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.

A few weeks ago, I wrote about why the agentic web needs contracts, not consent. I want to revisit that idea, as there’s a recent blog post that exposes the real risk of what happens if we don’t shift our fundamental thinking on how to maintain agency for us.

Someone spent a weekend reading 34 pages of legal documents so you don’t have to. Their post, I Verified My LinkedIn Identity. Here’s What I Actually Handed Over, is one of the most clarifying recent pieces of digital surveillance writing I’ve come across. The short version is that clicking verify on LinkedIn’s personal verification process redirected them to a company called Persona Identities, which is a company most people have never heard of. What followed was a three-minute process that captured their passport, selfie, facial geometry, NFC chip data, national ID number, IP address, device fingerprint, and behavioral biometrics, including hesitation detection. Then, Persona cross-referenced all of it against government databases, credit agencies, and mobile network providers.

All for a blue checkmark.

Persona’s subprocessor list, which are the companies that actually touch your data, includes 17 vendors, all of them based in North America (outside of EU jurisdiction, which mattered for the blog writer). The CLOUD Act means that even if your passport scan sits on a server in Frankfurt, a US court can compel Persona to hand it over without telling you. Oh, and if things go wrong? Their liability cap is $50 USD. Fifty dollars. For your face, your passport, and the mathematical geometry of your skull.

This is what consent looks like in 2026. A three-minute tap, a 34-page document nobody reads, and a surveillance chain that runs straight through AI infrastructure including Anthropic, OpenAI, and Groqcloud, all listed as subprocessors doing Data Extraction and Analysis of your identity documents.

Pretty sobering, if you ask me.

So, who exactly is “us”?

When I wrote The Case for Us I was talking about people who want genuine agency over their digital lives, and not just aspirationally, but operationally. The LinkedIn example shows why urgency matters. It’s not an edge case. Linkedin is a mainstream platform, where you may be compelled to get verified, without realizing that you handing over data to a surveillance chain most users never see. Now imagine that multiplied across every AI-powered interaction in the next decade, happening at machine speed, with no human in the loop to read anything before the terms are already set.

That’s the uncomfortable truth about where we are today. The surveillance model isn’t slowing down, rather it’s accelerating, and the agentic web will give it more surface area than ever before. But I believe the very scale of the problem is what makes this moment different. When the cost of the surveillance bargain becomes visible, as in your passport running through 17 companies, your face worth $50 in liability, three AI platforms doing “data extraction” on your government ID, people should start paying attention in ways they didn’t before. The case for individual agency isn’t a niche manifesto anymore.

It’s Time to Architect a Better Way

For nearly two decades, the Vendor Relationship Management community argued that customers should manage their own relationships with businesses, not the other way around. While this concept was aspirational, the enterprise software world largely ignored it as, for starters, there wasn’t a standard to adopt.

That’s changing. IEEE 7012, the MyTerms standard, is now officially available. It gives personal AI agents a machine-readable language for asserting your privacy terms before an interaction begins. As I explored in Beyond the Surveillance Bargain this isn’t just a privacy tool, it’s the first mechanism for genuinely bilateral customer experience. Your agent arrives at every interaction carrying your context and your conditions. Services either meet them or don’t get access. No more manufactured consent.

And as I wrote in Customer AI Agents are the New API this flips the entire CX model. You stop being a callable function, a data object to be processed, and become a platform with your own terms. Companies that adapt to that reality will have an opportunity to thrive in a balanced world. There’s so much more opportunity for value co-creation when both parties operate on agreeable terms. Companies that resist this change are running a countdown clock on a model that was always more fragile than it looked.

The Road Ahead

MyTerms itself is in its infancy. Most people have never heard of any of this. The surveillance chain is live and scaling right now. But, I believe we have a foundational element to rebalance the relationship between individuals and the entities they interact with. MyTerms is an endorsed standard, and is actively being worked on. The economic case for respecting customer agency is becoming undeniable as the costs of surveillance pile up.

The case for us isn’t that we’ve won. It’s that we finally have a foundation. That’s worth a lot.

For nearly two decades, the Vendor Relationship Management (VRM) community made an argument that most enterprise software vendors dismissed as irrational: what if customers managed their own relationships with businesses, rather than the other way around? What if, instead of a thousand CRM systems each maintaining a fragmented, vendor-curated version of “you,” you maintained your own authoritative record and extended selective access on your terms?

The enterprise world largely ignored this possibility, and instead kept building more invasive data collection methods to fill increasing bigger data lakes, all with the hope of delivering better customer experiences. The imbalances of this data collection framework, however, not only complicated every interaction, but also eroded the very core of successful customer experiences, trust.

The challenge of truly independent customers was as much infrastructure as interest. Customer sovereignty sounds elegant in a manifesto. It sounds impossible when you’re staring down the operational reality of millions of asynchronous, unstructured customer interactions. How do you honor a customer’s stated preferences in real time, at scale, without a human in every loop? You couldn’t. So the surveillance model won by default, not because it was better, but because it was executable and offered short term gains. While the surveillance model provided quick wins, the weight of inaccurate data and inaccurate insights has led to a ‘Customer Experience’ environment that is simply a mess.

As we see the rise of Agentic AI, there’s an opening to reset the relationship.

Personal AI agents change the infrastructure equation entirely. IEEE 7012 MyTerms contracts give customer-side agents a machine-readable language for asserting customer preferences before an interaction even begins. What VRM sketched out philosophically, the agentic layer can now run operationally. The inversion that I wrote about in my last article isn’t just coming, It’s being compiled into executable code.

What “Inversion” Actually Means for Enterprise CX

Let’s be specific about what flips. In the current model, CRM systems are the systems of record. They assume ‘ownership’ in defining who the customer is, what they’ve done, what they’re worth, and how they should be treated. Essentially, the customer is treated as an object in your data model. They have no formal mechanism to contest that representation, update it in real time, or route around it when it doesn’t serve them.

In the inverted model, the customer’s personal AI agent is their system of record. It arrives at every interaction already carrying context — purchase history, stated preferences, communication boundaries, even negotiating parameters. Your CRM becomes a participant in a conversation your customer’s agent is already running, not the authority defining that conversation’s terms.

This isn’t a minor UX upgrade. It’s a fundamental shift in who holds the authoritative data asset.

The Bilateral Contract as a CX Primitive

MyTerms in this context is more than a privacy protocol, it is the first technical mechanism for a true, bilateral Customer Experience where value is co-created at every interaction. Traditional service terms are unilateral — a vendor publishes terms, a customer clicks accept or walks away. The relationship is legally asymmetric from day one; however consent isn’t the same as contract.

A MyTerms contract negotiated between a customer agent and a brand’s service layer is a different kind of artifact. It’s a living agreement, machine-enforceable, that defines the conditions under which value exchange happens. It clarifies data use scope, communication frequency, escalation pathways, consent renewal triggers, and that’s just for starters.

For enterprise CX leaders, this reframes the entire concept of a “customer profile.” The profile is no longer something you build about a customer. It’s something the customer’s agent presents to you — with access levels you earn through the quality of your service, not the sophistication of your data collection.

Time to Pay Attention to Customers Intent

Any enterprise platform sitting on a surveillance-based value proposition needs to start asking honest questions. If the data moat is the core differentiator, and customers begin reclaiming that data behind agent-managed contracts, what’s left? The answer has to be service quality, interaction speed, reliability, and the sophistication of what you can actually do for a customer when they show up with full context already loaded.

Interestingly, this should be good news for companies that have always invested in genuine service capability but have been outcompeted by platforms that simply out-collected them on data. The MyTerms inversion levels a playing field that was never level to begin with.

The VRM community, led by Doc Searls, spent twenty years being right too early. The agentic web is making them right on time. For enterprise CX, the strategic question isn’t whether this inversion happens, as the infrastructure is here, the standards are maturing, and the customer motivation is obvious. The question is whether you’re building toward a world where customers choose to engage your systems, or assuming they’ll continue to have no choice.

One of those is a strategy. The other is a countdown.

In my last article, I made the case for why individuals need personal AI agents that operate with machine-readable contracts like IEEE 7012 (MyTerms) to reclaim control in digital relationships. When customers’ agents can automatically negotiate terms and route around services that don’t respect those terms, the power dynamic fundamentally shifts. Now let’s dig deeper into what happens to the businesses on the other side of those relationships.

The Callable Function Model Is Breaking

I also recently wrote about how many organizations risk reducing their human employees to just “callable functions.” This model implies that humans are simply API endpoints that AI could invoke when it needed something, get a response, then move on without context, without deeper learning, and simply treat the humans as transactional actors in a AI-driven process. If AI-driven processes scale too quickly, we may end up treating customers the same way, as customer interactions reduced to objects to be processed, without much context or insight.

We, the customer, often feel this way already. Marketing automation “calls” us with targeted messages. Data systems “invoke” us for information. Many CRM/CX operations and systems architecture assume customers are stateless endpoints in their processing pipeline. We intuitively know that’s an unfair, and unfortunate reality. But, what happens when customers get their own AI Agents?

The Shifting Power Dynamic

If customers control all their own data through AI agents operating under their terms, how does the established dynamic shift?

Traditional CX platforms create value through data aggregation. Collect customer data, build unified profiles, generate insights, ultimately, to drive revenue. But when customers’ personal AI agents carry their own complete data profiles and can reject terms that don’t respect customer privacy, that entire value proposition evaporates. Just like treating workers as callable functions undervalues expertise, treating customers as data endpoints undervalues the relationships that create sustainable business outcomes.

From Data Landlord to Service Provider

As customer-side AI agents enter the CX ecosystem, a fundamental pivot is required from owning customer data to optimizing how we interact with customers who control their own data.

In the AI agent-driven economy, competitive advantage will likely shift to speed of agent response, quality of interactions, reliability of commitments, and sophistication of services. It’ll require a mindset shift from being a data landlord to being a service provider. More fundamentally, it’ll move from treating customers as callable functions in your workflow to recognizing them as platforms that have sophisticated interests that have greater context, and recognizing that customers, too, actively run their own workflows and processes.

The MyTerms Inversion

This is what Doc Searls and the VRM (Vendor Relationship Management) community have been championing for nearly two decades. It’s a future where customers regain a balance of power and become the center of their own relationship networks rather than objects in vendor CRM systems.

Personal AI will make this practical at scale. While company CRM systems will continue to be critical for success to orchestrate customer-centric value co-creation, customers controlling their own data will begin the process of rebalancing customer experience dynamics. When customers stop being callable functions, and have their own platforms, greater opportunities for value co-creation will appear.

The surveillance-based model never delivered on the promise of great CX—it just made vendors better at manufacturing consent and treating customers as data objects to manipulate. An emerging agent-driven model, built on bilateral contracts and customer sovereignty, might actually deliver on that promise.

The companies that stop treating customers as callable functions and start treating them as platforms with their own agency won’t just survive the transition. They’ll thrive in ways the current vendor-controlled model never allowed.

I’m exhausted by trying to manage the surveillance infrastructure that surrounds my digital presence. You probably are too.

Nearly every app I use, every website I visit, every “free” service I sign up for are all playing a similar game. They collect my data, attempt to track everything I do, sell manufactured insights about me to advertisers, and then act like they’re doing me a favor. That world has to fundamentally change, especially if we are to protect our data rights in an era of Agentic AI.

Something significant is happening to alter the path forward. MyTerms, the IEEE 7012 standard for Machine Readable Personal Privacy Terms is now officially available to build with. I believe it’s a missing part of the protocol framework that will make the emerging agentic web actually work for people instead of against them. (full disclosure, my fellow board members at Customer Commons were deeply involved in the creation of MyTerms).

The Great Unwinding Demands New Rules

In my recent post on the year ahead, I described how we’re witnessing the “great unwinding” of enterprise software stacks. The traditional monolithic application will be hollowed out, replaced by a new orchestration layer where AI agents coordinate actions across fragmented systems without human intervention.

Among other things, this orchestration layer fundamentally will break the old “notice and consent” model. When AI agents are making thousands of decisions per second on your behalf, such as checking supply chains, updating forecasts, drafting contextualized outreach, there’s simply no time for humans to read privacy policies and click consent buttons.

We’re moving from a world where you go to software to a world where software works for you. But who controls that software when it’s working for you? Under what terms? With what constraints?

Why Consent Becomes Impossible

The shift from apps to actions, from interfaces you control to orchestration that happens autonomously, makes traditional consent literally impossible to execute. When your personal AI agent encounters a new service to accomplish a given task, it can’t stop and ask you to review a 10,000-word privacy policy. These consent requirements are bemusing in our current world and farcical in the agentic web that’s taking shape. This is where most people assume we lose. That the agentic future is inevitably one where we surrender even more control to surveillance-oriented systems that make decisions about our data without our meaningful input.

But there’s another path. And it requires replacing consent with something more robust: bilateral agreements, contracts, that can accomplish what we’ve had in the physical world for milennia, a durable mechanism to engage equally between two parties.

Contracts as part of the Protocol Layer

Binding agreements between parties is precisely what IEEE 7012 enables. Instead of “take it or leave it” consent, MyTerms provides the protocol for genuine bilateral contracts in the orchestration layer.

The mechanics of this are quite simple, and scalable. You choose your privacy terms from a standard roster maintained by a neutral nonprofit. These terms are simple and clear: what data can be collected, how it can be used, whether it can be shared, and what rights you have. They are published as human readable variants of legalese and are machine readable for agentic execution.

When your AI agent encounters a new service while orchestrating actions on your behalf, it automatically presents your terms to their system. The service reads those terms and either accepts them or proposes an alternative from the same standard roster. If both sides agree, the contract is digitally signed, recorded, and enforceable. If they don’t agree, your agent logs that as well, and gives you a choice on how to proceed. This will happen in milliseconds, automatically, at the orchestration layer. But unlike consent, it’s a real agreement with legal weight on both sides.

The New Surface Needs New Foundations

As I described in my look ahead, we’re also seeing interfaces become spatial as the world itself become an interactive surface. When the interface is everywhere and AI operators are managing complex systems too fast for human cognition, the only way to protect individual agency is through machine-readable contracts that travel with your data.

MyTerms becomes the essential protocol that allows you to maintain sovereignty even as you benefit from the orchestration layer. Your terms become enforceable conditions that any service wanting to interact with your agent must respect.

Building a Fair Agentic Web

Without MyTerms, the orchestration layer defaults to the current state of surveillance-driven control. Services dictate terms, agents comply, and individuals become even more thoroughly the product. This will only accelerate disempowerment of individuals and with dubious consent agreements.

But with MyTerms as a foundational protocol, something different may become possible. When millions of personal agents operate under MyTerms contracts, services that abuse customer data may become unsustainable on the agentic web. They won’t be able participate in the orchestration layer because agents representing people simply expose irrational, or onerous terms, and route around them.

Meanwhile, services that embrace MyTerms will become discoverable and preferred. The orchestration layer will favor companies that compete on how well they respect customer terms rather than how cleverly they exploit customer data.

The Path Forward

IEEE 7012 is now officially available. Although there’s been nearly a decade worth of investigation, research, and development to establish MyTerms, the work to enable it is just emerging. The people who get involved now will shape the next phase. That means learning about agent-to-agent contracts, supporting services that implement MyTerms, and demanding that the orchestration layer respects individual agency.

In the age of AI operators and spatial interfaces, consent is obsolete. Contracts are essential. And MyTerms is the protocol that makes a fair, robust, and human-centered agentic web possible.

Are you in? Learn more, and sign-up for the MyTerms Alliance here.

We’ve all had that moment. You want to make a simple, or strategic, change to your processes, like tweaking a customer journey, and you hit a wall. While the business expects to be dynamic, it’s now IT where the bottleneck appears, “We can’t touch those systems right now. Your changes will require us change hard-coded logic that is deep in the database schema. Check back in Q3.”

It’s frustrating, but whose fault is it? For years, we’ve built companies on a bit of a flawed premise that our Systems of Record (our ERPs, CRMs, and databases) were the place where our business logic should live. We took our dynamic, breathing strategy and locked it into static, rigid code. As I wrote a few weeks back, the most profound impact of Agentic AI will be in extracting this logic upwards to a dynamic orchestration layer, something that Forrester Research calls Systems of Agency. This orchestration layer will take years to stabilize, but the core will be a shift from writing rigid rules based logic to goal oriented guidance, largely for agentic AI guide-rails.

AI agents won’t blindly follow a script, they’ll use reasoning, context, and navigate through paths which are not all established. While there will be risks, there are a couple of clear benefits of this evolution:

• Killing the “Toggle Tax”: Much of ‘work’ for human employees today requires constant context switching, from one interface to another, and back. The ‘toggle tax,’ or I often call it integration at the keyboard level, makes work wildly inefficient and is a productivity killer. Also lost in this process is the actual, underlying knowledge that is created while shoving data between poorly integrated processes.

• The “USB-C” for Enterprise: While not a perfect comparison, as USB-C itself has all kinds of complexity, having standards like Model Context Protocol (MCP) will begin to move disparate enterprise process flows away from proprietary APIs toward a more standard approach. The standardization will enable business logic to move out of database architectures, and into a more dynamic orchestration layer.

And that’s just the beginning. This long road ahead isn’t about replacing people, rather freeing them up to expand their value and creativity. Enough of the toggle tax already. The future of the enterprise isn’t about better applications or databases. It’s about smarter orchestration. And honestly? It’s about time.

In my last post, I talked about the rise of the Agentic Operating System—that invisible, fluid layer where AI doesn’t just assist you, but actively operates your enterprise. We moved from the Future Intense idea of unwinding monolithic stacks to a world where software is no longer a destination you visit, but a set of callable functions that work for you.

But as I watch these agentic ecosystems come online, a new tension is bubbling up. It’s no longer about capability (can the agent do the task?); it is about permission (should the agent do the task, and how do we know it did it right?).

We’re entering the dawn of the paradoxical next phase, one of managed autonomy.

The Runaway Risk

The reality on the ground is that we are starting to hand over the keys to the kingdom to AI agents with lofty goals. We aren’t planning to just let AI summarize emails anymore; we are planning to activate AI to reroute supply chains, authorize payments, and negotiate with vendors.

The big research firms are validating this trend. Gartner recently predicted that by the end of 2026, 40% of enterprise applications will feature task-specific AI agents, a massive jump from less than 5% just a year ago. That is a staggering velocity of adoption.

But Forrester warns that without proper guardrails, we are heading for a “reality check,” predicting that up to 40% of agentic AI projects could be canceled by 2027 simply due to inadequate risk controls.

The problem isn’t the intelligence rather it’s the autonomy. When you have thousands of agents making micro-decisions every second, you can’t rely on a human “checking the work” in the traditional sense. The speed of the orchestration layer will outpace the speed of human audit.

Enter the Guardian Agent

So, how will we solve this? Paradoxically, a solution to too much AI may be... more AI.

Gartner calls this new emerging layer Guardian Agents. These agents will be specialized AI designed not to do the work, but to watch the workers. They are the digital auditors, the compliance officers, and the security guards of the Agentic OS.

Think of it as a trusted secondary immune system for your enterprise. While your “Operator Agents” are out there optimizing logistics or personalizing marketing campaigns, your Guardian Agents will silently observe the data flows, enforcing policy barriers, and flagging anomalies before they become liabilities.

This won’t be a nice-to-have, it’ll become the new market standard. In fact, Gartner predicts that these Guardian Agents will capture 15% of the AI market share in the next few years. If you are building an orchestration layer without a governance layer, you aren’t building a scalable business, rather you’re building a casino where there’s little to no control.

From Operator to Architect

This shift will fundamentally change our role as humans in the loop. In the first article of this series, I mentioned that AI is moving from Copilot to Operator. That’s true. But where does that leave us?

The parallel phase will be to enable Operator AI and Governance AI. These Architects of Governance will help humans to design the machine and set the safety limits.

BCG has a great framework for this, describing a move toward Supervised Autonomy. In this model, the agent stages the action, but the human sets the “confidence threshold.” If the agent is 99% sure, maybe it executes automatically. If it’s 85% sure, it pings a human (or a Guardian Agent) for sign-off.

This is where my Future Intense concept really lands as the intensity isn’t just in the computing power; it’s in the trust. Trust becomes the currency of the autonomous enterprise. If you can trust your governance layer, you can let the orchestration layer run at full speed.

The Optimism of Control

It is easy to look at this unchecked autonomy and feel a twinge of dystopian dread. But this transition won’t happen overnight, and I choose to remain optimistic as the entire orchestration ecosystem will co-develop.

McKinsey estimates that agentic AI could automate 30% of all work activities by 2030. That is not just efficiency but, optimistically, that is liberation. It’s the return of time, our most non-renewable resource, for more creative tasks…or to just relax!

So, as we head deeper into 2026, don’t just ask what your agents can do. Ask who is watching them. Because in an orchestrated world, control isn’t about slowing down; it’s the only thing that lets you go fast.