Can AI Break the Hype Cycle?

Generative AI and its agentic offspring may be foundational technologies, but they are still following the normal technology hype cycle: excitement at the potential, followed by news of failed deployments, followed by a more mature understanding of what’s required for success. The third stage generally comes down to a realization that the main obstacles are organizational, not technical: projects fail due to poor internal alignment, change management, reward systems, and so on. Similarly, when it comes to customer data (and all other business data), there’s a dawning recognition that whether the data resides in a CDP, cloud warehouse, or marketing platform, the real challenge is still transforming raw data into usable information through quality management, unification, and mapping. 

We’re just beginning to see the third-stage insights from the industry’s more advanced thinkers. It's true that they’re important: the only way people will be convinced to address these issues is by seeing real data and hearing about actual experience. But they are also utterly predictable. 

What’s less predictable is that AI has the potential to remove those barriers. If AI radically changes how work gets done, then the organizational structures designed for existing business processes (and blocking progress) will change as well. For data, AI could – at least in theory – handle preparation tasks that have always been the main roadblocks to deployment. 

These are not subtle ideas and I don’t consider them particularly brilliant insights. But I also don’t see them being discussed (at least explicitly). Instead, most of the AI conversation is still about using AI to replace individual tasks within existing workflows or about creating AI-based workflows within existing organizational structures. What I’m suggesting is a clearer focus on using AI to remove the traditional roadblocks to technology deployment: in addition to redesigning organizations around AI capabilities (with a particular focus on accommodating future change), this might also mean developing AI coaches to help existing organizations with change management. For data management, it means developing AI tools to automate end-to-end data preparation. 

I will somewhat airily leave the details to people who make their living helping organizations with technology management; they will no doubt have many more and better ideas than I can offer here. My goal is simply to convince them to adopt roadblock removal as a conscious objective in their work. This is what will empower AI to deliver the fundamental changes that we all see as its potential.

Fitting Agents into the Sales and Marketing Mix

Much has been written recently about how marketing and sales processes change when human buyers and sellers are replaced by buyer and seller agents: abbreviated, inevitably, as “A2A” marketing. It’s a fascinating topic but just one model that will coexist in the near future with human (or, more precisely, non-agentic) buyers interacting with agentic sellers, agentic buyers interacting with human sellers, and, lest we forget, humans interacting with humans. Any consultant will immediately recognize that this cries out for a 2x2 matrix, or perhaps a pair of 2x2 matrices if you want to distinguish business marketing from consumer marketing. For the moment, let’s stick with the single matrix model:

It’s worth making these admittedly-obvious distinctions because each situation raises separate issues, which are otherwise easily jumbled into a confusing heap. Let’s look at each situation in turn.

Human to Human (H2H)

Beyond the literal situation of one seller talking to one buyer, I’d argue this also includes humans interacting with traditional broadcast media, web search, and even non-agent websites. The common thread is that the human buyer does most of the work of asking questions and processing answers. The seller is largely reactive, although there are some situations where she makes choices such as selecting a personalized “next best action”, embedding dynamic content in a website, and setting up conventional search engine optimization. Those choices may be informed by predictive models or some other type of AI, but every step in the workflow is ultimately managed by humans, not agents.

I can’t point to specific data but am pretty sure that H2H interactions still account for the vast majority of today’s sales and marketing activity. This means that marketing and sales teams should still give significant amounts of attention to improving them, even though agentic interactions are vastly more fun to think about. If you absolutely must bring AI and agents into the picture, you can use them behind the scenes to speed up workflows, optimize performance, and analyze results.

Agentic Buyers to Human Sellers (A2H)

This is probably the situation that gets the most attention today. It includes true “buyer agents” (controlled directly by buyers) and “buyer-supporting” agents such as AI search engines and browsers. I call these “buyer-supporting” because they’re not controlled by the buyer, but instead by a company like OpenAI or Google which provides them to buyers at little or no cost.

The distinction matters because companies that offer “buyer-supporting” agents have their own agendas, which don’t necessarily align with the interests of actual buyers. In particular, these companies are increasingly interested in monetizing their products by serving ads within AI search and browser results. Some of these ads will be clearly labeled while others may be subtly embedded in the results themselves. These ads are an opportunity for marketers but may be problematic for users, who could be led to question the objectivity of the AI results.

Concern about biased AI search results could in turn lead to significant interest in true “buyer agents” that consumers pay for themselves. History suggests this will be an uphill battle: as we’ve seen with streaming video, large majorities of consumers typically chose free, ad-supported services over paid, ad-free subscriptions. Still, as streaming video has also shown, a significant fraction of consumers will pay for subscriptions in return for a better experience. This could be a large enough market to support a profitable business. Business buyers are even more likely to purchase agent subscriptions, since they don’t pay with their own money and can easily justify the expense based on better quality results. The precedent here is ad-supported versions of office productivity apps, which have never been broadly successful. There’s a chance that agents could be funded by charging advertisers for access to their owners, although such models have also failed in the past.

Advertising aside, most A2H discussions in martech and adtech circles focus on how sellers can adapt their systems to get the best results from buyer-side agents. This often involves advice on optimizing website design to accommodate search and browser agents, so a given brand receives the best possible treatment. Traditional SEO vendors are frantically expanding their products to meet this need and new AEO (AI Engine Optimization) specialists are also appearing. So far, the solutions are pretty basic: systems run sample queries to measure how often a given brand is mentioned in AI search results and vendors offer design tips to expose the kinds of data that AI agents are looking for. The next level is to look beyond measuring and influencing whether the brand is presented, to how it’s presented in terms of positioning and value. We’ll surely see more of that.

The thing to remember about “buyer-supporting” AI search and browser agents is they are generally driven by a big LLM model that draws from the same information for all users. True “buyer agents” would supplement the more-or-less static LLM models with custom research that visits seller websites to find answers to buyers’ specific questions. For example, one buyer might be interested in pricing details while another cares more about product quality. Beyond exposing all possible information, a seller might aim to present its product differently depending on what appear to be the buyer’s priorities. This is largely similar to today’s (non-agentic) website personalization. What’s more intriguing is the possibility that sellers can find a way to identify individual buyers’ agents over time, perhaps by requiring registration in exchange for detailed information. This would let the seller build a buyer profile and tailor responses to this profile. Piercing the buyer agents’ veil of anonymity would be hugely valuable.

There is a third situation: where the “H” in “A2H” is an actual human, not a non-agentic system. One current example is humans responding to agent-generated Requests for Proposals, which will likely be joined by other formats such as email inquiries or even telephone surveys. The growing volume of agent-generated requests is already a nightmare for business sellers faced with the cost of responding to them. The obvious solution is to let seller agents respond to the buyer agents, but it may be a while before most firms can deploy this capability. In the interim, sellers will be increasingly pressed to qualify buyers before deciding how to respond. Insofar as responding to qualification questions requires effort by the buyer, this imposes a cost on the buyer that should help to eliminate frivolous requests. At some point it might make sense for sellers to impose a literal cost – that is, to charge a fee – for responding to agent-generated sales queries. A less obvious concern is that buyers who rely on agent-generated research questions may fail to understand their true needs, removing a substantial portion of the value gained from a good purchasing project.

Human Buyers to Agentic Sellers (H2A)

Traditional websites may use AI-driven personalization but they are still non-agentic systems. In the future, we can expect true agentic interactions to become increasingly common. The best current example would be chat interfaces connected to an agentic back-end, enabling them to engage in true conversations with potential buyers. These have already evolved in some situations to full-scale agentic business development reps (who send those those super-annoying emails complementing your latest blog post and asking for an appointment) and sales reps (engaging in lengthy dialogs).  Agentic customer support reps are even more common and, often, better than humans at many tasks. While the distinction between AI-based and agent-based interactions can be vague, it’s fair to say that agentic interactions will be significantly more responsive to individual situations. This, in turn, makes them more reliant on capturing real-time data, both for customer behaviors and surrounding context.

Letting autonomous agents interact directly with customers raises major concerns about governance, output quality, and risk. These are widely recognized, as are the challenges of integrating agent-based systems with existing infrastructure. That being the case, I won’t rehash them here, apart from noting that they currently present substantial barriers to adoption of H2A models.

Agentic Buyers to Agentic Sellers (A2A)

Agents selling to other agents is the obvious endpoint of agentic adoption. It’s appealing if only for the amusing prospect of agents merrily jabbering with each other without any human involvement. But apart from a few highly structured interactions, such as programmatic advertising, it’s still largely in the future. A2A can’t become more common until the industry first solves the separate challenges of agentic buyers and agentic sellers. It must then overcome the additional challenges of connecting the two. Once the plumbing issues are addressed, there will be another level of adoption as buyers and sellers work to turn the interactions to their advantage. How will price negotiations work when buyers want the lowest price possible and sellers want the highest price? How will sellers discover the actual needs of buyers so they can make the best recommendations – and is what’s best for the seller necessarily what’s best for the buyer? How will seller agents decide which information to offer and which to exclude? How will agents build trust with each other? And how will companies manage the computing costs of agent-to-agent interactions, which could be substantial if the interactions are extensive?

Plenty of smart people are surely working through these issues. We already see some technical foundations being laid in protocols such as MCP and Google’s A2A. But it’s probably too soon for most marketers to put much energy into worrying about A2A deployment. Mastering the intermediate steps of A2H and H2A should come first and will put them in a better position to deal with A2A when the time is right.

Summary

The impact of AI in general, and agentic AI in particular, is overwhelming. While this piece offers some ideas and makes some prediction, my real goal is much simpler: to suggest that distinguishing the different types of human and agent interactions is a way to split the topic into smaller, more tractable pieces. I hope that helps.

Predicting the Future of AI-Based Customer Management: Lessons from Machine Tool Motors

As someone who has spent his career tracking new technologies, I’ve long been fascinated by how those technologies spread over time. The standard model of technology diffusion, developed by Everett Rogers and based on New Deal studies of how farmers adopted new crops, offers the familiar segmentation of users into innovators, early adapters, early majority, late majority, and laggards. 

It’s probably best known in the tech world in its Crossing the Chasm incarnation, which adds the all-important chasm between the second and third stages.

But there’s another model I find more helpful.  This looks not at who adopts new technologies but at how they’re deployed over time. There's a consistent pattern:

• Substitution (the new technology is deployed to exactly replace the old technology without changing the surrounding processes or workflows)
• Transformation (the process is adapted to take advantage of the capabilities of the new technology) 
• Infrastructure change (new infrastructure is developed to support the transformed process)
• Business model change (a new business model emerges to take advantage of the transformed processes and infrastructure)

I haven’t found any single source that presents this model in quite these terms, which may mean I could name it after myself, although for now I'll just use STIB. Some close matches include Ruben Puentedura’s SAMR Model for educational technology (substitution, augmentation, modification, redefinition), Deloitte’s Three Horizons Model (process optimization, process flow and quality, new business models) and  Carlota Perez’s theory of technological revolutions (radical innovation, irruption or substitution, incremental innovation, maturity).  Probably the best match is William Abernathy and James Utterback's Dominant Design Framework (fluid phase, transitional phase, specific phase). 

The paradigmatic example, at least for me, is deployment of electric power in U.S. factories.  This started with connecting electric motors to the shaftworks that were previously driven by waterwheels or steam engines (substitution).  After several intermediate changes, the endpoint was attaching a separate motor directly to each machine tool (transformation). This enabled an infrastructure change: no longer constrained by the limits of mechanical power transfer or proximity to water or coal, factories became cleaner, larger, single story, and located near other resources or markets. The new infrastructure resulted in a new business model featuring centralized mass production and standardized national brands. 

Perhaps you can't relate to machine tools.  Fair enough.  Consider the automobile instead. The first cars were literally horseless carriages, pretty much the same as horse-drawn carriages except that they used a mechanical motor instead of a horse.  That was substitution.  Over time, the design of the cars was modified to take better advantage of having an internal combustion engine.  That was transformation.  Once it became clear the new model was going to be a success, manufacturers developed new, mass-production business models that better suited the new products and processes.  Ultimately, this was supported by a new infrastructures of roads, filling stations, repair shops, traffic rules, licensing requirements, insurance products, and ultimately an entire, auto-based suburban landscape. 

This may seem academic, but it applies directly to something you probably care about: the growth of AI.  Seen through the STIB framework, most of the AI applications we see today are substitutions: an AI copywriter replaces a human copywriter in an unchanged workflow. The omnipresent co-pilots are another, even less disruptive type of substitution: they help humans perform the same tasks more efficiently, again without changing the workflow.

But it’s clear that these substitutions don’t take full advantage of AI’s potential. Again referring to the STIB framework, the question is what the transformed applications will look like.  To answer this, we conduct a thought experiment: what would the process look like if it were redesigned to make best use the new technology? For electric motors in factories, the final form was motors attached directly to machine tools. For most customer data and customer-facing operations, the final form of AI is likely to be a single process that completes all the previously-separate steps at once. This is because AI is not limited by the need to have different specialists perform each step in the workflow, a constraint which results from the inability of mere humans to master more than one specialty, and from the need for experts to check the output of each step before moving on to the next.

But I don't think unified execution is the final form of the AI transformation.  A deeper change is likely to remove discrete units such as customer segments, content pieces, campaign flows, and maybe even standardized products. Those exist because humans could only manage small numbers of segments, messages, campaigns, and products. An AI could handle more-or-less infinite numbers of these, which in practice would mean treating each customer and each interaction individually.

This leads to an end-state of “hyper-personalized” messaging, where content is custom generated on the fly for each customer and context. 

Imagine an all-knowing, all-seeing bot that listens to what’s happening in the market and jumps into action each time it sees an opportunity to do something useful. The action will be optimized using all relevant data, including the company’s own information about the customer; second-party, third-party, and public information about the customer; behaviors of other customers; and market conditions, inventory, and who knows what else. In another dimension, this listening can extend beyond company-owned systems such as websites and contact centers, to include appearance of customers on third-party sites (already available to some degree through programmatic ad bidding) and even in walled gardens (which already receive lists of customers to watch for; the change would be to open a channel that lets the company assess the situation, generate the optimal message, and send it back for the walled garden for delivery.)*

It should immediately be clear that this vision requires infrastructure and business model changes from what’s available today. A much-improved data sharing infrastructure is needed to monitor behavior and access data outside of company-owned systems. This implies new business models to compensate external data owners for access to their information. Perhaps the data owners would charge a fee for letting companies monitor their data streams or query their data stores; or maybe they would only charge for data that a company uses; or perhaps the fees will be based on outcomes such as clicks or sales. Most of these schemes will ultimately require some way to estimate the value contributed by a particular piece of data.

Hyper-personalized message delivery requires more infrastructure and business model innovations. One of the most important developments in marketing today is the emergence of new channels that allow direct customer interaction: these include interactive TV, social commerce, online games, retail media, and even  interactive podcasts and out-of-home advertising. All are alternatives to common web display ads, social media ads, and search ads, which are also becoming more interactive. As with data, the key change made possible by AI is the ability to monitor vastly more opportunities at once, to evaluate the potential of each opportunity in real time, and to take advantage of the opportunities offering the greatest value.

I certainly hope that everyone reading this realizes that what I’m describing is far beyond the capabilities of today’s AI systems. The data access process requires AI to continuously ingest, clean, and integrate data from multiple sources and to automatically adapt as new sources appear and established sources change. Remember that AI is just beginning to address the bottleneck of incorporating new data sources into today’s CDP and warehouse systems. Similarly, we’re just beginning to see AI systems deliver intermediate steps on the way to hyper-personalized messaging. Today’s cutting edge is automated campaign design, which at best (and with much-needed human quality checks) could transform a user’s prompt into a complete campaign package of audience selection, content, and delivery rules and then execute that package. While impressive, this still uses the conventional structure of a few, discrete segments, content pieces, and campaign flows. That makes it closer to substitution than true transformation.

Another way to look at this is that the vision offers a roadmap for future AI development. The current frontiers in AI are goal-seeking agents, access to external data (Anthropic’s Model Context Protocol), and agent cooperation (Google’s Agent-to-Agent). (See this Medium post for a good overview of these.) If my vision is correct (which is by no means certain), steps beyond those frontiers will include proactive data gathering and integration, automated data value assessments, greater situation awareness, and better simulation of human behaviors. (I'd really like to say "understanding" of human behaviors but don't think we can quite attribute that to AI.)  AI will also need more economical processing and reliable guardrails against hallucinations, biases, privacy breaches, and generally bad behavior.

The table below offers a more detailed view of where I think things are headed. It looks at four major customer data processes: customer data management, people issues related to customer data management, customer data activation, and advertising. For each process, it lists the required capabilities for each of the four diffusion stages.  You can think of these as requirements for new, AI-based products.

AI Applications for Customer Data (STIB Model)
	Data Management	People	Activation	Advertising
Substitute [execute via co-pilots and agents]	Data collection, ID resolution, connectors, metadata	Understand applications, requirements, training	Segmentation, analytics, prediction, sharing, privacy	Audience assembly, media buys, data buys
Transform [execute via unified AI systems]	Unified process, data as service	Management tools, define goals/prompts, explore opportunities	Hyper-personalize messages	Deliver best customer, data, channel/media; optimize spend
Infrastructure [required capabilities; many delivered via AI]	Automated data access, security, privacy, quality, transforms	Learning systems, training systems, process design systems	Efficient processing, attribution, instant commerce, buyer agents	Secure data sharing, consented data assembly, contextual targeting, marketplaces, fractional billing
Business Model [rely on AI for analytics and operations]	Value-based pricing	Pay for skill achievement	Goal achievement, Sales as a service	Goal achievement, audience as a service, value-based pricing

Where does all this lead? Here are the main points I hope you’ll take away:

1. The impact of AI on customer data is just beginning. We can expect AI to be deployed in the same pattern as other technologies.  At first, it will substitute for humans or non-AI systems by performing the same tasks within existing workflows. Over time, it will transform those workflows into new processes that take full advantage of what AI can do. Ultimately, the industry will develop new business models and infrastructures to support the transformed processes.

2. For developers: consider which stage your AI project is targeting. While substitution is low-hanging fruit, bear in mind that current processes will soon be obsolete. Consider developing products that support transformed processes and their related business models and infrastructures.

3. For users: current self-service AI tools may enable you to build your own substitutions with minimal investment. Unlike system developers, you can afford to deploy these now and then discard them when something better comes along. 

4. If you have a vision for a transformed process, you could try to build it.  It won't achieve its full potential because the supporting AI capabilities, infrastructure, and business models are not yet available.  But it might deliver enough value to justify the investment: imagine hyper-personalization based only on one company's own data and deployed only on the company's own customer-facing systems.  

5. The final form of the transformed process will emerge over time.  As companies experiment with different approaches, the industry will converge on an optimal design.  Commercial developers will then build systems with this design and flesh out the supporting AI features, infrastructures and business models.  As with most complex systems, commercial vendors will probably own most of the market because they can afford to invest more in their products than most individual firms.

6. Standardized integration mechanisms will play an important role in this new world.  This assumes I'm right that the transformed process will rely heavily on connecting to external data and external delivery channels. This should make integration a particularly fruitful area for investment if you're a developer, or for building expertise if you're a user.

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*As a practical matter, it's unlikely that thousands of companies would separately monitor thousands of data sources and delivery touchpoints.  It's more likely that data sources and delivery systems will broadcast event information which marketers can read and react to as they see fit.  Perhaps the sources and touchpoints will package their events into channels covering different customer groups or event types and let marketers subscribe to the channels they want.  

Unleashing the Power of Customer Data Platforms (CDPs) and AI: A Game-Changer for Modern Marketing

For some unknown reason, my last three presentations all started as headlines (two created by someone else) which I then then wrote a speech to match. This isn’t my usual way of working. It does add a little suspense to the writing process – can I develop an argument to match the title? It also dawns on me that this is the way generative AI works: start with a prompt and create a supporting text. That’s an unsettling thought: are humans now imitating AI instead of the other way around? Or have I already been replaced by an AI but just don’t realize it? How would I even know?

The latest in this series of prompt-generated presentations started when I noticed that the title shown in a conference agenda didn’t match the speech I had  prepared. When I pointed this out, the conference organizers said I could give any speech I want, but the problem was, I really liked their title: “Unleashing the Power of Customer Data Platforms (CDPs) and AI: A Game-Changer for Modern Marketing”. 

The idea of “unleashing” a CDP to run wild and exercise all its powers, is not something I get to talk about very often, since most of my presentations are about practical topics like defining a CDP, selecting one, or deploying one. And I love how “and AI” is casually tucked into the title like an afterthought: “there’s this little thing called AI, perhaps you’ve heard of it?”

And how about “game changer for modern marketing”? That’s an amazing promise to make: not just will you learn the true meaning of modern marketing, but you'll find how to change its very nature so it’s a game you can win. Who wouldn’t want to learn that?

This was definitely a speech I wanted to hear. The only problem was, the only way for that to happen was for me to write it. So I did. Here’s a lightly modified version.

Let’s start with the goal: winning the modern marketing game. The object of the game is quite simple: deliver the optimal experience for each customer across all interactions. And, when I say all interactions, I mean all interactions: not just marketing interactions, but every interaction from initial advertising impressions through product purchase, use, service, and disposal. I also mean every touch point, from the Internet and email through call centers, repair services, and the product itself.

That’s a broad definition, and you will immediately see the first challenge: all the departments outside of marketing may not want to let marketing take charge of their customer interactions. Nor is your company’s senior management necessarily interested in giving marketing so much authority. So marketing’s role in many interactions may be more of an advisor. The best you can hope for is that marketing is given a seat at the table when those departments set their policies and set up their systems. This requires a cooperative rather than a controlling attitude among marketers.

The second challenge to winning at marketing is the fragmented nature of data and systems. Most marketing departments have a dozen or more systems with customer data; at global organizations, the number can reach into the hundreds.  Expanding the scope to include non-marketing systems that interact with customers adds still more sources such as contact centers and support websites. Again, marketing will rarely control these. At best, they may have an option to insert marketing recommendations directly into the customer experience, such as suggesting next best actions to call center agents.

The third challenge is optimization itself. It’s not always clear what action will result in the best long-term results. A proper answer requires capturing data on how customers are treated and how they later behaved as a result. Some of this will come from non-marketing systems, such as call center records of actions taken and accounting system records of purchases. Again, those systems’ owners may not be eager to share their data, although it’s harder for them to argue against sharing historical information than against sharing control over actual customer interactions.

But the challenge of optimization extends beyond data access. Really understanding the drivers of customer behavior requires deep analysis and no small amount of human insight. Some questions can be answered through formal experiments with test and control groups. But the most important questions often can’t be defined in such narrow terms. Even defining the options to test, such as new offers or marketing messages, takes creative thought beyond what analysis alone can reveal.

And even if you could find the optimal treatment in each situation, the playing field itself keeps shifting. New products, offer structures, and channels change what treatments are available. New systems change the data that be captured for analysis. New tools change the costs of actions such as creating customer-specific content. These all change the optimization equation: actions that once required expensive human labor can now be done cheaply with automation; fluctuating product costs and prices change the value of different actions; evolving customer attitudes towards privacy and service change the appeal of different offers. The optimal customer experience is a moving target, if not an entirely mythical one.

None of this is news, or really even new: marketing has always been hard. The question is how “unleashing” the power of CDPs and AI makes a difference.

Let’s start with a framework. If you think of modern marketing as a game, then the players have three  types of equipment: data systems to collect and organize customer information; decision systems to select customer experiences; and delivery systems to execute those experiences. It’s quite clear that the CDP maps into the data layer and AI maps into the decision layer. This raises the question of what maps into the delivery layer. We’ll return to that later.

First, we have to answer the question: Why would CDP and AI be game changers? To understand that, you have to imagine, or remember, life before CDP and AI. Probably the best word for that is chaos. There are dozens – often hundreds -- of data sources on the data layer, and dozens more systems making choices on the decision layer. The reason there are so many decision systems is that each channel usually makes its own choices. Even when channels share centralized decision systems, those are often specialized services that deal with one problem, whether it’s product recommendations or churn predictions or audience segmentation.

CDP and AI promise to end the chaos by consolidating all those systems into one customer data source and one decision engine. Each of these would be a huge improvement by itself:

• the CDP makes complete, consistent customer data available to all decision systems, and
• AI enables a single decision engine to coordinate and optimize custom experiences for each individual.

Yes, we’ve had journey orchestration engines and experience orchestration engines available for quite some time, but those work at the segment level. What’s unique about AI is not simply that it can power unified decisions, but that each decision can each be tailored to the unique individual. 

But we’re talking about more than the advantages of CDP and AI by themselves. We’re talking about the combination, and what makes that a game-changer. The answer is you get a huge leap in what’s possible when that personalized AI decisioning is connected to a unified source of all customer information. 

Remember, AI is only as good as the data you feed it. You won’t get anything near the full value of AI if it’s struggling with partial information, or if different bits of information are made available to different AI functions. Connecting the AI to the CDP solves that problem: once any new bit of information is loaded into the CDP, it’s immediately available to every AI service. This means the AI is always working with complete and up-to-date data, and it’s vastly easier to add new sources of customer data because they only have to be integrated once, into the CDP, to become available everywhere.

To put it in more concrete terms, one unified decision system can coordinate and optimize individual-specific customer experiences across all touch points based on one unified set of customer data. 

That is indeed a game-changer for modern marketing, and it only reaches its full potential if you “unleash” the CDP to consolidate all your customer data, and “unleash” the AI to make all of your experience decisions.

That’s a lot of unleashing. I hope you find it exciting but you should also be a little bit scared. The question to ask is: How can I take advantage of this ‘game changing potential’ without risking everything on technology that is relatively new and, in the case of AI, largely unproven. Here’s what I would suggest:

Let’s start with CDP. So far, I’ve been using the term without defining it. I hope you know that a CDP creates unified customer profiles that can be shared with any other system. No need to get into the technical details here. What’s important is that the CDP collects data from all your source systems, combines it to build complete customer profiles, and makes them available for any and every purpose.

Some people reading this will already have a CDP in place but most probably do not. So my first bit of advice is: Get one. It’s not such easy advice to follow: a CDP is a big project and there are dozens of vendors to choose from, so you have to work carefully to find a system that fits your needs and then you have to convince the rest of your organization that it’s worth the investment. I won't go into how to do that right now. But probably the most important pro tips are: base your selection on requirements that are directly tied to your business needs, and ensure you keep all stakeholders engaged throughout the entire selection process. If you do those two things, you can be pretty sure you’ll buy a CDP that’s useful and actually used.

That said, there are some specific requirements you’ll want to consider that tie directly to ensuring your CDP will support your AI system. One is make sure you buy a system that can handle all data types, retain all details, and handle any data volume. This does NOT mean you should load in every scrap of customer-related data that you can find. That would be a huge waste. You’ll want to start with a core set of customer data that you clearly need, which will be data that your decision and delivery systems are already working with. This lets the CDP become their primary data source. Beyond that, load additional data as the demand arises and when you’re confident the value created is worth the added cost.

The second requirement is to ensure your CDP can support real time access to its data. That’s another complicated topic because there are different kinds of real time processes. What you want as a minimum is the ability to read a single customer profile in real time, for example to support a personalization request. And you want your CDP to be able to respond in real time to events such as a dropped shopping cart. Your AI system will need both of those capabilities to make the best customer experience decisions. What’s not included in that list is updating the customer profiles in real time as new data arrives, or rebuilding the identity graph that connects data from different sources to the same customer. Some CDPs can do those things in real time but most cannot. Only some applications really need them.

The third requirement relates to identity management. The CDP needs to know which identifiers, such as email, telephone, device ID, and postal address, refer to the same customer. At CDP Institute, we don’t feel the CDP itself needs to find the matches between those identifiers. That’s because there’s lots of good outside software to do that. We do feel the CDP needs to be able to maintain a current list of matches, or identity graph, as matches are added or removed over time. That’s what lets the CDP combine data from different sources into unified profiles.

My second cluster of advice relates to AI. I’d be surprised if anyone reading this hasn’t at least tested ChatGPT, Bing Chat Search, or something similar. At the same time, there’s quite a bit of research showing that relatively few companies have moved beyond the testing stage to put the advanced AI tools into production.

And that’s really okay, so my first piece of advice is: don’t be hasty.  You should certainly be testing and probably deploying some initial applications, but don’t feel you must plow full speed ahead or you’ll fall behind. Most of your competitors are moving slowly as well. 

That said, you do need to train your people in using AI. They don’t necessarily need to become expert prompt writers, since the systems will keep getting smarter so specific prompting skills will become obsolete quickly. But they do need to build a basic understanding of what the systems can and can’t do and what it’s like to work with them. That will change less quickly than things like a user interface. The more familiar your associates become with AI, the less likely they are to ask it to do something it doesn’t do well or that creates problems for your company.

Third, pay close attention to AI’s ability to ingest and use your company’s own data. Remember the game-changing marketing application for AI is to create an optimal experience for each individual. The means it must be able to access that individual’s data. This is an ability that was barely available in tools like ChatGPT six months ago, but has now become increasingly common. Still, you can bet there will be huge differences in how different products handle this sort of data loading. Some will be designed with customer experience optimization in mind and some won’t. So be sure to look closely at the capabilities of the systems you consider.

Fourth, and closely related, the industry faces a huge backlog of unresolved issues relating to privacy, intellectual property ownership, security, bias, and accuracy. Again, these are all evolving with phenomenal speed, so it’s hard for anyone to keep up – even including the AI specialists themselves. Unless that’s your full time job, I suggest that you keep a general eye on those developments so you’re aware of issues that might come with any particular application you’re considering. Then, when you do begin to explore an application, you’ll know to bring in the experts to learn the current state of play.

Similarly, keep an eye on the new capabilities that AI systems are adding. This is also evolving at incredible speed. Some of those capabilities may change your opinion of what the systems can do well or poorly. Some will be directly relevant to your needs and may form the basis for powerful new applications. We’re still far away from the “one AI to rule them all” that will be the ultimate game-changer for marketing. But it’s coming, so be on the alert.

This brings us back to the third level of marketing technology: delivery. Will there be yet one more game-changer, a unified delivery system that offers the same simplification advantages as unified data and decision layers? The giant suite vendors like Salesforce and Adobe certainly hope so, as do the unified messaging platforms like Braze and Twilio. The fact that we can list those vendors offers something of an answer: some companies think a unified delivery layer is possible and would argue they already provide one. I’m not so confident because new channels keep popping up and it’s nearly impossible for any one vendor to support them all immediately.

What seems more likely is a hybrid approach where most companies have a core delivery platform that handles basic channels like email, websites, and advertising and supports third-party plug-ins to add channels or features they do not. These platforms are already common, so this is less a prediction of the future than an observation of the present, which seems likely to continue. The core delivery platform offers a single connection to the decision layer run by the AI. This gives the primary benefits gained from a single connection between layers, although I wouldn’t call it a game-changer only because it already exists.

My recommendation here is to adapt the delivery platform approach, seeking a platform that is as open as possible to plug-ins so it can coordinate experiences across as many channels as possible. In this view, the delivery platform is really an orchestration system. Which channels are actually delivered in the delivery platform and which are delivered by third-party plug-ins is relatively unimportant from an architectural point of view.  Of course, vendors, marketers, and tech staff will all care a great deal about which tools your company chooses.

While we’re discussing architectural options, I should also mention that the big suites would argue that data, decision, and delivery layers should all be part of one unified product, reducing integration efforts to a minimum. That may well be appealing, but remember that most of the integrated suites were cobbled together from separate systems that were purchased by the vendors over time. Connecting all the bits can be almost as much work as connecting products from different vendors.

And, of course, relying on a single vendor for everything means accepting all parts of their suite – some of which may not be as good as tools you could purchase elsewhere. The good news is most suite vendors have connectors that enable users to use external systems instead of their own components for important functions. As always, you have to look in detail at the actual capabilities of each system before judging how well it can meet your needs.

So, where does this leave us?

We’ve seen that the object of the modern marketing game is to deliver the optimal experience for each customer. And we’ve seen that challenges to winning that game include organizational conflicts, fragmented data, and fragmented decision and delivery systems.

We’ve also seen that the combination of customer data platforms and AI systems can indeed be game-changing, because CDPs create the unified data that AI systems need to deliver experiences that are optimized with a previously-impossible level of accuracy.

CDPs and AI won’t fix everything. Organization conflicts will still remain, since other departments can’t be expected to turn over all responsibility to marketing. And fragmentation will probably remain a feature of the delivery layer, simply because new opportunities appear too quickly for a single delivery system to provide everything.

In short, the game may change but it never ends. Build strong systems that can meet current requirements and can adapt easily to new ones. But never forget that change is inherently unpredictable, so even the most carefully crafted systems may prove inadequate or unexpectedly become obsolete. Adapting to those situations is where you’ll benefit from investment in the most important resource of all: people who will work to solve whatever problems come up in the interest of your company and its customers.

And remember that no matter how much the game changes, the goal is always the same: the best experience for each customer. Make sure everything you do is has that goal in mind, and success will follow.

In a World Run by AI, The Best Data Wins

Like everyone else in martech land, I’ve been pondering the future of marketing in a world populated with AI. Most research I’ve seen agrees with this Hubspot report  that marketers’ top application for generative AI has been content creation (48%), followed closely by data analysis (45%) and learning how to things (45%). So it’s fairly clear that the immediate impact of AI will be to let marketers create vastly more copy, including real-time messages tailored to specific individuals. 

Extending this a bit, we can expect those real-time messages to be delivered within ever-more-finely tailored campaign flows (something else that AI can easily generate) or without formal campaign structures at all. These messages will be orchestrated and optimized across all channels, fulfilling the omnichannel vision that has hovered like a mirage on the industry horizon for decades. 

(Whether this results in more or fewer martech applications is a separate question. I tend to agree with this GP Bullhound study, which argues that “Any application introduced as an AI-enhanced alternative to an existing application or as a feature to a platform will likely become redundant when that incumbent platform implements the same AI features.”  This suggests that fewer specialist martech products will be needed, since truly new applications are relatively rare. Moreover, the productivity benefits of integrated suites are magnified when AI can easily orchestrate tasks within the suite, but not those on the outside. And, AI systems are inherently more adaptable than traditional software, which must be explicitly programmed for each new task. So extending existing applications becomes easier when AI enters the picture.)

Of course, what the humans reading this piece really care about is the role that they will play in this AI-managed universe. (Come to think of it, the AIs reading this may also care more than we know.) We’re frighteningly close to living the old joke about the factory of the future, where the one human employee's job is to feed the dog, and the dog's job is to keep the human away from the equipment. 

The conventional answer to that question is that humans will still be needed to provide the creative and emotional insight that AIs cannot deliver. (That’s what ChatGPT told me when I asked.)  Frankly, I don’t buy it: just as people who can fake sincerity, have it made, the AIs will quickly learn to mimic creativity and find which emotion-based messages work best.

Still, let’s be a bit optimistic and assume the marketing department of the future includes one human whose job is to feed the AI. Let’s even assume that human adds some creative and emotional value. The net result across the marketing industry as a whole will be that every company produces a wonderfully high and consistent level of marketing outputs. While that’s great in many ways, it also means that better marketing will no longer be a competitive differentiator. It’s a repeat of the situation in manufacturing since the 1970’s, when quality best practices were applied everywhere, so the differences between the best and worst products were often too small to matter. The winners in that world were companies who could use marketing to differentiate what in fact were commodity products. But when AI lets every company produce great marketing, then marketing itself is also a commodity.

So, how will companies compete in this new world? I’ve already argued it won’t be through emotional insight or creativity. I briefly thought that people might do better than machines at adapting to rapid change. The theory was that AI can only be trained on historical data so it will flounder when faced with unexpected events – which are increasingly common. But, let’s face it: humans also flounder in the face the unexpected.  It’s not at all clear they’ll be better than machines at predicting abrupt change, recognizing changed circumstances, collecting new evidence, and finding the new best actions. In fact, humans are heavily biased in favor of making decisions based on past experience, so I’d probably bet on the AI.

But all is not lost. I still see two ways for companies, and the humans who run them (for now), to distinguish themselves.

The first is customer experience. If you consider the true commodity industries, such as telecommunications, air travel, hotels, and financial services, what makes customers loyal to one or another provider is rarely the actual product or price. Rather, it’s the way they are treated. As a baseline, customers expect reasonable service, delivered pleasantly. But loyalty is really won or lost when there’s a problem or special request. This gives the company a chance to distinguish itself, either against an actual competitor or against a customer’s expectations of how they should be treated. 

Company policies and systems play a large role in what’s possible, but ultimately it’s the front-line employee whose training, attitudes, and choices make or break the experience. In the future, AI will surely play a larger role in managing these interactions. But, as with marketing, this will yield largely similar results because companies will be using similar AI systems. The differentiator will be the company’s people.

That's good to know, but customer experience is rarely under marketers’ direct control. This leaves one final straw for them to lean on: the data used to train their AIs.

Remember, AI programs themselves will be widely available. As with any other technology, the difference in results will depend on how they’re used, not any difference in the technology itself. Once AI systems are fully deployed, most decisions about things like content and program design will be made by the system, limiting the impact of user choice on the outcomes. But the one thing that will remain under users’ control is the data fed into the AI systems. It’s differences in that data that will drive differences in outcomes. In short: whoever has the best data, wins.

This is an area where marketers have a major role to play. They may not control the various internal and external systems who provide customer data. But they will have a large say in what those systems feed into the primary customer data store which, in turn, will feed the AI. Marketers who select the best data feeds will have a more effective AI and, thus, better final results. This will be true even though AI makes data collection easier: that will reduce the technical barriers to data gathering at all companies, but most barriers are actually organizational and budgetary. Those are company-specific decisions whose outcomes marketers can affect.

This may not be the cheeriest news you’ve heard today. Few marketers chose their career because they wanted to fight political battles with IT and customer success teams. But it does mean that many marketers can continue to play a major role in the success of their organizations, even as most of the traditional marketing tasks are taken over by AI. No doubt, the marketers who remain employed will sneak a few of their creative and emotional insights into AI prompts.  Maybe their inputs will even make a positive difference. But what will really matter is how good a job they do at feeding the AI with the best possible data.  That's what will empower it to deliver better results than the competition.

Remember: in a world run by AI, the best data wins.

Chat-Based Development Changes the Build vs Buy Equation

The size of most markets is governed by a combination of supply and demand. Typically one or the other is limited: while there may be a bottomless appetite for chocolate, there is only so much cocoa in the world; conversely, while there is a near-infinite supply of Internet content, there are only so many hours available to consume it. The marketing technology industry has been a rare exception with few constraints on either factor. The supply of new products has grown as software-as-a-service, cloud platforms, low-code development tools, and other technical changes reduce development cost to something almost anyone with a business idea can afford, and widely available funding reduces barriers still further. Meanwhile, the non-stop expansion in marketing channels and techniques has created an endless demand for new systems. Further accelerating demand, the spread of digital marketing to nearly all industries has powered development of industry-specific versions of many product types.

Despite the visibility of these factors, the uninterrupted growth of martech has been accompanied almost from the start by predictions it will soon stop. This based less on any specific analysis than a fundamental sense that what goes up must come down, which is generally a safe bet. There’s also an almost esthetic judgement that a market so large is just too unruly, and that the growing number of systems used by each marketing department must indicate substantial waste and lost control.

One common metric cited as evidence for excess martech is utilization rate: Gartner, the high priests of rational tech management, reported last year that martech utilitzation rates fell from 58% in 2020 to 42% in 2022 and ranted in a recent press release that “The willingness to let the majority of their martech stack sit idle signifies a fundamental resource disconnect for CMOs. It’s difficult to imagine them leaving the same millions of dollars on the table for agencies or in-house resources. This trade-off of technology over people will not help marketing leaders accelerate out of the challenges a recession will bring.” They were especially incensed that their data showed CMOs are increasing martech’s share of the marketing budget, comparing them to “gamblers looking to write-off their losses with the next bet.” (They probably meant “recoup”, not “write-off” their losses.)

This isn’t just a Gartner obsession. Reports from Integrate, Wildfire, and Ascend2 also cite low utilization rates as evidence of martech overspending.

It aint necessarily so.

For one thing, underutilization is common in all departments, not just marketing.  Nexthink found half of all SaaS licenses are unused. Zylo put the average company-wide utilization at 56% and Productiv put it at 45%. (These studies measure app usage, not feature usage. But you can safely bet that feature usage rates are similarly low through the organization.)

More fundamentally, there’s no reason to expect people to use all the features of the products they buy. What fraction of Excel or Powerpoint features do you use? What’s important is finding a system with the features you need; if it has other features you don’t need, that’s really okay so long as you’re not paying extra or finding they get in your way.  Software vendors routinely add features required by a subset of their users. Since that helps them serve more needs for more clients, it’s a benefit, not a problem.

The real problem isn’t presence of features you don’t need, but the absence of features you do. That’s what pushes companies to buy new systems to fill in the gaps. As mentioned earlier, the great and on-going growth of the martech industry is due in good part to new technologies and channels creating new needs which existing systems don’t fill. That said, it's true that some purchases are unnecessary: buyers don’t always realize that a system they own offers a capability they need. And, since vendors add new features all the time, a specialist system may become redundant if the same features are added to a company’s primary system.

In both of those situations, avoiding unnecessary expense depends on marketers keeping themselves informed about what their current systems can do. This is certainly a problem: thanks to the miracle of SaaS, it’s often easier to buy a new system than the fully research the features of systems already in place. (Installing and integrating the new system will probably be harder, but that comes later.) So we do see reports of marketers trying to prune unnecessary systems from their stacks: for example, the previously-cited Integrate report found that 26% of marketers expected to shrink their stacks. Similarly, the CMO Council found 25% were planning to cut martech spend and Nielsen said 24% were planning martech reductions. (Before you sell all your martech stock, you should also know that each report found even more marketers were planning to increase their martech budgets: 32% for Integrate, 36% for CMO Council, and 56% for Nielsen.)

Let’s assume, if only for argument’s sake, that marketers are reasonably diligent about buying only products that close true gaps. New gaps will continue to appear so long as innovation continues, and there’s no reason to expect innovation will stop. So can we expect the growth in martech products will also continue indefinitely?

Until recently I would have said yes (unless budgets are severely crimped by recession). But the latest round of AI-based tools has me reconsidering. 

Specifically, I wonder whether marketers will close gaps by building their own applications with AI instead of buying applications from someone else. If so, industry expansion could halt.

Marketers have been using no-code technologies to build their own applications for some time. Some of these may have depressed demand for new martech products but I don’t think the impact has been substantial. That’s because no-code tools are usually constrained in some way: they’re an interface on top of an existing application (drag-and-drop journey builders), a personal productivity tool (Excel), or limited to a single function (Zapier for workflow automation). Building a complete application with such limited tools is either impossible or not worth the trouble.

The latest AI systems change this. Chat-based interfaces let users develop an application by describing what they want a system to do. This enables the resulting system to perform a much broader set of tasks than a drag-and-drop no-code interface. That said, the actual capabilities depend on what the model is trained to do. Today, it still takes considerable technical knowledge to include the right technical details in the instructions and to refine the result. But the AIs will quickly get better at working out those details for themselves, drawing on larger and more sophisticated information about how things should be done. Microsoft’s latest description of copilots and plug-ins points in this direction: “customers can use conversational language to create dataflows and data pipelines, generate code and entire functions, build machine learning models or visualize results.”

What’s important is that the conversational interface will drive a system that automatically employs professional-grade practices to ensure the resulting application is properly engineered, tested, deployed, and documented. In effect, it pairs the business user with a really smart developer who will properly execute what the user describes, let the user examine the results, and tweak the product until it meets her goals. Human developers who can do this are rare and expensive.  AI-based developers who can do this should soon be common and almost free.

This change overcomes the fundamental limitation of most user-built apps: they can only be deployed and maintained by the person who built them and are almost guaranteed to violate quality, security and privacy standards. This issue – let’s call it governance – has almost entirely blocked deployment of user-built systems as enterprise applications. Chat-built systems remove that barrier while fundamentally altering the economics of system development.  More concretely: building becomes cheaper so buying becomes less desirable.  This could significantly reduce the market for purchased software.

Anyone who has ever been involved in an enterprise development project will immediately recognize the flaw in this argument: there’s never just one user and most development time is actually spent getting the multiple stakeholders to agree on what the system should do. Agile methodologies mitigate these issues but don’t entirely eliminate them. 

Whether chat-driven development can overcome this barrier isn’t clear.  It will certainly speed some things up, which might enable teams to build and compare alternative versions when trying to reach a decision. But it might also enable independent users to create their own versions of an application, which would probably lead to even stiffer resistance to adopting someone else’s approach. 

One definite benefit should be that chat-based applications will learn to explain how they function in terms that humans can understand. Responsible tech managers will insist on this before they deploy systems those applications create.

In any event, I do believe that chat-built systems will make home-built software a viable alternative to packaged systems in a growing number of situations. This will especially apply to systems that fill small, specialized gaps created as new marketing technologies develop. Since filling these gaps has been a major factors behind the continuous growth of martech, industry growth may slow as a result.

Incidentally, we need a name for chat-based development. I’ll nominate “vo-code”, as shorthand for voice-based coding, since it fits in nicely with pro-code, low-code, and no-code. I could be talked into “robo-code” for the same reason.

Let's Stop Confusing LEGO Blocks with Computer Software

Can we retire Lego blocks as an analogy for API connections?  Apart from being a cliché that’s as old as the hills and tired as a worn-out shoe, it gives a false impression of how easy such connections are to make and manage.  

Very simply, all Lego block connectors are exactly the same, while API connections can vary greatly.  This means that while you can plug any Lego block into any other Lego block and know it will work correctly, you have to look very carefully at each API connector to see what data and functions it supports, and how these align with your particular requirements.   Often, getting the API to do what you need will involve configuration or even customization – a process that can be both painstaking and time consuming.  When was the last time you set parameters on a Lego block?

There’s a second way the analogy is misleading.  Lego blocks are truly interchangeable: if you have two blocks that are the same size and shape, they will do exactly the same thing (which is to say, nothing; they’re just solid pieces of plastic).  But no two software applications are exactly the same.  Even if they used the same API, they would have different internal functions, performance characteristics, and user interfaces.  Anyone who has tried to pick a WordPress plug-in or smartphone app knows the choice is never easy because there are so many different products.  Some research is always required, and the more important the application, the more important it is to be sure you select a product that meets your needs.  

This is why companies (and individuals) don’t constantly switch apps that do the same thing: there’s a substantial cost to researching a new choice and then learning how to use it.  So people stick with their existing solution even if they know better options are available.  Or, more precisely, they stick with their existing solution until the value gained from changing to a new one is higher than the cost of making a switch.  It turns out that’s often a pretty high bar to meet, especially because the limiting factor is the time of the person or people who have to make the switch, and very often those people have other, higher priority tasks to complete first.

I’ve made these points before, although they do bear repeating at a time when “composability” is offered as a brilliant new concept rather than a new label for micro-services or plain old modular design.  But the real reason I’m repeating them now is I’ve seen the Lego block analogy applied to software that users build for themselves with no-code tools or artificial intelligence.  The general argument is those technologies make it vastly easier to build software applications, and those applications can easily be connected to create new business processes.

The problem is, that’s only half right.  Yes, the new tools make it vastly easier for users to build their own applications.  But easily connected?  

Think of the granddaddy of all no-code tools, the computer spreadsheet.  An elaborate spreadsheet is an application in any meaningful sense of the term, and many people build wonderfully elaborate spreadsheets.  But those spreadsheets are personal tools: while they can be shared and even connected to form larger processes, there’s a severe limit to how far they can move beyond their creator before they’re used incorrectly, errors creep in, and small changes break the connection of one application to another.  

In fact, those problems apply to every application, regardless of who built it or what tool they used.  They can only be avoided if strict processes are in place to ensure documentation, train users, and control changes.  The problem is actually worse if it’s an AI-based application where the internal operations are hidden in a way that spreadsheet formulas are not.

And don’t forget that moving data across all those connections has costs of its own.  While the data movement costs for any single event can be tiny, they add up when you have thousands of connections and millions of events.  This report from Amazon Prime Video shows how they reduced costs by 90% by replacing a distributed microservices approach with a tightly integrated monolithic application.  Look here for more analysis.  Along related lines, this study found that half of “citizen developer” programs are unsuccessful (at least according to CIOs), and that custom solutions built with low-code tools are cheaper, faster, better tailored to business needs, and easier to change than systems built from packaged components.  It can be so messy when sacred cows come home to roost.

In other words, what’s half right is that application building is now easier than ever.  What’s half wrong is the claim that applications can easily be connected to create reliable, economical, large-scale business processes.  Building a functional process is much harder than connecting a pile of Lego blocks.

There’s one more, still deeper problem with the Lego analogy.  It leads people to conceive of applications as distinct units with fixed boundaries.  This is problematic because it creates a hidden rigidity in how businesses work.  Imagine all the issues of selection cost, connection cost, and functional compatibility suddenly vanished, and you really could build a business process by snapping together standard modules.  Even imagine that the internal operations of those modules could be continuously improved without creating new compatibility issues.  You would still be stuck with a process that is divided into a fixed set of tasks and executes those tasks either independently or in a fixed sequence, where the output of one task is input to the next.  

That may not sound so bad: after all, it’s pretty much the way we think about processes.  But what if there’s an advantage to combining parts of two tasks so they interact?  If the task boundaries are fixed, that just can’t be done.  

For example, most marketing organizations create a piece of content by first building a text, and then creating graphics to illustrate that text.  You have a writer here, and a designer there.  

The process works well enough, but what if the designer has a great idea that’s relevant but doesn’t illustrate the text she’s been given? Sure, she could talk to the writer, but that will slow things down, and it will look like “rework” in the project flow – and rework is the ultimate sin in process management.  More likely, the designer will just let go of her inspiration and illustrate what was originally requested.  The process wins.  The organization loses.

AI tools or apps by themselves don’t change this.  It doesn’t matter if the text is written by AI and then the graphics are created by AI.  You still have the same lost opportunity -- and, if anything, the AIs are even less likely than people to break the rules in service of a good idea.

What’s needed here is not orchestration, which manages how work moves from one box to the next, but collaboration, which manages how different boxes are combined so each can benefit from the other.

This is a critical distinction, so it’s worth elaborating a bit.  Orchestration implies central planning and control: one authority determines who will do what and when.  Any deviation is problematic.  It’s great for ensuring that a process is repeated consistently, but requires the central authority to make any changes.  The people running the individual tasks may have some autonomy to change what they do, but only if the inputs and outputs remain the same.  The image of one orchestra conductor telling many musicians what to do is exactly correct.  

Collaboration, on the other hand, assumes people work as a team to create an outcome, and are free to reorganize their work as they see fit.  The team can include people from many different specialties who consult with each other.  There’s no central authority and changes can happen quickly so long as all team members understand what they need to do.  There’s no penalty for doing work outside the standard sequence, such as the designer showing her idea to the copywriter.  In fact, that’s exactly what’s supposed to happen.  The musical analogy is a jazz ensemble although a clearer example might be a well-functioning hockey or soccer team: players have specific roles but they move together fluidly to reach a shared goal as conditions change.

If you want a different analogy: orchestration is actors following a script, while collaboration is an improv troupe reacting to each other and the audience.  Both can be effective but only one is adaptable.

Of course, there’s nothing new about collaboration.  It’s why we have cross-functional teams and meetings.  But the time those teams spend in meetings is expensive and the more people and tasks are handled in the same team, the more time those meetings take up.  Fairly soon, the cost of collaboration outweighs its benefits.  This is why well-managed companies limit team size and meeting length.

What makes this important today is that AI isn’t subject to the same constraints on collaboration as mere humans.  An AI can consider many more tasks simultaneously with collaboration costs that are close to zero.  In fact, there’s a good chance that collaboration costs within a team of specialist AIs will be less than communication costs of having separate specialist AIs each execute one task and then send the output to the next specialist AI.

If you want to get pseudo-mathy about it, write equations that compare the value and cost added by collaboration, with the value and cost of doing each task separately.  The key relationship as you add tasks is that collaboration cost grows exponentially while value grows linearly.*    This means collaboration cost increases faster than value, until at some point it exceeds the value.  That point marks the maximum effective team size.   

We can do the same calculation where the work is being done by AIs rather than humans.  Let’s generously (to humans) assume that AI collaboration adds the same value as human collaboration.  This means the only difference is the cost of collaboration, which is vastly lower for the AIs.  Even if AI collaboration cost also rises exponentially, it won’t exceed the value added by collaboration until the number of tasks is very, very large. 

Of course, finding the actual values for those graphs would be a lot of work, and, hey, this is just a blog post.  My main point is that collaboration allows organizations to restructure work so that formerly separate tasks are performed together.  Building and integrating task-specific apps won’t do this, no matter how cheaply they’re created or connected.  My secondary point is that AI increases the amount of profitable collaboration that’s possible, which means it increases the opportunity cost of sticking with the old task structure.  

As it happens, we don’t need to imagine how AI-based collaboration might work.  Machine learning systems today offer a real-world example the difference between human work and AI-based collaboration.  

Before machine learning, building a predictive model typically followed a process divided into sequential tasks: the data was first collected, then cleaned and prepped for analysis, then explored to select useful inputs, then run through modeling algorithms.  Results were then checked for accuracy and robustness and, when a satisfactory scoring formula was found, it was transferred into a production scoring system.  Each of those tasks was often done by a different person, but, even if one person handled everything, the tasks were sequential.  It was painful to backtrack if, say, an error was discovered in the original data preparation or a new data source became available late in the process.

With machine learning, this sequence no longer exists.  Techniques differ, but, in general, the system trains itself by testing a huge number of formulas, learning from past results to improve over time.  Data cleaning, preparation, and selection are part of this testing, and may be added or dropped based on the performance of formulas that include different versions.  In practice, the machine learning system will probably draw on fixed services such as address standardization or identity resolution.  But it at least has the possibility of testing methods that don’t use those services.  More important, it will automatically adjust its internal processes to produce the best results as conditions change.  This makes it economical to rebuild models on a regular basis, something that can be quite expensive using traditional methods.

Note that it might possible to take the machine learning approach by connecting separate specialist AI modules.  But this is where connection costs become an issue, because machine learning runs an enormous number of tests.  This would create a very high cumulative cost of moving data between specialist modules.  An integrated system will have fewer internal connections, keeping the coordination costs to a minimum.

I may have wandered a bit here, so let me summarize the case against the Lego analogy:

• It ignores integration costs.  You can snap together Lego blocks, but you can’t really snap together software modules.
  

• It ignores product differences.  Lego blocks are interchangeable, but software modules are not.  Selecting the right software module requires significant time, effort, and expertise.
  

• It prevents realignment of tasks, which blocks improvements, reduces agility, and increases collaboration costs.  This is especially important when AI is added to the picture, because expanded collaboration is a major potential benefit from AI technologies.

Lego blocks are great toys but they’re a poor model for system development.  It’s time to move on.
 

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* My logic is that collaboration cost is essentially the amount of time spent in meetings.  This is a product of the number of meetings and the number of people in each meeting.  If you assume each task adds one more meeting and one more team member, and each meeting last one hour, then a one-task project has one meeting with one person (one hour, talking to herself), a two-task project has two meetings with two people in each (four hours), a three-task project has three meetings with three people (nine hours), and so on.  When tasks are done sequentially, there is presumably a kick-off at the start of each task, where the previous team hands the work off to the new team: so each task adds one meeting of two people, or two hours, a linear increase.  

There’s no equivalently easy was to estimate the value added by collaboration, but it must grow by some amount with each added task (i.e., linearly), and it’s likely that diminishing returns prevent it from increasing endlessly.  So linear growth is a reasonable, if possibly conservative, assumption.  It's more clear that cumulative value will grow when tasks are performed sequentially, since otherwise the tasks wouldn't be added.  Let's again assume the increase is linear.  Presumably the value grows faster with collaboration than sequential management, but if both are growing linearly, the difference will grow linearly as well.
 

Will ChatGPT Destroy Martech?

Like everyone else, I’ve been pondering what generative AI means for martech, marketing, and the world in general.  My crystal ball is no clearer than yours but I’ll share my thoughts anyway.

Let’s start by looking how past technology changes have played out.  My template is the transition from steam to electric power in factories.  This happened in stages: first, the new technology was used in exactly the same way as the old technology (in factories, this meant powering the shafts and belts that previously were powered by waterwheels or steam engines).  Then, the devices were modified to make better use of the new technology’s capabilities (by attaching motors directly to machine tools).  Finally, the surrounding architecture was changed to take advantage of the new possibilities (freed from the need to connect to a central mechanical energy source, factories went from being small, vertical structures to large horizontal ones, which allowed greater scale and efficiency).  We should probably add one more stage, when the factories started to produce new products that were made possible by the technology, such as washing machines with electric motors.

During the earliest stages of the transition, attention focused on the new technology itself: factories had “chief electricians” and companies had “chief electricity officers”, whose main advantage was they were among the few people who understood new technology.  Those roles faded as the technology became more widely adopted.  The exact analogy today is “prompt engineer” in AI, and will likely be even shorter-lived as a profession.  

Most of the discussion I see today about generative AI is very much stuck in the first phase: vendors are furiously releasing tools that replace this worker or that worker, or even promising a suite of tools that replace pretty much everyone in the marketing department.  (See, for example, this announcement from Zeta Global https://zetaglobal.com/press-releases/zeta-introduces-generative-ai-agents-powered-by-zoe/  .)  Much debate is devoted to whether AI will make workers in those jobs more productive (hurrah!) or replace them entirely (boo!)   I don’t find this particular topic terribly engaging since the answer is so obviously “both”: first the machines will help, and, as they gradually get better at helping, they will eventually take over.  Or, to put it in other terms: as humans become more productive, companies will need fewer of them to get their work done.  Either way, lots of marketers lose their jobs.  

(I don’t buy the wishful alternative that the number of marketers will stay the same and they’ll produce vastly more, increasingly targeted materials.  The returns on the increasing personalization are surely diminishing, and it’s unrealistic to expect company managers to pass up an opportunity to reduce headcount.)

While the exact details of the near future are important – especially if your job is at stake – this discussion is still about the first stage of technology adoption, a one-for-one replacement of the old technology (human workers) with the new technology (AI workers).   The much more interesting question is what happens in the second and third stages, when the workplace is restructured to take full advantage of the new technology’s capabilities.

I believe the fundamental change will be to do away with the separate tasks that are now done by specialized individuals (copywriters, graphic designers, data analysts, campaign builders, etc.).  Those jobs have evolved because each requires complex skills that take full time study and practice to master.  The division of labor has seemed natural, if not inevitable, because it mirrors the specialization and linear flow of a factory production line – the archetype for industry organization for more than a century.  

But AI isn’t subject to the same constraints as humans.  There’s no reason a single AI cannot master all the tasks that require different human specialists.  And, critically, this change would bring a huge efficiency advantage because it would do away with the vast amount of time now spent coordinating the separate human workers, teams, and departments.  There would be other advantages in greater agility and easier data access.  Imagine that the AI can get what it needs by scanning the enterprise data lake, without the effort now needed to transform and load it into warehouses, CDPs, predictive modeling tools, and other systems.  Maintaining those systems takes another set of specialists whose jobs likely to vanish, along with all the martech managers who spend their time connecting the different tools.  

Of course, the vision of “citizen developers” using AI to create sophisticated personal applications on the fly is entirely irrelevant when the citizen developers themselves no more have jobs.  Thousands of independent applications that make up today’s martech industry may vanish, unless the marketing Ais build and trade components among themselves – which could happen.

So far, I’ve predicted that monolithic AI systems rather than teams of (human or robotic) specialists will create marketing programs similar to today’s campaigns and interactions.  But that assumes there’s still a demand for today’s types of campaign and interactions.  This brings us to the final type of change: in the outputs themselves.

Again, we may be in for a very fundamental transformation.  The output of a marketing department is ultimately determined by the how people buy things.  It’s a safe bet that AI will change that dramatically, although we don’t know exactly how.   For sake of argument, let’s assume that people adopt AI agents to manage more of their personal lives for them (pretty likely) and that they delegate most purchasing decisions to those agents (less certain but plausible, and again already happening to a limited degree).  If that happens, our AI marketing brains will be selling to other Ais, not to people.  To imagine what that looks like, we again have to move beyond expecting the AI to do what people do now, and look at the best way for an AI to achieve the same results.

If you think about marketing today – and for all the yesterdays that ever were – it’s based on the fundamental fact that humans have a limited amount of attention.  Every aspect of marketing is ultimately aimed to capturing that attention and feeding the most effective information to the human during the time available.  

But AIs have unlimited attention.

If an AI wants to buy a product, it can look at every option on the market and collect all the information available about each one.  Capturing the AI’s attention isn’t an issue; presenting it with the right information is the challenge.  This means the goal of the marketing department is to ensure product information is available everyplace the AI might look, or maybe in just one place, if you can be sure the AI will look there.   Imagine the world as a giant market with an infinite number of sellers but also buyers who can instantly and simultaneously visit every seller and gather all the information they provide.  The classical economist’s fantasy – perfect market, perfect information, no friction – might finally come true.

And, also as the classical economists dream, the buyers will be entirely rational, not swayed by emotional appeals, brand identity, or personal loyalties.  (At least, we assume that AI buyers are rational and objective, although it won’t be easy to ensure that’s the case. That relates to trust, which is a topic for another day.)

If the role of marketing is to lay out virtual products on a virtual table in a virtual market stall, there’s no need for advertising: every buyer will pass by every stall and decide whether to engage.  With no need for advertising, there’s no need for targeting or personalization and no need for personal data to drive that targeting or personalization.  Privacy will be preserved simply because advertisers will no longer have any reason to violate it.

The key to business success in this world of omniscient, rational buyers is having a superior product, and, to a lesser extent, presenting product information in the most effective way possible.  There’s still some room for puffery and creativity in the presentation, although presumably mechanisms such as consumer reviews and independent research will keep marketers reasonably honest.  (Trust, again.)   There’s probably more room for creativity in developing the products themselves and constructing a superior experience that extends beyond the product to the full package including pricing, service, and support.

We can expect the AIs to play a major role in developing those new and optimal products and experiences, although I suspect the pro-human romantic in everyone reading this (except you, Q-2X7Y) hopes that people will still have something special to contribute.  But, wherever the products themselves come from, it will be up to the marketing AI to present them effective to the AI shoppers.

(Side note: today’s programmatic ad buying marketplace comes fairly close to the model I’m proposing.  The obvious difference is the auction model, where buyers bid for a limited supply of ad impressions.  It’s conceivable that the consumer marketplace would also use an auction.  Again, just because most of today’s shopping is based on a fixed price model, we shouldn’t assume that model will continue in the future.  Come to think of it, an auction would probably be the best approach, since buyers could adjust their bids based on their current needs and preferences, and sellers could adjust them based on inventory and current demand.  In the traditional marketplace, this would be called haggling, or negotiating, and it's the way buying has been done for most of history.  With perfect information on both sides, the classical economists would be pleased yet again. It could be fruitful to explore other analogies with the programmatic marketplace when trying to predict how the AI-to-AI marketplace will play out.)

(You could also argue that Amazon, Expedia, and similar online marketplaces already offer a place for virtual sellers to offer their virtual wares to all comers.  Indeed they do, but the exact difference is that searching on Amazon requires a painfully inefficient use of human time.  If Amazon evolves a really good AI-based search method, and can convince users to share enough data to make the searches fully personalized, it could indeed become the basis for what I’m proposing.  The biggest barrier to this is more likely to be trust than technology.  It’s also worth noting that traditional marketing barely exists on those marketplaces.  The travel industry, where most marketing is centered on loyalty programs, may be an early indicator of where this leads.)

So what role, exactly, do humans play in this vision?  

As consumers, humans are no longer buyers.  Instead, they receive what’s purchased on their behalf.  So, their main role is to pick an AI and train it to understand their needs.  Of course, most of that training will happen without any direct human effort, as the AI watches what its owner does.  (I almost wrote “master”, but it’s not clear who’s really in charge.)  For people with disposable income, purchases are likely to move away from basic goods to luxury goods and experiences.  Those are inherently less susceptible to purely rational buying decisions, so there’s a good chance that conventional buying and attention-based marketing will still apply.

As workers, humans are in trouble.  Farming and manufacturing have been shrinking for decades and AI is likely to take over many of the remaining service jobs.  Some conventional jobs will remain to do research and supervise the AI-driven machines, and there may more jobs where it matters that the worker is human, such as sports and handcrafts, and where human interaction is part of the value, such as healthcare.  But total employment seems likely to decrease and income inequalities to grow.  It’s possible that wealthy nations will provide a guaranteed annual income to the under-employed.  But even if that happens, meaningful work will become harder to find.

I’ll admit this isn’t a terribly pleasant prospect.  The good news is, predictions are hard, so the odds are slim that I’m right.   I’m also aware that we’re at the peak of the hype cycle for ChatGPT and perhaps for AI in general.  Maybe what I’ve described above isn’t technically possible.  But, given how quickly AI and the underlying technologies evolve, I wouldn’t bet on technology bottlenecks blocking these changes indefinitely.  Quantum AI, anyone?

All that said, of the three major predictions, I’m most confident about the first.  It’s pretty likely that a monolithic marketing AI will emerge from the specialized AI bots that are being offered today.  The potential benefits are huge, the path from separate bots to an integrated system is an incremental progression, and some people are already moving in that direction.   (Pro tip: it’s easier to predict things that have already happened.)

The emergence of an AI-driven marketplace to replace conventional human buying is much less certain.  If it does happen, the delegation will emerge in stages.  The first will cover markets where the stakes are low and buying is boring.  Groceries are a likely example.  How quickly it spreads to other sectors will depend on how much time people have and how much intrinsic enjoyment they derive from the shopping itself.

The role of humans is least predictable of all.  Mass under-employment probably isn’t sustainable in the long run, although you could argue it’s already the reality in some parts of the world.  The range of possible long-term outcomes runs from delightful to horrific.  Where we end up with depend on many factors other than the development of AI.  The best we can do is try to understand developments as they happen and try to steer things in the best directions possible.