Why Agentic Campaigns Are Not The Future of Marketing

My last post, presenting ChatGPT’s description of AI-based marketing, generated thought-provoking comments on LinkedIn. Key insights included:   

• Organization issues as roadblocks to successful deployment (Chris Adelman and Lee Hammond)
• Buyer agents as increasing the importance of trusted information sources (Jon Miller, Dan Cote)
• Some vendors are already delivering end-to-end agentic campaigns (Matthew Niederberger, Duarte Garrido)
  

Thanks to everyone who contributed.

What I didn’t see was push-back against the vision that ChatGPT presented. Maybe people were being polite or maybe they fear that ChatGPT could hold a grudge. But my own initial take was triggered by words including “narrative” and “journey”. These led me to think that ChatGPT was still describing today's standard of multi-step marketing campaigns applied to customer segments. On closer examination, the story is a bit more complicated: the various components included in ChatGPT’s list are not necessarily a consistent whole, but rather a collection of predictions that ChatGPT has gathered by scraping of industry discussions. Oddly enough, ChatGPT's summary provides a clearer statement of an alternative approach:

AI-native marketing is:

• Continuous (not campaign-based)
• Conversational (not broadcast)
• Collaborative (AI agents on both sides)
• Generative (creating narratives, products, and experiences dynamically)
• Ethically-audited and explainable (trust is as important as persuasion)

Of course, that ChatGPT says something is no guarantee that it’s correct. So, although I intuitively agree with the prediction, I still need to convince myself (and, hopefully, others) that it’s right. One test is to compare the prediction with cutting-edge industry developments. Here’s a list of trends I’ve been tracking:

• Agentic search: AI-generated summaries replace traditional, link-based search results
• Ads in chat replies: Chat responses include paid advertising, identified with varying degrees of transparency
• Social commerce (a.k.a."live shopping"): Make purchases of items featured in social media discussions without leaving the social media platform
• Commerce via agentic browsers: Advertising and shopping links presented by browsers based on what they see the user doing (a privacy nightmare but we'll ignore that for now)
• Interactive ads: Advertisements that link to a conversational interface rather than conventional landing pages or websites
• Buyer agents: AI agents that do product research and, perhaps, make purchases on behalf of users without users visiting seller websites
• Conversational websites: websites that present chat-style interactions or hyper-personalized contents in dialog with the user

All but the last of these occur at the discovery stage of the buyer cycle. That's encouraging because marketers have been very concerned about they reach new customers when traditional search no longer drives traffic to their websites and consumers increasingly ignore traditional email and display messages. But what's really important is that these items all change the role of marketing from creating campaigns that broadcast messages to consumers, to executing continuous conversations with consumers. This is indeed what ChatGPT described. It’s not something that is accomplished by using AI generate conventional broadcast campaigns.

In fact, I’d argue that the current broadcast campaign model developed precisely because the then-existing technology couldn’t handle a conversational approach. Branching campaigns with personalized content nodes were the best that could be done when marketers were limited to deterministic, manually-crafted workflows. If you recall the once-popular cliché of marketing automation as a way to replicate the personal relationship of customers to their “corner grocer,” you quickly realize the real-life grocer had conversations, not campaigns.

Think a bit deeper and you see that the reason the grocer was able to have conversations was data. He knew the history of each customer and was able to discern their current situation by seeing and talking to them. Multi-step marketing funnels exist because marketers don’t have that kind of information available in real time. Instead, they have to make a guess at where each customer might be in the buying cycle. The funnel is a framework for making those guesses. Provide real information and it’s no longer needed.

I think this is important. Marketers, vendors and analysts have focused largely on how AI can reduce the cost and time of generating marketing campaigns. This allows companies to create near-infinite amounts of personalized content. But unless that content is driven by near-complete, real-time data, there’s a severe limit to how relevant it can be. Data, not content creation, is what ultimately limits the effectiveness of AI marketing.

This may strike you as bad news. Data has always been a problem for marketers, and real-time data is a particular challenge. Agentic campaign creation won’t solve the data problem, although perhaps it will highlight data's role as a bottleneck. The good news is that other AI and agent applications can in fact improve data access, something that data management vendors are already pursuing. 

It’s true that better data management isn’t as exciting, or directly tied to revenue, as better customer engagement tools. This is probably why most vendors focus on adding engagement features. But as autonomous engagement become increasingly common, the need for better data will become more obvious. Vendors who use AI to deliver better data (and convince the market that they are leaders in doing it) will have an increasing advantage in growing their business.

I’ll give the final word to Abhi Yadav of iCustomer, one of those very smart people who see further, sooner than most of us. He recently shared a substack post that captures my opinion quite eloquently:

We used to drag customers to our properties (site, app, landing pages). Now we take capabilities to where customers already live: agent/app ecosystems, workflow-native GPTs, and search/ad surfaces that embed decisions at the edge. ... If your data stays scattered and your channels siloed while this shift accelerates, AI won’t transform your business it’ll just perform visual eye candy. AI should drive a paradigm shift in your business, not a party trick.

What would an AI-native marketing process look like? ChatGPT's answer is better than I expected

If you look at recent vendor announcements, it seems that the cutting edge in applying AI to marketing is 'end to end' campaign generation, where a collection of agents work together to build and execute a complete marketing campaign with no human effort (although, for practical purposes, still under human supervision).  As I've written before, this strikes me as an advanced type of 'substitution', where the new tech is used to execute the old processes better and faster.  That's fine and valuable but it's only the first step in taking advantage of the new technology.  The final step is to develop new, "transformed" processes that are optimzed to make full use of the new tech's capability.  

I've recently been asking myself and others what they think that "transformed" process would look like for marketing.  I'm still collecting my thoughts but, as part of the process, asked ChatGPT for its opinion.  The answer was better than I expected.  I don't necessarily agree with all of it but thought it was interesting enough to present to the larger world for comment.  So, please, let me know what you think.

Prompt: if marketing techniques were designed from scratch to take full advantage of AI, what would they look like?

That’s an excellent and forward-thinking question — and the answer’s pretty transformative.

If we were to design marketing from the ground up for AI (instead of retrofitting AI into traditional marketing), we’d end up with a system that looks less like “campaigns” and more like continuously learning ecosystems that respond in real time to individuals and social dynamics.

Here’s a breakdown of what that might look like:

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1. Autonomous, Adaptive Market Intelligence

Instead of periodic market research and segmentation,
AI-first marketing would run on constant sensing loops:

• AI agents continuously scan social media, purchase data, and search patterns.
• They detect cultural shifts, emerging memes, and micro-trends as they form.
• Personas aren’t static — AI builds and dissolves them dynamically based on live data.

Analogy: You don’t define “target audiences.” You discover living patterns of attention in real time.

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2. Hyper-Personalized Narrative Engines

Instead of a single ad creative for millions of viewers,
AI-first marketing generates unique narrative journeys per person:

• Language models craft individualized story arcs and product framing that match someone’s identity, tone preferences, and current emotional state.
• Multimodal models (text, image, video, audio) assemble personalized content on the fly — like an AI “showrunner” creating micro-stories for each user.
• Brand consistency is enforced by a central “brand DNA model” ensuring all outputs feel coherent and on-brand, even when autonomously generated.

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3. Predictive and Generative Commerce

Instead of advertising then selling,
AI-first marketing anticipates needs and builds products or bundles before people articulate them:

• Predictive demand modeling uses behavioral and environmental data to suggest what products should exist.
• AI may prototype and A/B-test new offerings via virtual storefronts, gauging interest before production.
• Marketing and product design merge — you market possibilities before they exist, and only produce what the market proves it wants.

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4. Multi-Agent Relationship Systems

Instead of one-way brand broadcasts,
AI-first marketing uses relationship agents that represent both consumers and brands:

• Consumers might have personal “shopping AIs” negotiating with brand AIs.
• Trust, transparency, and reputation become measurable assets traded between AIs.
• The marketing goal shifts from persuasion to mutual optimization — aligning a person’s needs and a company’s offerings through agent collaboration.

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5. Real-Time Ethics and Trust Architecture

AI-driven marketing would require built-in ethical governors:

• Explainability modules ensure users understand why they’re being shown something.
• Consent becomes dynamic and contextual (you can allow certain data uses for limited time).
• Bias-detection systems continuously audit generative campaigns for fairness and cultural sensitivity.

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6. Emotionally Intelligent Interaction

Instead of static messaging,
AI-first marketing converses, empathizes, and co-creates:

• Emotional state detection (via tone, text, or visual cues) guides responses.
• The AI adjusts its persuasive approach — sometimes offering information, sometimes humor, sometimes retreat — like a human skilled in timing and empathy.
• Brands become more like adaptive characters than faceless institutions.

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7. Closed Feedback Loop: Learn → Generate → Test → Learn

Every interaction feeds a continuous reinforcement loop:

• Each ad, email, or product suggestion is an experiment.
• The system measures micro-responses (hover time, tone change, conversion intent).
• Those signals retrain the creative and strategy models daily.

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In summary

AI-native marketing is:

• Continuous (not campaign-based)
• Conversational (not broadcast)
• Collaborative (AI agents on both sides)
• Generative (creating narratives, products, and experiences dynamically)
• Ethically-audited and explainable (trust is as important as persuasion)

 

How Will GenAI Change the Consulting Industry?

Reports on the threat that generative AI poses to knowledge workers usually focus what might be called “structured creativity”: tasks like copywriting or computer programming where outputs are clearly specified through things like creative briefs or system requirements. One group that generally doesn’t consider itself at high risk are the analysts and consultants who write the reports themselves. After all, their work requires insights and judgements that are beyond the current capabilities of genAI, which ultimately does nothing more than play back the training data it's fed.

Case in point: This widely reported BCG study found that genAI tools substantially improved performance of consultants at product planning, with the greatest improvements among below-average performers. A second finding of the same study was less widely noted: AI depressed performance at generating insights from interviews and raw data. This dovetails nicely with the idea that AI can help with structured tasks such as campaign management but isn’t (yet) capable of developing new insights. One reason that AI struggles with insights is its well-known tendency to hallucinate, which would translate into false conclusions about the meaning of input data. (See, for example this BBC study that found 47% of AI chatbot answers contained significant errors about data sources or accuracy.)*

So it seems that the survey analyzers are indeed safe, at least for the time being. But while insights are an important part of the value provided by analysts and consultants, they’re just a small fraction of their work. In my experience, the main reasons that companies hire consultants are to access their skills at organizing a project and their knowledge of vendors to consider hiring.** Those are things that genAI does extremely well: ChatGPT will gladly provide a plausible project plan for nearly any task and will just as merrily answer your questions about which vendors to consider, providing detailed comparisons on relevant features. It's true that those comparisons will often be inaccurate (see the BBC study or do your own test in a field you know well). But, let’s face it, consultants also make mistakes about product capabilities, which change much faster than even the most diligent industry expert can keep up. So, whether a vendor list comes from genAI or a human consultant, it still has to be validated with current information before you base any decisions on it.

Even more to the point: just as it’s always been hard for clients to tell truly knowledgeable consultants from pretenders, it’s now equally hard to tell accurate genAI outputs from hallucinations. This applies to frameworks, vendor lists, and even those insights where humans are still clearly superior. GenAI is famously capable of providing reasonable-sounding arguments based on false premises, citing false evidence, and leading to wrong conclusions. A true optimist would argue those can lead to fruitful discussions even when they start with an error. A more realistic expectation is that some large fraction of people will accept them as delivered. 

This is especially true when it comes to comments on industry trends and futures, where there is no clearly right answer. A large part of the industry conversation today, especially in media like LinkedIn, is exactly that sort of discussion. AI-generated comments are increasingly common and, truth be told, can be hard to tell from the human-generated ones. Sure, that's a tough situation for industry members trying to get good information. But save some sympathy for the poor human consultants who are trying to be heard – and recognized as legitimate experts – amid all the noise. 

Indeed, the flood of content is arguably the biggest change in marketing in recent years. Not so long ago, publishing good content on a regular basis was an effective, low-budget path to success. The mechanism was simple: good content was picked up via social media sharing and, if it became popular enough, registered in search engine results. Today, not only is there too much content for anyone who’s not already famous to gain a significant share of attention, but AI search summaries will scoop up good information without leading readers back to the source. The alternative path of publishing on high-traffic news sites is equally blocked since AI search engines stifle traffic to those sites as well. Of course, a handful of influencers who are skilled at attracting attention can still become Internet-famous.  But what gains attention isn’t necessarily what’s of true value.***

I’ll skip the usual moaning about bad information driving out the good, since the consequences are all around us. Instead, I’ll posit that the situation is untenable because people simply will not be willing to consume such huge volumes of content indefinitely. There is already a backlash among people who want to reduce the time they spend trying to keep up. (In fact, time spent on social media peaked in 2022.)  The obvious solution is to let AI view media for you and summarize what’s worth seeing. Some people may find that acceptable but I’d like to think that most will recognize that AI can’t be trusted to make the right choices – especially if the AI is run by corporations with interests of their own. The alternative would seem to be relying on human curators – remember “editors?” -- to assess the available materials and select those worth consuming. Of course, deciding which curators to trust will itself require consumers to do some work. This path also requires an alternative to the failing economics of traditional, ad-supported media, which will probably be consumer-paid subscriptions that compensate both the content creators and the curators. A middle ground might be AI curators that are funded by consumers rather than self-interested corporate providers.

I won’t pretend to know approach will dominate. Perhaps different groups will adopt different approaches, with access to accurate information becoming a luxury good like ad-free TV. For now, people can already be selective in their content consumption by avoiding the undifferentiated (or, more precisely, platform-selected) streams in news and social media feeds. The dilemma is sharper for experts relying on content creation to build their business: do they try to keep up with content bots that can easily outproduce them, recognizing that even consistently brilliant, unique, and insightful content won’t necessarily be noticed? Or do they find other ways to reach prospective clients, perhaps through live events or less mediated platforms like reddit and medium?

Mind you, this is occurring in a world where AI can legitimately replace some of the expertise consultants are used to selling, such as methodologies, technical skills, and vendor knowledge. This leads to smaller engagements where clients are buying only the AI oversight and creative insights that humans alone can provide. So does the total market for consulting services shrink at exactly the same time that selling those services becomes harder? Or, more optimistically, does lower cost enable consultants to expand the market by selling to firms that couldn’t previously afford their services? (See, for example, this new WPP offering of AI-enabled “self-service” ad campaign development.) While AI-powered services don’t themselves create more human jobs, they do presumably offer an opportunity to sell insight-based projects that do require paying humans.

I'll say it again: the future is unknown. But I do believe that analysts who are calmly assessing the threat that AI poses to other people’s jobs might give more urgent thought to the threat AI poses to their own. 

Taking Advantage of AI Search

The past few weeks has seen a flurry – more of a blizzard, really – of announcements from Search Engine Optimization companies that have added tools for AI search ranking. This is a natural outcome of the meteoric growth in AI search: referrals from ChatGPT grew 20% from June to July this year while ChatGPT agent visits doubled in the same period. 

Before you get too excited, bear in mind that LLM vendors’ share of global search is still a minuscule: 0.13% so far in 2025 (for ChatGPT and Perplexity combined). Given that Google still has nearly 90% of all search traffic, it’s much more important that more than half of Google searches now return an AI overview.

Probably the most important statistic is that organic click-through rates drop by 20% to 40% when AI Overviews are displayed.  One important caveat is that AI overviews show up primarily on “informational” queries, as opposed to commercial, transactional, or navigational queries. The caveat to the caveat is that informational queries are by far the most common: 88.1 % of the total, compared with 8.7% commercial, 1.4% transactional and 1.8% navigational. So, if your web strategy has been based on providing information to attract visitors, you have some adjusting to do.

Or do you? Most AI optimization tools replicate SEO by focusing on how often a brand appears in AI search results. This Evertune press release provides a detailed explanation of how their product works, which is probably typical: the system runs a set of common prompts on a topic and tracks how often the target brand is mentioned. That’s very similar to traditional SEO, which reports how the target brand ranks in search results. In both cases, the goal is to be seen more often.

It's true that tactics to reach those goals will differ. Traditional SEO is based on links and keywords, while AI search engines care more about detailed information and reviews. More broadly, content that attracts humans will often be insightful or entertaining, while bots respond best to clearly presented data. So some changes are needed if you want to switch your goal from SEO to AI search ranking.

The deeper question is whether appearing in AI search results is the right goal. If you’re a brand whose aim is building awareness and credibility, the answer is probably yes – so long as the AI results present you in a positive light. It will be even more important as search bots morph into shopper bots, which recommend (and will eventually execute) purchases on behalf of human buyers. Shopper bots will no doubt engage with brand-owned seller bots, a category (or e-commerce system feature) that I can't recall seeing but is surely gestating as you read this.

The answer is different if you’re a publisher. There’s little value in having your materials cited in an AI response that doesn’t generate a click-through to your site. In fact, showing your materials gives users less reason to visit. Adding injury to insult, responding to the AI search engine’s crawler actually adds to your website operation costs. Put simply, publishers need traffic.  Visitors provide inventory for ad impressions and names to capture for future promotions. The drop in organic traffic forces publishers to seek other traffic sources, including both paid search and social forums such as reddit, Instagram, and YouTube.

• Paid search itself will evolve along with AI search. Ads already appear alongside Google and Microsoft AI search results. Looking ahead, Google Research recently published a paper describing how advertisers could bid on the text of the responses themselves. There are also other ways to reward AI search engines for driving traffic to advertisers, such as affiliate relationships. Consumers won’t be pleased when paid and unpaid recommendations mingle indistinguishably in search results, but it’s not clear they’ll have much choice in the matter. The problem facing publishers is that many won’t be able to afford the cost of paid advertising or will need to reengineer their business to generate revenues to cover the expense.  

• Social traffic is a more appealing option for publishers since it’s “free” to the degree they can generate it. That should be possible: after all, publishers' core competency is to create appealing content. One implication is a growing overlap between traditional publishers and influencers, a distinction that has already become quite blurry. It’s worth noting that we’ve recently seen substantial growth in software and services to support influencer marketing, including tools to find influencers, build audiences, and streamline content creation, as well as ad networks and media buying tools to simplify ad placement on influencer sites. Most have been aimed at individual influencers, but enterprise-grade products will probably appear with publishers as the target clients. Social marketing will increasingly look like SEO, except aimed at optimizing content for social algorithms rather than search algorithms. Since SEO companies and social media management vendors both have a legitimate claim to this turf, it will be interesting to see who ends up owning it.

In short, we’re just beginning to see the changes resulting from AI search. It has already disrupted the business model of many web publishers, who no longer receive the organic search traffic that powered their audience creation. Traditional SEO will be supplemented by AI SEO, especially for brands who can benefit from appearing in AI search results. But while traditional search generated large volumes of free traffic, AI search results will likely be dominated by paid placements. Publishers, who gain less than brands from AI search ranking, will need to find alternative sources of traffic and will increasingly resemble influencer business models. Above all, we’ll see continued development of supporting technologies (“infrastructure” in terms of the STIB model) as entrepreneurs identify new opportunities to help companies operate in the new environment. Ultimately, again according to STIB, we’ll start to see new business models emerge that are best suited to the dynamics of an AI search-based economy.

CDP Relay42 Bought by Supermetrics: The Embedded CDP Trend Continues

After a flurry of CDP acquisitions in January, things had been quiet on that front – until this morning, when we learned that Relay42 is being purchased by Supermetrics. The deal reverses the pattern of earlier CDP acquisitions, which had been been by customer-facing companies in content management (Contentstack/Lytics), customer service agents (Uniphore/ActionIQ), and ecommerce (Rokt/mParticle). Earlier CDP acquistions had also largely involved customer-facing businesses such as retail marketing (Bloomreach/Exponea), email (Sitecore/Boxever) and messaging (Twilio/Segment). But bear in mind that Relay42 is what we call a campaign CDP, meaning it offers journey orchestration, personalization, and outbound campaigns, as well as advanced analytics and AI agents. So the underlying logic is the same: vendors want to provide both data assembly and activation. While other acquisitions added a CDP’s data assembly capabilities to an existing activation system, Relay 42 adds activation to Supermetrics data assembly. Either way, you end up with a combination of both.

The trend towards embedded CDPs is now so clearly visible that it’s barely worth a mention. A more interesting question might be why Relay42 chose to sell itself at this moment: after all, as a bona fide CDP, it already had data assembly features similar to what Supermetrics provides. I suspect the answer here is brutally simple: money. Despite having an excellent product and marque accounts, Relay42 has struggled in recent years, dropping (per LinkedIn) from 69 employees in June 2023 to 50 as of June 2025. Over that same period, Supermetrics has grown a relatively modest 12% but, more important, its total headcount is now 407. At eight times the size of Relay42, Supermetrics provides the financial resources and customer base to power future growth. Without additional resources, Relay42 would have been hard-pressed to continue to develop its product, let alone make the sales and marketing investments needed to expand.

I had intended to write a longer post this week expanding on the trend towards embedded CDPs. I even had a nifty graphic prepared to illustrate the changes. (Actually, I prepared it for a workshop I’m giving in July at the Vibe Marketing Tech Fest in Manchester, UK, if you'd like to attend.) But plenty has already been written on this topic and I’m not sure a more detailed exploration would add much value. The only point I haven’t already made is that the trend towards integrated customer data/activation systems runs counter to the trend towards assembling systems from separate components. At the risk of being boring, I’ll mildly suggest that both will find some takers. What the Relay42 deal does illustrate is that small, independent CDPs will find it very hard in the future to compete outside of niche markets.

Mapping AI-Based Martech Using the STIB Innovation Diffusion Model

TL;DR: This post fleshes out the STIB model of innovation diffusion, tests it by classifying recent customer management product announcements into STIB categories, and draws some insights about the model and customer management trends.  As a bonus, it proposes a future of "hyper-personalized, financially-optimized treatment of each customer during each interaction across all channels."

 

The Substitution-Transformation-Infrastructure-Business Model (STIB) model of innovation diffusion that I presented in my last blog post is intriguing, but does it reflect reality? One measure is whether it provides a useful classification of industry products. To test this, I looked at the past ten weeks of product news in the CDP Institute Daily Newsletter, which came to about 50 items. Results were quite interesting.

But before we get to that, let’s flesh out the model a bit.  It describes two core elements: 

• Innovation: the development whose impact is being measured.  This might be electric motors or internal combustion engines.  (More formally, innovation is a change in technology, where technology is defined as the tools, methods and knowledge used to perform a task.  "Technology" in this sense isn't always "technical": a new marketing methods and financial tools can be important innovations.  But I digress.)
  

• Application: the process or product changed by the innovation being studied. This might be manufacturing in factories (a process) or transportation vehicles (a product).  The application is assembled from components, which might be physical objects in a product or steps in a process workflow.  The same innovation might affect many different applications but it's often more useful to study just one.

The model describes how an application changes in response to an innovation.  This change comes in three stages:

• Substitution: the innovation is deployed as an exact replacement for one component in the applications, without changing anything else.  Electric motors were hooked to turn factory shaftwork instead of water wheels; internal combustion engines replaced horses for powering carriages.

• Intermediate States: this is a period of experimentation. Multiple components are changed as the industry explores how to best take advantage of the innovation. The innovation itself continues to mature during this period, opening up additional possibilities. 
  

• Transformation: the industry settles on an optimal design incorporating the innovation.  This "final" design is fundamentally stable although incremental improvements continue. Factory tools incorporate built-in electric motors; motor vehicles locate the engine and drive train most efficiently.

The model also tracks changes over time to technologies beyond the application itself:  

• Infrastructure: this includes products, suppliers, and processes that support the transformed application or are changed as a result.  Electric motors require generating stations, power grids, and new machine tools, while they result in more efficient factory designs. Parts and materials manufacturers support motor-driven vehicles while auto ownership spawns new roads, filling stations, employment opportunities, legal frameworks, and more.

• Business model: this describes business methods such as revenue sources, pricing models, marketing, distribution, customer service, funding, and ownership models. Larger factories lead to scale economies that favor large, national brands.  Complex auto technology favors large organizations with deep resources for research, mass production, national advertising, and dealer networks. 
  

The graphic illustrates relationships among these elements.* 

• In the initial state, the application, infrastructure and business model are all in their baseline (i.e., pre-innovation) configuration.  

• This is followed by substitution, where the innovation is applied to make a slight change in the application while infrastructure and business model are unaffected.  
  

• In the third stage, intermediate states, developers try variety of changes to the application and the infrastructure starts to mature in response to growing demands.  Some will be a dead end (direct current electrical systems, steam powered cars). Others may hit on the transformed design before infrastructure is available to support them.  Vendors may also fail if they don't change quickly enough to keep up with competition.  
  

• In the final stage, developers converge on a transformed configuration and application change comes more slowly.  Some companies drop out or merge as the market consolidates. The infrastructure continues to evolve and new business models start to emerge.  
  

The Future of Customer Management

Applying the STIB model requires specifying the application being changed and the innovation that is changing it. This might be tricky in some situations, but it’s pretty simple in the current context: the application is customer management while the innovation is artificial intelligence.

What’s not simple is predicting the form of the transformed application. Hindsight about factory equipment or auto design is easy, but we don’t know how customers will be managed in the future.  This prediction isn't strictly necessary: we can simply observe changes as they occur without assessing their relation to the final state.  But that assessment helps to organize the analysis and gives buyers and developers decide where to invest their resources.

Predicting the transformed state is a thought experiment: identify the constraints imposed by the current technology and imagine what the ideal design would look like if those constraints were removed (and replaced by whatever constraints the innovation imposes.)

As I see it, the dominant feature of customer management today is the number of separate steps that are performed by different people. There’s a creative team with a multi-step process to develop content, a targeting team with a multi-step process to select audience segments, a media team with a process to buy advertising, and multiple operations teams with multi-step processes to deliver messages via websites, email, social media, connected TV, digital video, games, out-of-home, podcasts, and elsewhere. The reason we have so many teams with so many steps is the limited capability of individual humans: each can be expert in only a narrow area, so many must work together to deliver a complete result. And, because they’re human, each person can only deliver a relatively small number of outputs over time.  Thus, each output must apply to many people to deliver messages to everyone.

AI removes this limit. At least in theory, a single AI agent can expertly execute all steps in the messaging process, combining content creation, audience selection, media buying, and delivery. In practice, a single super-agent is less likely than a master agent that calls on specialized subordinate agents.  But so long as those agents complete their work almost instantaneously, the process will still function as a single step. Ideally, these agents would view all available data and collaborate with each other: content development would be informed by audience characteristics; media buying would take into consideration other channel opportunities; and so on. All these decisions would be coordinated to produce the highest total value: for a given advertising impression, the system(s) might simulate the results of several different creative treatments for several different offers for several different products, and then select the best creative/offer/product combination – or conclude that even the highest possible value for that impression is too low to justify the investment. This requires a sophisticated value prediction algorithm which would consider long-term as well as immediate results.

In addition to speed and coordination, the process would be completed at near-zero incremental cost.

In short, I see the transformed state as hyper-personalized, financially-optimized treatment of each customer during each interaction across all channels.

  

This is a radical change.  (That's why they call it a transformation.)  There are no more campaigns (defined as a predefined sequence of messages), no more audiences (defined as a group of customers who receive the same messages), no more content (defined as a fixed combination of text, images, and offers), and no more media buys (defined as group of impressions purchased together). What we have instead is a master agent orchestrating customer interactions across all channels to optimize use of budgets, data, customer attention, products, staff, and computing power. That agent would call internal and external data to guide its actions and would deliver messages across both internal (owned) and external (paid) media.

In the transformed world, today's creative, analytics, media, and operations departments have largely vanished, apart from a few human(?) experts left behind to monitor the AI. On the other hand, there might be more need for humans to do strategy and product development.

This vision also points to new infrastructure and business models:

• Supporting infrastructure would include vendors building the base AI systems, which are too complicated for most companies to build for themselves (although non-technical users might use no/low-code tools to tune them); data tools to prepare, integrate, and expose data from all sources in real time; and analytical tools to measure interaction value.

• Surrounding infrastructure would include new media that provide real-time access to their customers; low-friction integration methods to connect marketers with these systems; new billing and analytical methods; and social/legal frameworks to govern customer data collection, sharing and privacy.

• New business models would be needed for companies and the systems, data, and media providers that support and surround them. With each interaction managed separately, providers might become on-demand services that charge for value provided in each case rather than billing based on labor, system use, or impressions.

I’m not sure this will happen. At most, I’d bet one of my later-born children on it. I’m laying it here out because the STIB model works best if you measure against a transformed state (and because it’s fun to think about.)

Mapping against the STIB Framework

That said, let’s try mapping recent product news against the STIB framework. 

Application

The application we’re analyzing is customer management, which is roughly what we cover in the CDP Institute daily newsletter. This gives us a reasonable collection of announcements to work with, although it’s just a small sample based on items that happened to appear during a relatively brief period.

If the STIB model is correct, we should find clusters of products on a spectrum from business as usual, to substitution of AI within current processes, to complete transformation. Because transformation doesn’t happen all at once, we would expect to find several intermediate stages. Indeed, that’s the case.

Current State: The first cluster would hold products that execute the existing process without any change. AI-powered co-pilots might fit here. But co-pilots are so common that we don’t bother to write about them in the newsletter. Nor do we usually cover products that aren’t doing anything new. So this cluster is empty apart from one item about agentic helpers.

• CommerceIQ Offers Agentic Teammates to Ecommerce Marketers

Substitution: The second cluster is simple substitution: products that replace a discrete task with an AI-generated equivalent.  We see plenty of these, often offered as collections of (separate) AI tools for multiple tasks.  We also see toolkits for users to build their own.  So long as each AI tool executes one task separately from the others, this is still substitution.  Recent examples are:

• Optimizely Lets Users Create Embedded AI Agents
• HCL Unica Adds AI-Based Predictive Models
• HubSpot Launches a Range of New AI Agents
• Zeta Global Unveils AI Agent Studio
• Demandbase Launches GTM AI Agents
• BlueConic Adds No-Code Interactive Experiences

Intermediate Products: Now we move into products that change the underlying process but don’t reach the fully transformed state.  The news items seem to fall into three clusters.  It’s important to note that we didn’t define these in advance: they have emerged from the data itself. 

• Content Creation: This is a popular task to automate.  It’s a single task but more than substitution because most vendors connect their content generator to response data and use this to automatically optimize content over time.

• Unity Adaptive AI Generates Gaming Ads
• Sett Creates High-Performing Marketing Content
• Wix Introduces AI Adaptive Content for Websites
• Blueshift Customer AI Agents Optimize Personalized Messages

• Goal-Driven Workflow: this cluster holds systems that have automated development and execution of a multi-step workflow, such as audience segmentation, journey design, or media buying.  Like content creation, these usually let users specify a goal for the workflow and collect data to help measure results.  
  

• Simon Data Delivers AI-Driven Segmentation
• Emplifi Launches Automated Journey Builder
• Pubmatic Offers AI-Powered Media Buying

• Customer Management: these products collect data to support optimization, help users to create messages, and select and deliver messages across customer touchpoints.  They come the closest to the fully transformed state but don’t support all channels or create hyper-personalized messages in real time. 
  

• Wunderkind Integrates AI at Customer Touchpoints
• Twilio Connects Omnichannel Communications with Unified Profiles 
  

• Transformed Products: This cluster would hold products that deliver the fully transformed process.  There’s a good chance that some vendors have this mind, but we haven’t seen any products that deliver it.  So the cluster is empty.

Infrastructure

Infrastructure and Business Models won’t take their final shapes until the transformed process is fully deployed, but they do co-evolve with the application changes.  This applies especially to supporting infrastructure, parts of which must be in place for some intermediate product to function effectively.

Supporting infrastructures: The key supporting infrastructures for transformed customer management are inputs (data access and quality), internal processes (agent cooperation and analytics), and outputs (media integration).  We see announcements in all these areas.  

• Data Access: most data access announcements describe accessing data in real time without loading it into a separate database.  So far, these developments focus primarily on reading data in the company’s internal systems.  But remember that the full vision for transformation includes access to third-party data such as compiled customer behaviors and local weather.  We do see some of that, although none is in the current sample.
  

• Amplitude Brings Marketing and Product Data Together
• Databricks Launches Data Intelligence for Marketing
• Attentive Unveils Bi-Directional Data Sharing on Snowflake
• Algolia Gives AI Agents Real-Time Access to Salesforce, Adobe Data
• Tealium Integrates With MCP to Send Data to Agents
• Utiq Integrates With Adobe Real-Time CDP
• Sigma Now Connects Unstructured Content With Structured Data
• ChatGPT Lets Deep Research Query Company Files

• Data Quality: these tools prepare data for AI use.  Many data quality vendors have added features to support AI use and have added AI-powered offerings.  These apply to customer data but we don’t usually write about them, so this cluster is fairly sparse.  

• Informatica Unveils Agentic AI Offerings for Cloud Data Management
• Redpoint Global Launches Data Readiness Hub

• Agent Deployment: this describes technology for building agents and helping agents work together.  It’s another field with extensive activity that is largely beyond the scope of the daily newsletter.  It will be critically important if the transformed state involves teams of agents that cooperate closely with each other.

• Microsoft Simplifies AI Training
• Google A2A Boosts AI Agent Interoperability

• Analytics: any goal-seeking agent will need internal analytics to guide its decisions.  Still, there may turn out to be a market for independent agents that make their results available as a shared resource for teams of specialist agents.   This would apply especially to customer value analytics, which are needed to compare opportunities across different channels.

• Hightouch Adds Journey Simulations

• Media Integration: this is the output infrastructure that deliver messages created by the hyper-personalization.  The tools are steadily encompassing more channels. 
  

• Google Expands Its CTV Ad Offerings
• StackAdapt Brings Email and Data Hub to Programmatic Platform
• Adobe Real-Time CDP Integrates Roku Audiences
• OpenAP Unveils New Streaming Identity Solution for Video
• Google Automates Cross-Channel Marketing Content Creation
• Google Announces Immersive Ads Partnership With Roblox

Surrounding Infrastructures: changes to the surrounding infrastructure happen after the transformed application is in place.  This makes them harder to predict than supporting infrastructure, which develops sooner.  We do have some current developments that are likely to become more important as hyper-personalization matures.  There will no doubt be others.

• Integrated Commerce (Sales Agents): this describes shopping in non-traditional channels, including retail media, search engines, social media, video, connected TV, and mobile apps.  These can work without hyper-personalization but are vastly more effective when offers are tailored to the individual and context.  In many cases, the interface will be a chat-style, AI-based sales agent that has a real-time dialog with the customer.

• YouTube TV Offers a Version of Shoppable Ads
• Perplexity Partners With PayPal for In-Chat Shopping
• Amazon Brings Direct Commerce to Prime Video
• ChatGPT Adds Product Recommendation to Search
• OpenAI Appears Set to Bring Shopify to ChatGPT
• Amazon ‘Buy For Me’ Links to Products Sold Outside of Amazon
• Perplexity Integrates with firmly.ai Ecommerce Tech

• AI-Based Search (Buyer Agents): this describes marketing that is targeted at AI agents rather than humans.  Today's most common implementation is AI search overviews, which do research for buyers.  This changes the goal of search marketing from attracting traffic to appearing in genAI summaries. Other agents are evolving for other types of research and other stages in the sales cycle including making purchases on the customer's behalf.

Interacting with these buyer agents at scale and cost-effectively will require AI-based sales agents.   In some ways, it won’t matter whether the sales agent is interacting with a person or a bot: to the sales agent, both are collections of data that must be analyzed and responded to appropriately.  That said, the behaviors of humans and bots will be significantly different, so the sales bots will no doubt develop separate approaches to each.  Ultimately, marketing systems may give buyer agents direct access to (some of) the product and promotional information they provide to sales agents, bypassing the sales agents altogether.

 

Because this is a relatively new development, our news coverage includes more research reports than product announcements.

• Google Search Traffic Up 49% But Less Goes to Publishers: BrightEdge
• Google AI Overviews Cut CTR by More Than A Third: Ahrefs
• Product Content Dominates AI Search Links: Xfunnel
• Google Links AI Overviews to Its Own Search Pages
• Shoppers Lean Towards Trusting AI Agents: Salesforce
• Visa, Mastercard and PayPal Are Racing Into Agentic AI

Business Models

Few companies make announcements about changes in their business model, especially when those changes involve firing people.  As a result, we have few newsletter items on AI-based business models.  One change we do see is movement towards pricing based on value created rather than resources consumed.   The transformed process will surely create other opportunities, perhaps including “no staff” companies run almost entirely by AI or “no product” companies that source products in real time as they find interested customers. It's likely the new business models will include ones we haven't even imagined.

• Salesforce Extends Consumption-Based Pricing for Agentforce
• Publicis Sapient Describes Shift to Services as Software
• LUMA Predicts Move to Outcome-Based Measurement

What We’ve Learned

The main purpose of this blog post is to determine whether the STIB model provides a useful way to think about technology innovations.  Of course I’m biased, but I believe it does. Not only was it reasonably easy to slot products into different categories, but the process generated several helpful insights:

• Products that substitute AI for one step within an existing workflow are significantly different from products change the workflow.

• It helps to envision the final, fully transformed product so we can assess intermediate products, identify the supporting capabilities and infrastructures it requires, and predict the surrounding infrastructure and business models it is likely to generate. 

• We should acknowledge that our prediction could be wrong.  It may help to build scenarios around alternative outcomes.  

• Intermediate products appear before the final transformation. These become possible as new capabilities, infrastructures and business models appear. Observing the intermediate products helps to assess whether the changes are moving in the direction we expect or to adjust our prediction of the transformed state..

• A minimum set of capabilities, infrastructures, and business models must be in place before the final design can succeed. Companies will fail if they offer the transformed design before supporting infrastructures are in place.

• Capabilities, infrastructure, and business models continue to evolve after the (successful) transformed design is introduced. These developments make the product more effective and exploit the opportunities it creates.

• Continued evolution creates rapid growth and expands the value of the final design, giving it an increasing advantage over alternatives. This advantage ultimately locks other configurations out of the market, even though some may be technically superior.

Applying the STIB model to customer management offers additional insights. Our sample of product news is enough to show:

• Many current products do simple substitution. These are the easiest to deploy and can show immediate improvements in cost and quality. (There are also many products that support the existing workflow without any changes, but those don’t show up in our sample.)

• Some intermediate products are already available. Most of these automate a single stream of tasks within the customer management workflow, such as content creation, campaign design, media buying or analytics. 

• Intermediate products usually work within a single department. This makes them easier to drop into the larger workflow and reduces the number of users whose work is disrupted.

• Some products aim to automate an entire workflow, such as campaign design, development, and execution. Doing this with autonomous AI agents is the leading edge of the industry today.

• Some companies offer components that support the final vision.  These include application capabilities such as content optimization, response simulation, and advanced attribution, as well as infrastructure and business model changes including agent coordination, cross-company data sharing, touchpoint integration, and performance-based pricing.

• I haven’t seen any products that promise the predicted final state of “real-time, hyper-personalized, omni-channel messages.”  This has surely occurred to many smart people, so I’ll guess they have decided (correctly) that the capabilities, infrastructure and business models aren’t ready yet.

• The final state may be delivered by a single integrated product or by multiple agents working in concert. Remember that the final state requires close cooperation between advertisers and media companies and between different departments within the same company.  This makes a multi-agent solution more likely and reinforces the needed for data- and process-sharing technology..

Implications

Here are some practical implications of what we’ve discussed.

• Substitution products can be valuable, but the benefit won’t last. It’s tempting to argue that substitution is a poor investment because it offers only incremental improvements on today’s current processes and that companies, and software developers should instead focus on more profound solutions. But the plain fact is that substitution creates substantial benefits and is easier to deploy than changes that require process change. Product developers and business users shouldn’t shy away from substitution but they do need to recognize it has a relatively short shelf life. Product developers should also realize that nearly all incumbent vendors will add substitutions to their current products or have already done this.  That makes it hard to convince users to change to a new system on the basis of substitution alone. Attracting new clients will require solutions with more substantial advantages, which is what the intermediate product designs can offer.
  
  
• The goal of "real-time, hyper-personalized, omni-channel messaging" is not yet widely discussed. What leads me to expect this as the transformed state is the growth of direct sales in social media, search results, connected TV, podcasts, and pretty much every other channel. This "instant commerce" collapses the multi-step "awareness, interest, desire, act" cycle into a single moment when the customer is presented with an opportunity to buy. Taking full advantage of this moment requires marketers to connect with all message opportunities, to gather all data so they can assess the potential value of each opportunity, and to deliver the most effective possible message for each opportunity they purchase. Only AI can do this effectively at scale, most likely by presenting intelligent agents to interact with customers who engage.**
  
  
• The transition can be gradual. Instant commerce can be deployed in one channel at a time, can work with limited data, and doesn’t require advanced message optimization. Companies and vendors can move towards the fully transformed state in stages, building experience, product, infrastructure, and business models along the way.
  
  
• Instant commerce depends on long-term relationships. That may seem like a paradox but customers will only engage with companies that they trust. There’s no time to build trust during the interaction moment, so trust must be built in advance. The good news is that the importance of trust is widely recognized and the methods for building trust (among humans) are well understood, if not always well executed. The industry may need new lessons in building trust among AI agents.
  
  
• Infrastructure may offer the greatest opportunities. The biggest gaps between what’s currently available and what’s needed in the transformed state seem to be deep connections to collect external data and interact with touchpoint systems. 

• Shallow connections already exist, but real-time, hyper-personalized, omni-channel messaging implies real-time queries of external data sources about individual customers to collect up-to-moment information on behaviors. The touchpoints where those behaviors occur must capture, identify, assess, expose, and charge for that data in real time in a privacy-compliant fashion. Bear in mind I’m talking about touchpoints outside the company that wants to use this data. Very little technology exists to do that today. Data clean rooms are a start.
  
  
• Beyond sharing information, those same touchpoints need to receive ad messages and deliver them to their visitors, again in real time and with feedback on response. Most of the messaging technology will reside with the ad buyer or middlemen, who will need to receive notification of contact opportunities, gather data and assess those opportunities, select the appropriate message, bid on delivering the message, transmit the message on bids they win, and measure results. Again, the existing technology to do all this in real time, at scale, and across many channels is limited at best. (Today’s programmatic ad system is a partial model.) In addition to data movement, this process requires sophisticated evaluation models so marketers can accurately bid on the projected value of each interaction.
  
  
• Because all this interaction is happening between different companies, the infrastructure technology must be widely shared. This could imply broadly accepted standards implemented by many developers, or, more likely, proprietary technology built and sold by a few major suppliers. Competition to be one of those suppliers will be fierce but the rewards are likely to be huge. The rewards might diminish over time once the process is well enough understood to create open standards that are a viable, cheaper alternative.
  
  
• Data quality doesn’t get the attention it deserves. Survey after survey shows that data issues are the top roadblocks to marketing, personalization, and AI success. Yet companies rarely make data quality an investment priority. There isn’t much to say about this except that real-time, hyper-personalized, omni-channel messages make data quality more important than ever. This is yet another piece of infrastructure that’s ripe for improvement.
  
  
• Buyer bots might change everything. Buyers have limited attention, which is why gaining attention has always been the first step in successful marketing. This still applies to real-time, hyper-personalized, omni-channel messages -- so long as they're being sent to humans. But if customers delegate their purchasing activities to bots, the fundamental truth is no longer true: buyer attention will no longer be limited.  Developments such as search engine optimization and AI search overviews as early examples of marketing to bots: marketers must target the algorithms, not attract human attention. But search marketing of any kind is still aimed at putting messages in front of eyeballs. Truly automated purchasing will remove humans from the entire process. The change won't happen overnight but it seems plausible to expect a mix of human and bot buyers in the near future. A STIB model with bot buyers as the final transformed state would be quite different from the one I’ve presented here.
  

Summary

This post has explored whether current developments in customer management technology can be effectively analyzed with the STIB model of innovation diffusion and whether the results provide useful insights into industry trends. I believe the answers are Yes and Yes.  I've also presented a specific vision for the industry future, of "instant commerce" delivered through real-time, hyper-personalized, omni-channel messages.  I can't promise that this is correct, but it's an interesting starting point for discussion.

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* The graphic is conceptual but could be quantified by counting the number of components in each product that match the final transformed design or that differ from the initial state.  Fun!

**  Things are admittedly a bit more complicated for non-impulse purchases.  But I’d argue you’re still trying to motivate an action in the moment, even if it’s only saving an offer in a wallet for future consideration.