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, initial optimization, incremental innovations, maturity).

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.  

State of Martech Report: Customer Data Platforms Are Evolving, Not Dying

Scott Brinker’s State of Martech report has grown from a one-page logo jigsaw to a small industry, co-authored with MartechTribe’s Frans Riemersma and apparently produced by a small army of elves reviewing thousands of products each year. The latest edition, released yesterday, provides the now-expected deep analysis of industry trends covering category growth, the impact(s) of AI, stack architectures, and more. It’s all interesting and too complicated (or maybe complex?) to recap here, so I can only suggest that you read it on your own. 

In addition to data based on the 15,384 vendors now listed (up 9% from 2024), the report analyzes results from a survey of martech and marketing operations leaders. Again, there’s lots of fascinating information, but I was of course drawn to the sections related to Customer Data Platforms.

On its face, the report offers some pretty bad news for the CDP industry: the fraction of companies citing a CDP as the “center” of their martech stack fell from 15.5% in their similar 2024 survey to 12.5% in 2025. However, the 2025 survey is based on just 96 responses, meaning that’s a swing of three answers, so it’s not cause for too much alarm.* Still, it’s an interesting result, and even more intriguing when you separate B2B respondents (52% of the sample) from others (14% B2C and 34% mixed B2B/B2C). CDPs have never been widely adopted in the B2B world, and indeed, their share is unchanged from 2024 (7.9%) to 2025 (8.0%). The flip side to that is in the B2C and mixed group, the shift is larger: from 26.9% in 2024 to 17.4% in 2025. But, again, bear in mind the sample size: that 17.4% represents seven responses. A shift of three answers is well within the range of statistical noise.

Let’s put sampling issues aside and assume there’s some drop-off in reliance on CDP. The question is, what has taken its place?

Did you just answer “cloud data warehouse, of course”? That’s not entirely wrong – the data warehouse share grew from 20.9% to 23.9%. But the big winner was MAP/CEP (marketing automation/customer experience platform), which grew from 19.4% to 26.1%. CRM grew from 17.9% to 19.5%, or nearly as much as data warehouses. Multi-product suites fell from 1.4% to zero, which hints quite strongly that the respondents heavily skewed away from large enterprises.**

If we combine the MAP/CEP, CRM, and DXP or ecommerce categories into “customer-facing systems”, the combined share of that group grew from 43.3% to 52.1%. So if I were to read any trend from this data, it would be that companies are centering their martech stacks on customer-facing systems, not on data warehouses.

This is actually consistent with the trends we’ve seen in the CDP industry itself, where the most recent major acquisitions (ActionIQ by Uniphore , Lytics by ContentStack, mParticle by Rokt) all involved merging a CDP into a customer-facing product, and where customer-facing vendors like MessageGears, Klayvio, Insider, Listrak, and Braze have added CDP (or CDP-ish) capabilities***. The CDP Institute classifies all of these systems as CDPs, in addition to whatever else they do. So, the way I see it, companies that list those products as the “center” of their martech stack are still building their stack on a CDP, even if they don’t call it one. That’s good news for the industry, not bad.

The survey also takes another look at the role of data warehouses in the martech stack, asking “Do you have a customer data warehouse/lakehouse integrated with your martech stack?” More than half the respondents (56.2%) said they did, a number that climbs to 92% for B2C vendors and 80% for enterprise companies. What’s interesting here is the relation of those answers to the previous question: many more firms have integrated a warehouse than are using their warehouse as the center of their martech stack. This is far from shocking but, again, suggests that the mere presence of a warehouse doesn’t mean that warehouse is the primary customer data store.

If you’re a “warehouse-centric” composable CDP vendor, you could read the relatively small share of warehouse-as-central system either as cause for alarm – the market isn’t as big as you thought – or reason to rejoice – the potential for growth is huge. Both could be true at the same time. But if the primary industry trend is for CDP functions to migrate to customer-facing systems (run by marketing or other business units), then a shift of CDP functions toward the (IT-controlled) data warehouse seems to be the wrong way to bet.  (In this context, the recent sale of leading composable CDP Census to data movement platform Fivetran may signal incipient consolidation in the young-but-already-overcrowded composable CDP sector. That Census was bought by a data movement tool rather than a customer-facing system reinforces the notion that composable CDP products serve IT teams while customer-facing CDPs serve marketers and other end-users.)

Presumably all those non-central warehouses are acting as data sources to CDPs or customer-facing systems with a CDP inside.  Indeed, the Brinker/Riemersma report positions CDPs (stand-alone or embedded) as intermediaries between data systems and activation systems (which they call "systems of knowledge" and "systems of context," respectively), responsible for "organizing or framing the data in a way that best serves more situational needs."  I quite agree, and couldn't have said it better.  I would expand on this by noting that CDPs within customer-facing systems will have direct access to the data those systems generate, so the role of the data warehouse is limited to supplying data that the warehouse collects elsewhere. A CDP with direct access to customer-facing systems overcomes one of the main drawbacks of the warehouse-centric approach, which is that warehouses often can't provide the real-time access to behavior data that's needed for many customer-facing applications of CDP data.

I should stress that CDPs are a bit player in the Brinker/ Riemersma epic. Definitely download the report (it’s free and ungated) and focus on whichever portions you find most relevant. It’s all good.

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*The 2024 survey had 168 responses, of which 60% were B2B, 9% were B2C and 31% were mixed. Doing that math, that’s 67 responses in the B2C and mixed group, of which 18 cited CDP as the center of their stack.

**The 2025 report says 30 of the 96 responded were from ‘enterprise’ organizations, but doesn’t indicate how these split between B2B, B2C and mixed.

***In addition, at least one unacquired CDP (BlueConic) has repositioned as a customer-facing system.

Rokt Buys mParticle

 

mParticle announced yesterday it is being acquired by ecommerce offer personalization vendor Rokt for $300 million.  It’s the third acquisition of a leading CDP in a little over one month, following ActionIQ/Uniphore and Lytics/ContentStack. It’s also the first to announce the deal price.

I’ve already written in this blog about the other two deals and this one is similar enough that most of what I said before still applies. It’s clearly tough to be an independent CDP these days. ActionIQ, Lytics, and mParticle are all technically advanced products, with a particular stress on delivering real-time data – something that’s hard for cloud data warehouses like Snowflake and Google BigQuery. This is probably what made them attractive to their buyers, which are all in different businesses (conversation management, digital experience management, and ecommerce, respectively) but share the need to manage real-time customer interactions.

The buyers' businesses themselves increasingly overlap, so part of their motivation is likely to differentiate themselves from competitors.  Probably more compelling, a CDPlets them position their product as the foundation platform in their clients’ customer management architecture. The platform is a powerful position because it’s tough to change platforms; applications that read data from a platform are precarious because switching applications is relatively easy. (And I do mean relatively: it’s still a lot of work in most cases.) On a more practical level, adding a real-time CDP makes it easier to do personalization, which offers a concrete advantage to potential buyers of these products.  (And, yes, the data is essential for Artificial Intelligence as well.)

It’s interesting that all three buyers are relatively small firms (Uniphore has 851 employees, down 3% in the past two years; ContentStack has 609 employees, up 27% in the past two years; Rokt has 614 employees, up 21% in the past two years) and none are the biggest in their space. While they’re not struggling to survive, they do need a way to distinguish themselves from their many competitors. This is provided by the comprehensive data assembled by a CDP and the superior personalization it makes possible.

From the CDP vendors’ viewpoint, becoming part of a customer-facing system lets them reverse the trend of selling to IT and data departments and move back to the original CDP position of selling to business users. This is a more comfortable environment, since IT and data teams are much more likely than business users to want to build their own solution based on a cloud data warehouse. It also removes some competition from the giant enterprise cloud vendors like Salesforce, Adobe, and Oracle. Those companies are most appealing to central IT and data teams, although they certainly sell to business users as well.

There’s nothing radical about a CDP being embedded in a customer-facing system. In fact, statistics in our Industry Update report have long shown that what we call “campaign” and “delivery” CDPs comprise more than two-thirds of the industry. The truth is, the market long ago decided it preferred a CDP that was part of a larger product. So the latest round of acquisitions reflect a continuation of that situation, not a radical departure.

The obvious question is whether more acquisitions will follow. Likely candidates are firms that, like ActionQI, Lytics, and mParticle, have particularly advanced real-time technologies. This isn’t everyone, since many CDPs rely on standard database technology and differentiate in other ways such as industry expertise or regional presence. The biggest remaining independents (Tealium at 599 employees*, Optimove at 504 employees, and Treasure Data at 484 employees) might all be large enough to continue going it alone or at least might be too big for companies similar to Uniphore, ContentStack and Rokt to swallow. The next level down would be firms including Twilio Segment (a perpetual acquisition candidate as a spin-off from the rest of Twilio), Resulticks (293 employees), and Cordial (245 employees). Note they are still larger than mParticle (237 employees), ActionIQ (152 employees) and Lytics (49 employees).

More likely candidates are firms including BlueConic (177 employees), Redpoint Global (157 employees), and FirstHive (114 employees), as well as a variety of still-smaller firms including Blueshift, Simon Data, Lemnisk, Lexer, Commanders Act, Meiro, NGDATA, and Relay42. Several of this latter group are based outside the U.S., which may reduce their appeal, and of course every company has a unique situation.  So it’s not clear which would actually be open to being acquired or be technically interesting to a potential buyer. But it wouldn’t at all be surprising to see one or more of these join the list of CDPs embedded in customer-facing system.

________________________________________________________________________________________________________________________- *all data from LinkedIn, as captured in the CDP Institute’s latest Industry Update, available here.

CDP Lytics Bought By DXP Customerstack

 

Almost one month to the day since ActionIQ was bought by Uniphore, another first-generation CDP has been sold to a large customer experience vendor.  This time it's Lytics, founded in 2013, which announced this week that it had been purchased in December by composable CMS and digital experience platform Contentstack.

  

Like ActionIQ, Lytics is a technically advanced product that has kept up with industry trends, making its features available as components, stressing real-time capabilities, and offering a broad range of campaign management and personalization features.  In fact, just last October, Lytics announced integrations with major content management systems and a free self-service offering that built customer profiles and used them to make personalization recommendations available within any content management system.  

But Lytics never achieved the scale needed to support its vision.  The company raised a relatively meager $58 million, with its last round in 2019.  (By contrast, ActionIQ raised $145 million including a $77 million round in 2021.)  Headcount (per LinkedIn) fell from a peak of just over 200 in 2020 to 49 this January.  

The Contentstack combination will provide the resources needed to continue developing Lytics’ vision.  Contentstack has nearly 600 employees, up 24% in the past two years, and $169 million in funding, with their last raise of $80 million in 2022.  They also claim over 500 customers, many of whom can be expected to adopt Lytics technology. 

Indeed, the architectural fit between Lytics and Contentstack appears to be excellent.  Both offer composable products, which should make it easy to integrate Lytics profiles with Contentstack’s personalization engine.  This is exactly the ability that the company touts in its announcement of the acquisition.  While I can’t speak to the technical compatibility between the two systems, the fact that Lytics already had integrations with Drupal and Wordpress suggests exposing Lytics data to Contentstack will be fairly easy.

And what does the deal say about the larger CDP industry?

One observation is that the adoption of a composable approach was not enough to preserve the independence of either Lytics or ActionIQ.  Both those firms were early leaders in offering their systems as components.  Maybe this generated some revenue but it was clearly not enough to turn around their businesses.   I’d guess the price tags for components are too small to really help much, especially compared with the much larger fees these companies were charging for their integrated products.  

The large number of competitors in the component marketplace probably ensures that prices will stay low, which is good for buyers but not so good for vendors.  This doesn’t bode well for “composable CDP” vendors who only sell components, although presumably they have geared their organizations to be profitable while selling relatively low-ticket items.  Note that “composable CDP” vendors like Hightouch and Census have all added multiple components, enabling them to earn more money from each client.  But so long as competition keeps down the price of individual components, the revenue per client will remain low even from clients who buy all the components those vendors have to sell.

It’s likely that the investments that Lytics and ActionIQ made in “componentizing” their products made them more attractive to their ultimate buyers.  But, otherwise, those investments may have harmed their business by consuming resources that could otherwise have been spent on improving their core products. 

A second question is whether these transactions mark the start of the industry consolidation that analysts have been predicting pretty much forever. 

I think not.  Actual consolidation would involve mergers between competing CDP vendors, something that has almost never happened.  (Exceptions have been in Europe, where Spotler combined Datatrics and Squeezley, Splio acquired Tinyclues, and Easyence merged with mediarithmics .  You’ll note these are not exactly household names.)  What we have seen over the years is deals like Lytics/Contentstack, where a larger customer experience vendor buys a CDP to add profile building, and sometimes campaign management, capabilities.  I do expect these sorts of acquisitions to continue, as the growth of personalization and AI makes the need for unified data more pressing.  We’ll also presumably see a few of the small independent CDPs vanish entirely, although this too has been unusual: enough companies need profile-building technology that the intellectual property of even a struggling CDP can usually find a buyer.  In this regard, it’s probably not coincidental that both Lytics and ActionIQ were quite advanced technically, making them particularly appealing.

So, while I don’t see a literal consolidation in the sense of CDP vendors merging with each other, I do expect to see a continued drop in the number of independent CDP vendors.  These will be replaced by customer experience vendors who include a CDP inside their products, the better to support personalization.  To be clear, what will qualify these firms to be considered CDPs is the ability to work with external systems as data sources for unified profiles and as consumers of those profiles.  This data-sharing ability will become more important as the number of customer-facing channels continues to increase and as customers expect consistent, personalized treatments across all of those channels.  Since no single system can expect to manage all channels by itself, it becomes essential even for customer experience products to capture data from external systems and to support personalization across those systems.  That was, and remains, the defining feature of a CDP.