Rajesh Jain

Published April 8, 2025

1

The Missing Link

In today’s digital marketing landscape, a critical disconnect exists between what marketers need and what they’re getting. Marketers aren’t looking for more software—they’re seeking revenue growth solutions. Adtech has mastered this value proposition with elegant simplicity: “Give us your budget, and we’ll deliver clicks to your website and app.” This direct link between investment and outcome explains why approximately 90% of digital marketing budgets flow to acquisition through ad platforms.

Meanwhile, martech has positioned itself merely as a toolkit—offering better ways to convert those clicks into conversions. But this fundamental misalignment in value proposition has created a massive efficiency problem: an estimated 70% of paid acquisition spending goes toward reaching existing or former customers. This translates to a staggering $500 billion in annual AdWaste—money that should rightfully remain on brands’ P&L statements.

For martech to truly challenge adtech’s dominance, it must evolve from selling tools to delivering outcomes. Marketers don’t want the operational burden of staffing teams to handle customer segmentation, journey orchestration, campaign creation, and micro-analytics—all daily tasks that drain resources and focus. They want what adtech provides: clear business outcomes without the operational complexity.

This is precisely where the concept of “Progency” enters—a powerful fusion of product and agency designed to take ownership of business outcomes rather than just providing software. As a foundational pillar of NeoMarketing, Progency bridges the execution gap between martech’s powerful capabilities and the tangible business results brands demand, making it the essential implementation engine for the future of profitable customer relationships. While martech platforms offer sophisticated capabilities, the critical “service” element is missing entirely from the traditional SaaS (Software-as-a-Service) model. Martech companies have shown little interest in adding even a thin layer of outcome-driven services to complement their software.

I’ve previously identified three perennial marketing problems: the “Not for Me” problem (lack of personalisation), the “No Hotline” problem (ineffective communication channels), and the “No Alternative” problem (dependency on expensive ad platforms). Solutions like N=1 personalisation, NeoMails, and NeoN can address these issues. But a fourth challenge looms larger than all: “Who Will Do It?”

Agentic AI represents the breakthrough technology that could finally bridge this gap. By deploying specialised AI agents that coordinate with each other to complete complex marketing tasks, we can create a “mirror world” that predicts customer actions, reduces operational burdens on marketing departments, and maximises lifetime value—ultimately reducing the dependency on wasteful reacquisition.

This transformation offers martech companies a profound opportunity. By embracing the Progency model powered by Agentic AI fusing product, agents, and agency, they can evolve beyond selling software to delivering the outcomes marketers truly seek. In this series, we’ll explore how every martech company should consider creating a Progency division to offer marketing teams a compelling alternative to mere technology stacks—one that addresses not just what should be done but solves the critical question of who will do it.

2

Past Ideas

I have written about Progency in multiple essays over the past few years:

• Progency for Martech: The Missing Link
• Progency: The Profipoly Pathway
• Netcore Progency: A Profipoly Catalyst
• Profishare: A New Business Model for Enterprise Software
• Solving Marketing’s Three Zeros Problems via Progency
• Bundled Kaizen Services: An Advantage for Indian SaaS
• Commerce Metaverse: Shaping Marketing’s Future with Agentic AI and Digital Twin
• NeoSaaS: From India First to Global Leader in Big Martech
• How Martech Companies Can Usher in the NeoMarketing Era
• AI and Neo: The Twin Engines of Marketing’s Future

I asked Claude to provide a summary of the key ideas.

Definition: A Progency is a product-led agency that melds martech platforms with a specialised layer of agency services, operating on a performance-driven pricing model tied directly to outcomes.

Origin of Need: Marketing departments are overwhelmed with daily tasks yet underutilise martech platforms (typically only 30-40% of features). Simultaneously, they waste approximately 70% of their budgets on reacquiring existing customers through expensive adtech platforms.

Key Differentiator: Unlike traditional agencies that use third-party tools, a Progency is built by martech platform creators themselves, ensuring deep platform expertise and creating a sustainable competitive advantage.

Business Model: Performance-based compensation similar to adtech, where the Progency only gets paid for delivered outcomes (like revenue growth, reactivation rates, or customer lifetime value improvement).

Operational Focus: Primarily targets overlooked customer segments—the “Rest” and “Test” customers (dormant and at-risk segments) that marketing departments typically ignore while focusing on acquisition and “Best” customers.

Technological Edge:

• Operates on a proprietary “profipoly/NeoMarketing stack” that works in parallel with existing systems
• Creates a seamless data flow between brand operations and Progency initiatives
• Enables real-time insights and adaptation not possible with traditional agency models

Indian Advantage: For Indian SaaS companies, a “Bundled Kaizen Services” approach through Progency offers competitive pricing advantage against US competitors, allowing continuous improvement services to be bundled into product pricing.

Primary Use Cases:

• Reactivation of dormant customers (an alternative to costly reacquisition)
• Maximising revenue from “Rest” customers to convert them into “Best” customers
• Driving referrals from existing customer bases
• Using Best Customer Genome for targeted new acquisition

Market Opportunity: The $500 billion currently wasted on inefficient reacquisition represents the potential market for Progency services—approximately 10x larger than the traditional martech software market.

Metrics Framework: Uses the EnCoRe Triad (Engagement, Conversion, Retention) as key performance indicators, with “Earned Growth” as the North Star metric.

Value Proposition:

• For brands: Outsource outcomes, not just tasks; focus internal resources on high-value activities
• For martech companies: Transform from feature competition to a profit-sharing business model with unlimited upside (like adtech)
• For customers: Receive more relevant, personalised experiences based on their actual needs

Implementation Model: Typically starts with quick wins like reactivation campaigns, then progressively adds more sophisticated services (personalisation, referral programs, etc.)

Transformation Potential: Can deliver up to 500-700 basis points of profit improvement for brands through combined savings on acquisition costs and increased revenue from existing customers.

Future Evolution: With Agentic AI, Progency can leverage specialised agents to automate complex tasks, creating a scalable service layer that maintains human-like quality without proportional staff increases.

3

Agentic Power

Agentic AI represents the next significant evolution in artificial intelligence, building upon the foundations laid by predictive and generative AI systems. While predictive AI excels at forecasting outcomes based on historical data and generative AI creates content in response to prompts, agentic AI adds a crucial capability: autonomous action. These AI systems can understand goals, plan steps toward achieving them, execute actions across different environments, and learn from outcomes—all with minimal human oversight.

This evolution follows a natural progression. First-generation AI systems could analyse past customer behaviours to predict future actions (predictive). Second-generation systems could create personalised content at scale (generative). Now, agentic AI can independently orchestrate entire marketing workflows, coordinating multiple tasks while adapting strategies based on real-time feedback.

In marketing, agentic AI manifests in two primary forms:

1. Operational Agents

• AI Co-Marketer: Functions as a marketing executive’s virtual partner, overseeing strategy and orchestrating specialised sub-agents. This “manager agent” translates high-level objectives (like “increase Best customer revenue by 20%”) into tactical plans and assigns tasks to functional specialists.
• Functional Agents: Specialised AI systems that handle specific marketing tasks:
  • Content agents that create and optimise campaign materials
  • Analytics agents that process customer signals and identify patterns
  • Campaign agents that manage execution across channels
  • Testing agents that continuously refine approaches
  • Budget agents that reallocate resources in real-time based on performance

These operational agents work in concert, communicating with each other and coordinating complex, multi-step marketing initiatives without requiring constant human direction.

2. Customer-Representative Agents

• Segment Twins: AI systems that model the collective behaviours and preferences of customer segments (for example, Best, Rest, and Test customer groups). These analyse segment-level data to identify trends and opportunities.
• Singular Twins: The ultimate evolution—personalized AI companions representing individual customers. These digital twins understand specific preferences, historical behaviours, and likely future actions, enabling true N=1 personalisation at scale.

Customer-representative agents continuously learn from interactions, adapting to changing behaviours and preferences. By simulating customer responses to potential marketing initiatives before deployment, they dramatically increase effectiveness while reducing waste.

**

The power of this approach lies in the interaction between these agent types. When operational agents work alongside customer-representative agents, they create a self-organising and self-improving “emergent” system that can:

• Predict which offerings will resonate with specific customers
• Pre-emptively address friction points in the customer journey
• Allocate resources dynamically to maximise lifetime value
• Continuously adapt strategies based on real-time performance data

While marketing departments would ideally deploy these AI agents directly, implementation challenges remain significant. Most teams lack the technical expertise, data infrastructure, and operational frameworks needed to fully leverage agentic AI’s potential.

This creates a compelling alternative pathway: the Progency model. A Progency combines proprietary martech platforms with specialised human expertise, using AI agents internally to deliver outcomes rather than just technology. This approach shields marketers from implementation complexity while providing the benefits of agentic AI through a performance-based partnership focused on results rather than software adoption.

4

Workings

For martech companies looking to evolve beyond software provision, operationalising a Progency represents both a significant opportunity and a complex transformation. This transition requires rethinking organisational structures, talent acquisition, pricing models, and technology deployment. Here’s how martech firms can bring the Progency model to life:

Organisational Framework

Creating a Progency division necessitates a distinct organisational unit with its own leadership, KPIs, and operating model:

• Independent Business Unit: Establish a separate P&L unit with dedicated leadership, staffing, and resources to prevent the Progency being subsumed by the core product business
• Cross-Functional Expertise: Build teams that blend martech platform specialists with performance marketing strategists, data scientists, customer experience designers, and industry vertical experts
• Performance Culture: Cultivate a results-oriented environment where compensation and recognition tie directly to client outcomes rather than software adoption or utilisation

Technology Integration

The technology backbone of a successful Progency combines the core martech platform with AI agent architecture:

• Agent Infrastructure: Deploy the AI Co-Marketer alongside functional specialists (content, analytics, campaign agents) and AI Twins in a coordinated system
• Client Integration Layer: Develop lightweight APIs and connectors to access client data while minimising IT involvement from the client side
• Parallel Operations: Create systems that can run alongside existing martech implementations without disrupting current operations
• Dashboard Ecosystem: Build transparent reporting tools showing both real-time activities and business impact metrics

Service Delivery Model

Rather than traditional agency account management, a Progency operates through a blend of human expertise and AI execution:

• Scoped Outcome Agreements: Replace software contracts with clearly defined performance commitments focused on specific revenue or customer behaviour metrics
• Tiered Engagement Options: Offer entry points ranging from targeted use cases (like reactivation campaigns) to comprehensive marketing operations management
• Continuous Optimisation Practice: Implement methodical approaches to incremental improvement, similar to the “Bundled Kaizen Services” model
• Defined Handoffs: Establish clear boundaries between client responsibilities and Progency activities to prevent scope creep

Economics and Pricing

The financial model represents perhaps the most critical shift from traditional SaaS:

• Performance-Based Pricing: Structure compensation primarily around achieved outcomes rather than platform access, with baseline fees covering operational costs
• Revenue Sharing Mechanisms: Implement transparent calculations for sharing incremental revenue or cost savings, especially for reactivation and cross-sell initiatives
• Long-Term Partnerships: Design multi-year agreements that align incentives for sustained customer relationship development rather than short-term gains
• Investment Model: Recognise that initial implementation may require upfront investment before revenue sharing becomes profitable

Implementation Pathway

A measured approach to building Progency capabilities helps manage risk while validating the model:

1. Pilot with Strategic Clients: Select 2-3 existing customers with strong relationships for initial Progency engagements
2. Focus on Proven Use Cases: Begin with reactivation of dormant customers, where ROI is most clearly demonstrable (and AdWaste is the highest)
3. Build Agent Capabilities Incrementally: Start with core operational agents before advancing to more sophisticated customer twins
4. Document Value Creation: Rigorously track both inputs and outcomes to refine the economic model and build case studies

For martech companies, operationalising a Progency represents more than adding a services layer—it fundamentally transforms the business relationship with clients from vendor to partner. While this evolution requires significant investment and organisational change, it addresses the core “Who Will Do It?” problem that currently limits martech’s impact and opens access to the $500 billion opportunity currently lost to AdWaste.

5

Enhancements

While internal marketing departments possess deep brand knowledge, they face structural constraints that limit their effectiveness in fully leveraging martech capabilities. A Progency, with its specialised focus and performance-driven model, can deliver powerful enhancements that transform marketing outcomes. Here are the key capabilities that position Progency as superior to internal implementation:

Data & Intelligence Enhancements

• Holistic Data Integration: Combine traditionally siloed data streams—adtech performance, news cycles, social media trends, weather patterns, and competitive intelligence—into a unified decision framework, creating insights that cross departmental boundaries.
• Institutional Memory Preservation: Build an “infinite memory” of campaign successes and failures that persists despite staff turnover. Unlike internal marketing teams that suffer knowledge loss when employees move to higher-paying adtech roles, Progency maintains consistent historical insight.
• Cross-Client Pattern Recognition: Identify success patterns in and across verticals in similar categories, accelerating the discovery of effective strategies while avoiding proven pitfalls—something impossible for an isolated internal team.
• Competitive Intelligence Amplification: Deploy specialised tools to monitor competitor activities, pricing shifts, promotional calendars, and product launches, providing strategic early-warning capabilities beyond most internal teams’ resources.

Operational Excellence

• AI Agent Deployment: Overcome internal resistance to new technologies by implementing AI agents externally, sidestepping the natural human hesitancy to embrace automation that might threaten roles or require significant skills retraining.
• Continuous Improvement Framework: Apply rigorous Kaizen methodology for systematic enhancement of marketing operations, bringing industry-wide learnings and best practices to each client relationship.
• Resource Flexibility: Scale resources up or down based on seasonal needs, campaign launches, or market opportunities without the constraints of fixed internal headcounts or budget cycles, ensuring optimal resource allocation.
• Specialised Expertise On-Demand: Access niche specialists (like data scientists, behavioural economists, or vertical industry experts) when needed without maintaining these high-cost resources permanently on staff.

Performance & Accountability

• Clear ROI Measurement: Implement sophisticated attribution models that accurately connect marketing activities to financial outcomes, overcoming the internal political challenges of cross-department measurement.
• Zero-Based Budgeting Application: Continuously justify every marketing expenditure based on performance rather than historical budget allocations, eliminating low-value “we’ve always done it this way” spending.
• Objective Performance Assessment: Evaluate marketing tactics without the internal biases that often protect underperforming legacy initiatives championed by influential stakeholders.
• True P&L Responsibility: Accept genuine financial accountability for results, creating alignment between marketing investment and business outcomes that departmental structures often inhibit.

Technical & Martech Mastery

• Platform Utilisation Optimisation: Ensure the full capability spectrum of martech investments is leveraged, in contrast to internal teams that typically use only 30-40% of available features.
• Rapid Innovation Adoption: Accelerate implementation of emerging technologies without lengthy internal approval processes, bureaucratic IT roadblocks, or change management resistance.
• Technical Debt Prevention: Maintain clean, future-proof systems and processes rather than accumulating shortcuts and workarounds that commonly plague internal implementations.
• Integration Expertise: Seamlessly connect martech platforms with adjacent systems (CRM, e-commerce, customer service, inventory management) to create unified customer experiences across touchpoints.

The fundamental advantage of Progency lies in this comprehensive enhancement portfolio combined with its performance-based model. Unlike internal departments that receive fixed budgets regardless of outcomes, Progency succeeds only when clients succeed—creating perfect alignment between service provider and business results. For executives frustrated by the gap between martech’s promise and practical results, Progency offers a compelling alternative to both traditional implementation approaches and the wasteful adtech dependency cycle.

6

The Pitch

An Open Letter from a Progency Team to Forward-Thinking CMOs and CEOs

Dear Marketing Leader,

We understand the challenges you face today: rising acquisition costs, fragmented customer journeys, complex technology stacks, and the constant pressure to deliver measurable growth. Your marketing department works tirelessly, yet the full potential of your martech investments remains frustratingly elusive while your adtech spending continues to climb.

This isn’t a failure of strategy or talent. It’s a structural challenge that affects virtually every marketing organisation. Let me offer a candid assessment of where we might help—and just as importantly, where we won’t.

Where Progency Adds Value

Focus on the Overlooked Majority

Your team excels at new acquisition strategies and nurturing your best customers. What often falls through the cracks are your “Rest” and “Test” customers—those who have purchased but aren’t fully engaged, and those who have gone dormant. These segments represent an enormous untapped revenue opportunity that we can activate without disrupting your core marketing operations.

Elimination of AdWaste

Our analysis typically reveals that 60-70% of digital ad spending goes toward reaching people who are already in your database. This represents millions in potential savings. We don’t ask you to reduce your acquisition budget; instead, we focus on reducing the need for expensive reacquisition by reactivating these customers through your owned channels.

Outcome-Based Economics

Unlike traditional martech vendors who charge regardless of results, or agencies billing by the hour, our compensation aligns directly with measurable outcomes. If we don’t deliver increased revenue from reactivated customers or reduce your AdWaste, we don’t get paid. This transforms marketing from a cost centre into a predictable profit engine.

AI-Powered Scale

Our proprietary AI agent system can process millions of individual customer journeys simultaneously, delivering true N=1 personalisation at scale. This capability simply isn’t feasible for internal teams without significant technology investment and specialised AI expertise.

What We Don’t Replace

Your Brand Stewardship

Your team’s understanding of your brand’s voice, positioning, and heritage is irreplaceable. We complement this expertise rather than attempting to replicate it.

Your Strategic Direction

You set the business objectives and north star metrics. We align our efforts with these goals, focusing on specific segments and KPIs that support your broader strategy.

Your Core Creative Process

While our AI systems can optimise and personalise content, your creative teams remain essential for developing distinctive brand assets and campaign concepts that resonate with your audience.

A Practical Starting Point

We propose beginning with a focused 90-day pilot targeting a specific inactive customer segment. This allows us to demonstrate our capabilities while you evaluate the model with minimal risk. Typical results include:

• 20-30% reduction in reacquisition spending
• 15-25% increase in revenue from previously dormant customers
• Clear attribution linking our activities to business outcomes

The beauty of our approach is its complementary nature. Your core marketing functions continue unchanged while we work in parallel, focusing exclusively on areas currently receiving insufficient attention. As the model proves itself, we can explore expanding our scope based on measurable results.

What makes this moment particularly opportune is the convergence of AI agent technology with performance-based engagement models. For the first time, it’s possible to deliver agency-level service with software-level scalability and the economic alignment of a true business partnership.

We welcome the opportunity to discuss how a Progency relationship might enhance your marketing ecosystem and contribute to your growth objectives.

Sincerely,
The Progency Team

7

AI Critique and Improvements

I asked Claude, ChatGPT, and DeepSeek for an assessment.

Claude

Strengths of the Concept

The Progency model presents a compelling vision for addressing several critical gaps in today’s marketing ecosystem:

1. Value Proposition Alignment: The essay brilliantly identifies the core misalignment in the martech industry – selling tools rather than outcomes. By connecting compensation directly to results, Progency creates natural alignment with client objectives.
2. Focus on Overlooked Segments: The emphasis on “Rest” and “Test” customers represents a genuinely underserved opportunity. Most marketing departments concentrate resources on acquisition and high-value customers, leaving significant revenue potential untapped.
3. Practical Implementation Path: The suggested 90-day pilot approach with clear metrics creates a low-risk entry point for clients to test the model before broader commitment.
4. Technological Innovation: The integration of AI agents – both operational and customer-representative – represents a genuine innovation that could deliver capabilities beyond what traditional marketing departments can achieve.
5. Clear Economic Case: The focus on eliminating AdWaste provides a compelling financial justification, with a massive $500 billion opportunity that makes the business case immediately apparent.

Practical Challenges and Improvement Areas

Despite its promise, several challenges could hinder the Progency model’s practical implementation:

1. Data Integration and Access

Challenge: The model requires deep integration with client systems and comprehensive access to customer data – often a significant hurdle given privacy regulations, data silos, and security concerns.

Solution: Develop a tiered data access framework with clear governance policies. Start with anonymised or aggregated data for initial projects, progressively demonstrating value to earn deeper access. Create secure data exchange protocols that respect privacy regulations while enabling sufficient operational capabilities.

2. Skill Gap and Talent Acquisition

Challenge: Building a Progency requires a unique combination of martech expertise, data science capabilities, industry knowledge, and AI engineering talent – all in short supply.

Solution: Consider a “build-acquire-partner” talent strategy: identify key skills that must be in-house versus those that can be contracted initially. Develop upskilling programmes for existing martech professionals, potentially creating certification paths. Partner with universities to develop specialised curricula that prepare graduates for these hybrid roles.

3. Organisational Resistance

Challenge: The essay acknowledges potential resistance from marketing teams who may view Progency as threatening their roles or authority.

Solution: Position Progency as an enhancement team rather than a replacement, with clear boundaries around responsibilities. Implement collaborative workshops where client marketing teams participate in defining success metrics and strategic direction. Create explicit career development opportunities for client-side marketers to evolve alongside the Progency, perhaps through joint innovation programmes or secondments.

4. Measuring Causality and Attribution

Challenge: Accurately attributing business outcomes specifically to Progency activities will prove challenging, particularly in isolating their impact from other marketing initiatives.

Solution: Implement controlled experiments with holdout groups where possible. Develop sophisticated multi-touch attribution models that account for both Progency and client marketing activities. Establish baseline measurements before engagement and agree on measurement methodologies upfront to prevent disputes about performance-based compensation.

5. Scalability vs Customisation Balance

Challenge: While AI agents promise scalability, each client will require significant customisation of systems, processes, and metrics – potentially undermining economic viability.

Solution: Create a modular approach to Progency implementation with standardised components that can be configured rather than custom-built for each client. Develop industry-specific templates for common use cases (e.g., retail reactivation, B2B engagement) to accelerate deployment. Build a maturity model that gradually increases customisation as client relationships mature.

6. Competitive Response

Challenge: If successful, traditional agencies, consultancies, and martech vendors will rapidly attempt to replicate the model, potentially with deeper resources.

Solution: Focus on building proprietary AI agent architecture that creates sustainable competitive advantage. Secure exclusive client relationships with multi-year contracts that include innovation roadmaps. Consider creating an industry consortium of Progency providers to establish standards and best practices, positioning early movers as thought leaders.

Enhancements to Strengthen the Model

1. Hybrid Team Integration

The current proposal positions Progency as somewhat parallel to the client’s operations. Consider deeper integration models where hybrid teams of client marketers and Progency specialists work together, allowing for knowledge transfer in both directions while maintaining accountability.

2. Capability Development Path

Extend the model to include a clear capability development path for clients, where initial engagements eventually build client capacity rather than creating perpetual dependency. This would position Progency as a transformation partner rather than just a service provider.

3. Cross-Client Insights Network

Expand the value proposition by creating anonymised, aggregated insights networks across clients (with appropriate permissions). This “collective intelligence” could provide competitive advantage impossible for standalone marketing departments.

4. Regulatory-Compliant Data Framework

Develop specific frameworks for managing data in increasingly regulated environments, particularly addressing how AI twins can operate effectively while respecting privacy regulations like GDPR and evolving legislation.

5. Environmental and Ethical Considerations

Strengthen the model by explicitly addressing the environmental impact of AI operations and establishing ethical guidelines for AI agent decision-making, particularly regarding personalisation boundaries.

Conclusion

The Progency model represents a genuinely innovative approach to solving critical marketing challenges. Its focus on outcomes rather than inputs, overlooked customer segments, and AI-powered execution creates a compelling value proposition. However, successful implementation will require thoughtful approaches to data integration, talent acquisition, organisational change management, and measurement methodologies.

With appropriate refinements to address these practical challenges, Progency could indeed become the crucial missing link in marketing’s evolution toward truly accountable, customer-centric operations that eliminate wasteful spending while driving sustainable growth.

ChatGPT

1. What Works Well

a) Clear Recognition of Martech’s Gaps

Your essay effectively outlines how traditional martech vendors sell “toolkits” rather than end-to-end solutions. By focusing on outcomes rather than mere software features, Progency repositions martech as a driver of genuine revenue growth and retention. This framing resonates with the industry’s increasing demand for turnkey, results-focused partnerships.

Why This Matters
Many brands remain overwhelmed by the operational requirements of sophisticated martech platforms. Progency’s emphasis on performance-based services—rather than merely licensing software—addresses a real pain point for marketers who lack time, skill, or personnel to exploit 100% of any given platform’s capabilities.

b) Performance-Driven Pricing

Your advocacy for aligning pricing with delivered outcomes is a significant strength. Much of adtech’s success comes from tying budget outlay directly to measurable returns (like clicks or conversions). Applying a similar logic to martech via performance-based pricing (for instance, sharing incremental revenue from reactivation) strongly incentivises the Progency team to solve the “Who Will Do It?” problem.

Why This Matters
Many martech solutions fail to achieve adoption precisely because internal marketing teams do not have the capacity to deploy or optimise them. By adopting a performance model, a Progency effectively assumes both the financial and operational risk, which can be compelling to brands reluctant to invest in more tools without guaranteed payback.

c) AI at the Core: Agentic AI and Co-Marketers

Your essay’s integration of Agentic AI and the concept of an AI-powered “Co-Marketer” is well-articulated. Moving beyond simple automation into agentic autonomy and orchestration suggests Progency’s potential to operate “in parallel” with existing marketing stacks, delivering quick wins like reactivation, then layering more advanced campaigns over time.

Why This Matters
Most AI in martech is still limited to predictive analytics or segment-based personalisation. Your essay’s vision of operational and customer-representative AI agents coordinating complex tasks gives Progency a competitive edge. This helps justify the value proposition: brands are not just buying more software—they’re accessing a self-improving AI-driven service layer.

d) Holistic Approach to “The Three Perennial Problems”

By describing the “Not for Me”, “No Hotline”, and “No Alternative” challenges—and then clarifying the fourth, “Who Will Do It?”—you showcase a thorough grasp of marketing’s structural weaknesses. Progency’s integrated approach (platform + services + performance-based incentives) is a logical remedy and sets it apart from fragmented “agency + third-party martech” models.

Why This Matters
Many solutions address one or two marketing challenges but not all. Progency’s holistic scope—coupled with your emphasis on bridging the execution gap—feels refreshingly comprehensive and could appeal to mid-market and enterprise brands alike.

2. Where It Could Be Stronger

a) Talent and Organisational Readiness

The essay mentions the need for a dedicated business unit within a martech vendor to run Progency. However, it does not deeply explore how the necessary talent and organisational culture will be built.

Potential Issue

• Managing a performance-driven services arm requires very different skill sets from running a B2B SaaS product team. You will need experts in data science, campaign operations, creative strategy, and account management, all comfortable working under revenue-sharing or outcome-based compensation.

Suggested Improvement

1. Talent Strategy: Include clear guidelines on acquiring, training, and retaining staff with both martech and agency expertise.
2. Cultural Bridge: Emphasise how Progency staff will integrate with or remain distinct from core product teams, ensuring that neither group is overshadowed.

b) Detailed Business Modelling for Profit-Sharing

While the concept of “performance-based pricing” is compelling, there is a gap around specific commercial structures. Performance-based deals can be difficult to manage when attributing incremental revenue to Progency activities versus a brand’s other marketing efforts.

Potential Issue

• Conflicts may arise when multiple vendors or in-house teams contribute to revenue gains. Determining the “incremental uplift” or “counterfactual” scenario can become contentious.

Suggested Improvement

1. Attribution Framework: Propose an agreed model for measuring the baseline and the incremental revenue or performance lifts that Progency claims.
2. Tiered Engagements: Define scenarios where a client might pay a small baseline fee to fund overheads, with bonuses only if certain thresholds (revenue or retention lifts) are exceeded.
3. Case Studies or Pilots: Outline real or hypothetical examples demonstrating how the fee structure might work, thus reducing friction over measurement or “false positives”.

c) Handling Client Data and Systems Integration

The essay states Progency will likely operate “in parallel” with a brand’s existing marketing stack, minimising direct interference. However, in practice, data synchronisation and systems integration can be major bottlenecks. Without robust data flows, the AI cannot deliver the personalisation promised.

Potential Issue

• Onboarding new clients could be lengthy if they have legacy CRMs or incomplete data pipelines. Progency’s effectiveness depends heavily on timely, high-quality, and appropriately permissioned data.

Suggested Improvement

1. Integration Blueprint: Provide a step-by-step approach for connecting Progency’s AI layer to a client’s data warehouse, CRM, e-commerce platform, etc.
2. Parallel Ops Explanation: Offer more detail on how you minimise disruption. For instance, do you deploy your own tracking pixels? Do you create a “mirror data layer” fed from existing systems?

d) Scalability and Profitability Concerns

Performance-driven pricing holds undeniable appeal, but as Progency grows, each engagement might require deep involvement from AI experts, data analysts, and marketing strategists. The essay could better address how to scale these resources.

Potential Issue

• The labour-intensive nature of multi-client management can strain margins if not carefully planned. “Service + platform” is often more lucrative than plain SaaS, but it also has higher labour costs.

Suggested Improvement

1. Agentic AI Roadmap: Show how, over time, more tasks can be handled by AI agents to keep operational overheads stable even as client numbers grow.
2. Service Delivery Tiers: Perhaps propose gold/silver/bronze tiers to cater for varying client budgets and complexity, ensuring that not every client demands your top experts.

e) Risk Allocation and Contractual Safeguards

The essay highlights the need for trusting, multi-year partnerships, but performance-based deals often mean forging a new level of contractual risk-sharing.

Potential Issue

• Clients might expect guaranteed improvements or unlimited campaign iterations. Conversely, Progency might overpromise on performance or become entangled in legal disputes if incremental revenue is disputed.

Suggested Improvement

1. Contractual Clarity: Add detail on how you will handle performance shortfalls—e.g., do you offer “money back guarantees” or partial refunds if targets are missed?
2. Shared Success: Explain how you manage circumstances (e.g., brand crises unrelated to marketing) that hamper results. Clear out-clauses or “force majeure” style clauses could be needed.

3. Practical Suggestions for Strengthening Progency

1. Gradual Roll-Out and Pilots

1. • Proof of Concept: Start with 2–3 “strategic clients” willing to try performance-based reactivation campaigns. Document successes (and lessons learned) to refine your attribution methods and fee structure.
   • Use Cases: Reactivation is an excellent initial angle—once trust is built through small-scale wins, expand into bigger, broader marketing transformations.

2. Adoption of Industry Benchmarks

1. • Maturity Models: Provide a “Progency Maturity Model,” so clients see how they progress from basic reactivation or personalisation to advanced AI orchestration.
   • Best Practice Community: Encourage knowledge sharing among Progency clients, fostering a collective sense of innovation and helping cross-vertical learnings accelerate.

3. Ethical AI Governance

1. • Data Security & Privacy: Deepen your coverage of how Progency’s AI upholds privacy legislation (GDPR, CCPA), especially with advanced data usage.
   • Algorithmic Transparency: Offer periodic reports on how AI decisions are made (to the extent possible) to avoid brand mistrust or hidden biases.

4. Detailed Talent Roadmap

1. • AI/Martech Specialists: Clarify the mix of data scientists, marketing strategists, creative storytellers, and client success managers who will deliver Progency services.
   • In-House vs. Outsourced: Specify whether you foresee in-house teams, or if you’ll partner with smaller agencies and freelancers to quickly scale resources.

4. Overall Assessment

The Progency concept holds considerable promise, bringing together technology, services, and outcome-based commercial models at a time when brands are searching for genuine alternatives to both the “toolkit-only” martech approach and the “spray-and-pray” adtech treadmill. By focusing on delivering outcomes rather than mere software functionality, you position Progency as a potent solution to marketing’s perennial question: “Who will actually do the work?”

However, the real-world success of Progency will hinge on its capacity to tackle practical, everyday challenges. Data integration, talent acquisition, measurement and attribution, and the complexities of scaling a services-based business model all deserve deeper exploration. With more explicit roadmaps, sample commercial structures, and clarity on how you mitigate both operational and contractual risks, you can bolster Progency’s credibility and reduce the friction likely to arise when pitching performance-based partnerships to cautious brands.

In summary, your essay deftly captures why Progency matters now—reacquisition burn rates remain shockingly high, and brands are craving simpler, more direct ways of turning martech potential into real revenue and profit. By refining your plans to address practical hurdles and offer robust guidance on implementation, you’ll fortify Progency’s standing as an ambitious yet thoroughly viable new business model for martech and beyond.

DeepSeek

The Progency model presents a compelling vision for bridging the gap between martech capabilities and tangible marketing outcomes. Below is an assessment of its strengths, practical challenges, and suggestions for refinement, using UK English conventions.

Strengths

1. Addressing AdWaste: Progency’s focus on reactivating dormant customers (the “Rest” and “Test” segments) tackles a well-documented inefficiency: the $500 billion wasted annually on reacquiring existing customers. This aligns with brands’ growing need to maximise customer lifetime value (CLV) over constant acquisition.
2. Outcome-Driven Model: Shifting from SaaS subscriptions to performance-based pricing ensures alignment with client goals. This model mirrors adtech’s success (e.g., paying for clicks) while offering martech’s precision, making it attractive for ROI-focused marketers.
3. AI Agent Scalability: The integration of agentic AI (e.g., operational and customer-representative agents) could automate complex workflows, enabling hyper-personalisation and real-time optimisation at scale—tasks impractical for human teams alone.
4. Complementary Approach: Progency’s emphasis on working alongside internal teams—rather than replacing brand stewardship or creativity—minimises resistance. It positions itself as a force multiplier, not a disruptor.
5. Market Opportunity: Tapping into the $500 billion AdWaste pool offers a lucrative revenue stream for martech firms, with 10x the potential of traditional software sales.

Practical Challenges and Improvements

1. Technical Implementation Complexity

Issue:

• Deploying AI agents that integrate seamlessly with clients’ legacy systems requires robust APIs and data infrastructure. Many brands lack the technical maturity for such integrations, risking delays or failures.
  Solution:
• Pre-built connectors: Develop industry-specific integration templates (e.g., for Shopify, Salesforce) to reduce setup friction.
• Hybrid human-AI workflows: Use AI for data processing and predictions but retain human oversight for strategic decisions, ensuring adaptability to edge cases.

2. Data Privacy and Security

Issue:

• Centralising customer data across multiple clients increases exposure to breaches, especially under regulations like GDPR.
  Solution:
• Zero-trust architecture: Implement encryption, anonymisation, and strict access controls.
• Transparency protocols: Provide clients with granular audit trails showing how data is used and protected.

3. AI Reliability and Bias

Issue:

• AI agents may misinterpret context (e.g., cultural nuances) or inherit biases from training data, leading to tone-deaf campaigns.
  Solution:
• Bias mitigation frameworks: Regularly audit AI outputs using third-party tools and diverse testing cohorts.
• Client-specific guardrails: Allow brands to set rules (e.g., brand voice guidelines, ethical boundaries) that AI agents must follow.

4. Client Trust in Performance Metrics

Issue:

• Clients may sceptically view Progency’s attribution models, especially if internal teams dispute credit for outcomes.
  Solution:
• Third-party validation: Partner with independent analytics firms (e.g., Nielsen) to verify results.
• Real-time dashboards: Offer clients transparent, interactive reporting tools to track progress against KPIs.

5. Scalability Across Industries

Issue:

• Marketing needs vary widely between verticals (e.g., B2B vs. B2C). A one-size-fits-all Progency model may struggle to adapt.
  Solution:
• Modular service tiers: Offer industry-specific packages (e.g., e-commerce reactivation, SaaS upsell campaigns) with tailored AI agents.
• Vertical expertise hires: Build teams with deep domain knowledge in high-priority sectors (e.g., retail, fintech).

6. Economic Sustainability

Issue:

• Performance-based pricing could strain cash flow if outcomes take time to materialise, especially in long sales cycles.
  Solution:
• Hybrid pricing: Combine a reduced upfront fee with outcome-based bonuses to balance risk.
• Phased milestones: Tie payments to incremental metrics (e.g., 30% reactivation within 90 days).

Conclusion

The Progency model is a visionary response to martech’s execution gap, combining AI scalability with outcome accountability. Its strengths lie in addressing AdWaste, aligning incentives, and enhancing operational efficiency. However, technical complexity, data governance, and client adoption hurdles require strategic mitigation.

Recommendations:

• Start with niche verticals to refine the model before scaling.
• Invest in hybrid human-AI workflows to balance automation with human judgment.
• Prioritise transparency in metrics and data usage to build trust.

If executed thoughtfully, Progency could indeed become the “missing link” in profitable customer engagement—provided its proponents navigate these challenges with agility and client-centricity.

8

Deep Research

I asked OpenAI’s Deep Research for a more comprehensive report on the state of B2C martech and an assessment of the Progency idea.

Below is a comprehensive report on the current state of B2C martech, the opportunities now arising in the age of generative and agentic AI, and how martech providers can create their own “agency” layer. This report also addresses why martech has lagged adtech, how AI will transform retention and engagement, and why a thin services division can be the next strategic frontier for B2C martech companies.

1. Overview of B2C Martech

B2C martech platforms primarily focus on customer engagement and retention: push notifications, email/SMS marketing, in-app messaging, automation workflows, analytics, and personalisation.

Despite continuing innovation, most of these solutions still struggle to match the scope and simplicity of adtech: “Give us your budget, we’ll deliver X new clicks.” As a result, martech’s share of budgets has remained overshadowed by adtech spending, which regularly consumes the majority of marketing dollars.

2. Why Martech Lagged Adtech

2.1 Value Proposition Gap
Adtech excels at tying spend directly to outcomes—views, clicks, or conversions. Brands typically see a direct correlation between money in (ad spend) and leads or traffic out. In contrast, martech is often sold as software with a toolkit of features—data management, segmentation, analytics—that requires in-house teams or agencies to drive real returns. This gap in “service vs. software” partially explains why 90% of digital budgets flow to ad platforms, while retention efforts tend to be underfunded.

2.2 Operational Complexity
Martech implementations demand ongoing staff effort to create segmentations, build workflows, craft messages, and fine-tune personalisation. By contrast, ad platforms largely automate the “hard parts” (e.g., setting up campaigns, recommending bids) via wizard-like interfaces or fully managed options. This difference in day-to-day workload discourages deeper martech adoption.

2.3 Organization and Mindset
Many brands—especially large enterprises—are built around acquisition-focused KPIs. Retention rarely receives the same leadership attention or budget. Large marketing orgs often have entire teams dedicated to media buying and ad operations, but only a skeleton crew for lifecycle messaging and CRM.

3. Key Opportunities in the AI (Generative + Agentic) Age

With AI evolving from predictive models (forecasting) to generative (content creation) and now agentic (autonomous execution), B2C martech is on the verge of a new renaissance:

1. Hyper-Personalisation at Scale

1. • Generative AI can produce custom creative—emails, visuals, push notifications—tailored down to “segments of one.”
   • Agentic AI can orchestrate entire workflows: from building audiences to writing copy, testing variants, and optimizing in real time. Human marketers simply guide the strategic goals.
   • This eliminates a core limitation of martech: the burden of staffing large teams to craft and execute complex messaging journeys.

2. Reduced Dependence on Adtech

1. • Improved retention cuts reacquisition costs—addressing the massive “AdWaste” spent reaching existing customers via external platforms.
   • AI-empowered martech can autonomously reactivate dormant customers, effectively recapturing revenue that would otherwise go to expensive ad auctions.

3. Revenue-Sharing and Performance Models

1. • AI-driven martech can track incremental revenue uplift from retention/loyalty campaigns. This opens the door to performance-based pricing (e.g., a percentage of reactivated revenue).
   • Such arrangements could help martech providers replicate the straightforward “we deliver X leads for Y dollars” promise of adtech—but on the retention side.

4. Better End-to-End Customer Understanding

1. • Agentic AI unifies data from multiple sources—transaction history, real-time behaviors, social signals—and automates reengagement.
   • Over time, the system continuously self-improves through feedback loops, letting martech providers deliver unstoppable personalisation that makes each customer feel individually served.

4. The Need for a “Thin Services Layer”: Introducing Progency

One of the largest hurdles for brands is “Who Will Do It?”—who will build and run the day-to-day campaigns that fully leverage martech capabilities? Vendors rarely provide ongoing managed services, leaving marketers to figure out everything themselves. The documents refer to a “Progency” model—an agency layer combined with proprietary martech technology that aligns incentives with client outcomes rather than purely licensing software.

4.1 What is Progency?

• Definition: A product-led agency built on top of a martech platform, offering not just a toolkit, but a service layer that executes strategies on behalf of the brand.
• Value Proposition: Instead of marketers laboring over complex segmentation, campaign orchestration, and micro-analytics, the Progency unit takes ownership of driving growth and retention.
• Outcome-Based Pricing: Compensation can be tied to measurable business outcomes (e.g., share of reactivated revenue, cross-sell uplift).

4.2 Business Rationale

1. Bridging the Skills Gap

1. • Many marketing departments lack specialists in data science, lifecycle strategy, or AI-driven personalisation.
   • A Progency team, deeply versed in the martech platform, ensures that advanced features (like multi-agent AI orchestration) are actually used to their fullest.

2. Reducing Operational Friction

1. • Marketers often juggle multiple vendors—email providers, automation suites, design agencies—and rarely have time to execute every feature set.
   • A Progency model eliminates friction by combining platform + agency in one solution, guaranteeing cohesive execution.

3. Adtech-Like Simplicity

1. • By focusing on business outcomes (“we’ll deliver X% reactivation and Y% cross-sell”), a Progency approach gives martech the same clarity that has fueled adtech’s success.
   • Marketers can then compare acquisition vs. retention ROI on a more level playing field.

4. New Revenue Streams for Martech Providers

1. • Traditional martech is capped by subscription-based or usage-based fees.
   • A Progency unit can share in the upside if it boosts customer revenue, replicating adtech’s success in capturing a slice of brand budgets.

5. How a Progency Division Could Work

From a structural standpoint, a martech company adding a service layer might follow this path:

1. Dedicated Business Unit

1. • Operate separately from core product engineering so the services team can focus on client engagements, P&L accountability, and performance metrics.

2. AI-Driven Execution

1. • Deploy AI “Co-Marketers” and specialized sub-agents for segmentation, content generation, campaign orchestration, budget optimization, and analytics.

3. Performance-Based Pricing

1. • Incentivize the Progency division to deliver real revenue impact. For example, if the division reactivates 100,000 dormant users who generate $X million in additional revenue, the Progency earns a share.

4. Phased Implementation

1. • Launch small pilots with strategic clients to prove the model, focusing on quick wins like reactivation or upsell campaigns.
   • Scale up by adding agentic AI, forging multi-year performance partnerships with core enterprise clients.

5. Talent and Expertise

1. • A synergy of martech technologists + creative strategists + data scientists + industry experts.
   • A performance-based culture that differs from a typical software subscription model.

6. The Future of Retention and Engagement in an Agentic AI World

6.1 Autonomous Personalisation
Agentic AI systems can “own” daily marketing tasks such as:

• Testing endless creative variations to see what resonates
• Dynamically adjusting segments based on real-time user behavior
• Triggering orchestrations across email, mobile, SMS, WhatsApp, in-app messages, etc.

6.2 Seamless Customer Lifecycle Journeys
With AI Agents that coordinate across multiple channels and data sources, each user’s journey becomes a fluid conversation rather than a siloed set of campaigns. Dormant customers get reactivation nudges, brand advocates get loyalty boosters, and high-value VIPs receive “white-glove” messaging.

6.3 Reducing “AdWaste”
AI-based martech reengages users without paying third-party tolls repeatedly. Over time, marketing teams can cut reacquisition spend, reinvesting savings into deeper personalisation and brand experiences. In effect, martech becomes a profit engine—less about tool licensing, more about outcome-based marketing transformation.

7. Conclusion and Recommendations

7.1 Closing the Martech vs. Adtech Gap

Martech’s historical lag behind adtech is rooted in complexity, incomplete adoption, and a value proposition that demands too much from resource-constrained marketing teams. By adding a managed service “Progency” division, martech providers can finally offer the clear, outcome-centric proposition that acquisition platforms have long delivered.

7.2 Embrace Agentic AI

Generative and agentic AI are reshaping retention and engagement. Rather than solely delivering features, martech firms can harness AI to autonomously run campaigns, personalisation, and continuous optimisation, reducing the operational burden for brand teams.

7.3 Add a Services Layer to Unlock Revenue

Creating a Progency (product + agency) allows B2C martech vendors to:

• Remove friction for client marketing teams.
• Align martech usage with tangible revenue growth.
• Capture a share of that incremental value.

7.4 Capitalise on Retention’s Hidden ROI

By focusing on reactivation and cross-sell/upsell campaigns, martech providers can unlock massive latent revenue for brands—while building more reliable, high-margin annuity streams for themselves.

Final Thoughts

The next era of B2C martech is poised to revolve around generative and agentic AI, enabling true end-to-end, outcome-focused solutions. This shift can solve martech’s historical disadvantage relative to adtech. Done right, a thin yet potent services layer—integrated with advanced AI—will help martech vendors deliver turnkey results, rather than software complexity.

As more brands realise they are wasting a significant portion of budgets reacquiring known customers, retention efforts will finally receive their due. B2C martech companies that embrace Progency and AI-driven “agentic” capabilities will position themselves at the forefront of this transformation, capturing untapped market potential and redefining what modern marketing can be.

Published April 7, 2025

1

Overview

Just as humanity emerged from centuries of economic stagnation through the Enlightenment, marketing must now escape from its own dark ages – a period defined by waste, inefficiency, and outdated practices that strangle growth. John Wanamaker’s famous lament over a century ago – “Half the money I spend on advertising is wasted; the trouble is I don’t know which half –  remains eerily relevant. But today, the waste is no longer unknown; we know exactly where it is. Of the $700 billion spent on digital marketing, $500 billion is wasted on reacquiring customers brands already own. Like feudal lords taxing merchants for crossing bridges they never built, today’s digital ad platforms force brands to pay just to reach their own customers—trapping them in an auction-driven profit drain, and turning marketing into a system of enforced tolls rather than value creation. The time has come for marketing’s own Enlightenment—one powered by AI.

**

Over the past five years, I’ve written more than 150 essays challenging the status quo in marketing. Throughout this journey, one promise has remained constant: technology, when wielded wisely, can transform marketing from a costly burden into a powerful profit engine. As AI has evolved from buzzword to business reality, my focus has increasingly centred on how it might finally deliver on this long-held promise.

My AI exploration has spanned a breadth of topics: from the revolutionary potential of AI Co-Marketers to the transformative power of Digital Twins, from the promise of Vector Search to the paradigm-shifting concept of Generative Journeys. Through dozens of interconnected essays, I’ve examined how AI could reshape every facet of marketing – from customer understanding to campaign execution, from personalisation to performance optimisation.

Here are some of the AI-themed essays:

• Co-Marketer: Martech meets AI
• Synthetic Businesses: Can Gen AI Mirror Worlds Accelerate Sales?
• Vector Search: Made for AI
• How Agentic AI will Transform Digital Marketing
• Generative Journeys: Digital Marketing’s New Core
• CEO Memo: How Agentic AI can Power the Profipoly Quest
• Martech’s 10+1 Foundations in the AI Age
• Large Customer Model: Foundation for AI-first Martech
• Marketing’s AI Triad: Large Customer Model, Digital Twins, Co-Marketer
• AI-Powered Digital Twins: Marketing’s Marvel
• Commerce Metaverse: Shaping Marketing’s Future with Agentic AI and Digital Twin
• AI Twins: Digital Customer Representations That Will Transform Marketing (Forbes)
• Digital Twins in Marketing: Magical Minions
• AI Twins: The Future of Marketing Intelligence
• MyTwin: The Agentic AI That Powers N=1 Personalisation
• AI Twins in Action: Daily Allies for Smarter Marketing and Meaningful Connections
• AI and Neo: The Twin Engines of Marketing’s Future
• The Emergence Revolution: How Agentic AI Will Reinvent Marketing Teams

A compelling pattern emerges from my writings: marketing’s next evolution will pivot on two groundbreaking ideas. First, there’s the rise of AI Twins – sophisticated digital replicas that capture individual preferences, behaviours, and contexts in real time. These Twins promise to deliver the holy grail of “segment of one” personalisation, moving beyond broad-stroke campaigns to truly individualised engagement.

Second, we’re witnessing the emergence of Agentic AI – autonomous marketing systems that function as “departments of one.” These AI agents can orchestrate campaigns, analyse performance, and optimise outcomes with minimal human intervention. Rather than replacing human marketers, this technology amplifies their capabilities, enabling unprecedented scale and efficiency without proportional increases in overhead. (Twins can be thought of as a special type of Agents.)

Together, these innovations offer solutions to marketing’s three perennial challenges: the “Not for Me” problem of irrelevant messaging, the lack of reliable brand-customer “Hotlines,” and the absence of viable “Alternatives” to expensive ad platforms. By addressing these fundamental issues through enhanced personalisation, stronger owned channels, and AI-driven processes, we can finally break free from marketing’s dark ages of endless reacquisition.

In this series, I’ll trace AI’s evolution in marketing and illuminate the transformative future on our horizon. We’ll explore how the convergence of AI Twins and Agents could reshape everything from customer engagement to campaign optimisation, from data analysis to creative development. My aim is not just to document this transformation but to inspire a new vision for marketing’s future – one I call “NeoMarketing” – where technology enables genuine connections, eliminates wasteful spending, and drives sustainable growth.

The opportunity before us is extraordinary. With the right application of Agentic AI, marketing can finally evolve beyond its traditional role as a necessary expense to become a genuine catalyst for business growth – one relationship at a time.

2

History

Here is ChatGPT on marketing’s evolution.

Artificial intelligence has played an increasingly critical role in marketing over the past few decades, starting with basic rule-based systems in the 1990s, progressing to sophisticated predictive analytics in the 2000s and 2010s, and culminating in the recent surge of generative AI. Below is a deeply researched overview of how AI has evolved within marketing, with a focus on past and present applications.

1. Early Roots: Rule-Based Systems and Data Mining (1990s–2000s)

• Rule-Based Expert Systems. In the 1990s, “intelligent” marketing systems often followed manually coded rules. Marketers would define “if-then” conditions: for instance, “if a customer hasn’t purchased in 90 days, send a reactivation email.” These approaches were fairly static, relying on marketers’ domain knowledge rather than algorithmic learning.
  • Emergence of Predictive Models. As data warehouses expanded in the late 1990s, advanced statistical methods (e.g., regression, decision trees) became more widely applied to spot customer churn risks and segment audiences. This led to the first wave of “predictive marketing” in areas like direct mail, where marketers used regression-based models to decide which households might respond best to an offer.
  • Data Mining and CRM. As customer relationship management (CRM) platforms grew popular, data mining tools—early forms of AI—were integrated to identify high-value customers, forecast response rates, and predict product affinities. Most of these systems required specialized data scientists or analysts, so adoption remained limited to large enterprises.

2. First Wave of Predictive AI in Marketing (Mid-2000s–2015)

• Machine Learning for Targeting. With e-commerce expanding, marketers needed more granular ways to reach the right customers. Predictive algorithms (logistic regression, gradient boosting, random forests) gained traction for:
  – Propensity Modeling: Estimating the likelihood that a customer would respond to a particular campaign.
  – Next-Best-Offer Engines: Recommending what a customer might buy next based on their past purchases and behaviors.
  • Personalization at Scale. As cloud computing matured, large-scale personalization became more feasible. Early recommendation engines (e.g., Amazon’s “Recommended for You”) used collaborative filtering to tailor products and content. This era saw the beginnings of automated email journeys—brands would feed CRM data into email marketing platforms, which then personalized subject lines or product picks, albeit using relatively simple models.
  • Programmatic Advertising. The adtech boom introduced real-time bidding and predictive bidding algorithms on ad exchanges. Marketers could set target CPAs (cost per acquisition) or ROAS (return on ad spend) goals, letting the system optimize bidding in milliseconds. Though powerful, this mostly addressed “who to show an ad to” rather than deeper engagement or relationship-building.

3. Second Wave: Deep Learning and Advanced Predictive AI (2015–2020)

• Deep Learning Breakthroughs. Advances in neural networks—driven by research in image recognition, natural language processing (NLP), and speech—began spilling into marketing. Platforms integrated deep learning models for more accurate predictions. Notably, advanced recommendation engines could factor in broader contexts: user reviews, textual product descriptions, clickstream data, and more.
  • Customer Journey Orchestration. Multi-touch attribution models got more sophisticated, incorporating sequential patterns in user behavior. Marketers could see a user who clicked on Facebook ads, abandoned a cart on the website, and then came back via email, all in one journey path. AI algorithms helped decide how best to re-engage at each touchpoint, increasing conversions and reducing unsubscribes.
  • Predictive Analytics to Agentic AI (Initial Steps). Experiments with “autonomous” marketing agents began (though quite limited)—for instance, systems that automatically paused underperforming campaigns or reallocated budget to better-performing channels. While still relying heavily on marketer oversight, these prototypes set the stage for truly agentic AI.

4. The Generative AI Breakout (2021–Present)

Recent years have seen explosive interest in large language models (LLMs) and other generative systems.
• Large Language Models. Transformers—particularly GPT variants—demonstrated that AI could generate coherent text, handle Q&A, summarize content, and even write ad copy. Marketers quickly embraced tools like GPT-3 and ChatGPT to draft social media posts, product descriptions, email subject lines, and blog articles.
• Generative Images and Media. Tools such as DALL·E, Midjourney, and Stable Diffusion enabled AI-generated visuals at scale. Creative teams experimented with quickly producing ad images, banners, or concept art—cutting iteration times from days to minutes.
• Automated Content Workflows. Marketers began building end-to-end workflows where generative AI tools:
– Propose content ideas based on trends.
– Draft copy or imagery.
– Push final creative into social and email channels with minimal human editing.
• Conversational Agents. AI chatbots evolved well beyond FAQ-bots to handle more nuanced tasks, personal shopping advice, or interactive marketing (e.g., product quizzes that emulate personal assistants). Companies started to experiment with ChatGPT-like systems on their own websites, enabling advanced support and “guided selling” experiences.

5. Agentic AI: From Automation to Autonomy

While generative AI has transformed content creation, the emerging concept of Agentic AI stands to revolutionize marketing execution and strategy. Instead of waiting for prompts or instructions, advanced AI agents can:
• Set and Pursue Goals. A “Co-Marketer” agent might receive a high-level objective such as “increase cross-sells among our top 5% of customers by 10% next quarter” and autonomously coordinate segmentation, messaging, budget allocation, and performance tracking.
• Orchestrate Multi-Agent Systems. One AI agent focuses on ad copy. Another analyzes real-time data for budget optimization. A third personalizes email flows. Collectively, they function as a marketing “department of one,” collaborating and learning from each other.

Core Use Cases for AI in Marketing Today

• Predictive Targeting: Machine learning helps identify the highest-likelihood buyers or churn risks, enabling cost-effective campaigns.
• Recommendation Engines: Still among the most visible successes—recommending products, content, or services based on past behaviors and similar user patterns.
• Dynamic Pricing: Adjusting product prices in real time based on inventory, demand signals, and competitive benchmarks.
• Chatbots and Virtual Assistants: Handling routine inquiries and guiding shoppers toward relevant products, with advanced systems harnessing LLMs for natural, context-aware conversations.
• Content Generation: From automated email subject lines to fully drafted blogs or social ads, generative AI dramatically speeds up creative output.
• Campaign Orchestration: Beyond simple automation, some platforms use AI to shift budgets or rewrite ad text if performance dips, all with minimal human oversight.

Challenges and Considerations

• Data Quality and Silos. AI-driven marketing is only as good as the data feeding it. Many organizations still struggle with fragmented data architectures.
• Privacy and Trust. Regulations like GDPR and CCPA, as well as growing consumer concerns about data usage, place constraints on how AI can gather and leverage personal information.
• Bias and Ethics. AI models risk perpetuating biases if trained on biased data, potentially causing reputational or legal issues.
• Overreliance on Platforms. Despite the sophistication of AI, many brands remain heavily reliant on adtech duopolies, raising costs and limiting ownership of insights.

From Predictive to Generative: Key Observations

• Predictive AI excelled at classifying, segmenting, and forecasting but often required heavy data science expertise and was focused on optimizing within existing marketing paradigms (e.g., who to target, how to schedule).
• Generative AI adds creativity and language fluency, opening possibilities for new kinds of personalized storytelling, interactive campaigns, and even AI-managed strategy.
• The Shift to Agentic AI merges both predictive and generative. By analyzing customer data (predictive) and then autonomously creating or adjusting campaigns (generative + agentic), AI can address deep inefficiencies in reacquisition spending, unresponsive segments, and underpersonalized experiences.

The Road Ahead

Much as we’ve already seen AI integrate with CRMs and marketing automation tools, the next phase will likely expand toward:

• Self-Optimizing Campaigns. AI that runs ongoing A/B or multivariate tests to refine messaging across channels—in real time, at scale.
• AI “Departments of One”. Growing sophistication in multi-agent architectures means fewer manual handoffs. AI could coordinate a suite of specialized mini-agents for content, design, analytics, budget, etc., orchestrating them toward a marketer’s high-level goals.
• Deeper Personalization. True “N=1” marketing, where each individual receives a uniquely tailored experience, requires more advanced AI Twins that can respond contextually in real time.
• Ethical and Transparent AI. As AI agents become more autonomous, brands must invest in “explainability,” ensuring marketers and customers alike understand how decisions are made and data is handled.

Conclusion

From its early days in rule-based systems to the powerful predictive engines that shaped digital advertising, AI has methodically chipped away at marketing’s biggest inefficiencies. Now, with generative and agentic AI, we stand on the cusp of a deeper transformation—one that not only optimizes content and targeting but can fully automate and personalize entire marketing strategies.

In the near term, generative AI already frees human teams from repetitive creative tasks, while robust predictive analytics guards against guesswork in campaign planning. Long term, truly autonomous agentic AI could help marketers cut down on reacquisition spending, connect with customers in more meaningful ways, and even unlock new revenue streams. In short, AI’s evolving role in marketing is no longer about incremental improvements; it’s about redesigning the entire marketing function around intelligent, adaptive technology.

As marketing continues this transition—from predictive, to generative, to fully agentic—brands that harness AI responsibly and innovatively will likely outpace their competitors. The stage is set for an era in which marketers move beyond chasing endless new customer acquisitions and instead focus on delivering compelling, profitable experiences for each existing customer—one personalized AI interaction at a time.

3

Five Acts

For some additional colour, I asked the other AIs (Claude, DeepSeek, and Perplexity) to put together the story about the evolution of AI in marketing.

The story of artificial intelligence in marketing unfolds like a play in five acts: from the early days of rule-based systems and basic predictive analytics, through the big data revolution and the rise of machine learning, to today’s generative AI breakthrough, and finally to the emerging era of agentic intelligence. Each stage has built upon the previous ones, creating increasingly sophisticated capabilities for understanding and engaging customers.

Act I: The Foundation Years (1980s-2000s)

Early Beginnings: Rule-Based Systems

The journey began in the 1980s with rudimentary expert systems and neural networks. These early applications focused primarily on basic pattern recognition and decision support, with marketing applications limited to:

• Database analytics for customer categorisation
• Rule-based segmentation systems
• Basic predictive models for market analysis
• Statistical modeling for campaign planning

The limitations were significant: systems required extensive manual coding, couldn’t learn from new data, and operated on rigid, predefined rules. Yet these early efforts laid the groundwork for more sophisticated applications to come.

The Dawn of Predictive Analytics

As computing power increased and data storage costs decreased in the 1990s, more sophisticated analytical approaches emerged:

• Advanced Statistical Methods
  • Regression analysis for response prediction
  • Decision trees for customer segmentation
  • Cluster analysis for market segmentation
• Early CRM Integration
  • Basic customer value calculations
  • Churn prediction models
  • Campaign response forecasting

These systems, while more sophisticated than their predecessors, still required significant human oversight and expertise to operate effectively.

Act II: The Rise of Predictive AI (2000-2010)

The Recommendation Revolution

The early 2000s saw the first truly transformative AI applications in marketing, led by recommendation engines:

• Amazon’s Collaborative Filtering
  • Product recommendations based on purchase history
  • “Customers who bought this also bought” features
  • Personalised homepage recommendations
• Netflix’s Content Suggestion Algorithm
  • Viewing history analysis
  • Genre preference mapping
  • Personalised content rankings

These systems demonstrated the power of AI to deliver personalised experiences at scale, though they still relied primarily on historical behavior patterns.

Advanced Customer Analytics

The period also saw significant advances in customer analysis:

• Predictive Lead Scoring
  • Probability-based qualification
  • Multi-factor scoring models
  • Automated lead prioritisation
• Customer Lifetime Value Modeling
  • Future value prediction
  • Risk-adjusted CLV calculations
  • Segment-based value forecasting
• Marketing Automation
  • Triggered email campaigns
  • Basic journey mapping
  • Automated A/B testing

Act III: The Big Data Era (2010-2020)

The Machine Learning Revolution

The explosion of digital data transformed how AI could be applied to marketing:

• Advanced Machine Learning Applications
  • Deep learning for customer segmentation
  • Neural networks for behavior prediction
  • Natural language processing for sentiment analysis
• Real-Time Analytics
  • Programmatic advertising optimization
  • Dynamic pricing systems
  • Real-time personalization engines

Marketing Automation 2.0

Marketing automation platforms evolved significantly:

• Journey Orchestration
  • Cross-channel campaign management
  • Dynamic content personalisation
  • Automated workflow optimization
• Customer Intelligence
  • Advanced behavioral analytics
  • Social media monitoring
  • Attribution modeling

Act IV: The Generative AI Revolution (2020-2024)

Content Creation Transformation

The emergence of large language models and generative AI marked a fundamental shift:

• AI-Powered Content
  • Automated copywriting
  • Image generation for campaigns
  • Video content automation
  • Dynamic creative optimization
• Conversational AI
  • Advanced chatbots
  • Natural language understanding
  • Contextual response generation
  • Personalised messaging at scale

Campaign Intelligence

Marketing strategy itself became AI-enhanced:

• Strategic Planning
  • AI-driven campaign strategy
  • Automated performance optimization
  • Predictive audience targeting
  • Cross-channel attribution

Act V: The Agentic AI Era (2024-Present)

The Rise of AI Co-Marketers

The latest evolution brings truly autonomous systems to marketing:

• Strategic Orchestration
  • Autonomous decision-making
  • Multi-agent coordination
  • Real-time performance optimization
  • Campaign adaptation
• AI Twins
  • Individual customer digital replicas
  • Real-time preference tracking
  • Predictive behaviour modeling
  • N=1 personalisation at scale

Autonomous Operations

Marketing departments are being reimagined through self-organizing systems:

• Dynamic Resource Allocation
  • Automated budget optimization
  • Real-time campaign adjustments
  • Performance-based resource shifting
• Continuous Optimization
  • Self-learning algorithms
  • Automated A/B testing
  • Real-time strategy adaptation

4

Evolution and Future

Continuing with the story written by the AIs.

Key Transitions in Marketing AI Evolution

1. From Rules to Learning

• Early Systems: Fixed rules and basic automation
• Modern AI: Dynamic learning and adaptation
• Future: Autonomous decision-making and execution

2. From Segments to Individuals

• Early Systems: Broad demographic targeting
• Modern AI: Behavioral segmentation
• Future: True N=1 personalisation

3. From Assistance to Agency

• Early Systems: Tool-based support
• Modern AI: Intelligent automation
• Future: Autonomous marketing operations

4. From Data to Intelligence

• Early Systems: Basic analytics
• Modern AI: Predictive insights
• Future: Prescriptive strategy

Impact on Marketing Practice

1. Customer Understanding

• Evolution from demographic profiles to individual digital twins
• Real-time preference tracking capabilities
• Predictive behaviour modeling
• Zero-party data collection

2. Campaign Management

• Shift from manual planning to autonomous orchestration
• Real-time optimization capabilities
• Cross-channel coordination
• Dynamic resource allocation

3. Content Creation

• Transformation from human-only to AI-assisted creation
• Personalised messaging at scale
• Dynamic creative optimization
• Automated content generation

4. Performance Measurement

• Advanced from basic metrics to predictive analytics
• Real-time attribution capabilities
• Multi-touch attribution modeling
• Predictive ROI calculations

Challenges and Considerations

Technical Challenges

• Data quality and integration issues
• System interoperability
• Scale and performance demands
• Technical debt in legacy systems

Ethical Considerations

• Privacy protection requirements
• Algorithmic bias prevention
• Transparent decision-making
• Responsible AI usage

Organizational Challenges

• Skill gap in AI implementation
• Change management requirements
• Cultural adaptation needs
• Resource allocation decisions

The Future of AI in Marketing

Emerging Trends

• Deeper AI integration across operations
• Enhanced human-AI collaboration
• Greater system autonomy
• Improved personalisation capabilities

Key Opportunities

• Waste reduction in advertising
• Improved customer experiences
• Enhanced operational efficiency
• New revenue stream creation

Critical Success Factors

• Strong data foundations
• Clear ethical frameworks
• Skilled talent acquisition
• Effective change management

Conclusion: The Path Forward

The evolution of AI in marketing represents a journey from basic automation to truly intelligent, autonomous systems. Each era has built upon the previous one, creating increasingly sophisticated capabilities for understanding and engaging customers. As we move forward, the integration of AI Twins and AI Agents promises to revolutionise how brands connect with customers, potentially eliminating the wasteful cycle of continuous reacquisition while enabling genuine one-to-one relationships at scale.

The future of marketing lies not in better tools for marketers but in creating genuine value for consumers while enabling precise targeting through authenticated identity. This transformation promises to eliminate the massive waste in current marketing practices while creating new opportunities for sustainable, profitable growth.

For marketers beginning this journey, the key is to understand that AI is not just another tool but a fundamental reimagining of how marketing creates and captures value. Success will require not just technological adoption but a complete rethinking of marketing strategy, organization, and execution.

5

Agentic AI

While I have discussed Agentic AI in previous essays, here is some additional background.

Nvidia on how Agentic AI uses a four-step process for problem-solving:

1. Perceive: AI agents gather and process data from various sources, such as sensors, databases and digital interfaces. This involves extracting meaningful features, recognizing objects or identifying relevant entities in the environment.
2. Reason: A large language model acts as the orchestrator, or reasoning engine, that understands tasks, generates solutions and coordinates specialized models for specific functions like content creation, visual processing or recommendation systems. This step uses techniques like retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate, relevant outputs.
3. Act: By integrating with external tools and software via application programming interfaces, agentic AI can quickly execute tasks based on the plans it has formulated. Guardrails can be built into AI agents to help ensure they execute tasks correctly. For example, a customer service AI agent may be able to process claims up to a certain amount, while claims above the amount would have to be approved by a human.
4. Learn: Agentic AI continuously improves through a feedback loop, or
   “data flywheel,” where the data generated from its interactions is fed into the system to enhance models. This ability to adapt and become more effective over time offers businesses a powerful tool for driving better decision-making and operational efficiency.

An AWS article explains how AI Agents work.

• Determine goals: The AI agent receives a specific instruction or goal from the user. It uses the goal to plan tasks that make the final outcome relevant and useful to the user. Then, the agent breaks down the goal into several smaller actionable tasks. To achieve the goal, the agent performs those tasks based on specific orders or conditions.
• Acquire information: AI agents need information to act on tasks they have planned successfully. For example, the agent must extract conversation logs to analyze customer sentiments. As such, AI agents might access the internet to search for and retrieve the information they need. In some applications, an intelligent agent can interact with other agents or machine learning models to access or exchange information.
• Implement tasks: With sufficient data, the AI agent methodically implements the task at hand. Once it accomplishes a task, the agent removes it from the list and proceeds to the next one. In between task completions, the agent evaluates if it has achieved the designated goal by seeking external feedback and inspecting its own logs. During this process, the agent might create and act on more tasks to reach the final outcome.

I asked ChatGPT for an example of how a multi-agent system would work in personal finance:

1. LLM Foundation: Think of this like a basic financial encyclopedia. It “knows” about different types of investments—stocks, bonds, mutual funds—and can explain them if you ask. But it doesn’t actually manage your money, analyze your portfolio, or tailor recommendations. It’s just a general source of information.
2. Specialized LLM: Now imagine an AI advisor that focuses on a specific area of finance, such as retirement planning. It’s been trained on expert knowledge about 401(k)s, IRAs, tax implications, and typical life-stage investing strategies. It can give you precise, well-informed guidance—“Here’s how a Roth IRA differs from a traditional IRA”—but it still only provides advice. It doesn’t automatically invest or monitor the market for you.
3. AI Agent: At this level, the AI becomes your personal financial manager. You give it a high-level goal—“I want to retire at age 60 with enough money for a comfortable lifestyle”—and it takes care of the details. It researches the best funds, shifts allocations as market conditions change, and balances your portfolio according to your risk tolerance. You don’t need to micromanage; the AI Agent autonomously handles day-to-day decisions to keep you on track.
4. Multi-Agent System: Here, you have an entire “virtual finance department.” One AI specializes in budgeting, tracking income, and limiting expenses. Another AI focuses on long-term investing and asset allocation. A third monitors regulatory changes and ensures compliance. A fourth keeps an eye on tax strategies to minimize liabilities. They all communicate and collaborate to give you a seamless, end-to-end financial management experience, much like a full team of specialists coordinating to achieve one goal.

6

A New Vision

NeoMarketing is my vision for Marketing’s Agentic AI future. Here are some slides from a recent presentation I had done.

Marketing’s future mission must be to eliminate AdWaste and solve the three “perma-problems.”

Here is how I summarised the evolution of AI in marketing and its future.

Here are a few slides of the NeoMarketing future.

**

NeoMarketing envisions a future where brands engage each customer individually through AI-powered systems. At its core are two transformative technologies: AI Twins that deeply understand customers, and AI Agents that execute marketing tasks autonomously. Together with innovations like NeoMails (interactive engagement channels) and NeoN (cost-effective reactivation network), these create a new marketing paradigm where marketers set strategic goals while AI handles daily execution.

The impact is revolutionary, solving marketing’s three perennial problems:

• “Not for Me”: True N=1 personalisation at scale
• “No Hotline”: Reliable, daily customer engagement channels
• “No Alternative”: Freedom from expensive adtech platforms

This isn’t just evolution – it’s a complete reimagining of marketing, transforming it from a cost centre into a profit engine. Through Agentic AI, brands can finally build lasting, profitable customer relationships that grow through retention rather than endless reacquisition.

7

Department of Agents

Agentic AI represents a transformative leap in marketing technology. Unlike traditional generative AI that simply responds to prompts, Agentic AI systems can plan, coordinate, and execute tasks autonomously. This shift – from passive response to active agency – opens unprecedented possibilities for marketing teams and is a key pillar for NeoMarketing.

From Generation to Autonomous Action

While generative AI helps with content creation and campaign ideation, Agentic AI evolves these capabilities into autonomous “co-workers” that drive the entire marketing process. These systems don’t just wait for instructions—they:

• Set and pursue strategic goals
• Break down complex tasks into manageable components
• Orchestrate multi-channel campaigns
• Continuously optimize performance
• Adapt strategies based on real-time results

The “Department of One” Vision

Imagine a marketing operation where AI agents autonomously:

• Monitor performance metrics across channels
• Identify opportunities and challenges
• Launch analysis initiatives
• Create and test new content variations
• Adjust strategies based on results
• Deploy specialised sub-agents for specific tasks

This creates a self-improving system where AI agents (“emergents”) continuously watch performance trends and optimise campaigns without constant human intervention.

True N=1 Personalization at Scale

Agentic AI pushes personalisation beyond basic segmentation:

• AI Twins create digital replicas of individual customers
• Real-time interaction analysis informs personalisation decisions
• Continuous learning refines individual customer profiles
• Dynamic adjustment of content and offers based on immediate context
• Autonomous optimisation of customer journeys

Instead of generic segments, each customer receives truly individualised experiences aligned with their specific needs and preferences.

Multi-Agent Marketing Operations

The power of Agentic AI comes from specialised agents working in concert:

Strategy Agent

• Analyses market trends and opportunities
• Identifies target audiences
• Sets campaign objectives
• Allocates resources across channels

Content Agent

• Generates customised creative assets
• Adapts content for different channels
• Maintains brand consistency
• Tests and optimises messaging

Analytics Agent

• Monitors performance metrics
• Identifies patterns and anomalies
• Recommends optimisation opportunities
• Tracks ROI across initiatives

Campaign Agent

• Orchestrates multi-channel campaigns
• Manages timing and sequencing
• Adjusts tactics based on results
• Coordinates cross-channel messaging

All these agents operate under the supervision of an AI Co-Marketer that ensures alignment with overall marketing objectives and brand guidelines.

The Future of Marketing Operations

As Agentic AI matures, we’ll see:

• Increased automation of routine decisions
• More sophisticated multi-agent collaboration
• Deeper integration of AI into strategy
• Enhanced personalisation capabilities
• New forms of customer engagement

Marketing teams will evolve from day-to-day campaign management to focus on:

• Strategic innovation
• Creative direction
• Partnership development
• Brand evolution
• Customer experience design

**

Agentic AI represents more than just automation – it’s a fundamental reimagining of how marketing operates. By combining autonomous decision-making with continuous learning and execution capabilities, these systems transform marketing from a cost centre into a growth engine that’s always optimising, always learning, and always ready to capture new opportunities.

The key is understanding that Agentic AI doesn’t replace human marketers but rather amplifies their capabilities. It handles the complex orchestration of day-to-day marketing operations, freeing humans to focus on strategy, creativity, and innovation. This partnership between human insight and AI capability creates a new paradigm for marketing effectiveness.

NeoMarketing heralds marketing’s Agentic AI-powered future – an era where $500B in AdWaste dissolves into profit-driving customer relationships and growth fuelled by data-driven precision.

8

Tales of Two Eras: Claude

I asked Claude, ChatGPT, and DeepSeek to write stories about marketing’s present and future. Here is Claude.

Present Day: The Endless Chase

A Day in the Life of Maya, CMO at TrendStyle

Maya stares at her laptop screen, fighting back a headache. Another surge in customer acquisition costs on Meta. The quarterly marketing review is tomorrow, and she’ll have to explain why they’re spending 30% more to acquire the same customers they had last year.

Her phone buzzes. A Slack message from the email team: “Open rates down to 6% on the summer collection blast. Should we do a retargeting campaign?”

She sighs. Of course they’ll do retargeting—they always do. They’ll pay Google and Meta premium rates to chase after their own customers, who’ve grown numb to the endless stream of generic promotional emails. It’s the same costly dance they perform every month.

“Morning update?” her assistant pokes his head in.

“Right.” Maya grabs her coffee and heads to the daily standup. The room is tense with familiar frustrations:

“The product team launched a new feature but forgot to tell us—we need to update all our ad creatives.”

“Customer service is getting complaints about irrelevant recommendations.”

“The loyalty program numbers are dropping—we need more aggressive reactivation campaigns.”

Maya jots down action items, knowing they’re just Band-Aids. They’ll blast more emails, boost ad spend, and hope something sticks. By day’s end, she’s approved three “urgent” budget increases for various platforms.

Later, reviewing the day’s reports, she sees a pattern that’s become all too familiar: 65% of their new customers make exactly one purchase and disappear. They’ll spend the next six months—and considerable marketing budget—trying to win them back through increasingly expensive retargeting campaigns.

As she packs up for the day, a notification pops up: “Meta CPMs increasing by 15% next quarter.”

Maya closes her laptop with a heavy sigh. There has to be a better way.

2025: The Intelligent Orchestra

A Day in the Life of Maya, CMO at TrendStyle

Maya starts her morning by checking in with her AI Co-Marketer. “Good morning, Maya. I’ve identified three opportunities based on overnight analysis,” the system reports. “Our AI Twins detected shifting preferences in our athleisure segment. I’ve already briefed the content team’s AI agents to develop new messaging angles.”

She smiles, remembering the old days of blast-and-hope email campaigns. Now, their NeoMails system maintains daily micro-engagements with customers through personalized interactive content. Each customer’s MyTwin learns their preferences through natural conversations, ensuring every interaction feels relevant and timely.

A notification appears: “Customer segment showing early churn signals. Implementing proactive retention strategy.” Maya doesn’t need to intervene—the system will orchestrate personalized re-engagement campaigns through their owned channels, without wasting money on expensive ad platforms.

In the marketing review meeting, the discussion has transformed. Instead of debating ad budgets and platform costs, they’re exploring insights surfaced by their AI agents:

“The Analytics Agent identified a new micro-segment with unique buying patterns.”

“Our Content Agent has been testing novel storytelling approaches, seeing 40% better engagement.”

“The Campaign Agent automatically shifted resources to our best-performing channels, improving ROI by 25%.”

Maya pulls up the quarterly dashboard. Customer acquisition costs have dropped 60% since implementing their AI systems. Better yet, their one-and-done customer rate has plummeted to 15%—the AI Twins ensure each customer receives truly personalized experiences that keep them coming back.

A message from their NEON network pops up: “Revenue from authenticated advertising up 50% this quarter.” Their owned channels have become profit centers, not cost centers.

Later, Maya opens a strategy proposal from her AI Co-Marketer. It’s identified an opportunity to launch a new product line based on emerging customer preferences detected across their AI Twin network. The system has already drafted a go-to-market plan, complete with personalized journey maps for different customer segments.

As she heads home, Maya reflects on how different her role has become. No more endless chase for attention through expensive platforms. No more spray-and-pray campaigns. Instead, she’s leading an intelligent orchestra of AI agents that maintain genuine, profitable relationships with each customer.

The days of AdWaste feel like a distant memory. Marketing has finally become what it always should have been: a true engine for growth, powered by intelligence, driven by relationships.

The Contrast

These tales highlight the fundamental shift in marketing operations:

Present

• Reactive problem-solving
• Platform dependency
• Generic messaging
• Rising costs
• Constant reacquisition
• Manual orchestration
• Fragmented data
• Wasted spend

Future

• Proactive optimization
• Owned channel focus
• True personalization
• Declining costs
• Sustained relationships
• Autonomous execution
• Unified intelligence
• Profitable growth

The future of marketing isn’t about better tools for chasing customers—it’s about building intelligent systems that nurture genuine relationships at scale. Through Agentic AI, the endless cycle of acquisition and reacquisition finally gives way to sustainable, profitable growth.

9

Tales of Two Eras: ChatGPT

Maya’s Day in “Traditional” Marketing (Present)

It’s 9 a.m., and Maya—Chief Marketing Officer at a mid-sized consumer electronics company—opens her inbox to a flood of unread messages. A junior analyst has flagged that last night’s email campaign underperformed expectations by nearly 40%. Maya sighs; she’s juggling multiple priorities, and the mere thought of manually tweaking subject lines and audience segments again is draining. Despite having a decent predictive analytics tool, Maya’s team still relies on manual segmentation. “High-Value,” “Low-Value,” and “At-Risk” are the main buckets, but they don’t capture the nuances of individual customers who all have different budgets, preferences, and reasons for engaging.

Before lunch, Maya joins a Zoom call with their digital agency to discuss retargeting tactics on Facebook and Instagram. CAC (customer acquisition cost) is creeping upward because the brand has no real alternative to reacquire dormant customers—they keep paying for the same eyeballs. The agency suggests more granular audience creation, but the data-lake integration keeps stalling, so personalization remains superficial. Meanwhile, Maya signs off on an expensive direct-mail piece that might convert a tiny fraction of recipients. By the end of the day, she’s overwhelmed by the swirl of disconnected marketing tasks: a new influencer campaign to coordinate, next month’s budget for reactivation ad spend, and the weekly “emergency” to re-evaluate a promotional discount strategy. As Maya packs up for the evening, she wonders if her marketing team will ever break free from this perpetual scramble.

Maya’s Day with Agentic AI (Future)

It’s 9 a.m., but Maya’s inbox is refreshingly quiet. That’s because her AI Co-Marketer—an autonomous agent—has already handled most of the routine data checks and campaign tweaks overnight. Over coffee, Maya skims her custom “Morning Marketing Pulse,” a concise briefing automatically curated by her multi-agent system. One specialized AI agent focuses on new opportunities (like a surge in chatter about a rival product), another agent monitors brand sentiment across channels, and a third agent has quietly optimized today’s email schedules based on real-time engagement signals from each subscriber’s Digital Twin.

Maya’s biggest challenge today is purely strategic: She’s debating a new product launch approach. To explore scenarios, she simply sets a broad goal—“Drive a 20% lift among high-value customers in Q1”—and the Agentic AI system decomposes that goal into sub-tasks, mapping out potential offers, content angles, and predicted results. Over lunch, Maya meets briefly with her creative director to finalize brand guidelines; the AI will incorporate those guidelines into every campaign, ensuring consistent, on-brand output. Meanwhile, her reactivation budget is down 60% because the AI’s NeoMails and “hotline” channels are proactively engaging dormant customers, eliminating the need to repeatedly purchase ads from Google or Meta. By 5 p.m., Maya checks the system’s real-time “Customer Happiness Index,” sees it trending up, and walks out the door—confident that the AI’s daily adjustments will keep everything humming until she returns.

10

Tales of Two Eras: DeepSeek

The Present – CMO Maya’s Daily Grind

“The $500 Billion Treadmill”

It’s 7:30 AM, and Maya, CMO of a mid-sized e-commerce brand, is already scrolling through her dashboard. Her team spent $2 million last quarter on Google and Meta ads, yet revenue grew by just 3%. She knows why: 70% of that budget went to reacquiring customers who’d already bought from them.

Her morning begins with a crisis. An email campaign targeting dormant customers bombed—open rates at 8%, click-throughs at 0.5%. “They’re ignoring us,” her email manager sighs. Maya’s team debates solutions: Send more emails? Risk damaging domain reputation. Stop? Lose the connection. Run Meta retargeting? That’ll cost $50 per conversion. They choose the latter, adding another line item to their AdTech tax.

Next, a meeting about their “personalization” efforts. The team proudly shows off GPT-4-generated product descriptions and dynamic banners. But when Maya asks, “Are we tailoring this to individuals?” the room falls silent. Their “segments” are still broad: “Women aged 25–34 who bought shoes.” A customer who purchased running shoes six months ago gets ads for heels—a mismatch the AI can’t resolve without better data.

By noon, Maya reviews their latest campaign: a TikTok influencer partnership. The creative is generic, the targeting based on Meta’s black-box algorithms. The analytics team reports a 1.2% conversion rate—”better than industry average!” But Maya knows the truth: they’re paying to reach their own customers, who’d likely have returned anyway if they’d had a reliable “hotline.”

At 3 PM, she meets the CFO, who questions the $500K monthly AdTech spend. “We’re trapped,” Maya admits. “If we stop spending, sales drop. If we keep spending, profits vanish.” The meeting ends with a mandate: “Cut CAC by 20% next quarter.”

By 7 PM, Maya’s exhausted. She logs into ChatGPT, drafting a reactivation email. “Hi [First Name], we miss you!” It feels hollow. She knows customers are drowning in identical messages. As she leaves, she wonders: There has to be a better way.

The Future – CMO Maya’s AI-Powered Revolution

“The Department of One”

It’s 2026, and Maya’s morning starts with a notification from her AI Co-Marketer: “Q2 campaigns delivered 98% of targets. $2.1M saved in AdWaste. Ready for Q3 strategy review.”

In the office, Maya reviews her dashboard. Instead of fragmented metrics, she sees a unified view of customer lifetime value (LTV), Earned Growth (organic referrals), and AdWaste elimination. Her team’s focus? Building relationships, not chasing clicks.

AI Twins handle the heavy lifting:

• Each customer has a digital replica updated in real-time. A runner who bought shoes last year now gets trail gear recommendations—before she even searches.
• NeoMails deliver interactive experiences: quizzes, gamified rewards (Atomic Rewards), and in-email purchases. Open rates hover at 65%, with 30% converting directly in the inbox.

Maya’s Agentic AI system—a “department of one”—orchestrates everything:

• The Strategy Agent identified a niche market for sustainable activewear, allocating 40% of the budget there.
• The Content Agent generated 500 personalized video ads overnight, each tailored to individual preferences.
• The Campaign Agent paused underperforming Facebook ads autonomously, reallocating funds to high-potential TikTok collaborations.

At noon, Maya meets her team—not to troubleshoot campaigns, but to brainstorm experiences. “Let’s launch a Generative Journey,” she suggests. The AI Twins instantly simulate outcomes: customers who complete a fitness challenge (hosted in NeoMails) receive custom gear recommendations. The Analytics Agent predicts a 15% LTV increase.

By 3 PM, Maya reviews NEON’s performance. Instead of paying Meta, her brand monetizes its own customer attention. A travel company’s ActionAd appears in her NeoMails, targeting frequent travelers. Revenue splits are automatic, and her CFO celebrates the new profit stream.

As Maya leaves, her AI Co-Marketer sends a final update: “Customer #18392 reactivated after 18 months dormant. Zero AdTech spend. Strategy: Sent a personalized hiking gear offer via a partner’s NeoMail.”

Maya smiles. Marketing is no longer a cost center—it’s the engine driving sustainable growth, one individual at a time.

Contrasting Worlds

• Present: Reactive, wasteful, and platform-dependent. Maya fights fires, wastes budget on reacquisition, and struggles with impersonal campaigns.
• Future: Proactive, efficient, and customer-centric. Maya focuses on strategy and innovation, while Agentic AI eliminates waste and delivers hyper-personalized experiences.

The difference? In 2026, AI Twins and Agentic AI turn marketing from a treadmill into a rocket ship.