The conversation about knowledge graphs and context graphs in enterprise AI has matured quickly over the last several months. With industry analysts projecting that roughly one-third of enterprise applications will embed agent-based intelligence by 2028, the underlying data architecture required to power these systems is shifting. It is no longer a niche discussion; the market for enterprise knowledge graphs is actively accelerating, projected to expand from roughly $1.9 billion today to nearly $10 billion over the next several years.

Major hyperscalers are moving decisively into the space. A prime example is Amazon’s entry with AWS Context, a new knowledge graph service designed to dynamically surface organizational intelligence that continuously learns and sharpens through agent usage over time. As platform companies like Atlan, Glean, Neo4j, and a growing roster of data infrastructure providers converge on the same idea, a fundamental truth has emerged: AI agents cannot operate reliably without structured, governed context about how enterprises actually work.

The distinction between the two types of graph is also emerging across enterprise architecture conversations:

• The Knowledge Graph: Maps entities and their relationships at design time. It answers what things are, where data resides, and how corporate assets connect.

• The Context Graph: Extends that exact same structure at runtime, capturing decision traces, data shelf life, and behavioral policies. It answers why decisions were made, how the enterprise actually operates, and what tribal knowledge sits between traditional systems of record.

Both are emerging as critical infrastructure for the agentic era, and both are being built with real urgency inside enterprises right now.

But, in the conversations happening today, these graphs are largely built as internal, enterprise-only artifacts. That is a natural starting point, because the immediate, burning problem is helping corporate agents understand internal workflows. It is also, from a holistic perspective, structurally incomplete. If the three-body model of customer experience holds, then each of the three bodies will have its own unique knowledge and its own runtime context to contribute to the relationship. The graphs being engineered today are capturing only one-third of what the model will ultimately require.

Let’s quickly review how I’ve been thinking about the three bodies that produce durable customer value as the organization, the customer, and the product or service that binds the relationship. Each has knowledge and context native to its domain:

1. The Organization

The organization has structural knowledge about its products, its policies, its pricing, and its capabilities. It maintains runtime context about why an account executive made a specific concession, how escalations flow through support tiers, and where human expertise lives. This is what the current context graph conversation is about, and enterprises are right to build for it. But it remains an isolated silo.

2. The Customer

The customer possesses knowledge and context of a radically different kind. The customer knows what they were trying to accomplish when they used the product or service. They know what worked and what failed. They know what alternative solution they would have chosen if they had been better informed, how the product actually fits into their daily life or workflow, and which of the enterprise’s brand promises landed versus which ones fell short. Their context is the living decision trace of their own life as they navigate the vendor relationship.

Under a trust protocol like the developing IEEE 7012 MyTerms standard, this rich customer knowledge and context will become portable, structured, and available to the enterprise on machine-readable terms the customer has explicitly set rather than harvested silently and inaccurately through third-party cookies and fragmented telemetry.

3. The Product or Service

The product body is the one the current enterprise conversation is largely overlooking. As AI gets embedded into products themselves, the product will no longer be a passive instrument observed from the outside. It will feature its own localized AI model tuned to the product’s explicit purpose with an architecture capable of generating intrinsic product insights.

This is a fundamentally different kind of signal from the telemetry the industry has spent the last thirty years collecting. Telemetry is a lagging indicator; it is what a monitoring system observes from the outside (e.g., the user clicked a button three times). Product-side AI feedback is what the product itself has to say about its own life in the wild (e.g., the user is experiencing acute friction executing this specific data transformation workflow). This real-time edge context is structured so that both the customer and the enterprise can act on it immediately.

A Fully Enabled Graph

When all three bodies emit both design-time knowledge and runtime context, and when a trust protocol like MyTerms makes that emission collaborative rather than extractive, something entirely new becomes possible.

With a collaborative approach, the knowledge graph is no longer just what the enterprise knows about its own backend operations. It also incorporates what the customer, the product, and the organization together know about the relationship they share. The context graph is no longer just the internal decision traces of corporate software. It becomes the holistic decision trace of the entire three-body system, dynamically blending the customer’s evolving priorities, the product’s actual edge behavior, and the enterprise’s operational constraints.

The value that emerges from this synthesis is not incremental; it is a category shift. It represents a layer of shared understanding that could never exist inside a single organization’s silo. The customer’s agent will know what the user intended to do. The product’s internal model will know what actually happened at the execution layer. The organization’s graph will know what the enterprise committed to deliver and why. When all three are in a structured, multi-agent conversation with one another, they co-create value based on absolute ground truth.

This will fundamentally rewrite what enterprises build and buy over the next several years. The context layer cannot remain an internal-only asset. It must evolve into a collaborative surface where enterprise, customer, and product all contribute what they know under a robust governance framework.

The organizations that recognize this shift early will design for the three-body pattern from day one. The ones that treat the context graph as a glorified corporate database will find themselves completely rebuilding their data layer once user-side trust protocols mature and customers arrive at the relationship with structured representation of their own.

The knowledge and context graphs being deployed right now are the beginning of the infrastructure shift, and not the end of it. What comes next is an ecosystem that treats all three bodies as equal contributors to the shared understanding that the agentic era demands. When that arrives, the value created between customer and enterprise will be fundamentally different from anything the old cost-deflection era could produce, because for the first time, both sides will be building on the exact same graph of what the relationship actually is.

The idea of “owning the customer” has been the organizing metaphor of customer experience for as long as the discipline has existed. Account ownership, customer ownership, segment ownership. The language runs through every CRM implementation, every quarterly business review, every leadership scorecard. It made sense inside the enterprise, but it never made sense to the customer, because no customer has ever chosen to be owned. The enterprise got away with the fiction for thirty years because the customer had no structured way to push back. Customer-side agents are going to remove that limitation, and once they do, ownership will become a relic of a world where only one side of the relationship was allowed to bring structure to it.

What will replace ownership in the agentic era is something like a reputation ledger, and it will work fundamentally differently. Ownership was something the enterprise claimed. A reputation ledger is something the customer’s agent will keep, and the entries in it will not be permanent claims. The ledger will behave more like a running account than a title deed, and the customer’s agent will keep the books.

The best way to think about it is as a ledger the customer’s agent will maintain for every enterprise it interacts with on behalf of the customer. Every commitment the enterprise honors will add to the balance. Every promise it fails to deliver on will subtract from it. Every act of reciprocity, such as sharing useful context back, honoring the customer’s stated terms, or acting on feedback in a way the customer’s agent can verify, will add value. Every act of extraction, every dark pattern, every unexplained fee, will subtract value. The customer’s agent will keep this ledger in a way no individual customer ever could, because agents will remember everything, compare across vendors, and share notes with agents representing other customers.

The implications of the reputation ledger for the CX operating model will be significant, and most enterprises are underestimating them.

Marketing’s job will no longer be to manufacture preference. It will be to produce claims the customer’s agent can verify, so that positive claims add value to the ledger rather than being dismissed as noise. A campaign that overstates a capability will not just fail to convert. It will actively subtract value from the ledger, because the agent will notice the gap between claim and delivery and score the enterprise accordingly.

Product’s job will no longer be to ship features and let marketing tell the story. It will be to publish capabilities in a form the customer’s agent can evaluate, and to hold the delivered experience accountable to those capabilities in real time. The ledger will grow in value when the product does what it said it would do. It will shrink when it does not.

Support’s job will shift the most, because support is where the ledger will be tested most often. Every interaction will be a chance to add or subtract to the balance. In the deflection era, support was measured on how quickly it could close a ticket. In the ledger model, support will be measured on whether the resolution added to the value balance or subtracted from it. That is a fundamentally different scorecard, and it will make service the connective function it was always trying to be but was never structured to become.

Sales will look the most different, because the transaction itself will be the moment when the ledger opens. The terms agreed to, the promises made, the commitments booked. All of them will become entries the customer’s agent will check against reality over the life of the relationship. Sales will stop being the function that closes deals and become the function that opens accounts the rest of the enterprise will spend years honoring.

The through-line across all four functions will be that reputation is earned in every interaction and lost in every misalignment. No function will get to declare it has been earned. No leader will get to claim their team owns the customer. The customer’s agent will keep the books, and the enterprise’s job will be to make sure the running balance is one worth honoring.

The leaders who internalize this early will stop asking who owns the customer, and start asking how to build relationships worth co-creating value in, on both sides of the ledger.

Something has been shifting in the customer experience conversation over the last year, and most of the people having the conversation have not yet put it together. The full narrative is scattered across different discussions. Customer-side agents are being debated in privacy and consumer circles. Context graphs are being built by enterprise AI teams. Trust frameworks are being designed in standards bodies. Co-creation is being talked about as a strategy without anyone explaining how it would actually work. All of these conversations are describing different parts of the same model, which is what I’ll walk through in this piece.

The model rests on an observation that has been getting clearer as the parts have come into view. The customer is going to stop being something the enterprise does things to, and start being something the enterprise does things with. Every other shift in the conversation follows from that one.

The structure of the model is the three-body frame I introduced a while back, although I’ve shifted the definition of the tree bodies to reflect a more comprehensive operating model. The three bodies that produce durable customer value are customer, product or service, and organization. Each has its own gravity, generates its own context, and evolves on its own clock. For the entire history of customer experience, these three bodies have been navigating each other with no medium fast enough or rich enough to keep them aligned. The customer experiences a product the organization built on its best guess about what the customer would want. Each body senses the others through narrow channels, and context gets lost with every translation.

The arc of the last several months has been describing what happens when those channels widen and become structured. The change involves four different kinds of work happening between the three bodies, and each kind of work has a different character. Agents are the medium that carries the relationship. Context is the substance that flows between the bodies. Trust is the framework that governs how the substance moves. Co-creation is the result the system produces when the other three are working together.

Agents are the medium that carries the relationship between the three bodies. Customer-side agents will hold the customer’s context, enforce the customer’s interests, and negotiate on the customer’s behalf. Enterprise-side agents will hold organizational context, enforce internal policy, and act on behalf of the company. The product layer will be increasingly instrumented to emit usage signals, telemetry, and behavior data that agents on either side can act on. None of these participants will operate on reputation alone, because reputation does not survive contact with the volume and speed of agent-mediated interaction. The speed and structure of an agent-mediated relationship is different in kind from a human-mediated one, and that difference will show up at every other point in the model.

Context is the substance that flows between the bodies. Agents cannot do useful work without it. The customer-side context payload, the product telemetry, and the enterprise context graph are three sources of the same substance, and the work of the next several years is going to be making sure all three are emitting, structured, and legible to the agents that need to act on them. The companies that get this right will be operating with a much more complete picture of the customer than the deflection-era enterprise ever had access to.

Trust is the framework that governs how the substance flows. Context can only move safely between the bodies when both sides can verify what the other is doing with it. That verification has to be continuous, machine-readable, and provable in real time, because the parties involved are agents moving at machine speed. The framework has to make three things continuously verifiable: that the agents are who they claim to be and operating within their stated boundaries, that the context flowing between them is protected throughout the interaction, and that everything that happened leaves a tamper-proof record either side can audit later.

This is the area I have been watching most closely, because it is where the most important infrastructure for the entire model is being built right now. The contract side of the framework is being developed through standards like MyTerms (IEEE 7012), where the customer brings their own terms to the relationship rather than accepting whatever the vendor’s lawyers wrote. The infrastructure side is being built by companies like Opaque, whose confidential AI platform uses hardware-backed attestation, cryptographic enforcement, and tamper-proof audit logs to make trust between agents verifiable rather than assumed. The contract and the infrastructure together are what convert trust from a marketing claim into an operational guarantee.

Co-creation is the result the system produces when the other three forces are working together. The customer-company relationship stops being transactional and becomes continuous, bilateral, and generative. The customer shares context that previously stayed inside their head because they can see what the enterprise can and cannot do with it. The enterprise commits to outcomes that previously felt too risky to promise because it can prove what was delivered. The product roadmap improves because real usage signals flow back in a form the enterprise can act on. Pricing becomes fairer because both sides can see the value being created over time. Service stops being a defensive function and becomes the connective layer where new value is generated continuously rather than reconciled occasionally.

That is the shape of the model. Three bodies, four forces between them, and an operating model for customer experience that looks fundamentally different from what the deflection era produced.

The deflection era tried to run this same system without any of the forces in place. Customer-facing channels stood in for customer-side agents, siloed systems of record stood in for flowing context, consent banners and unread contracts stood in for verifiable trust, and loyalty programs and lifecycle marketing stood in for co-creation. Each of those substitutes did some of the work for a while, but the substitutes are reaching the end of what they can carry. The enterprises that try to bolt agentic AI onto a stack designed around those substitutes are going to find that the stack does not support the load.

The organizational implications are the part most CX leaders are still underestimating. The org chart was built to process customers as endpoints in a journey the enterprise designed. When the customer becomes a peer node in a relationship the enterprise no longer fully controls, the org chart starts to break down. The idea of ‘owning’ the customer loses meaning. The functional boundaries between marketing, product, sales, and service soften, because the work itself steps beyond those operational boundaries. The trust framework needs an organizational home that does not currently exist at most companies. Titles will evolve because the work underneath them will evolve.

The point of all of this is not the technology. The technology is what makes the medium possible. The point is what the medium unlocks, which is a fundamentally different economic relationship between enterprises and the customers they serve. A relationship in which both sides can invest more because both sides can verify more. One in which value is created continuously instead of extracted episodically. One in which the customer is represented in a structured way for the first time in the history of commerce, and the enterprise is held accountable in a structured way for the first time in the history of customer experience.

The shift is going to take several years to fully land. The leaders who recognize the model now and start operating on it before the org chart catches up will be the ones running the businesses that compound customer value through the rest of this decade. The ones who wait for the new model to be packaged and sold to them will spend the next several years explaining why their CX investments are not landing, and why their customers, increasingly equipped with agents the enterprise was not prepared to negotiate with, are no longer behaving the way the journey map predicted.

Customer, product, organization. Three bodies. Agents as the medium. Context as the substance. Trust as the framework. Co-creation as the result.

In the last piece I argued that verifiable trust is going to be the precondition for value co-creation, and that co-creation is the operating model that will define the next era of customer experience. Now let’s go deeper into what that change is going to look like inside the enterprise.

For as long as customer experience has been a discipline, the org chart has reflected a single assumption. The customer is an endpoint. Marketing acquires them. Sales closes them. Service supports them. Product builds for them. Each function has its own metric, its own budget, and its own definition of success, and the customer moves through the org chart as a unit of work that gets handed off between functions. The journey map is just the visual layer on top of this assumption. The deeper artifact is the org chart itself.

Looking back across several waves of enterprise software, this assumption has been the structural wall every customer-centric initiative has eventually run into. The CRM era promised a unified view of the customer and tried to deliver a unified database with the same functional silos still operating around it. The customer experience suite era promised journey orchestration and delivered better tooling for the same handoff model. Each generation got closer to the customer without actually moving them out of the endpoint position, because moving the customer out of the endpoint position is not a software problem. It is an organizational design problem, and software cannot solve it from underneath alone.

Customer-side agents are going to force the issue in a way previous waves did not. When the customer arrives at every interaction represented by an agent that holds their context, enforces their interests, and negotiates on their behalf, the enterprise can no longer treat them as the recipient of a process. The customer becomes a peer node in the relationship, and the org chart that was built to process endpoints does not know what to do with a peer node. That is the redesign question.

A few principles will define how that redesign actually unfolds.

The idea of “owning the customer” inside the enterprise is going to lose meaning. Account ownership, customer ownership, segment ownership were all artifacts of a world where the enterprise was the only side bringing structure to the relationship. When the customer brings their own agent and their own context, what the enterprise can earn is standing, not ownership. The functions that succeed in the new model will be the ones that learn to earn standing with the customer’s agent rather than insisting on ownership of a customer who is no longer asking permission to manage their own relationship.

The functional boundaries between marketing, product, sales, and service are going to soften, not because anyone decides to break them down, but because the work itself stops respecting them. When all four functions are operating against a shared context graph that includes the customer node, the handoff model that justified the boundaries becomes friction rather than structure. The functions still exist, but the question of which function owns which stage of the journey becomes the wrong question, because the journey is no longer the model.

The trust framework underneath the relationship is going to need an organizational home, and at most companies that home does not currently exist. The function that owns how customer context is received, governed, audited, and reciprocated is a function that has not been built yet at most enterprises. The principle that matters is that this function is going to be load-bearing, and treating it as a side responsibility of an existing team is going to underweight it.

Titles will evolve, because the work underneath them will evolve. Chief Customer Officer does not disappear, but the work that role does will look different when customers are no longer endpoints to be experienced and become peers to be co-created with. VP of Customer Experience does not disappear, but the experience being designed will no longer be a journey through stages. The titles will catch up to the work, the way titles always do. What the leaders in those roles do every day is what will shift first, and the language will follow.

The point I am making is that this is not just a technology tooling question alone. It is an organizational redesign question, and the redesign will run on principles long before it runs on a new org chart. The leaders who recognize that early, and start operating on the new principles before the chart catches up, will be the ones running the businesses that compound customer value through the rest of this decade. The ones who wait for the chart to change first will spend the next several years explaining why their CX investments are not returning the results that they had expected.

In the last piece I argued that the customer-shaped hole in the enterprise context graph can only be closed under a trust framework, something that is in active development. This piece is about what that framework is going to unlock.

Trust in the agentic era is going to be structurally different from trust in any prior era of customer experience. For thirty years it was carried by people, paperwork, and slow-moving processes. That arrangement was always more fragile than the industry liked to admit, and it is not going to survive contact with agents acting on either side at machine speed.

When a customer’s agent and a company’s agent enter a relationship, neither will be able to operate on reputation alone. The customer’s agent will have fiduciary obligations and cannot share context that might be misused. The company’s agent will have obligations to the enterprise and cannot accept context it cannot verify. The trust between them will have to be provable in real time, not assumed and reconciled later.

What is going to be needed underneath the relationship is a layer that makes three things continuously verifiable. The first is that the agents on both sides are who they claim to be and are operating within their stated boundaries. The second is that the context flowing between them is protected throughout the interaction, not just at the start and the end. The third is that everything that happened leaves a tamper-proof record either side can audit later. Attestation, enforcement, audit. Those are the foundational concepts, regardless of how the technology layer evolves to deliver them.

This matters because of what it is going to unlock, not because of how it will work. The difference between a relationship with verifiable trust and one without is not going to be incremental. It will be the difference between extracting value from customers and co-creating value with them.

A relationship without verifiable trust forces both sides to hold back. The customer holds back context because they cannot be sure how it will be used. The company holds back commitments because it cannot be sure what the customer will hold them to. Both sides operate behind narrow guardrails, which means the value that gets created is bounded by what each side is willing to risk in the absence of proof. That is the model that produced the deflection era of customer service, the consent-banner era of customer privacy, and the loyalty-program era of customer marketing. All three are different faces of the same underlying problem, which is that nobody fully trusted anybody, so nobody fully invested in the relationship.

When trust becomes verifiable, the dynamic will invert. The customer will be willing to share context that previously stayed inside their head because they can see exactly what the company can and cannot do with it. The company will be willing to commit to outcomes that previously felt too risky to promise because it can prove what was delivered. The product roadmap will improve because real usage signals can flow back in a form the enterprise can act on. Pricing will become fairer because both sides can see the value being created over time. Service will stop being a defensive function and become the connective layer where new value is generated continuously rather than reconciled occasionally.

Verifiable trust is going to be the precondition for co-creation, and co-creation is going to be the precondition for the kind of compounding customer value the deflection era never produced and never could. The leaders who win this era will be the ones who recognize, while it is still early, that trust is no longer going to be something you claim. It will be something you build, prove, and deliver, continuously, between two parties who can both verify it. The companies that start positioning for that shift now will be defining the operating model their competitors are still trying to understand. When that condition holds, value co-creation stops being a strategy deck and starts being the way the relationship actually works.

There’s been a real and useful conversation building over the last several months about context graphs and their role in enterprise AI. Gartner and Forrester have identified context graphs as foundational infrastructure for agentic AI, with a projection that by 2029, 80% of AI agent platforms using reasoning models will have aligned context layers in place, compared to fewer than 10% today. Microsoft just launched IQ at Build 2026 as an enterprise intelligence layer that grounds AI agents in workplace data and business systems. Glean, Atlan, Publicis Sapient, and a half dozen others are all converging on the same idea: AI agents need a structured understanding of how the enterprise actually decides, escalates, and resolves things, not just a flat snapshot of what’s in the systems of record.

I think the conversation is correct on the substance and structurally incomplete on the scope. Every framing I’ve seen treats the context graph as an enterprise-internal artifact. The decision threads in Slack, the escalation patterns in the ticketing system, the tribal knowledge that lives between employees. All of that matters, and the enterprise is right to be building infrastructure to make it queryable. But the graph stops at the moment the customer enters the picture, and that is where the most valuable context in the entire system actually lives.

The richest context about your product or service is not held inside your company. It is held by the people who actually use what you sell.

That sentence sounds obvious when you read it, and the obviousness is exactly the problem. CX has organized itself for decades around customer journey maps that treat the customer as a stage-completion function. Did they convert. Did they activate. Did they expand. Did they renew. In the previous piece I argued that the journey map was always a fiction the enterprise told itself. The deeper fiction underneath is that the customer is a callable function in your process rather than the most valuable node in your graph.

The customer holds the why. They know what they were trying to accomplish when they bought your product. They know which features they actually use and which ones they ignore. They know which workarounds they invented when your product didn’t do what they needed. They know how your service interaction made them feel about renewing. They know how your pricing landed against the value they actually derived. None of that lives in your CRM. Some of it lives in your support tickets, albeit captured in opaque ways. Most of it lives in the customer’s head, and it has been functionally invisible to the enterprise for the entire history of CRM/CX as a discipline.

Customer-side agents are about to change that, and I think the implication for the context graph conversation is bigger than the current focus is treating it. When a customer deploys an agent that represents them across the vendors they buy from, that agent accumulates exactly the kind of structured, queryable context that enterprises have been trying to manufacture internally. It knows what the customer wanted, what they got, what worked, what didn’t, and what they would have preferred. It carries that context in a form that can be shared, in whole or in part, with the vendors who have earned the right to receive it. That is the customer’s missing node, finally legible.

The hard part is what comes next, and it is not technical. Companies have to fundamentally reposition where the customer sits in their operating model. As long as the customer is a callable function inside a journey map, there is no place in the architecture to receive what their agent is offering. The customer has to move from being a target of the enterprise’s process to being a peer node in the enterprise’s graph. I think that is an organizational redesign question more than a data engineering question, and it is going to be the harder lift for most CX leaders to make.

It also requires a trust framework, because customer context only flows when both sides have a durable framework, like contract, that governs it. Consent banners are unilateral, brittle, and largely performative. What is required is a bilateral agreement, machine-readable, persistent, and auditable, that defines what the enterprise can do with the customer’s context, how long it can hold it, and what the customer gets back in return. That work is underway in standards bodies and in adjacent communities (MyTerms being one of them), and I think it becomes essential infrastructure for everything the agentic CX conversation is currently hand-waving past.

When both sides bring context into a relationship that has a contract underneath it, value co-creation stops being a strategy deck and starts being an operational reality. The enterprise’s context graph gets the missing node. The customer’s agent gets a counterpart that can actually respond to what it knows. Both sides end up holding a richer, more useful, and more honest view of the relationship they are actually in.

The context graph conversation is the right conversation. It just needs to include the customer. The technology is finally good enough to make that possible, and the leaders who do the work to receive what their customers’ agents are about to offer will end up running businesses that compound value in ways their deflection-era predecessors never could.

Every customer journey map I’ve ever seen drawn assumes the enterprise is the cartographer, charting a pathway for an uninformed customer. Awareness, consideration, purchase, onboarding, adoption, renewal. The stages are tidy, the arrows point in one direction, and the map gets pinned to the wall in the CX war room as if customers consult it on their way through the funnel. They don’t. They never have. I’ve come to think the journey map was always a convenient fiction the enterprise told itself so that marketing, sales, and service could coordinate handoffs across a relationship the customer was actually running on their own terms.

In the last piece, I argued that agentic AI will move CX from deflection into co-creation, where both sides keep adding value to the relationship after the sale rather than periodically reconciling its failures. That argument has a quiet assumption baked into it, and I want to pull it out into the light. Co-creation only works if the enterprise accepts that the customer is now an equal party in the relationship. Looking at the trend-line, the coming arrival of customer-side agents is what makes that shift impossible to ignore.

A customer-side agent doesn’t enter your funnel. It enters your product, your pricing page, your terms of service, your competitor’s product, your competitor’s pricing page, three independent review sources, two regulatory filings, and a Reddit thread from 2024, and it does all of that before the customer it represents has even decided whether to engage with you directly. The agent carries fiduciary trust on behalf of its principal. Its job is not to be persuaded by your marketing, it’s to optimize an outcome for the person it works for. I think that changes the structure of every interaction the enterprise has been organized to run.

The implications take a minute to settle. In a B2B context, the agent shows up to a negotiation with deeper knowledge of your product than your own sales team has, because it doesn’t have a quota to achieve and it doesn’t have a script follow. It has read your documentation more carefully than your customers ever did. In some cases, the customer’s agent has cross-referenced your SLAs against your historical incident reports. It has compared your renewal terms against the three vendors who would happily replace you. The information asymmetry that used to favor the enterprise is now inverted, and I think the inversion is central to realigning assumptions. No amount of sales enablement closes that gap, because the gap isn’t about training, it’s about whose interests the intelligence is optimized for.

The B2C version is even more interesting, because there’s no sales cycle to soften the moment of transaction. When I look at how this plays out for a consumer brand, there’s no quarterly business review, no procurement window, no relationship manager working the account. The customer’s agent just arrives, fully briefed, with a clear instruction from its principal. Find me a better mortgage. Switch my insurance if the renewal exceeds last year by more than five percent. Buy the running shoes I liked last time unless something measurably better has appeared at the same price. The brand never gets a meeting. It gets a verdict, delivered at machine speed, against criteria the customer set and the agent enforced.

That moment is where every consumer marketing assumption I’ve watched the industry build for a generation runs out of road. Loyalty programs, brand affinity, retargeting, lifecycle email, the whole apparatus was designed to influence a human who could be nudged. An agent acting on fiduciary instructions cannot be nudged. It can only be convinced by evidence its principal would value, and it will collect that evidence from sources the brand has no relationship with and no ability to shape.

This is where I think most CX strategies will break, and where the next era of value creation actually begins. The only durable response to an informed, fiduciary, agentic counterparty is to make the long-term value of what you sell genuinely defensible. Not the marketing narrative around the value, the value itself. If your product works, if your pricing is fair, if your service actually solves the problem, the customer’s agent will surface that and reinforce the relationship. If any of those things are weaker than the story you’ve been telling, the agent will surface that too, and it will route its principal toward whoever can do better.

I know this can sound threatening if you read it as the end of marketing leverage. I don’t think it is. I think marketing itself is being reshaped, and the reshape is healthier than the function’s recent decade has been. The narrative center of gravity moves from the promise of value to the delivery of value, and the leverage moves with it. The brands that win in an agent-mediated world are the ones whose marketing is grounded in what the product actually does, what the pricing actually costs over time, and what the service actually resolves. That’s still marketing. It’s just marketing that has to be true to be effective, because the customer’s agent will check.

I think this is the beginning of a much healthier economic relationship between enterprises and the customers they serve. The companies that lean into this deepen their commitment to long-term product quality, transparent pricing, and service that compounds value over time. Marketing becomes the function that translates real value into language the customer’s agent can verify, rather than the function that manufactures preference in the absence of verification. The customers who deploy agents on their behalf get representation in a relationship where they have historically been outgunned. Both sides win, and the win is structural rather than promotional.

The journey map was the enterprise’s fantasy of control. The customer-side agent is the customer’s restoration of a durable balance in the relationship. The leaders who internalize that shift early will build the businesses that are still standing when the dust settles, and they will do it by competing on the only thing that survives an agent-mediated relationship, which is whether what you sell is actually worth what you charge for it.

The brand promise is made in marketing, priced in sales, and tested in service. That sequence has been true for as long as customer experience has been a discipline, and yet most enterprises still invest as if the promise is fulfilled the moment the contract is signed, or product purchased. Marketing gets the budget for the promise, sales gets the comp for the close, and service gets the headcount cap and the deflection target. The part of the journey where brand value is actually proven, the long arc after the transaction where customers decide whether they got what they were sold, sits inside the function with the least strategic capital in most organizations. The math here has always been backwards, and the technology of the last thirty years gave the enterprise no real reason to fix it.

In the last piece, I argued that AI lets service stop being a translation layer absorbing the enterprise’s failures of imagination, and start operating as the nervous system of the enterprise, sensing where value is leaking and routing the signal to wherever the response actually has to come from. That reframes service from a cost center into a context-routing function. The next move is bigger. Once context is moving across the enterprise in real time, the relationship between the company and the customer changes shape entirely. Service stops being the place where oneway, company-side, value gets defended and becomes the place where value gets co-created, both company and customer benefit.

This shift is what agentic AI actually unlocks, and most CX leaders aren’t yet planning for it. The agentic conversation today is dominated by automation framing: agents that resolve tickets, agents that handle returns, agents that complete tasks without human escalation. That framing keeps service trapped inside the deflection mindset wearing a new technology coat. The more interesting move is the one the technology genuinely makes possible. When AI agents operate with full enterprise context on behalf of the customer, and when the customer’s own agents operate with full personal context on behalf of the buyer, the post-transaction relationship stops being transactional at all. It becomes continuous, bilateral, and generative. Both sides are now in a position to keep adding value to the relationship rather than periodically reconciling its failures.

That is co-creation, and it is structurally different from anything CX has been organized around before. Co-creation assumes the customer is an active participant in defining what good looks like, not a recipient of what the company decided good was at the time of sale. It assumes the product roadmap, the pricing model, the service experience, and even the contractual terms are living artifacts shaped by what the enterprise learns from how customers actually use what they bought. Agentic infrastructure makes this operationally feasible at scale for the first time. The customer’s agent can negotiate terms, surface unmet needs, flag friction, and request adjustments. The enterprise’s agents can respond with context that spans product, finance, and operations rather than the narrow script the service rep was handed in 2015.

The economic implications of this are worth sitting with. A deflection-oriented service organization extracts value from each interaction by minimizing its cost. A co-creation-oriented service organization compounds value from each interaction by feeding it back into the things the customer actually paid for. One model treats every contact as a tax on operations. The other treats every contact as an investment in the relationship’s future yield. Over a multi-year horizon those two models diverge dramatically, and the gap shows up in retention, in expansion, in pricing power, and in the kind of brand equity that no amount of marketing spend can manufacture after the fact.

This is also where the strategic positioning of CX changes inside the enterprise. For decades, service has reported into operations because the dominant frame was containment. Co-creation pulls service toward the strategic core, alongside product and finance, because the function is now generating the signal that shapes what the company sells next, how it prices it, and what it commits to. That is a different operating model, and it requires CX leaders to argue for a different seat at the table.

The promise is tested after the sale. The technology is finally good enough to honor that fact. The leaders who act on this stop investing in service as the place where the brand gets defended, and start investing in it as the place where the brand gets renewed every day the customer is still in the relationship. That is what comes after deflection, and it is the only frame that takes agentic AI seriously.

Customer service has historically been the place where the enterprise’s failures of imagination meet the customer. The product team didn’t think of that edge case. Pricing didn’t anticipate that scenario. Engineering shipped a feature that assumed the customer would think the way the org chart does. Service catches all of it, and the only reason that arrangement worked for decades is that humans inside service teams were absorbing the cost of routing context the rest of the enterprise couldn’t.

This cost is one that very few understood or priced. The reality was that customer service wasn’t a department, it was a load-bearing translation layer holding the rest of the business together. The activity got contained in a department because the technology of the time couldn’t route enterprise context any other way.

AI changes that constraint, and the strategic question for CX leaders shifts with it. The question isn’t how to deflect more contacts or close more tickets faster, it’s how to activate the entire enterprise on behalf of the customer, in real time, with the context required to actually solve the problem rather than simply deflect it.

Amdahl Comes for Value Delivery Too

In the previous piece I argued that Amdahl’s Law caps productivity gains: speed up 30% of a workflow and the other 70% defines your ceiling. The same law applies to value delivery, just with a different focus.

If your service organization can resolve a customer’s stated issue in two minutes but the underlying product gap takes six quarters to fix, your actual service excellence is bounded by your product cycle. If your agents can identify a billing pattern that predicts churn but finance can’t act on the signal for ninety days, your CX intelligence is bounded by your finance cadence. The constraint on continuous value delivery isn’t service capacity, it’s the enterprise’s capacity to absorb what service is learning.

This is why deflection metrics are a dangerous north star. They optimize a fragment of the true value journey of customers. Optimizing the part of the system that touches the customer, while the parts that produce durable value for the customer stay slow. This just produces a more efficient veneer over an unchanged business.

From Task Orientation to Process Orientation

The hard part of AI in CX isn’t getting it to handle tasks well, it’s getting it to step above tasks into the process layer where value actually moves.

Task-oriented AI answers the question, summarizes the ticket, drafts the response, and routes the case. It’s useful, and it’s where most deployments live today. Process-oriented AI does something different: it watches the patterns across thousands of customer interactions, identifies which of them encode signals that the rest of the enterprise needs, and routes those signals to the teams that can act on them. Product hears the friction pattern that’s driving 12% of contacts before it shows up in the next NPS survey. Finance sees the dispute trend before it becomes a quarterly variance. Engineering gets the product failure pattern before it lands on product review websites.

That’s the shift, and it’s harder than it sounds, because most enterprises aren’t structured to receive signals from service. Service was supposed to absorb the problem, not propagate it back upstream. Process-oriented AI breaks that thinking, and the redesign work is in deciding how the rest of the enterprise responds when it does.

The End of the Deflection Mindset

The deflection mindset treats every customer contact as a cost to minimize. The process mindset treats it as a context signal to absorb.

These two mindsets produce radically different economics over time. Deflection optimizes the existing product against the existing customer base. Learning compounds, because every interaction adds to the enterprise’s understanding of how customers actually use what they bought, what they were trying to accomplish, where the product met reality and where it didn’t.

That last piece of the puzzle is the prize. Most enterprises have detailed data on what customers buy and almost no data on the context in which they use it. AI agents that operate at the process layer, routing context across the enterprise rather than burning it inside service tickets, finally make that context legible at scale.

The CX organizations that internalize this won’t look like service organizations anymore. They’ll look like the nervous system of the enterprise, sensing where value is leaking and routing the response to wherever it actually has to come from.

That’s what the next phase looks like, and it starts the moment you stop focusing solely on deflection.

In the midst of all the AI hype, CX leaders are challenged to solve the same problem in two languages. The first language is quarterly: the board wants AI wins, velocity, cost reductions, and ROI to demonstrate to their shareholders. The second is structural, and it lands six months later when the same board asks why CSAT moved three points but revenue per agent didn’t, and why the P&L doesn’t reflect greener dashboards.

These languages aren’t contradictory, rather they’re sequential, and conflating them is what sits underneath every “AI is everywhere except in the bottom line” narrative that seems to be everywhere.

The Phase One Fixation

McKinsey’s recent piece on where AI will and won’t create value is, on a careful reading, a memo about timing. It names three waves: productivity gains, differentiation, and transaction-cost reduction. Most organizations are deep into the first wave and organizing as if it’s the only one that matters.

That’s the risk. McKinsey argues productivity gains rarely expand profit pools, because competition erodes them and customers capture most of the value (as it should be). Phase One is table stakes, not the prize. Real value comes later, when AI changes what you can offer customers and reshapes the economics of the markets you compete in. Treat Phase One as the destination and you’ll miss the transformation you think you’re executing.

Even Phase One Isn’t Compounding

The issue is that, in the real world, most organizations aren’t winning Phase One either. Goldman Sachs puts task-level savings around an hour a day, and Deloitte shows 66% of organizations reporting productivity gains while only 20% see revenue growth from them. As I wrote about previously, Amdahl’s Law, is in full swing with Phase One.

If AI accelerates 30% of a workflow, the improvement is capped by everything it doesn’t touch. Approval chains, handoffs, exception paths, and process structures built for constraints that no longer exist all stay in place, and speeding up part of a workflow that was never designed for speed just produces a faster version of the same output.

Stanford’s research puts numbers on this: companies that redesigned workflows around AI saw median productivity gains of 71%, while those that deployed the same AI without redesign saw 30%. The only variable was the redesign.

Beyond Phase One: Redesigning work

Stuart Winter-Tear’s recent Substack post has the cleanest framing for this angle. The moment AI touches live work, he writes, “it stops being ‘just software.’” It becomes behavior on the organization’s behalf, which means you’re changing how work moves, where judgment lives, and who absorbs the consequences when the workflow is wrong.

He names the unpriced cost as AI Translation Debt: the work of repairing meaning where work crosses teams, systems, vendors, or approvals. Humans used to absorb that debt invisibly, but AI doesn’t remove it. It collects it, amplifies it, and presents it back as “review.” That’s why CX teams feel busier rather than lighter after the first wave. The tools work, but the workflow was never redesigned to carry them.

Framing for the Next Phase

The CX leader’s job is to hold two horizons at once: short-term wins that prove value this quarter, and a redesign thesis that prepares the organization for the subsequent phases. Quick wins without a thesis don’t compound, and a thesis without quick wins doesn’t survive the budget cycle. There are essentially four moves to keep both clocks running in strategic alignment.

Pick workflows, not tasks. Choose end-to-end CX workflows where delegation could plausibly change unit economics: onboarding, escalation, retention. “Deflect this query” isn’t a workflow, it’s a task, and tasks alone don’t serve the long term time horizon.

Budget the intangibles. Stanford’s data is unambiguous: for every dollar of AI technology, organizations that achieve real productivity gains spend up to ten dollars on process redesign, reskilling, and change management. If your AI budget is mostly licenses, you’re underwriting a J-curve you won’t survive.

Make the seams visible before you scale. Where are humans currently filling in what the workflow doesn’t specify? Name it, price it, and decide whether that judgment stays human, gets delegated, or gets engineered out. Skip that step and Translation Debt scales invisibly, surfacing later as escalation load and margin leakage.

Anchor every quick win to a Phase Two thesis. Each deployment should answer one question: which moat is this building or eliminating? Proprietary data, workflow embedding that raises switching costs, faster learning cycles? A quick win that doesn’t compound into structural advantage is a productivity demo, not a strategy.

The CX leaders who define this era won’t be the ones who optimized hardest for Phase One, they’ll be the ones who used short-term wins to fund the longer redesign.

And what’s coming for CX aren’t faster sales, service, marketing departments, it’ll be the dissolution of the department line itself. For example, customer service and support were never genuinely a department, they were artifacts of a system that couldn’t route enterprise context fast enough to act otherwise. AI changes that constraint. The next phases of value will come from teams that step past “the job to be done” and design for the value to be provided to the customer, wherever in the enterprise that value lives.

More on this line of thinking next week.

Last week, I explained that, while Adam Smith wrote two books, most corporate strategy has spent 250 years acting on one. The wealth side runs as background logic in nearly every business decision, whether anyone names it or not. The worth side, the operating substrate that lets specialization and exchange compound, sits on shelves.

This is clearly manifested in enterprise software. The wealth side has had a pricing model for the last two decades. It is called per-seat, per month SaaS. You count the humans, you charge for the access, you book the revenue. The worth side has not had a similarly scalable pricing model. That is part of why it has been so easy to ignore. That asymmetry is starting to break, especially with the arrival of AI-driven workflows.

The CX pricing landscape in 2026 is in a major state of flux. Per-seat licensing is in structural decline as AI agents propose to reduce the human seats that justified the model. Outcome-based pricing is moving fast in the other direction, with vendors like Intercom and Sierra charging only when a conversation actually resolves. There’s a lot to learn in this shift, as it goes beyond how products and outcome are priced. They are the first serious attempts to put human judgment, AI work, and customer outcomes into a single currency that the CFO can read. That is what the worth side has needed all along.

Consider what per-seat pricing actually measured. It measured access. It measured how many humans were authorized to log in. It did not measure whether the work was good, whether the customer came back, whether the relationship compounded. The wealth side accepted this because it was billable and predictable. The worth side disappeared into the gap, because nothing in the contract priced it.

Outcome-based pricing closes that gap, at least partially. When a CX vendor charges only when a conversation resolves, the vendor and the buyer are now looking at the same ledger. Both sides get some pre-defined value from the interaction. The customer experience is no longer a downstream consequence of the software purchase. It is the unit of the purchase itself. That is a meaningful repricing of where value sits.

There’s still a lot of experimentation to come, but the trend toward value is real. The pricing architecture finding a way to reward augmentation. If a human escalation produces a saved relationship, that outcome can be priced. If an AI agent resolves a routine query without judgment, that can be priced too. The two are no longer in competition for the same headcount budget. They are contributing to a shared revenue line, denominated in customer outcomes.

The contrarian read is that this only works if both sides commit to defining outcomes honestly. If an AI marks a ticket resolved and the customer reopens it three days later, was that a resolution? If a human escalation saves a renewal, who gets credit, the AI that flagged the risk or the human who closed the save? These are not edge cases, rather they are the central definitional work of worth-side pricing, and the vendors who get them right will set the standard for the next decade.

For now, at least, the path forward looks like a hybrid. Subscription pricing for the platform layer, where access is genuinely the value. Consumption pricing for the volume layer, where the work is routine and metered. Outcome-based pricing for the moments that decide whether the customer comes back, where judgment actually shows up in the bill. Three currencies, one ledger.

This does not fully solve the worth-side problem. Trust, reputation, the willingness to refer, the long tail of relational value, and these still do not have line items, and probably never will. But pricing the moments that matter is a real start. Perhaps the worth side now has a vocabulary the wealth side recognizes.

Smith would have understood that how these two are symbiotic. The two books were always meant to be read together. Are we now coming to a realization that future CX pricing needs to achieve equilibrium with both?

Adam Smith published The Theory of Moral Sentiments in 1759. The Wealth of Nations came seventeen years later. He never thought of them as separate arguments. We did that.

I have been thinking about this division as I read the layoff memos of 2026. In the first quarter alone, roughly 78,000 tech workers were let go, with close to half of those cuts traced directly to AI and workflow automation. The pattern is familiar by now, and most visible in instances like when Oracle let go of thousands within days of a stellar earnings report. Salesforce’s CEO summed up the logic last fall in five words: “I need less heads.” Meta and Microsoft each announced new reductions in recent days. The capital plan tells the rest of the story. The four largest hyperscalers are on track to spend close to $700 billion this year on AI infrastructure.

These are big numbers. They look like the wealth of nations in motion.

What is missing from most of the memos is the other book. Smith’s argument in Moral Sentiments was not primarily about feelings. It was about the architecture of judgment that lets a market work in the first place. Trust, reputation, the expectation that a counterparty will behave a certain way next month, the willingness to repurchase, the willingness to refer. These are not soft assets. They are the production function for everything Smith would later describe in The Wealth of Nations with the pin factory. A factory only generates wealth because there is a working system of trust around it that nobody on the assembly line is paid to maintain. Smith took that system for granted because it was everywhere around him. We are now running a real-time experiment on what happens when we no longer can.

For Customer Experience numbers tell that story in ServiceNow’s CX Shift study, which surveyed more than 34,000 people earlier this year, found that 51 percent of consumers cite a lack of empathy as their top frustration with service. In plainer terms, that is the absence of human judgment at the moment they need it. Only 20 percent of executives ranked it as a priority. Fifty-three percent of customers say they would switch providers after a single bad interaction. The wealth side is being measured in compute and quarterly margin. The worth side is being measured in churn that rarely shows up on the earnings call.

This is not a values issue, rather it is a model of where value actually comes from in a service economy. The wealth of nations, in Smith’s pin factory, was created by specialization and exchange. The worth of people, in Smith’s other book, was the operating substrate that made specialization and exchange durable enough to compound. Strip the worth of people and the factory still runs, in theory, but the customers will lose an ever critical connection.

I’m hopeful that we are not heading for a dystopia of clinical efficiency. I think the role of the customer will be elevated to differentiate, where the customer will do the sorting. They are the ones noticing which interactions feel like a transaction and which feel like a relationship, which moments resolve and which loop back, which companies treat them like a ticket number and which still recognize them on the second call. The 53 percent who say they would switch after one bad interaction are not expressing a preference. They are describing behavior already in progress.

What the customer is asking, in effect, is not whether AI should be in the loop. They have already accepted that. They are asking which moments in their journey still get a person, and what kind of judgment that person brings. They are answering it in real time, while companies are still deciding whether to ask. The ones running the wealth-side play without the worth-side foundation will hear the answer in their renewal numbers soon enough. Smith would not have called that a soft signal. He would have called it the whole point.

The question that came out of Salesforce’s TrailblazerDX last week would have been unthinkable three years ago: “Why should you ever log into Salesforce again? Maybe you never will.”

It’s worth pausing on where that question is coming from, and what the company asking it has been right about before.

Salesforce practically invented the on-demand CRM category in 1999, when the enterprise software consensus was that anything critical had to run on-premises. They were early on the mobile enterprise wave. They moved into platform-as-a-service before most of their competitors understood what that meant. Each of those moves looked aggressive at the time and obvious in hindsight. Marc Benioff has a specific talent for arriving at the destination before his customers know they’re going there, then making the road himself. That history is the right frame for evaluating Headless 360, because dismissing it as a defensive pivot misses the pattern.

That said, being early and being right are different things, and the gap between them is where the risk lives.

Three weeks ago, I wrote about how the per-seat model collapsed because it measured logins instead of value. AI agents do real work that the usual measures have no way to account for. If users stop logging in, the seat stops being a useful unit of anything. Headless 360 is Salesforce’s architectural answer to that problem: expose the entire platform so that AI agents can operate it without a human in the loop. Benioff’s framing was direct: “No Browser Required. Our API is the UI.” He recognized that the value question was never about the browser as the UI, and associated seat based pricing, it was always about where value gets delivered, and who or what is doing the delivering.

The margin problem hasn’t gone away, and Headless 360 doesn’t resolve it. Traditional software margins were built on a simple structural reality: write the code once, sell it infinitely, and the cost of serving the next customer is nearly zero. AI breaks that. Every agent interaction burns real compute. Every workflow execution carries a cost that doesn’t disappear with scale. Salesforce’s stock is down roughly 28% year-to-date partly because the market is repricing what software economics look like when the product has to do actual work on every transaction.

The pricing picture is where my most recent piece on the open questions in every AI contract connects most directly. What Salesforce has not fully disclosed is the price of the Headless 360 infrastructure that sits underneath their current Agentforce initiative. Currently, it ships bundled with existing platform licenses. That sounds straightforward until you read Salesforce’s history of introducing capabilities in a base tier, letting customers build dependencies on them, and separating them into a premium tier later. It’s a playbook they’ve run before, and it has worked. SaaStr’s Jason Lemkin captured the logic clearly: when agents are doing the work, the CRM isn’t a filing cabinet anymore. It’s the brain. Switching costs go up, not down. That’s a genuine competitive advantage. It also describes exactly the kind of architectural dependency that makes future pricing conversations harder for buyers.

Info-Tech analyst Scott Bickley put it plainly: “CIOs should be asking about pricing now, before building in architectural dependencies on features that might land in a premium cost tier.”

Salesforce has earned the benefit of the doubt on bold architectural bets. They’re asking for it again here, on a bigger bet, in a market where the pricing rules are still being written. The architecture for the agentic world is still settling, but Salesforce’s directionally right. Whether the economics work for buyers at the scale Salesforce is projecting is the question nobody on either side of the table can fully answer yet.

The incumbent is taking another swing at setting the market standard. It’ll be interesting to see where this goes.

Most homeowners discover they’re underinsured after the fire, not before it. The policy said “replacement cost,” but when the check arrives, it reveals something different. What happened between those two things was a set of definitions that were in the contract the whole time, written in language that few read until the claim is denied.

Many customer experience leaders are in a similar situation right now with their AI vendor contracts.

In the first two articles in this series I took a look at the problem from a higher altitude. First, seat-based pricing collapsing because AI agents doing work that the usual pricing measures have no standardized way to account for. Second, the margin problem underneath is structural: AI runs on compute, compute doesn’t produce software margins, and venture capital is covering the gap for now. In this piece, I want to come down to ground level. Specifically, to the conversation customer experience leaders are increasingly being asked to have: how do you frame AI returns honestly, without building a case on numbers that don’t yet have solid foundations?

The core tension is that while individual user AI productivity gains are real, the organizational value is often less apparent. Ninety-seven percent of executives report personal benefit from AI tools, but only 29% of organizations see significant ROI at the organizational level. That gap traces back to three assumptions embedded in most AI contract, not because vendors are obscuring them, but because the industry hasn’t yet settled on what the right units of measurement even are. Both sides are still working this out, often mid-contract.

The three open questions

When you sign an AI contract today, regardless of pricing model, you are making three commitments in areas where the industry hasn’t reached consensus. That’s not a criticism of vendors, rather it is a description of where we are.

The first open question is your vendor’s cost trajectory, and where it lands relative to your own usage growth. AI processing costs have fallen roughly 280 times in 18 months, and the optimistic case is that this continues. But every AI contract implicitly assumes something about where those costs land relative to your own usage growth. Agentic workflows, the kind that handle customer journeys end to end, use 5 to 30 times more processing per task than a simple chatbot. If your usage grows faster than your vendor’s costs fall, the economics get worse as you scale. Many buyers haven’t modeled this as a risk in their AI planning.

The second is how your own consumption patterns can be reliably modeled, on either side of the table. AI systems don’t use resources neatly. An agent that fails and retries uses compute whether it succeeds or not. The gap between pilot budgets and production reality is significant enough that 62% of organizations experienced major unexpected costs in their first year of scaled AI deployment. The projections were built on pilot behavior, not the messier reality of running at full scale.

The third is the varying definition of “resolved” in a real-world environment. This risk sounds like a detail but is actually the heart of the pricing problem. Under outcome-based pricing, you only pay for successful resolutions, which aligns incentives in exactly the right direction. The industry is generally moving here, and it’s the most promising development in AI pricing in two years. The broadly unresolved question is identifying, at scale, what a resolution is, and at what granular level resolution is achieved. In CX deployments, for example who decides when a customer issue counts as closed? If a customer contacts you again within 24 hours about the same problem, does the first interaction count? These aren’t gotchas buried in fine print, rather they’re genuinely hard operational questions that vendors and buyers are still working out together. The contract you signed reflects where that conversation stands today, which is incomplete. Having built value models for many enterprise CX deployments, I’ve seen this resolution question as the most challenging one in estalishing a success baseline.

A cautious reason for optimism

None of these issues are a reason to slow down. Pricing models are genuinely maturing. Eighty-four percent of CEOs now expect returns from AI to take longer than six months, which gives buyers real leverage to negotiate better terms. Organizations running AI in production for three or more years are already documenting a 25% reduction in cost per customer interaction. A workable equilibrium is forming albiet slowly, imperfectly, but visibly.

The key is to dial-in realistic definintions of success after a pilot implementation before launching a project to deploy AI.

The insurance analogy is worth revisiting. Two homeowners can have identical policies and walk away from a claim with very different expectations, because one understood what they were covered for before anything happened. That knowledge doesn’t change the policy. It changes what you do with it.

The same logic applies to your AI vendor relationship. The three open questions aren’t traps. They’re the natural byproduct of an industry that is still figuring out how to price something genuinely new. The customer experience leaders who navigate this well won’t necessarily have better contracts than everyone else. They’ll just have gone in with a clearer picture of where the gaps are, and that changes every conversation that follows.

Last week I wrote about why the seat-based SaaS pricing model has largely collapsed. The short version: the seat was always a proxy for value, not a measure of it, and AI agents blew that proxy apart by doing real work without a clear user model. If you haven’t read it, start there. This piece picks up where that one left off.

Matt Barrie, CEO of Freelancer, published a remarkable piece last week that goes deeper than anything I’ve seen on the economics underneath the pricing crisis. It’s long and technically dense in places, but worth the read if you want the full picture. What he’s really asking is that even if the industry figures out how to price AI correctly, can the underlying economics ever actually work?

His answer, at least for now, is no. And the reasoning is worth understanding even if you’ve never looked at a margin calculation in your life.

Here’s the simplest version. Traditional software is almost free to copy. Once a company writes the code, serving the next customer costs almost nothing extra. That’s why software companies can keep 70 to 85 cents of every dollar they bring in. AI is different. Every time you ask it a question, somewhere a server burns electricity, processes your request, and generates a response. In otherwords, runtime execution is doing net-new work. That cost is real, it recurs every single time, and it doesn’t get cheaper just because you have more customers. As Barrie puts it, the unit economics of AI are “at their core, the unit economics of compute, not software. And compute has never commanded software margins. It never will.”

He’s right that the numbers are ugly right now. OpenAI spent $8.4 billion on inference alone in 2025 and lost money on its most expensive subscription tier. Sam Altman has said publicly that the $200 per month plan loses money. Anthropic has spent roughly two dollars for every dollar it’s earned since it was founded. These are not rounding errors. They are structural deficits being covered, for now, by venture capital.

Which raises the obvious question: how long does that last?

Probably longer than skeptics expect, and shorter than optimists hope. The cost of running AI is falling fast, far faster than any previous technology. Per-token inference costs have dropped roughly 280 times in 18 months. Anthropic’s gross margins have moved from deeply negative in 2023 to around 50 percent today, with a credible internal projection of 77 percent by 2028. If that trajectory holds, it starts to look like software economics. The companies actually making money right now, like NVIDIA selling the chips that run everything and cloud providers renting the infrastructure, are running at margins that would make any SaaS CFO envious. The problem is concentrated at the model layer, not across the whole ecosystem.

What optimistic takes, which are taking shape miss is that even as the cost per query falls, the number of queries is exploding. Agentic AI workflows use, often, 5x to 20x times more compute per task than a simple chatbot. Every efficiency gain creates demand for more AI, not less. The total bill keeps rising even as the per-unit cost falls. Meanwhile, open-source models are closing the quality gap fast, which puts a ceiling on what anyone can charge, and that ceiling is dropping every year.

So where does that leave the industry? The seat was a bad proxy for value because it measured logins instead of outcomes. Consumption pricing is also an imperfect proxy because it measures compute instead of results. Outcome-based pricing, charging only when something actually gets resolved or completed, is directionally correct, but only about 10 percent of vendors have managed to implement it at scale.

The companies that survive this are the ones building toward that model, not defending what came before.

Barrie frames the whole thing better than I can: the AI industry’s current answer to “who pays for all this?” is venture capitalists and debt markets, paying on the promise that someday the unit economics will fix themselves.

Someday is doing a lot of work in that sentence.

Charles Goodhart was a British economist who, in 1975, articulated something anyone who has worked inside a large organization already knew: when a measure becomes a target, it ceases to be a good measure. It’s been confirmed so consistently across economics, medicine, education, and public policy that it functions less like a theory and more like gravity.

The seat was always a proxy. It was never actually measuring value.

That’s the underlying challenge worth understanding about the SaaSpocalypse, which has wiped out over two trillion dollars in software market capitalization since February 2026. Most of the mainstream coverage still frames this as AI threatening to replace human workers, which makes per-seat pricing economically absurd. That is partially a factor in this chaos. But the deeper story is that the seat was never measuring what anyone thought it was measuring, and AI has simply made the gap between the proxy and reality too wide to ignore.

Think about what a seat license actually tracked. Not work completed. Not value delivered. Not outcomes achieved. A login. An interface rendered on a screen for a human who may or may not have been productive, engaged, or even actively using the thing. The seat was a rough stand-in for “a person doing something useful with this software,” close enough to value, for long enough, that the entire financial architecture of SaaS got built on top of it. ARR, NRR, the Rule of 40, two decades of premium revenue multiples. All of it resting on the assumption that counting logins was a reliable signal for value delivery.

It worked, until it met something that delivers value without logging in, in the traditional sense, at all.

When an AI agent resolves a support ticket, qualifies a sales lead, or drafts a procurement contract, it isn’t via a rendered GUI. It doesn’t generate a seat usage metric. It just does the work. The per-seat model registers that output as, at best, one license for the agent itself, regardless of how much human-equivalent work it produced. When Monday.com replaced 100 sales reps with AI agents, those weren’t 100 seats quietly consolidating. They were 100 seats gone, permanently, with no replacement count coming. The measure and the thing it was supposed to measure had finally, visibly, separated.

The vendor response has been to reach for consumption-based pricing: charge per token, per API call, per action taken. Which is logical, except it carries its own proxy problem. Tokens consumed and API calls fired are no more connected to business value than logins were. An AI agent that fails and retries three times burns more tokens than one that succeeds cleanly, generating more revenue for the vendor.

You’ve built a model that rewards your product for not working.

The anxiety this produces is measurable. Zylo’s 2026 SaaS Management Index, covering $75 billion in enterprise software spend, found that 78% of IT leaders experienced unexpected charges from consumption-based AI pricing in the past year, and 61% cut projects because of it. The production gap is even starker: 67% of companies report gains from AI pilots, but only 10% ever scale to production. The pricing model is a significant reason why. When your meter runs whether the agent succeeds or fails, you stop running the agent. The industry traded one bad proxy for another and is now living in the consequences.

What’s beginning to work, messily and without a settled playbook, is outcome-based pricing. It measures what the seat and the token never could: the thing that actually happened. A support ticket resolved. A lead that qualified. A workflow that completed. Intercom charges $0.99 per resolved ticket for its Fin AI agent; zero for failures. Sierra, Bret Taylor’s startup, hit $100 million ARR in under two years running exactly this model. Others are using this emerging standard as the default for their AI agents. Hybrid structures, a predictable subscription floor with outcome meters layered on top, are now how 41% of vendors are structuring deals, up from 27% a year ago.

None of this is clean yet. FASB issued new accounting guidance in September 2025 specifically because AI development doesn’t fit the old linear model that’s governed software cost capitalization since 1998. Valuation frameworks are openly in flux: traditional SaaS trades at 6x revenue post-crash, AI-native companies at 25-30x, and analysts are actively debating which metrics should anchor those multiples when ARR tied to human seats no longer tells the story. New proxies are being tested, things like Credit Consumption Velocity, Agentic Work Units, and Cost per Resolved Request, and it’s not yet clear which of them will avoid Goodhart’s fate.

That last part matters. The history here is not reassuring. Every time the industry finds a metric that approximates value well enough, it builds a financial architecture on it, and then the metric drifts until something forces the reckoning. The seat drifted for twenty years before AI made the gap undeniable. There’s no guarantee the replacement proxy won’t do the same thing.

The SaaSpocalypse is proxy collapse at scale. Two trillion dollars repricing the distance between a measure and the thing it stopped measuring.

The seat was a genuinely useful solution to a hard problem: how do you price something as diffuse as the value of software to an organization? Counting the humans who used it was close enough, for a long time. AI didn’t break that by being disruptive. It broke it by being accurate, by doing real work without generating the signal the model was trained to count.

What software vendors are actually selling now is outcomes. The ones who figure out how to price that cleanly will be fine. The ones still defending the seat are defending a measure that already lost contact with the thing it was measuring.

Last week I argued that the most valuable thing in a customer conversation lives in the slow, exploratory, inefficient middle: the flow conversation that efficient AI is systematically designed to skip past. That argument was about what gets lost when we optimize for speed.

This week I want to go one level deeper as it turns out the problem isn’t just that efficient AI skips the conversation. The more sophisticated threat is that it’s learning to replace it with something that feels, from the outside, almost indistinguishable from the real thing.

The Uncanny Valley of Empathy

The aspirational AI customer service deployments today aim to not feel cold or mechanical. They are meant to be warm, patient, apparently attentive. They’ll remember what you said three turns ago. They’ll acknowledge your frustration. They tell you they understand.

But is that true understanding, or a very precise simulation of understanding?

There is a meaningful difference between a conversation that constructs genuine shared meaning between two parties, and a system engineered to produce the feeling of being heard. The first is what I called the flow conversation last week. The second is its uncanny valley twin where something that looks like empathy, is calibrated to feel like empathy, and is in fact a resolution optimization tool wearing empathy’s face.

The customer leaves initially satisfied. The underlying signal, what they actually needed, feared, or were about to do, was never captured. As a result, the feeling of resolution substituted for the reality of it.

The distinction matters more than it might seem. Customers may not be able to name what feels off, but they feel it. And when the gap between performed empathy and real investment becomes clear, the relationship doesn’t recover easily.

What’s Coming Into View

This is actually the design opportunity hiding inside the problem. Build a system with enough self-awareness to know when speed is the right answer and when a customer needs something slower and more human, and you've built something most of the market hasn't.

The dominant market pull, it seems like, is running in the opposite direction. And the implications extend well beyond CX.

It’s worth noting that last week Mustafa Suleyman, CEO of Microsoft AI, published a piece in Nature that covers this topic from a higher vantage point. He calls it seemingly conscious AI, or the deliberate engineering of the illusion of inner life. Go read it. It's short, and it reframes the stakes considerably. Suleyman closes with a line that I think belongs on the wall of every CX team making AI architecture decisions right now: "The simulation is getting better every day." What he leaves unsaid, and what I think is the defining question for us in CX, is whether better simulation is the goal, or whether we're still capable of wanting something more than that.

Let’s talk about river systems, and what they reveal (stay with me…).

A river channeled into a straight, concrete-lined aqueduct moves water more efficiently than a meandering natural stream. The metrics are unambiguous, whether it’s volume per minute, loss to evaporation, or predictability of flow. The aqueduct wins on every engineering measure, yet the river loses something in its straightening. The perceived inefficiencies of natural flowing rivers are where ecosystems of all types, from fish spawning areas, to aquifers recharging, happen. This is where branches, leaves and other elements often pile up. These snag piles, what a civil engineer would call obstructions, are where biodiversity concentrates. You can optimize the river right out of being an ecosystem.

I think this analogy is important when we frame usage of AI in customer experience. Are we hoping to build very good aqueducts, aimed at measures that miss bigger opportunities? Are we sure we know what we’re losing?

Efficiency as a First Principle

The modern industrialized society runs on the idea that efficiency is the master value, the lens through which all systems should be evaluated and improved. Over a century ago, Frederick Taylor turned it into a science. The twentieth century turned it into a religion. And the twenty-first century, so far, is busy handing it to a GPU cluster and telling it to go faster.

The results, in purely technical terms, are astonishing. We can now handle millions of customer service interactions with no human involvement, at a fraction of the cost, with accuracy rates that would have seemed like science fiction a decade ago.

But efficiency is a value, not a fact. It is always efficiency toward something, but the question of toward what is not a technical question. It is a philosophical one. When we build AI systems that are primarily optimized for customer service efficiency, we have already made a profound choice about what customer experience is for, and I believe many have made that choice almost entirely without examining the real impact for both customers and companies.

The Flow Conversation as a Human Act

There’s a concept in psychology called flow (read more from the excellent book of the same name), that describes a state of optimal engagement where challenge and capability are in rough balance, and consciousness temporarily forgets itself in the work. It’s most associated with creative and athletic performance, but it applies equally to customer experiences. [I owe a special shout out to my brilliant friend Dr. Michael Wu, who introduced me to the concept of flow in the context of CX.]

The best customer conversations I’ve observed over the years have this quality of flow. A customer comes in with a problem that is, on the surface, a billing dispute or a technical question. What eventually unfolds when both parties are present, and the pacing, is right is something more like collaborative meaning-making. The customer discovers what they actually need. The representative discovers insights that extend beyond the transactional engagement, and a relationship, in the deepest sense, is renewed.

This is not a sentimental description. It has material consequences for both sides. For the company, this engagement impacts retention, expansion, referral, and, often, forgiveness when things go wrong later in the relationship. For the customer, the added value impacts usefulness of the product or service offered by the company. These are the most economically valuable outcomes in the customer lifecycle, and they emerge reliably from conversations that would be flagged as “too long” in any efficiency-optimized operations dashboard.

The flow conversation is the inefficiency that generates the value. And the current frenzy around AI, and AI agents for customer experience are, largely, systematically engineering it out of existence.

What AI Is Actually Optimizing For

Let me put it in a more provocative way. AI in its current dominant deployment model is not aimed at optimizing for customer experience. It is optimizing for the appearance of customer experience at the lowest possible cost.

This isn’t as a critique of the technology, in fact the technology is extraordinary. I say it as a critique of the problem formulation and the choices, made upstream of any engineering decision, that impact what success means.

When we define success as containment rate, handle time, and CSAT scores collected thirty seconds after a transaction closes, we have defined a problem that AI can absolutely solve. And it will. Beautifully, cheaply, at scale. The numbers will be good. The dashboard will be green.

What will not appear on the dashboard is the slow erosion of the thing those metrics were originally designed to protect, such as a customer’s sense that they are known by the companies they do business with. That they are, in some meaningful sense, in a relationship rather than a transaction sequence.

This matters beyond sentiment. An economy built on transactional efficiency, where every human interaction is a cost to be minimized, is a very different civilization than one built on relational depth. We are, right now, making architectural choices that will determine which of those we inhabit.

The Intention Economy, Revisited

Doc Searls wrote The Intention Economy more than a decade ago and the book remains prophetic in ways that have not been fully absorbed by customer experience experts. His central argument is the future of commerce isn’t companies getting better at predicting and capturing customer attention. The future is a combination customers getting better at expressing and acting on their own intentions, with true agency, and companies building the infrastructure to receive and respond to those signals with trust.

What he described is a fundamentally inefficient model by the standards of current CX thinking. It requires listening. It requires friction, not the bad kind, but the kind that slows things down enough for intention to fully form and be expressed. It requires the company to relinquish some control over the interaction in order to actually hear what the customer is saying.

AI can be deployed to support that model, and there are active developments to evolve to this more natural, yet digital, state for CX. One of the intentions of MyTerms is to enable the foundational, contractual, ability for customer-side AI agents to navigate a richer engagement model than one that is purely built on efficiency. The dominant market pull right now is in the opposite direction, toward AI that replaces the messy, expensive, slow work of understanding with the cheap, fast, scalable work of processing. These are not the same thing. The gap between them is the gap between a river and an aqueduct.

What We Should Be Building

I don’t think the answer is less AI. I think it’s AI designed around a different first principle, not efficiency, but fidelity to the customer’s context.

That means AI deployments that treat clarification as a feature and that it treat extended conversation as a signal of complexity worth honoring, not a failure of containment. It also means that capturing the full texture of a customer interaction, including the subtle subtext, and treating that as data worth having, not noise to be suppressed. It means building the right amount of friction back into systems that have been too thoroughly smoothed. Not because friction is good in itself, but because some things of value only emerge in slowing down.

I’d argue that the most important question in CX AI right now is not how fast can we resolve this? It is what are we missing by resolving it so fast?

The companies that learn to ask, and answer , that second question are going to build something that looks less like a better IVR and more like a genuine relationship at scale. That is the actual frontier. And it is, by design, not the most efficient path to it.

Which, I would argue, is exactly the point.

There’s a conflation in how most enterprise software teams think about the relationship between customers and data that’s worth pulling apart, because it matters architecturally. Consent and contract are not the same thing. They’re not interchangeable. And the entire history of surveillance-driven CX is, in a precise technical sense, a story about what happens when you treat consent as a substitute for contract, and watch customer trust erode along the way.

When a user clicks “I agree” on a terms-of-service page, they’ve consented to a one-sided agreement. They haven’t entered a contract in any meaningful sense. The document they agreed to was written unilaterally by the organization, optimized for the organization’s interests, and designed to be as broad in scope as legally defensible. The customer has no standing to negotiate, no mechanism to enforce the terms on their behalf, and no recourse when those terms shift. This isn’t just a legal technicality, it’s the structural foundation of the surveillance model by which most internet relationships operate. These broad, unilateral terms of service exist to empower organizations, largely, to extract perceived insight from behavioral signals, not to define a mutually agreed upon relationship. At their core, they’re a permission structure masquerading as an agreement.

The practical consequence for your data architecture is more severe than most organizations appreciate. The signals your surveillance stack collects are signals from a relationship defined entirely on your terms. They reflect what customers do inside a context you control, under conditions customers can’t meaningfully alter, in response to incentives you’ve designed. Behavioral inference built on this foundation isn’t just ethically compromised, it’s structurally contaminated. A customer’s behavior in a surveillance relationship is a modified behavior, defensive and accommodating to the context. Your models train on the modification, not on genuine intent. And the edge layer, where the referral dynamics, community influence, and network context that actually drives revenue at scale, remains invisible precisely because it exists outside the surveillance perimeter. This is where customers make real decisions on their own terms.

MyTerms, the IEEE 7012 standard for machine-readable personal privacy terms, addresses this flaw directly. The critical word in its full name isn’t “privacy,” it is “terms.” MyTerms is a contract standard, not a consent standard. When a customer’s AI agent presents MyTerms terms to your systems, both parties are entering a durable bilateral agreement, one that defines scope, defines purpose, defines expiry conditions, and defines recourse. The customer has standing. The organization has obligations. The relationship is mutually defined, and it becomes mutually beneficial in ways that unilateral surveillance relationships structurally can’t be.

This distinction is the key to understanding why MyTerms agreements form a spectrum of unlocked value rather than just a menu of privacy options that aren’t enforceable or verifiable. At the foundational end, a basic bilateral relationship agreement does something that sounds unremarkable but has profound downstream consequences: it establishes a relationship where both parties know and have agreed to what the data exchange entails. The customer is no longer behaving defensively inside a context they didn’t choose. The signals your system receives are from a relationship the customer has actively agreed upon and engaged. That’s a categorically different training input than behavioral data extracted under unilateral terms, and it’s the architectural precondition for everything that follows.

Move one step along the spectrum to data portability and the bilateral foundation starts generating compounding returns. When the contract includes the customer’s right to their own copy of the relationship data, you’ve created something consent alone never can: a customer with genuine skin in the game. Their preference profile becomes an asset they own, maintain, and carry across services. They have a strong incentive to keep it accurate and current because it directly serves their own interests. You’re no longer trying to infer preferences from click patterns, rather you’re receiving a preference profile the customer has actively invested in.

Further along the spectrum, in the territory of declared intent, the contract foundation enables something surveillance architectures simply can’t produce: edge context that’s shared voluntarily. When the bilateral relationship is trusted and durable, customers have a concrete reason to share not just their own preferences but the contextual signals that connect them to their networks. These signals may include referral relationships, community affiliations, and the influence dynamics that form the missing graph. These signals don’t arrive as behavioral inference from click clustering. They arrive as declared context, shared because sharing them serves the customer’s interests inside a relationship they trust. This is what finally makes the edge layer legible. The edge emerges not because you’ve built a better inference engine, but because you’ve built a relationship structure where customers are willing to share what they actually know about themselves and their world.

At the far end of the spectrum, where customers actively co-develop the intelligence that serves them by contributing to AI training, developing collective knowledge projects, and generating shared data goods, the bilateral contract foundation isn’t just useful, it carries load-bearing value. Co-development requires a level of mutual commitment and mutual accountability that consent banners can’t establish and surveillance relationships actively undermine. A customer who is a genuine contractual partner has standing and expects that their contribution improves not only their experience, but the overall value generated from the collaboration. From a data governance standpoint, this is also the cleanest possible origin for training data: explicit, scoped, and revocable consent backed by a durable bilateral agreement rather than a unilateral click.

Building MyTerms compatibility isn’t just a technical upgrade, it’s the foundation for the next era of Customer Experience. The shift from surveillance-based CX to collaboration-based CX is arguably the most significant architectural transition since CRM moved to the cloud. Your current CX stack will need to evolve. The teams that start building this foundation now are the ones who will own the relationship layer when the agentic web arrives, and it’s arriving fast. The value compounds progressively as you move along the spectrum, and every position on that spectrum is inaccessible until you’ve built the bilateral contract foundation underneath it. Consent gave organizations permission to watch. Contract gives both parties a reason to build something together. That’s not an incremental improvement in CX. It’s a new category of it.

In my last article, I laid out the case that surveillance-driven customer experience isn’t just ethically compromised, it’s likely commercially self-defeating. I want to push that argument further, because I think there’s a more technical dimension that hasn’t gotten enough attention: the surveillance model doesn’t just collect the wrong data. It actively suppresses the formation of the most valuable data structure in your customer ecosystem.

That structure is, essentially, a hidden graph. And most enterprise CX platforms are architected in ways that make it invisible.

Here’s the frame I want to borrow from something I wrote earlier: Your Company’s Neural Network Is Growing Right Now—Whether You’re Managing It or Not. The premise there was that organizational intelligence forms through repeated interaction patterns; that the data flowing through your systems aren’t just records, that data are training signal. The same logic applies to customers. Every interaction produces signal; but, the surveillance model is capturing the wrong layer of it.

When your CRM logs a customer interaction, it captures a node: this customer, this product, this timestamp, this outcome. What it systematically fails to capture is the edge activity: this customer referred that friend, this user’s endorsement influenced that group, this complaint traveled through a community forum and influenced six renewal decisions. The node-level data is what surveillance optimizes for. The edge-level data, where the interaction network resides, is where the actual commercial value lives. And because surveillance-based systems can’t obtain it consensually, they approximate it with behavioral inference that is, structurally, a poor substitute.

Think about what this means architecturally. You’re running ML models trained on incomplete graphs. You’re building recommendation engines that optimize for individual conversion signals while ignoring the network dynamics that actually drive revenue at scale. This transaction focused data capture ignores the graph layer which drives long term value, especially in consumer-oriented business.

This is the idea I’ve been building in The Great Unwinding: From Digital Panopticon to Collaborative Intelligence. The panopticon architecture that centralizes observation, actives asymmetric data control that aims for one-directional signal extraction, doesn’t just create compliance risk and erode trust. It produces structurally degraded data. The behavioral signals you extract from a relationship built on surveillance are contaminated by the defensive behaviors surveillance provokes. A customer who knows they’re being watched modifies their behavior. Your model trains on the modified behavior, not on the genuine signal. The feedback loop degrades the very intelligence it claims to build.

The technical solution is a different data architecture entirely. That’s what MyTerms, the working name for IEEE 7012, the Standard for Machine Readable Personal Privacy Terms, actually represents. At a technical level, MyTerms introduces a bilateral contract layer that sits between the customer and the company’s service system. The customer’s agent presents machine-readable terms: data scope, interaction parameters, consent expiry conditions, escalation protocols. The company’s system acknowledges and operates within those terms.

What this creates, technically, is a consent layer that enables higher-quality signal. When a customer voluntarily shares context like stated intent, explicit preferences, and defined relationship parameters, you’re working with first-party intentional data rather than third-party behavioral inference. The training signal quality difference is significant as intent data doesn’t require a proxy. It arrives labeled and permission.

The graph problem also begins to resolve under this architecture. When customers engage through contractual, agentic frameworks, the referral and influence dynamics become legible in ways surveillance never enabled. A customer who trusts the relationship will share context about their network, not because they’re being tracked, but because sharing context serves their own interests. That’s opt-in edge data. It’s not theoretical; it’s the kind of signal that has historically only been available through high-touch relationships that have been the foundation of commercial interaction for millennia. With MyTerms as a core component, these high-touch relationships now have a scalable protocol, critical for high value co-creation in an AI-agentic world.

I’ve made the case about what this unlocks in The Intention Economy Meets AI: Why Your Customers Are About to Become Your Best Partners. When permissioned AI agents operate on both sides of a customer interaction, where the customer’s agent presenting intentions, the company’s agent responding within stated terms, you have a new class of interaction data entirely. Not “what did this customer click” but “what did this customer’s agent negotiate for, and what was the resolution.” That’s a semantically richer, structurally cleaner, and commercially more valuable data type than anything the surveillance stack produces.

The reality is that MyTerms has just been approved by the IEEE, and the technical development is still in its infancy. The agentic infrastructure for customer-side AI agents is early-stage, with interoperability challenges that haven’t been fully resolved. Most enterprise CX platforms would need significant architectural work to receive and process bilateral contract terms. This is a two-to-five year transformation, not a quarterly roadmap item.

But the architectural direction is clear enough to begin planning for. The surveillance model was never optimal from a data quality standpoint; it was simply the only available approach when customers lacked the tools to present their own terms. Those tools are arriving. The companies investing now in consent-first data architectures, customer-presented preference layers, and agentic interaction frameworks will find themselves operating on a fundamentally cleaner and more valuable graph when the standards mature.

The missing graph has always been there. We just haven’t built systems capable of seeing it honestly.