Published August 14, 2025
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AI Next
Wikpedia defines superintelligence thus: “A superintelligence is a hypothetical agent that possesses intelligence surpassing that of the brightest and most gifted human minds. “Superintelligence” may also refer to a property of advanced problem-solving systems that excel in specific areas (e.g., superintelligent language translators or engineering assistants). Nevertheless, a general purpose superintelligence remains hypothetical and its creation may or may not be triggered by an intelligence explosion or a technological singularity. University of Oxford philosopher Nick Bostrom defines superintelligence as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest”.”
It adds that artificial systems have several potential advantages over biological intelligence:
- Speed – Computer components operate much faster than biological neurons. Modern microprocessors (~2 GHz) are seven orders of magnitude faster than neurons (~200 Hz).
- Scalability – AI systems can potentially be scaled up in size and computational capacity more easily than biological brains.
- Modularity – Different components of AI systems can be improved or replaced independently.
- Memory – AI systems can have perfect recall and vast knowledge bases. It is also much less constrained than humans when it comes to working memory.[15]
- Multitasking – AI can perform multiple tasks simultaneously in ways not possible for biological entities.
Coursera writes: “Whereas AI mimics human behavior, superintelligence proposes to go beyond that—it is its form of thinking and behavior, far superior to that of a human. AI currently exists and supports humans at work, using machine learning algorithms to perform specific tasks, such as chatbots and self-driving cars. However, superintelligence requires further advancements in computer science and technology to function, with the goal of performing cognitively better than humans in technical and scientific problems.”
From IBM: “A big step toward developing an ASI would be to realize an artificial general intelligence (AGI) or Strong AI. An AGI is a next-generation AI system that can understand the world and learn and apply problem-solving intelligence as broadly and flexibly as a human can. AGI would be capable of cross-domain learning and reasoning with the ability to make connections across different fields. Just like ASI, true AGI has yet to be developed.”
Some recent news:
- In an essay entitled “The Path to Medical Superintelligence”, Microsoft wrote: “The Microsoft AI team shares research that demonstrates how AI can sequentially investigate and solve medicine’s most complex diagnostic challenges—cases that expert physicians struggle to answer. Benchmarked against real-world case records published each week in the New England Journal of Medicine, we show that the Microsoft AI Diagnostic Orchestrator (MAI-DxO) correctly diagnoses up to 85% of NEJM case proceedings, a rate more than four times higher than a group of experienced physicians. MAI-DxO also gets to the correct diagnosis more cost-effectively than physicians.”
- Mark Zuckerberg wrote in a recent memo announcing the formation of Meta Superintelligence Labs: “As the pace of AI progress accelerates, developing superintelligence is coming into sight. I believe this will be the beginning of a new era for humanity.”
- Harmonic, the artificial intelligence lab leading the development of Mathematical Superintelligence (MSI), announced a $100 million Series B funding round to “to accelerate the development of Mathematical Superintelligence and integrate it into useful and delightful real-world applications.”
- Economic Times: “Schmidt laid out a roadmap that reads more like science fiction than emerging reality. Within the next 12 months, he believes, most programming jobs could be replaced by AI. Not only that, AI systems will be able to outpace the brightest graduate-level mathematicians in structured reasoning tasks like advanced math and coding. At the core of this shift is what he calls recursive self-improvement—AI systems that write their own code using protocols like Lean, making them exponentially more efficient with each iteration…He refers to ASI, or Artificial Super-intelligence, as a leap beyond individual human intellect—something that could soon exceed the collective intelligence of all humans. “This occurs within six years, just based on scaling,” he said…“People do not understand what happens when you have intelligence at this level, which is largely free.””
- Economist: “Since the breakthroughs of almost a decade ago, AI’s powers have repeatedly and spectacularly outrun predictions. This year large language models from OpenAI and Google DeepMind got to gold in the International Mathematical Olympiad, 18 years sooner than experts had predicted in 2021. The models grow ever larger, propelled by an arms race between tech firms, which expect the winner to take everything; and between China and America, which fear systemic defeat if they come second. By 2027 it should be possible to train a model using 1,000 times the computing resources that built GPT-4, which lies behind today’s most popular chatbot… The way to look at another acceleration, if it comes, is as the continuation of a long miracle, made possible only because people embraced disruption. Humanity may find its intelligence surpassed. It will still need wisdom.”
As discussion about AGI (artificial general intelligence) and superintelligence heats up, I have been thinking (with help from the AIs) about how it will transform marketing, and more specifically, martech/retention/CRM.
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Multi-Agent Systems – 1
This is from ChatGPT Deep Research.
One intriguing possibility [to getting to superintelligence] is through multi-agent systems – effectively, AI collectives working together – which some see as a route to greater intelligence and capability.
Traditional AI systems often operate as single agents that perform tasks in isolation (even if they’re very sophisticated single agents, like a large language model answering questions). In contrast, multi-agent systems (MAS) involve many AI agents interacting within a shared environment to solve problems collaboratively [demandbase.com]. Each agent can have specialized roles or objectives, and they communicate or negotiate with one another to achieve an overall goal. This approach draws inspiration from human teams and even from nature (think of colonies of ants or bees, which together exhibit intelligent behavior beyond any individual insect).
Tech leaders increasingly view multi-agent AI as a key frontier. Google in particular has highlighted this as the next step in AI evolution. At Google’s 2025 Cloud Next conference, executives noted that AI progress is moving “from chatbots to single-agent systems to multi-agent systems working across multiple functions.” [fiercehealthcare.com] Instead of an AI merely automating a single task, multiple AI agents could collaborate to handle complex, multi-step workflows. A Google Cloud director described the vision: “Imagine entire systems powered by multiple AI agents working together autonomously to optimize things… This isn’t just about automation. It’s about creating self-optimizing ecosystems.” [fiercehealthcare.com] In other words, in a multi-agent setup, AI doesn’t just assist humans on one step – it could conceivably run an entire process end-to-end, with different agents handling different aspects and coordinating with each other. Google has even introduced an Agent-to-Agent communication protocol to facilitate such ecosystems, aiming to let agents built on different frameworks talk to each other [fiercehealthcare.com].
Why is this significant for achieving advanced AI or superintelligence? One reason is that specialization and cooperation can lead to emergent capabilities. In human society, no single individual knows everything or does everything – our greatest achievements come from collaboration and dividing tasks among specialists. Similarly, a network of AI agents could leverage diverse expertise: one agent might excel at data analysis, another at creative generation, another at long-term planning, etc. When these agents share information and jointly decide on actions, the system as a whole can solve problems that none of the agents could fully tackle alone. Importantly, unlike human teams, AI agents can communicate at electronic speed and remain perfectly aligned to a common goal (if designed correctly), which might enable a level of integrated intelligence beyond a human group. Indeed, Bostrom’s definition of superintelligence allows for the possibility it could be an “ensemble of networked computers” rather than a single computer mind [nickbostrom.com] – the key is that the system behaves as an integrated intellect. The difference between, say, a loose consortium of experts and a true collective intelligence is in how tightly they can coordinate. Early attempts at multi-agent AI hint that tight AI coordination is feasible: for example, research agents can already divide tasks like searching for information, writing summaries, and double-checking facts among themselves to produce a final report with minimal human guidance [fiercehealthcare.com].
Another advantage is robustness and adaptability. Multiple agents can negotiate or even compete in limited ways to find better solutions. If one agent’s approach isn’t working, another agent can provide feedback or an alternative strategy. This resembles having checks and balances that make the whole system more reliable. In complex environments (like a chaotic real-time market scenario, or a strategic game), multi-agent setups can explore different angles simultaneously and then converge on the best solution.
Over the next few years, we can expect multi-agent systems to become more common in business settings – not as sci-fi curiosities, but as workhorse solutions. Instead of thinking “I have one AI model that does X,” companies might orchestrate swarms of AI agents that together handle complex workflows in operations, customer service, finance, and marketing.
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Multi Agent Systems – 2
This is from Claude.
Leading AI researchers now predict artificial general intelligence (AGI) by 2026-2030, with superintelligence following within 2-5 years. Multi-agent systems emerge as the most viable pathway to superintelligence.
The theoretical framework for superintelligence, established by Nick Bostrom’s seminal 2014 work, defines it as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest.” Recent developments show this definition shifting from abstract possibility to near-term probability, with expert timelines dramatically compressing in 2024-2025.
Current predictions from leading researchers reveal unprecedented consensus around short timelines: Sam Altman expects AGI “in a few thousand days” (by 2032), Dario Amodei predicts AGI by 2026, and Leopold Aschenbrenner forecasts AGI by 2027 with rapid progression to superintelligence. Even previously conservative voices like Ray Kurzweil have moved their predictions from 2045 to 2032. This acceleration reflects tangible progress indicators: 4-5x annual increases in training compute, models approaching human performance in coding and scientific reasoning, and the emergence of capable AI agents in production environments.
The three types of superintelligence identified by Bostrom – speed superintelligence, collective superintelligence, and quality superintelligence – each offer distinct pathways and implications for marketing applications. Speed superintelligence could complete PhD-level market research in hours, collective superintelligence could coordinate thousands of specialized marketing agents, and quality superintelligence could solve customer understanding problems beyond human comprehension.
Multi-agent systems demonstrate the most promising path to superintelligence
Multi-agent systems have emerged as the leading pathway to superintelligence, with breakthrough research in 2024-2025 demonstrating collaborative scaling laws that enable effective coordination among over 1,000 agents. Recent studies by Chen Qian and colleagues show that multi-agent performance follows predictable logistic growth patterns, with collaborative emergence occurring earlier than traditional neural emergence.
The technical foundations for multi-agent superintelligence are solidifying rapidly. OpenAI’s Swarm framework, Microsoft’s Magentic-One, and Google’s multi-agent research demonstrate sophisticated coordination mechanisms using directed acyclic graphs for task orchestration, Byzantine fault tolerance for consensus, and stigmergy for indirect communication. These systems exhibit emergent behaviors that exceed the sum of their parts, with OpenAI’s hide-and-seek research documenting six distinct strategy phases emerging from simple competitive objectives.
Current implementations showcase remarkable capabilities. Microsoft’s Magentic-One achieves competitive performance on complex benchmarks through an orchestrator directing specialized agents (WebSurfer, FileSurfer, Coder, ComputerTerminal). Google’s multi-agent debate systems demonstrate 90.2% performance improvement over single-agent approaches while reducing communication costs by 79.92% through sparse topologies. These results suggest that collective intelligence may achieve superintelligent capabilities through coordination rather than individual agent improvement.
However, significant challenges remain. Communication overhead scales exponentially with agent count, consensus mechanisms remain computationally expensive, and emergent behaviors can be unpredictable. The “alignment problem” becomes more complex with thousands of coordinated agents, as individual agent alignment doesn’t guarantee collective system alignment. Despite these challenges, experts assign moderate to high likelihood that multi-agent systems will achieve superintelligence, contingent on breakthroughs in coordination mechanisms, alignment solutions, and computational efficiency.
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Multi Agent Systems – 3
This is from Gemini.
Multi-Agent Systems (MAS) are a foundational concept in Artificial Intelligence, predating the recent rise of generative AI. They emphasize functional independence, where individual agents—whether human or artificial—operate as distinct entities, each possessing its own decision-making capabilities and engaging in dynamic collaboration. This architectural approach stands in contrast to monolithic AI systems, promoting a more distributed and resilient design.
MAS maintain distinct boundaries between their components while enabling complex interactions, much like natural ecosystems. This design allows for inherent modularity, facilitating distributed problem-solving and enhancing system resilience. Key characteristics that make MAS highly adaptable and scalable include:
Decoupling: Components within a MAS, such as individual agents or large action models, can communicate and interact without needing internal knowledge of each other’s systems. This decoupling simplifies overall system design, maintenance, and integration.
Scalability: The messaging-coupled nature of MAS makes it straightforward to add more components, allowing for flexible expansion of capabilities as needs evolve.
Coordination: Agents within a MAS coordinate effectively through well-defined protocols, all while preserving their distinct boundaries and roles. This structured coordination enables the emergence of complex collective behaviors that surpass the capabilities of any single agent.
The traditional conception of the Technological Singularity often centers on a single Artificial General Intelligence (AGI) recursively enhancing its own performance to achieve superintelligence. However, recent advancements in distributed cognition, swarm intelligence, and language model agents suggest an alternative pathway: cooperative intelligence achieved through multi-agent self-enhancement.
Interdependent networks of AI agents engaged in collective self-enhancement can demonstrably outperform any single AI system. These “coalitions” of interacting, competing, or co-evolving agents are poised to precipitate an emergent explosion of intelligence. Such collective systems can exhibit meta-learning processes that surpass the abilities of individual architectures, leading to a form of distributed intelligence.8 Crucially, the first AGI might not manifest as a singular, all-encompassing system, but rather as “a conglomeration of multiple AIs with disparate capabilities and objectives that form a collective intelligence”. If multi-agent systems indeed emerge as the first true AGIs, they could be significantly more dynamic and adaptable in their operations, though their internal interactions might also present challenges related to fragility and unpredictability. This aligns with the inherent modularity advantage of artificial intelligence, allowing for the flexible assembly of diverse intelligent components.
The idea that superintelligence can emerge from a “conglomeration of multiple AIs” and through “collective self-enhancement” fundamentally redefines how businesses might achieve “superintelligent” capabilities. It suggests that an organization does not necessarily need to await the arrival of a singular, all-powerful AI. Instead, by strategically deploying and orchestrating a network of highly specialized, autonomous, and continuously improving AI agents, a company can cultivate an emergent collective intelligence. This collective intelligence, operating within specific domains, can surpass human capabilities, effectively achieving a form of “organizational superintelligence.” For businesses, this shifts the strategic focus from acquiring a single, general-purpose AI solution to building a robust, interconnected ecosystem of specialized AI agents. This necessitates a strategic architectural approach to AI integration, emphasizing interoperability, defining clear communication protocols between agents, and establishing mechanisms for collective learning and adaptation, rather than merely deploying isolated AI tools.
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Netcore’s AI Agents Collective
Here is a recap of what we have discussed so far.
Superintelligence is defined as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest.” It represents AI systems that don’t just mimic human behaviour but develop their own superior forms of thinking and problem-solving.
Three Types: Bostrom identifies three distinct pathways:
- Speed superintelligence – operating at vastly accelerated timescales
- Collective superintelligence – coordinating multiple specialised agents
- Quality superintelligence – solving problems beyond human comprehension
Multi-Agent Systems as the Optimal Path
Beyond Single Agents: Rather than relying on one monolithic AI system, multi-agent systems involve networks of specialised AI agents working collaboratively. This mirrors how human achievements emerge from teams of specialists rather than individual brilliance.
Emergent Collective Intelligence: The key insight is that superintelligence may emerge not from a single recursive self-improving AI, but from “coalitions” of interacting agents that collectively exhibit capabilities exceeding any individual component.
Technical Advantages:
- Specialisation and cooperation enable emergent capabilities through division of labour
- Robustness and adaptability through built-in checks, balances, and alternative strategies
- Electronic-speed communication with perfect alignment to common goals
- Scalability – recent research shows effective coordination among 1,000+ agents
The fundamental shift is from waiting for one superintelligent system to strategically orchestrating networks of specialised agents that can achieve “organisational superintelligence” within specific domains – making this pathway more immediately actionable for businesses.
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I will use “Agentic Marketing” to describe the coming world of superintelligence in marketing. It is something we have been building at Netcore.
Our vision centres on creating an Agent Collective System that orchestrates specialised marketing agents, each with distinct capabilities and objectives. The Co-marketer serves as the strategic orchestrator, coordinating eight specialised agents including Segment Agents for deeper customer understanding, Content Agents for multi-channel creative generation, and Shopping Agents for optimised product recommendations. This mirrors the multi-agent superintelligence pathway described earlier—rather than waiting for a single all-powerful AI, we’re building a network of specialised agents that collectively achieve superhuman marketing capabilities.
The agent collective cycle demonstrates how tasks flow through agent selection, execution, and outcome delivery, creating a continuous optimisation loop. Each agent brings specialised expertise while contributing to emergent collective intelligence that exceeds what any individual component could achieve.
Our Multi-Agent Framework already powers real martech applications, with agents generating campaign insights, creating audience segments, producing cross-channel content, and delivering personalised shopping experiences. The framework integrates seamlessly with existing martech stacks, transforming static tools into dynamic, intelligent systems.
This illustrates our multiplier effect: when the Futuristic Agentic Core combines with traditional Martech Stack capabilities, it creates exponential improvements across intelligent product discovery, frictionless purchase experiences, personalised engagement, and predictive analytics. This represents the practical realisation of marketing superintelligence—not as a distant possibility, but as an immediate competitive advantage through coordinated AI agent ecosystems.
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The Roadmap – 1
This has been co-created with AIs.
The journey from today’s marketing automation to tomorrow’s marketing superintelligence follows a clear evolutionary path through increasingly sophisticated multi-agent systems. As demonstrated by current implementations at companies like Netcore, the foundation is already being laid through specialised AI agents that handle distinct marketing functions—from content creation and audience segmentation to campaign optimisation and customer insights.
The Emergence of Collective Intelligence
The path to marketing superintelligence begins with what researchers call “collaborative emergence”—the point where coordinated AI agents achieve capabilities that exceed the sum of their individual parts. Current multi-agent marketing systems like Netcore’s demonstrate early signs of this phenomenon. When Segment Agents create deeper customer understanding while Content Agents generate personalised messaging and Shopping Agents optimise recommendations, their coordination produces insights and outcomes no single agent could achieve alone.
Recent research shows that multi-agent performance follows predictable logistic growth patterns, with collaborative emergence occurring earlier than traditional neural scaling. This means marketing superintelligence may arrive not through a single breakthrough AI model, but through the sophisticated orchestration of specialised agents working in concert.
The Technical Foundation for Superintelligent Marketing
The technical architecture supporting this evolution relies on three critical components: advanced coordination mechanisms, unified data integration, and recursive self-improvement loops.
The key breakthrough lies in the agent collective cycle: task initiation, intelligent agent selection, coordinated execution, and outcome delivery with continuous feedback. This creates a self-optimising ecosystem where agents learn not just from their individual performance, but from the collective intelligence of the entire system. When the Co-marketer orchestrator coordinates insights from Campaign Analytics with Creative Generation and Customer Segmentation, it creates emergent strategic capabilities that approach superintelligent performance within the marketing domain.
Speed and Scale Advantages
Marketing superintelligence emerges from the compound advantages of artificial systems: processing speed seven orders of magnitude faster than human cognition, perfect memory and recall across vast datasets, and the ability to multitask across thousands of customer interactions simultaneously. Current implementations already demonstrate 20-30% ROI improvements through real-time optimisation across channels—a preview of the exponential gains possible as these systems achieve true collective intelligence.
The Multiplier Effect
The transition from tool-assisted marketing to agentic marketing represents a fundamental shift in competitive dynamics. When Netcore’s Futuristic Agentic Core combines with traditional MarTech Stack capabilities, it creates exponential improvements across intelligent product discovery, frictionless purchase experiences, and predictive analytics. This “multiplier effect” suggests that marketing superintelligence won’t just improve existing processes—it will enable entirely new categories of customer understanding and engagement that were previously impossible at scale.
The evidence strongly indicates that this evolution is already underway. Companies implementing sophisticated multi-agent marketing systems are gaining competitive advantages that compound over time, creating a flywheel effect where better data enables smarter agents, which generate better outcomes, which attract more customers, providing richer data for continuous improvement.
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The Roadmap – 2
The transformation of martech, retention, and CRM through agentic marketing systems represents the most immediate and economically significant manifestation of marketing superintelligence. As these systems achieve collective intelligence capabilities, they fundamentally reshape how businesses understand, engage, and retain customers across the entire lifecycle.
From Reactive Automation to Predictive Orchestration
Traditional CRM systems operate reactively—responding to customer actions after they occur. Marketing superintelligence through multi-agent systems enables proactive customer orchestration, where AI agents predict customer needs, anticipate churn risks, and design intervention strategies before problems arise. This shift from “what happened” to “what will happen” to “what should we make happen” represents the core value proposition of superintelligent marketing.
Netcore’s multi-agent framework demonstrates this evolution in practice. The Insights Agent continuously analyses behavioural patterns while the Segment Agent creates dynamic customer cohorts and the Content Agent generates personalised interventions. The Shopping Agent optimises product recommendations based not just on past purchases, but on predicted future preferences. This coordinated intelligence creates a customer experience orchestrator that manages millions of individual journeys simultaneously with unprecedented precision.
Hyper-Personalisation at Superintelligent Scale
The transformation of retention and CRM centres on achieving true one-to-one marketing at massive scale. While current systems can segment customers into thousands of micro-audiences, marketing superintelligence enables individual customer modelling—where each person receives completely unique messaging, timing, offers, and experiences optimised for their specific context and predicted lifetime value.
This capability emerges from the compound intelligence of specialised agents working together. The Segment Agent identifies not just demographic patterns but behavioural micro-signals. The Content Agent generates millions of message variations. The Scheduler Agent optimises timing down to the individual level. The Designer Agent creates personalised creative assets. When orchestrated by the Co-marketer, these agents achieve personalisation granularity that approaches clairvoyance—predicting customer needs before customers themselves are consciously aware of them.
The Economics of Superintelligent CRM
Current implementations already demonstrate substantial economic impacts: 15-30% campaign performance improvements, 20-40% time savings on routine tasks, and 90%+ customer inquiry resolution rates. However, these early results merely hint at the economic transformation possible through full marketing superintelligence.
Superintelligent CRM systems will achieve perfect customer lifecycle optimisation—maximising lifetime value through precisely timed interventions, preventing churn before it occurs, and identifying upselling opportunities with near-perfect accuracy. The economic impact compounds because these systems improve continuously through recursive self-improvement, creating competitive moats that become increasingly difficult for competitors to overcome.
Organisational and Strategic Implications
The shift to superintelligent martech requires fundamental organisational evolution. Human roles transition from operational execution to strategic oversight, creative direction, and ethical governance. Marketing teams become “AI orchestrators” who design agent behaviours, interpret collective intelligence outputs, and ensure alignment with brand values and customer wellbeing.
This transformation also creates new competitive dynamics. Companies with sophisticated multi-agent marketing systems will achieve customer understanding and engagement capabilities that appear almost supernatural to competitors using traditional approaches. The gap between AI-native marketing organisations and traditional marketing departments will become a chasm, forcing rapid industry-wide adoption or competitive obsolescence.
The Three-Year Horizon
Within the next three years, we can expect marketing superintelligence to manifest through increasingly autonomous agent ecosystems that handle the majority of customer interactions, campaign optimisation, and strategic decision-making with minimal human intervention. The agents will evolve from following pre-programmed rules to exhibiting genuine strategic reasoning, creative problem-solving, and adaptive learning that approaches or exceeds human marketing expertise.
The companies building these capabilities today—like Netcore with its Agent Collective System—are creating the foundation for tomorrow’s marketing superintelligence.
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The Impossibility Play
What Agentic Marketing enables is the ability to make the impossible inevitable. Today, marketing teams find it impossible to create more than a handful of customer segments because each segment requires regular content creation and analytics. But as the IBM 2025 CMO Study reveals: “While previous generations of CMOs orchestrated campaigns with start and end dates, tomorrow’s marketing leaders will build perpetual growth engines powered by agentic AI that continuously learns, adapts, and optimizes toward business objectives.”
Agentic Marketing transforms this impossibility by moving from a few “segments of many” to many “segments of few”, and eventually towards a “segment of one.” This isn’t theoretical—it’s an operational reality that solves what the IBM study identifies as modern marketing’s core challenge: 64% of CMOs say they are now responsible for profitability, with 58% accountable for revenue growth.
The Three Impossible Challenges Becoming Inevitable
- Hyper-Personalisation at Scale
Segment AI Twins, and eventually Singular AI Twins, enable precision marketing—delivering the right message to the right person at the right time on the right channel. This solves what the IBM research calls “the execution gap,” where 84% of demand leaders recognize AI as the game-changer, yet remain paralyzed in first gear—their operations too rigid and too fragmented to harness the very technology that could save them.
Traditional marketing makes personalisation impossible due to resource constraints. But AI Agents working collectively can create thousands of micro-segments and personalised journeys simultaneously. As one IBM study participant noted: “We’re moving from rule-based personalisation to predictive and generative personalisation with systems that can anticipate needs, not just react. This means our data architecture needs to be more agile, more integrated, and AI-ready.”
- Shopping Agent Evolution
Agentic Marketing will power shopping agents that guide customers through their journey across the brand’s properties—website, app, and in-channel experiences. We can expect agent-to-agent interaction where the brand’s agents will collaborate with a customer’s personal agent for recommendations and advice, creating a new paradigm of customer service that operates 24/7 without human intervention.
This addresses what the IBM study identifies as a critical shift: consumers now place personalised interactions and proactive support at the top of their priority list, along with trust and security. Meanwhile, high-quality products and intuitive purchasing experiences have slipped to the bottom.
Imagine a Best customer browsing products late at night—their personal AI agent communicates directly with the brand’s shopping agent to surface the perfect recommendation based on their purchase history, current needs, and even their budget preferences, all without any human intervention.
- The Department of One
The marketing team of the future will be a “department of one” working with multiple agents. This isn’t about replacing humans—it’s about amplifying human creativity and strategic thinking through AI collaboration. As the IBM study emphasises: The most valuable marketing currency isn’t data—it’s the uniquely human capacity to create emotional connections through intuition, empathy, and creative brilliance. Yet in a landscape transformed by AI, human talent alone isn’t sufficient.
This transformation requires what IBM calls “hiring for heart and training for AI”—cultivating professionals who can direct AI tools with strategic vision while infusing the output with emotional resonance that algorithms can’t generate.
From Cost Centre to Profit Engine
Agentic Marketing is one of the core pillars for NeoMarketing, and the key to unlocking systematic, sustainable, “Rule of 40” profitable growth. It converts marketing from a cost centre to a profit centre whilst eliminating the 20-30% revenue tax and $500 billion AdWaste that have plagued brands in recent times.
The impossibility play works because it addresses the fundamental challenge identified in the IBM research: CMOs face a brutal paradox. The traditional playbook—more data, more campaigns, really more of anything—has hit its ceiling. Without fundamental operational transformation, marketing aspirations increasingly outrun execution capabilities, creating a dangerous disconnect between promise and delivery.
Making Impossible Inevitable
By making what seems impossible today inevitable, Agentic Marketing represents the future that CxOs and customers should look forward to. It’s not about incremental improvement—it’s about fundamental transformation that makes sustainable profitable growth not just possible, but systematic.
As one marketing leader in the IBM study observed: “What’s important now is are you curious? Are you adaptable? Can you employ systems thinking? Are you passionate about harnessing insights and sparking emotions? We’re no longer just creating campaigns. We’re creating enduring storylines, conversations, experiences, and customer relationships.”
The impossible becomes inevitable when we stop thinking about marketing as a series of campaigns and start building it as an intelligent, always-on system that learns, adapts, and grows more valuable with every interaction. The question isn’t whether this transformation will happen—it’s whether your organisation will lead it or be left behind by it.
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Future Stories
This has been written by Claude.
Imagine it is 2027. Agentic Marketing has made marketing superintelligence a reality. The transformation that seemed impossible just two years ago is now the foundation of competitive advantage.
Maya: The Department of One
Maya starts her Tuesday morning as she has for the past year—not with frantic dashboard checks or crisis calls from her team, but with her AI Co-marketer’s strategic briefing delivered over coffee.
“Good morning, Maya,” her AI assistant begins. “Three customer segments showed unusual engagement patterns overnight. Segment 47,382—young mothers in Tier-2 cities—responded exceptionally well to our new parenting content series. The Content Agent has already generated 40 variations for today’s email campaigns. Should we accelerate budget allocation?”
Maya reviews the recommendation in seconds. What once required hours of analysis, team meetings, and manual campaign setup now happens autonomously. Her role has evolved from tactical coordinator to strategic conductor—setting brand vision while her AI Agents Collective executes with precision she never thought possible.
By 9 AM, her Progency partner has already optimised journeys for 127,000 Rest customers, moving 1,847 of them into higher engagement tiers overnight. The economic impact is immediate: ₹2.3 crore in incremental monthly revenue that would have been impossible with her previous team structure.
Maya’s marketing “department” now consists of herself, one creative strategist, and eight AI agents that never sleep. They manage 2.4 million individual customer journeys simultaneously—each one as personalised as a handwritten letter, yet scalable across millions.
Arun: The Empowered Customer
Arun’s morning begins with his personalised financial newsletter, powered by NeoMails technology that has transformed his relationship with brands. Instead of the generic broadcasts that once cluttered his inbox, he now receives genuinely valuable content that adapts to his interests in real-time.
Today’s newsletter includes a Mu-rewarded quiz about market predictions (his 15-day streak earns him premium access to investment research), a Micron explaining blockchain developments in just 60 seconds, and an ActionAd for sustainable investment opportunities that caught his attention last week.
The beautiful thing? He completes his investment directly within the email—no website redirects, no forgotten bookmarks, no friction. The entire transaction takes 90 seconds and automatically integrates with his existing portfolio tracking.
His financial newsletter has evolved beyond information delivery into a sophisticated AI assistant that learns from every interaction. When he spends extra time reading about green energy stocks, future content naturally shifts to include more sustainability coverage. When he skips cryptocurrency sections, those fade from his personalised feed.
Ria: The Seamless Shopper
Ria’s shopping experience represents the pinnacle of agent-to-agent collaboration. As she browses skincare products late at night, her personal AI agent—which understands her sensitive skin, budget constraints, and upcoming wedding—communicates directly with the brand’s shopping agent.
“Ria seems interested in this serum,” her agent notes, “but her previous purchases suggest she’s price-sensitive. Also, her calendar shows a wedding in two months—she might value faster results over gradual improvement.”
The brand’s shopping agent responds instantly: “Based on her skin type analysis and timeline, I recommend our accelerated treatment kit. Given her loyalty score and upcoming occasion, I can offer a 15% discount and include complimentary consultation with our skincare specialist.”
This entire negotiation happens in milliseconds, invisible to Ria. She simply sees perfectly tailored recommendations that feel almost telepathic in their relevance. Her purchase decision feels natural and unforced because the recommendation genuinely serves her needs.
The Compound Effect
By December 2027, the transformation is undeniable:
Maya’s company has achieved Rule of 40 performance—30% revenue growth with 12% profit margins—while reducing marketing headcount by 40%. Their customer lifetime value has increased 60% through AI-powered personalisation that operates at a scale no human team could match.
Arun has become a brand advocate, referring three friends to services he discovers through his trusted newsletters. His customer journey represents the new reality: engaged customers who actively seek brand communication because it consistently delivers value.
Ria represents the empowered consumer of 2027—one who receives truly personalised attention at scale, where every interaction feels crafted specifically for her needs because, quite literally, it was.
The impossible has become inevitable. Marketing has transformed from a cost centre desperately chasing attention into a profit engine that systematically grows customer relationships. The future Maya once struggled to envision has become her competitive reality.
The quiet revolution is complete: AdWaste has been eliminated not through restriction, but through precision. Customer acquisition costs have plummeted not through budget cuts, but through retention mastery. Profit margins have exploded not through cost reduction, but through value creation.
In 2027, the brands that embraced Agentic Marketing two years earlier aren’t just surviving—they’re defining entirely new standards for what customer relationships can become.
