How Agentic AI will Transform Digital Marketing

Published June 6-15, 2024

Internet to AI

In late 1994, I was at the right place at the right time. My image processing venture was not making any headway; I realised I would need to shut it down and do something different. The Internet was beginning to weave its magic in the form of the World Wide Web. And I sensed there could be an opportunity there. And so it was that I came to launch IndiaWorld, India’s first Internet portals, in early 1995.

30 years later, a similar revolution is underway with the mainstreaming of AI. Like the Internet, it will impact every industry, every business, every individual, and everything we do. There will be winners and losers as Generative AI will make and break companies and business models. It is a “reset”. The process of creative destruction is underway. For those like me who are in their late 50s, this is fourth big technological revolution in our lives after the computer, Internet, and mobile. For incumbents and startups, this is a time of great opportunity – and peril.

In my essay Resetting ESP and Adtech Industries, I had quoted from a 2004 Harvard Business Review essay by Anita M. McGahan: “You can’t make intelligent investments within your organization unless you understand how your whole industry is changing. If the industry is in the midst of radical change, you’ll eventually have to dismantle old businesses. If the industry is experiencing incremental change, you’ll probably need to reinvest in your core. The need to understand change in your industry may seem obvious, but such knowledge is not always easy to come by. Companies misread clues and arrive at false conclusions all the time…To truly understand where your industry is headed, you have to shut out the noise from the popular business press and the pressure of immediate competitive threats to take a longer-term look at the context in which you do business.”

She adds: “Industries evolve along four distinct trajectories—radical, progressive, creative, and intermediating—that set boundaries on what will generate profits in a business… [These] trajectories…can help you anticipate how change will unfold in your industry—and how to take advantage of opportunities as they emerge. To get out from under industry threats, your company must cultivate a deep understanding of how changes to the industry will unfold over time. How will buyer and seller relationships be affected? And are intangible assets like brand capital and knowledge capital truly adaptable across industries? The work of systematically analyzing the business environment is not easy, but the payoff is great: better strategic decision-making for your company.”

What’s becoming clear is that Generative AI is a radical change. This big shift will bring along many small shifts – just like small tremors follow a big earthquake. In this series, I will discuss some history (with the help of the AIs) about industry transformations, and then discuss the present and future of the industry I know best – digital marketing.

Industry Revolutions

ChatGPT wrote about some historical examples of industry transformations and their impact on both incumbents and disruptors.

The Industrial Revolution (Late 18th to Early 19th Century)

Transformation: The introduction of mechanized production transformed agriculture and textile manufacturing in Europe and the U.S., transitioning from manual labor to machines.
Impact on Incumbents: Traditional craftsmen and artisans found it challenging to compete with the efficiency and scalability of machine production. Many were either forced out of business or had to adapt to new manufacturing processes.
Impact on Disruptors: Innovators who adopted and improved upon industrial technologies—like Richard Arkwright with his water frame—were able to build large-scale factories that dominated industries. This period also saw the rise of industrial magnates and a shift in the workforce from rural to urban settings.

The Automotive Industry (Early 20th Century)

Transformation: The mass production techniques pioneered by Henry Ford made automobiles affordable and accessible to a large segment of the population, which was previously dominated by carriage and locomotive manufacturers.
Impact on Incumbents: Many traditional carriage and harness makers went out of business or significantly downsized, unable to compete with the cost and functionality of automobiles.
Impact on Disruptors: Ford and other early automobile companies like General Motors rapidly expanded, fundamentally changing transportation, city planning, and consumer culture.

The Information Technology Revolution (Late 20th Century)

Transformation: The advent of personal computing and later the internet changed how businesses operate, how information is processed, and how products are marketed.
Impact on Incumbents: Companies that failed to adapt to new IT capabilities (like Kodak with digital photography or Blockbuster with video streaming) struggled or failed.
Impact on Disruptors: Companies such as Apple in personal computing, Amazon in e-commerce, and Google in online search and advertising leveraged new technologies to create dominant new economic paradigms and business models.

Renewable Energy Shift (21st Century)

Transformation: Growing environmental concerns and technological advances have driven a shift from fossil fuels to renewable energy sources such as solar and wind.
Impact on Incumbents: Traditional energy companies face regulatory and market pressure to transition to or incorporate renewables into their energy mix, with varying degrees of resistance and adaptation.
Impact on Disruptors: New entrants like Tesla in electric vehicles and NextEra in wind energy have capitalized on these shifts, challenging established players and often leading the market.

Digital Streaming and Entertainment (Early 21st Century)

Transformation: Digital streaming technologies have transformed media consumption, moving from physical and broadcast media to on-demand streaming.
Impact on Incumbents: Media companies reliant on traditional broadcasting and cable television have had to pivot significantly or partner with tech companies to survive.
Impact on Disruptors: Platforms like Netflix, Spotify, and YouTube have grown from niche players to major industry leaders, setting new standards for content distribution and consumption.

These examples illustrate how technological innovations and shifts in consumer preferences have historically led to significant industry transformations. They also highlight the recurring theme of “creative destruction,” where new technologies disrupt existing market structures, often leading to the decline of incumbents and the rise of new leaders. This pattern is likely to be reflected in how AI will reshape industries in the coming years.

[Rajesh] If we reflect on our lives today compared to just two or three decades ago, the magnitude of change is staggering — the way we consume news and information (Google Search and social platforms like Twitter/X, Instagram, Facebook), how we entertain ourselves (Netflix and Amazon Prime), how we shop (Amazon and dozens of shopping sites), how we communicate (WhatsApp), how we network (LinkedIn). What’s remarkable is that all these transformative changes have occurred within the span of just a single generation. The pace of innovation shows no signs of slowing down. In the realm of artificial intelligence, this pace is even more accelerated. What once took a generation now transpires in a year, if not months or even weeks, underscoring the transformative era we live in.

Benedict Evans

Here are a few slides from a Benedict Evans presentation from December 2023:

In a recent essay (April 2024), Benedict Evans writes: “We’ve had ChatGPT for 18 months, but what’s it for? What are the use-cases? Why isn’t it useful for everyone, right now? Do Large Language Models become universal tools that can do ‘any’ task, or do we wrap them in single-purpose apps, and build thousands of new companies around that?… We would still have an orders of magnitude change in how much can be automated, and how many use-cases can be found for LLMs, but they still need to be found and built one by one. The change would be that these new use-cases would be things that are still automated one-at-a-time, but that could not have been automated before, or that would have needed far more software (and capital) to automate. That would make LLMs the new SQL, not the new HAL9000.”

Agentic AI – 1

Note: I want to thank Gautam Mehra, co-founder and CEO of ProfitWheel, for introducing me to the world of Agentic AI.

Volodymyr Zhukov writes: “Agentic AI marks a fundamental shift in the very nature of artificial intelligence. It describes a class of AI systems specifically designed to understand complex workflows and pursue intricate goals autonomously, with little to no human intervention. In essence, agentic AI functions more like a human employee, grasping the complex context and instructions provided in natural language, embarking on goal-setting, reasoning through subtasks, and adapting decisions and actions based on changing conditions… Agentic AI can automate and optimize operations, adding significant value to business processes. These enhancements go beyond mere automation, offering advanced problem-solving capability and strategic planning to tackle complex tasks.” He lists the features that set it apart:

Autonomy: Unlike traditional AI systems, agentic AI is built to take initiative, performing directed actions independently, without constant human supervision.
Reasoning: Agentic AI possesses an advanced degree of decision-making, allowing it to make contextual judgments, weigh trade-offs, and set strategic actions.
Adaptable planning: In dynamic and changeable conditions, agentic AI demonstrates flexibility and responsiveness, adjusting its goals and plans based on the prevailing circumstances.
Language understanding: With an advanced ability to comprehend and interpret natural language, agentic AI can meticulously follow complex instructions, enhancing its capability to tackle sophisticated operations.
Workflow optimization: Agentic AI exhibits an uncanny skill to move fluidly between subtasks and applications, executing processes with optimum efficiency while ensuring the end goal is achieved.

Ken Rheingans writes: “[AI Agents] are teams of AI-powered virtual assistants that collaborate to solve complex problems. Unlike traditional AI systems that generate a single output based on a given prompt, AI Agents work together, sharing goals and making collective decisions to tackle tasks more effectively. This collaborative approach allows for more sophisticated interactions and decision-making processes, ultimately leading to improved efficiency and significantly more accurate results across various applications. Agentic Workflows advance the concept of AI Agents by incorporating iterative refinement and feedback mechanisms. In an Agentic Workflow, AI Agents can generate drafts, receive guidance to improve those drafts, and iterate on their output. This process can lead to more accurate and refined results, as the AI Agents receive specific feedback and have the opportunity to adjust within the parameters of the assigned task. This improved accuracy is the amazing part where good LLM outputs become superhuman great outputs.”

Margo Poda writes: “Enterprise-wide use cases require more than just well-thought-out responses — enterprises need AI agents that can reliably manage complex goals and workflows. This demand has driven the emergence of agentic capabilities like autonomous goal-setting, reasoning, decision-making, robust language understanding, and the ability to connect with enterprise systems using plugins. These agentic capabilities unlock a new generation of enterprise AI solutions — including AI copilots. These tools are being designed to operate without constant human oversight across varied domains. In this way, agentic systems interpret instructions more accurately, set subgoals to accomplish multi-step tasks, and make adaptive choices adjusting to real-time developments, enabling reliable automation of convoluted business objectives.”

Economist: “There is a way…to make large language models (LLMs) perform such complex jobs: make them work together. Researchers are experimenting with teams of LLMs—known as multi-agent systems (MAS)—that can assign each other tasks, build on each other’s work or deliberate over a problem in order to find a solution that any one, on its own, would have been unable to find. And all without the need for a human to direct them at every step. Teams also demonstrate the kinds of reasoning and mathematical skills that are usually beyond stand-alone AI models. And they could be less prone to generating inaccurate or false information.”

Agentic AI – 2

Daniel Warfield writes: “It’s quickly becoming apparent that, while LLMs are exciting, they’re not a silver bullet. AI needs clever designers to wrangle it into a focused and powerful offering so it can actually be useful to consumers. Agentic systems seem to be the shining north star towards building successful LLM powered applications…Imagine if, instead of asking a language model to give you some output immediately, you asked a language model to do things like this: “You’ve been given a complex question, think about what to do next”, “You have access to a few tools, think about which one you can use them to best assist the user”, “You just output some information. Was it correct? Would you like to revisit that idea or move on?”. Essentially, Agents create a framework which allows a language model to reason about it’s previous output and decide to use tools to seek external sources of information.

Andrew Ng has been discussing this in his newsletters.

March 6, 2024: “Although today’s research agents, whose tasks are mainly to gather and synthesize information, are still in an early phase of development, I expect to see rapid improvements. ChatGPT, Bing Chat, and Gemini can already browse the web, but their online research tends to be limited; this helps them get back to users quickly. But I look forward to the next generation of agents that can spend minutes or perhaps hours doing deep research before getting back to you with an output. Such algorithms will be able to generate much better answers than models that fetch only one or two pages before returning an answer.”

March 20, 2024: Today, we mostly use LLMs in zero-shot mode, prompting a model to generate final output token by token without revising its work. This is akin to asking someone to compose an essay from start to finish, typing straight through with no backspacing allowed, and expecting a high-quality result. Despite the difficulty, LLMs do amazingly well at this task! With an agent workflow, however, we can ask the LLM to iterate over a document many times. For example, it might take a sequence of steps such as: plan an outline, decide what, if any, web searches are needed to gather more information, write a first draft, read over the first draft to spot unjustified arguments or extraneous information, revise the draft taking into account any weaknesses spotted, and so on. This iterative process is critical for most human writers to write good text. With AI, such an iterative workflow yields much better results than writing in a single pass.

Andrew shared a framework for categorising design patterns for building agents:

Reflection: The LLM examines its own work to come up with ways to improve it.
Tool Use: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data.
Planning: The LLM comes up with, and executes, a multistep plan to achieve a goal (for example, writing an outline for an essay, then doing online research, then writing a draft, and so on).
Multi-agent collaboration: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would.

This video has more. [Kitty (Sijia) Shen has a summary.]

Agentic AI – 3

ChatGPT

Agentic AI involves the design and deployment of artificial intelligence systems that possess a degree of self-governance, enabling them to perform tasks and make decisions autonomously based on predefined objectives. These systems are characterized by their ability to interpret their environments, respond to changes, and initiate actions without human intervention. Common applications include autonomous vehicles that navigate traffic, robots that perform complex tasks in variable conditions, and smart systems that manage home or industrial operations. The development of agentic AI requires integrating advanced machine learning algorithms, cognitive computing techniques, and robust decision-making frameworks to ensure they can handle real-world unpredictability and complexity safely and effectively.”

Gemini

Taking Charge: Autonomy is Key

Unlike traditional AI designed for specific tasks, Agentic AI is built to pursue complex goals and workflows with minimal human oversight.
Imagine an AI assistant that can not only book your travel but also handle unexpected delays, re-routing your trip and updating your itinerary on the fly.

Smarter Decisions: Planning and Adapting

Agentic AI can break down complex tasks into subtasks, reason through them, and make decisions based on the situation.
This allows it to adapt to changing circumstances. For instance, an agentic AI system managing inventory might adjust ordering patterns based on real-time sales data.

Understanding the Context: Natural Language Power

Agentic AI can understand and interpret complex information, including natural language instructions and goals.
This enables more sophisticated interactions, allowing humans to communicate with the AI in a natural way.

The Benefits of Agentic AI

Increased Efficiency: Automating complex workflows and adapting to changing situations can significantly improve efficiency.
Reduced Costs: Less human oversight translates to reduced labor costs.
Enhanced Innovation: Agentic AI can explore new possibilities and solutions, aiding human creativity.
Personalized Experiences: AI agents can tailor their actions and services based on individual needs and preferences.

The Future of Agentic AI

This technology is still under development, but it holds immense potential across various industries. Here are some areas where agentic AI might make a significant impact:

Enterprise Automation: Streamlining complex back-office processes and supply chain management.
Customer Service: Providing personalized and efficient support through AI chatbots and virtual assistants.
Financial Services: Optimizing investment strategies and fraud detection.
Healthcare: Assisting doctors with diagnoses and treatment plans.

[Rajesh] AI agents mark a significant evolution from traditional LLM-based chatbots like ChatGPT, which primarily assist with content creation, coding, and pattern-matching tasks. While chatbots utilise large language models to analyse data and generate responses, AI agents enhance this by autonomously making decisions, initiating actions, and adapting in real-time. This capability transforms them from reactive tools into proactive partners, opening new avenues for business innovation. These agents are set to revolutionise industries by automating complex decision-making processes and boosting operational efficiencies, particularly in roles like Co-Marketers and Digital Twins in digital marketing. This is what we will explore next.

First Principles

My Netcore colleague, Pratik Bhadra, had a very good LinkedIn post discussing how marketing needs to change (in the context of ecommerce companies).

The traditional model ecommerce marketers follow to market their customers is exactly what it is. TRADITIONAL.

The model is

– Setup ad campaigns on Google and Meta
– Pump in HUGE budgets to acquire customers
– Get customers to visit, do a transaction, leave
– Continue pumping money to acquire more and more customers

This model is not scalable anymore. The ecommerce market is mature and crowded. The cost to acquire a customer (CAC) is significantly high and ROAS, let’s admit, is s**t.

Add to that, the competition is cutthroat. Customers have endless options to shop from.

If you continue following this, you’ll eventually run out of business.

At Netcore Cloud, we believe The MODERN way of marketing should be

– Increased focus on existing customers
– Engage them continuously across all available channels digitally
– Keep taking feedback on how your ecom business could do better
– Reward them for providing their feedback
– Prompt dormant users to shop again with incentives
– Provide exclusive experiences to loyal customers for repeat purchases

How will this new model help you?

– Spending on acquisition will reduce
– You’ll connect with your customers on a deeper level
– Your brand name and value skyrockets
– Your end customers will eventually start advocating for you

Takeaway
– Don’t put all your money on acquisition. You can only get so far. You’ll end up broke
– Customers today engage with ads at lower rates
– Make your customer the center of everything. From acquisition to engagement to retention
– The game of acquiring new customers has pivoted to engaging and retaining them
– Focus on repeat business to sustain in the long run

Marketing is as simple as that: focus on existing customers and growing them, and then ensuring they refer their family and friends, rather than spending most of the marketing on acquisition and reacquisition. This is a theme I have been consistently writing about in my 100+ essays: make martech rather than adtech at the centre of digital marketing. [Among the more recent essays, see The Profipoly Quest and The Profipoly Quest: Maya’s Story.]

In martech, three are four emerging themes:

Age of AI: Co-Intelligence
Consolidation: Point Solutions to Platforms
Profitable Growth: Focus on Retention
Co-Ownership: Progency

Among them, the biggest impact will be because of AI. While Gen AI is already making an impact on content creation, the multiplier will come from Agentic AI with its ability to create a Co-Marketer for the marketing department and a Digital Twin for every customer. They will work together in a Mirror World powered by a Large Customer Model. Think Department of One for a Segment of One (N=1). For martech companies, the shift will be from 1S (Software) to 4S (Strategy, Software, Services, Sharing). This is the coming world of Agentic AI which will transform digital marketing for brands and martech companies alike, creating new winners – either incumbents who are able to reinvent themselves or disruptors who build AI-first Martech solutions.

Co-Marketer

[This builds on my previous essay Co-Marketer: Martech meets AI and inputs from AIs.]

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

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

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

Co-Marketers excel in areas such as campaign planning and optimisation, leveraging their predictive capabilities to forecast campaign performance and proactively suggest improvements based on real-time data. They can also assist in content creation and personalization, utilizing their understanding of customer preferences and industry trends to generate tailored and engaging content across multiple channels.

Moreover, Co-Marketers serve as valuable sources of market intelligence and trend spotting, scouring vast amounts of data to identify emerging consumer preferences, competitive moves, and industry disruptions. This empowers CMOs to make informed strategic decisions and stay ahead of the curve.

Crucially, Co-Marketers do not operate in isolation but foster collaborative decision-making. By engaging in interactive dialogue with CMOs and their teams, these AI agents can brainstorm ideas, explore scenarios, and provide objective recommendations, complementing the team’s expertise and intuition.

Furthermore, Co-Marketers play a crucial role in team enablement, offloading routine tasks and analytical work, allowing marketing teams to focus on higher-level strategic initiatives and fostering a more collaborative and innovative environment.

As Co-Marketers continuously learn and adapt their knowledge based on feedback and real-world outcomes, they become indispensable partners for CMOs, empowering them to make data-driven decisions, optimize marketing efforts, and drive business growth while fostering enduring customer relationships.

The power of Co-Marketers extends beyond strategic planning and campaign execution. They also play a pivotal role in interacting with “Digital Twins” of customers – dynamic and continuously learning virtual models that mirror the behaviour, preferences, and characteristics of individual customers. These Digital Twins enable brands to forge highly personalised and efficient marketing strategies, maximising each customer’s lifetime value and fostering enduring relationships.

By leveraging the insights and recommendations provided by Digital Twins, brands can create highly targeted and effective marketing campaigns, offering products and services that resonate deeply with each customer’s unique needs and preferences. This level of N=1 hyper-personalisation and customer-centricity not only drives increased sales and customer loyalty but also fosters a sense of trust and connection between the brand and its customers.

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

Digital Twins

[This combines my previous writings with inputs from AIs.]

The advent of Agentic AI is poised to revolutionise the way businesses approach customer relationships and lifetime value maximisation. At the core of this transformation lies the concept of “Digital Twins” – AI-driven virtual representations of individual customers that possess a profound understanding of their behaviours, preferences, and interactions.

Digital Twins are not mere static customer profiles; they are dynamic, continuously evolving models that mirror the real-world customer’s journey with a brand. Powered by ML algorithms, large customer models (LCMs), and vast troves of customer data, Digital Twins become living embodiments of the customer experience.

The creation of these Digital Twins begins with the aggregation and analysis of data from various sources, including CRM systems, purchase histories, browsing patterns, social media interactions, and demographic information. This could even be augmented with real-world news and events which could impact shopping behaviour. This data is then fed into sophisticated LCMs, akin to the large language models used in natural language processing, enabling them to learn and adapt to the unique characteristics of each customer.

As customers interact with a brand across various touchpoints, their Digital Twins continuously analyse and incorporate these interactions, refining their understanding of the customer’s preferences, needs gaps, pain points, and potential future actions. This deep, personalised understanding empowers Digital Twins to anticipate customer needs, make tailored recommendations, and guide the customer journey, thus enabling brands to maximising their lifetime value.

Moreover, Digital Twins play a crucial role in the “mirror world” – a simulated environment where they can interact with the brand’s Co-Marketer, an AI-powered marketing assistant. Within this virtual playground, an infinite number of scenarios and hypotheses can be tested, enabling the Co-Marketer to identify the optimal strategies and personalised experiences for each individual customer.

By leveraging the insights and recommendations provided by Digital Twins, brands can create highly targeted and effective marketing campaigns, offering products and services that resonate deeply with each customer’s unique needs and preferences. This level of hyper-personalisation (N=1) not only drives increased sales and customer loyalty but also fosters a sense of trust and connection between the brand and its customers.

Furthermore, Digital Twins empower brands to optimise customer journeys, ensuring that every touchpoint is tailored to the individual’s preferences and needs. From personalised content and messaging to dynamic promotions and predictive recommendations, Digital Twins enable brands to deliver a truly seamless and engaging customer experience.

The integration of Digital Twins and Agentic AI into marketing strategies represents a significant step towards realizing the true potential of customer relationships and maximizing lifetime value. By continuously updating customer profiles in real-time and providing actionable insights, Digital Twins ensure that every marketing message is highly personalized and timed to perfection, improving customer satisfaction, and enhancing the efficiency of marketing spend.

In essence, Agentic AI and Digital Twins work in tandem, empowering businesses to navigate the complexities of the modern marketing landscape with unprecedented precision, efficiency, and personalization. By harnessing the power of these technologies, brands can foster enduring customer relationships, maximise lifetime value, and ultimately achieve a sustainable competitive advantage in an ever-evolving marketplace.

The Prize

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

For martech providers caught in the red ocean of competition, Agentic AI is the gateway to unlocking new pools of profits in a non-zero-sum world. Today, when one martech provider replaces another in a brand, they are typically doing at a lower MRR – leading to a scenario where it becomes harder and harder to just stay in place in a world where staff and operating costs keep increasing. Agentic AI combined with a shift in business model from SaaS to 4S (strategy, software, services, and sharing) is what will unlock new growth opportunities for martech companies. For example, email service providers could create new monetization opportunities based on “Action Ads” in email. Martech providers could use the Progency model to create new revenues and share in the upside. They could also provide a people-based services offering to refine AI models, simplifying the complexities for CMOs. These are ideas I have discussed in some of my recent essays:

The good news is that the Agentic AI future will create huge value for the marketing ecosystem. Once CMOs, CFOs, and CEOs open their eyes to the fact that the AdWaste that consumes half their digital marketing budget is the key to unlocking profitable growth, they will fashion changes in the way they acquire, retain, and grow customers. Someone in the CxO suite needs to rise and become the Chief Profits Officer and bring to life the Agentic AI future – one where AI is not just generating nice content but becoming a true “co-intelligence” for both marketers and their customers.

Consider the world of digital today. Brands creates websites and apps, and then use push messages to bring their existing customers to their digital properties for transactions. If that doesn’t work well (and in most cases, it doesn’t), they amp up spending on adtech for retargeting their existing customers and attracting new ones. New technologies are about to transform this world which has prevailed for the past quarter century.

Push channels are becoming two-way and shoppable. In-channel conversion in email and WhatsApp (and perhaps SMS and RCS) will remove the friction of clicking through to the digital properties. The websites and apps could be replaced with “agent” interfaces – smart chatbots which customers can interact with as they would be with a friendly salesperson in a store. And this only in the unlikely scenario that the digital twin has not delivered the right product recommendation at the right time!

To summarise: Agentic AI, manifesting as Co-Marketers and Digital Twins, promises to unlock $250 billion in AdWaste, marking a pivotal shift toward profitability for consumer-facing brands currently battling high customer acquisition costs. This technological leap will simplify martech to the ease of adtech, propelling marketing departments into a new era of efficiency and strategic depth. For martech providers trapped in a fiercely competitive market, Agentic AI offers a pathway to untapped profit pools through innovative “4S” business models. These models not only foster revenue generation but also enable a partnership approach in the marketing ecosystem. As we stand on the brink of this transformation, it’s crucial for CxOs to recognise the vast potential of Agentic AI. By reorienting strategies towards AI-driven technologies, businesses can dramatically enhance their operational efficiencies and customer relationships. Imagine a future where digital marketing transcends traditional platforms, enabling seamless in-channel conversions and intelligent agent interfaces that predict and fulfil customer needs effortlessly. As digital marketing becomes increasingly central to our lives, the integration of Agentic AI will redefine the landscape for adtech and martech companies alike, heralding a new era of innovation and helping brands in their Profipoly Quest.

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