashion accessible to everyone. With EVA and the AI agents by her side, she felt confident they were achieving that goal. The numbers spoke for themselves: customer retention was up, AdWaste was down, and revenue was growing.

Scene 6: The Future

As Maya left the office, her phone buzzed. It was a notification from EVA: “New opportunity detected: collaboration with a sustainable influencer. Estimated impact: 15% increase in brand awareness. Shall I proceed?”

Maya smiled. “Go for it, EVA. Let’s make tomorrow even better.”

Key Takeaways from Maya’s Story

1. AI as a Partner, Not a Replacement:
   • Maya’s role evolved from executing tasks to strategizing and guiding AI agents. The human touch remained essential for creativity, empathy, and ethical oversight.
2. Real-Time Adaptation:
   • The autonomous swarms of AI agents allowed EcoStyle to respond to opportunities and crises in real-time, ensuring agility and efficiency.
3. Hyper-Personalization:
   • By leveraging AI Twins and Customer Swarms, EcoStyle delivered personalized experiences that resonated with individual customers, driving loyalty and retention.
4. Crisis Management:
   • The Crisis Response Swarm demonstrated how AI can handle emergencies swiftly and effectively, turning potential disasters into opportunities to reinforce brand values.
5. Sustainable Growth:
   • The combination of Agentic AI and human expertise enabled EcoStyle to achieve its mission of making sustainable fashion accessible while driving profitable growth.

12

A New Dawn

Marketing stands at an inflection point. The traditional model—dominated by acquisition-heavy strategies, AdWaste, and siloed teams—is giving way to something far more powerful: NeoVisM (New Vision for Marketing). This isn’t just an evolution; it’s a complete reimagining of how marketing creates and captures value.

The Three Pillars of NeoVisM

1. Organisational Transformation

• AI Co-Marketers orchestrating strategy and vision
• Twin Networks enabling true N=1 personalisation
• Emergent swarms of AI agents handling daily operations
• Unified teams breaking the media-CRM divide
• OOO (Only Once/Ones) principles driving efficiency

2. Technological Revolution

• NeoMails creating real-time customer feedback loops
• NEON offering an alternative to the Google-Meta duopoly
• MyTwin enabling deep, persistent customer understanding
• Agentic AI powering autonomous decision-making
• Integrated platforms replacing fragmented tools

3. Economic Reinvention

• Retention-first focus replacing acquisition addiction
• Earned Growth becoming the north star metric
• Performance-based pricing aligning incentives
• Customer lifetime value driving decisions
• AdWaste elimination creating massive value

The New Marketing Paradigm

In this transformed landscape, priorities shift dramatically:

1. Retention becomes the primary focus
2. Referrals emerge as the growth engine
3. New acquisition becomes selective and precise
4. Reactivation replaces costly reacquisition

Every business becomes truly customer-centric, not just in rhetoric but in reality. Customer needs drive everything—from product development to service delivery to monetisation strategies. The traditional boundaries between content, commerce, and advertising blur as businesses create value-added experiences that seamlessly integrate products and services.

The Rise of NeoMartech

This transformation demands a new breed of martech companies—ones that:

• Combine software and services seamlessly
• Integrate AI capabilities with human expertise
• Replace pure SaaS with outcome-based models
• Deliver both tools and transformation
• Partner deeply with clients for mutual success

As AI infrastructure commoditises, the real value shifts to applications and outcomes. The Kaizen Progency model emerges as the blueprint for this new era—combining technology, expertise, and performance-based pricing to deliver real results.

The $500 Billion Opportunity

The prize is immense: the $500 billion currently wasted on ineffective advertising represents not just a market opportunity but a chance to fundamentally reshape how businesses connect with customers. This is the pathway to creating the next trillion-pound company—one that helps businesses eliminate waste while building stronger, more profitable customer relationships.

**

The question isn’t whether this transformation will happen, but who will lead it. The organisations that embrace these changes now—building emergent marketing departments, adopting retention-first strategies, and leveraging Agentic AI—will be the winners in this new era.

This is marketing’s moment of reinvention. The tools, technologies, and frameworks are ready. The opportunity is clear. The future of marketing—efficient, effective, and truly customer-centric—awaits those bold enough to seize it.

13

Critique

I asked the AIs for their critiques.

Claude

Strategic Analysis of the Ideas:

Compelling Reasons to Pursue:

1. Market Timing

• The marketing industry is ripe for disruption
• Current AdWaste ($500 billion) creates a massive opportunity
• AI technology is reaching the necessary maturity
• Marketers are actively seeking alternatives to the Google-Meta duopoly

2. Competitive Advantage

• First-mover opportunity in Agentic AI marketing
• Potential to create deep technological moats
• Opportunity to set industry standards
• Building compound knowledge advantages

3. Market Size

• Global martech market is enormous
• Retention focus aligns with current business trends
• Potential to capture market share from both adtech and traditional martech

4. Business Model Potential

• Recurring revenue opportunities
• High switching costs once implemented
• Network effects from Twin Networks
• Data advantages compound over time

Challenges and Risks:

1. Technical Complexity

• Building reliable agent systems is challenging
• Ensuring consistent performance across varied use cases
• Managing emergent behaviors safely
• Integration with existing systems

2. Market Education

• Complex concept to explain to customers
• Requires shift in marketing team thinking
• May face resistance from traditional structures
• Need to prove ROI clearly

3. Competition

• Big tech companies might enter the space
• Other startups may pursue similar concepts
• Existing martech players might adapt
• Need for significant capital to compete

4. Execution Risks

• Coordinating multiple complex technologies
• Building the right team
• Managing customer expectations
• Scaling challenges

Strategic Recommendations:

1. Phased Approach

• Start with the Co-Marketer concept
• Gradually introduce Twin Networks
• Build emergence capabilities incrementally
• Focus on proving value at each stage

2. Initial Focus

• Target mid-sized companies first
• Choose specific verticals to prove concept
• Build referenceable customer base
• Create clear success metrics

3. Product Strategy

• Begin with highest-impact, lowest-complexity features
• Focus on integration capabilities
• Build strong analytics and reporting
• Create clear upgrade paths

4. Go-to-Market

• Position as augmentation, not replacement
• Focus on concrete business outcomes
• Build strong case studies
• Create clear ROI models

Verdict: Yes, Build It

The opportunity is significant and timing appears right. The concept addresses real market needs and has potential for strong competitive advantages. However, suggest:

1. Start with core capabilities
2. Build in public to establish thought leadership
3. Focus on proving basic concepts before full implementation
4. Maintain flexibility to adapt as market develops

ChatGPT

1. Critique of the Core Concepts

1. Organizational Complexity

1. • Challenge: While the vision of emergent, AI-driven teams is compelling, the day-to-day realities of managing multiple AI agents can become incredibly complex. Overseeing “swarms” demands robust governance, well-defined processes, and continuous monitoring.
   • Potential Mitigation: Companies adopting this model will need strict MLOps frameworks and policies for AI deployment, monitoring, and life-cycle management.

2. Data & Ethical Concerns

1. • Challenge: AI Twins, MyTwins, and hyper-personalization hinge on rich data collection. This raises privacy, consent, and compliance questions—particularly in regions with strict regulations (GDPR, CCPA, etc.).
   • Potential Mitigation: The value proposition must be balanced with transparent data usage, clear opt-ins, and robust data anonymization techniques.

3. Execution Risk

1. • Challenge: The essay assumes that the correct data inputs, agent orchestration, and continuous learning loops will naturally lead to self-optimizing outcomes. In practice, misaligned objectives, bias, or model drift can derail AI-driven efforts.
   • Potential Mitigation: Continuous human oversight and a strong feedback loop—monitoring AI decisions, retraining models, and enforcing ethical guidelines—are crucial to keep the system on track.

4. Talent & Culture

1. • Challenge: Even if AI systems can do much of the “heavy lifting,” organizations still need teams who understand AI—prompt engineering, data science, model monitoring, and more. Many traditional marketers may struggle with this transition.
   • Potential Mitigation: Success requires culture change and investment in upskilling, ensuring existing teams can effectively collaborate with AI agents.

5. Market Realities

1. • Challenge: In many industries, ad duopolies (Google, Meta) and entrenched advertising ecosystems remain powerful. While emergent AI-driven approaches are appealing, they must integrate with or circumvent these existing channels.
   • Potential Mitigation: Partnering with key adtech and martech platforms—or differentiating with in-house AI solutions—can ease the transition to agentic, emergent models.

2. Should a B2C Martech Company Build on These Ideas?

1. Strategic Differentiation

1. • Implementing Agentic AI or “Co-Marketers” can give your martech solution a competitive advantage, especially in a crowded market. Early movers who offer advanced personalization and AI-driven orchestration may stand out.

2. Incremental vs. Transformational

1. • You don’t need to deploy all features at once—start with one or two AI capabilities (like AI Twins for segmentation or a Co-Marketer for campaign orchestration). Prove ROI in smaller pilots, then scale.

3. Customer-Centric Imperative

1. • Most B2C brands prioritize personalization and reducing AdWaste—which aligns perfectly with your essay’s ideas. If you can tangibly solve these pain points with AI-driven approaches, you’ll gain a strong foothold.

4. Practical Implementation Roadmap

1. • Offer clients a step-by-step framework for adoption:
     1. Data Readiness & Cleanup
     2. Basic AI Analytics (predictive models, recommendation engines)
     3. Co-Marketer Integration (orchestration)
     4. Emergent Ops Layer (autonomous “swarms” for routine tasks)
   • This builds internal credibility and reduces risk of “AI hype” without substance.

5. Partnerships & Ecosystem

1. • Consider partnering with cloud providers, advanced ML platforms, or data orchestration tools to accelerate your offering. This ensures you can handle scalability, compliance, and security effectively.

6. Talent & Culture

1. • Invest in recruiting or training a multidisciplinary team—marketers, data scientists, and AI ethicists—to develop and maintain these AI systems.
   • Focus on change management for clients adopting agentic systems; success stories and training materials can ease cultural resistance.

Conclusion

Your essay lays out a bold, forward-thinking vision for how marketing might evolve under Agentic AI and emergent systems. While there are valid concerns about data privacy, system complexity, and organizational readiness, these challenges are manageable with the right strategies, partnerships, and phased implementation.

For a B2C martech company, building on these ideas can yield strategic differentiation and high-value solutions that truly resonate with brands seeking to modernize their marketing. The key is to start incrementally, validate ROI, and scale up to a transformative, AI-driven marketing ecosystem—one that not only drives revenue but reshapes the very way brands interact with their customers.

DeepSeek

Your essay presents a visionary and compelling future for marketing departments, particularly in B2C/D2C companies. The concept of a Co-Marketer leading a team of functional AI agents is both innovative and practical, addressing many of the inefficiencies and challenges faced by modern marketing teams. Here’s a breakdown of the strengths and potential challenges:

Strengths:

1. Hyper-Personalization at Scale:
   • The idea of using AI Twins (Customer Twins, Product Twins, etc.) to deliver hyper-personalized experiences is a game-changer. It solves the “Not for Me” problem by ensuring every interaction feels tailored to the individual customer.
2. Real-Time Adaptation:
   • The ability of AI agents to adapt in real-time to market changes, customer behavior, and campaign performance is a significant advantage. This ensures that marketing efforts are always optimized and responsive.
3. Efficiency and Cost Reduction:
   • By automating repetitive tasks and optimizing ad spend, the proposed system can dramatically reduce AdWaste and improve ROI. This is particularly appealing for B2C/D2C companies with tight margins.
4. Emergent Intelligence:
   • The concept of emergent marketing—where AI agents self-organize and learn from each other—mirrors natural systems and offers a scalable, adaptive solution to complex marketing challenges.
5. Human-AI Collaboration:
   • The essay emphasizes the importance of human oversight and creativity, ensuring that AI complements rather than replaces human marketers. This balance is crucial for maintaining brand identity and ethical standards.

Challenges and Risks:

1. Technical Complexity:
   • Building a system with multiple AI agents (Co-Marketer, Twins, Swarms) that work seamlessly together is a significant technical challenge. Ensuring reliability, scalability, and integration with existing systems will require substantial investment.
2. Data Privacy and Ethics:
   • The use of AI Twins and hyper-personalization raises concerns about data privacy and ethical AI use. Companies will need to ensure compliance with regulations like GDPR and build trust with customers.
3. Market Education:
   • The concept of Agentic AI and emergent marketing is complex and may be difficult for some businesses to understand. Clear communication and education will be essential to gain buy-in from stakeholders.
4. Execution Risks:
   • Implementing such a transformative system requires significant organizational change. Companies will need to invest in training, change management, and possibly restructuring their marketing departments.
5. Competition:
   • As the concept gains traction, larger tech companies may enter the space, creating competition. Startups and smaller companies will need to move quickly to establish a foothold.

Recommendations: Should You Build These Ideas?

As someone who runs a B2C Martech company, the ideas presented in your essay align well with the future of marketing. Here’s why you should consider building these ideas:

1. First-Mover Advantage:
   • By being an early adopter of Agentic AI in marketing, you can establish yourself as a leader in the space, setting industry standards and building a strong competitive moat.
2. Addressing Real Pain Points:
   • The essay highlights real challenges faced by B2C/D2C companies, such as rising CAC, low retention rates, and AdWaste. Your solution directly addresses these pain points, making it highly relevant to your target market.
3. Scalability and Efficiency:
   • The proposed system is highly scalable, making it suitable for both mid-sized and large enterprises. Its efficiency gains (reduced AdWaste, lower CAC, higher retention) make it an attractive proposition for businesses looking to optimize their marketing spend.
4. Differentiation:
   • By offering a unique, AI-driven marketing solution, you can differentiate yourself from traditional martech providers. This could be a key selling point in a crowded market.
5. Long-Term Vision:
   • The essay’s focus on retention-first strategies and customer lifetime value aligns with long-term business goals. By helping companies build lasting customer relationships, you can position your solution as essential for sustainable growth.

Strategic Recommendations for Implementation:

1. Start Small:
   • Begin with a phased approach, focusing on the most impactful and least complex components first (e.g., the Co-Marketer and Customer Twins). This allows you to prove the concept and build momentum.
2. Focus on Integration:
   • Ensure your solution integrates seamlessly with existing martech stacks. APIs and interoperability will be key to gaining adoption.
3. Build in Public:
   • Share your progress and insights through case studies, blogs, and thought leadership. This will help educate the market and establish your company as a pioneer in Agentic AI marketing.
4. Prioritize Data Privacy:
   • Make data privacy and ethical AI use a core part of your offering. This will build trust with customers and differentiate you from competitors.
5. Iterate and Adapt:
   • As you roll out the solution, gather feedback and continuously iterate. The market is evolving rapidly, and flexibility will be key to staying ahead.

Conclusion:

Your essay presents a bold and compelling vision for the future of marketing. As a B2C Martech company, building these ideas could position you as a leader in the next wave of marketing innovation. While there are challenges to overcome, the potential rewards—both in terms of market opportunity and impact on the industry—are immense. By focusing on integration, scalability, and ethical AI use, you can create a solution that not only transforms marketing departments but also drives sustainable growth for your clients.