Every AI CRM vendor selling into 2026 has an AI agent story by now. The differentiator is no longer whether agents exist, but who is allowed to build the next one, how long that takes, and what happens to the bill once it works. For decades, CRM has promised growth and mostly delivered data entry, decaying from a system of action into a system of record. The agentic shift changes that, and with it the questions buyers should ask. Let’s put Creatio's AI CRM to those questions, following a deal from lead to order to see how much orchestration ships out of the box and how much a revenue team must assemble. The findings are published in full in my report, AI CRM for Revenue Growth: Inside Creatio's AI-Native No-Code Platform.

The company behind the platform

Creatio is a privately held, AI CRM and no-code workflow automation company headquartered in Boston, founded in 2014 by Katherine Kostereva, who remains CEO. It ran as bpm'online until a 2019 rebranding, bootstrapped until its first institutional round in 2021. A $200 million round led by Sapphire Ventures in June 2024 lifted its valuation to $1.2 billion; total funding raised now stands at roughly $268 million, and it reported around 50 percent year-over-year revenue growth at the time.

Creatio employs around 1,000 people and sells through more than 500 implementation partners worldwide. The company’s partner program has held a 5-star rating in CRN's Partner Program Guide for eight consecutive years. Customers span more than 100 countries, among them AMD, Colgate-Palmolive, and MetLife, with millions of workflows launched daily.

One platform, two studios

The product serves marketing, sales, and service on a single unified data model. Creatio Studio sits on top, split into Business Studio for no-code applications and AI Studio for autonomous agents, both sharing one data, security, and governance model. An in-app AI Twin now lets end users build their own agents from an IT-approved library without leaving the CRM. What makes this an AI CRM rather than a CRM with AI attached is where the intelligence sits: Creatio combines predictive, generative, and agentic AI in a single Creatio.ai architecture, reachable by end users in natural language, instead of bolting a chatbot onto a system of record.

Two authoring patterns cover most agent use cases. Prompt agents are simple assistants defined by a natural-language instruction plus the tools and skills the agent is allowed to use. Workflow agents are multi-step processes built on the same drag-and-drop designer that powers the rest of the AI CRM. Both are built by the same business-side practitioner who already configures pipelines and dashboards. There is no separate developer queue, AI-specialist hiring profile, or code repository in the middle.

Creatio was named a Leader in Nucleus Research's November 2025 LCAP Technology Value Matrix, and it was the only Leader in Forrester's 2024 Wave for low-code platforms built for citizen developers. That recognition shows up in practice too: BSN Sports runs its entire deployment for 2,600 users with just three administrators, while Howdens rolled out to 7,000 users across more than 800 depots in twelve weeks. Nucleus has separately measured 61 percent faster lead response, 70 percent faster implementation, and 37 percent lower total cost of ownership against legacy systems. Industry editions — including an agentic banking Solution that provides the basis for a Banking Blueprint that covers onboarding, lending, and KYC/AML — extend the platform into regulated sectors.

The pricing bet

In 2026, Creatio introduced an Unlimited plan tied to its Unlimited Enterprise operating model. One subscription covers unlimited users, custom agents, applications, workflows, custom objects, and API calls as a single platform fee, with AI included rather than metered. Beneath it, credit-based consumption is the default and per-user licensing remain available; AI Studio and AI Studio Twin add no incremental license.

The test: five agents, one deal

To test the authoring claim directly, I looked at a five-agent scenario across a single deal's lifecycle, combining shipped Creatio.ai agents with customer-specific ones authored in AI Studio:

• An ICP-fit agent and an engagement-fit agent jointly qualify inbound leads, built on Creatio's Account Research and Lead Scoring agent patterns, promoting a lead to sales-accepted once both clear their thresholds.
• An opportunity-health agent layers S/M/L risk sizing on Creatio's native MEDDPICC scoring, reads the opportunity record and call transcripts, and gates stage advancement until the criteria are met.
• A SPIN-style coaching agent proposes concrete next moves on a stalled deal but cannot act without rep approval.
• A service-brief agent, built on the shipped Customer Support and Knowledge Base agents, compiles ticket history, sentiment, and invoice status into an on-demand pre-call summary.

All five are registered, monitored, and governed in Creatio's unified administration layer, with PII policy, approval gates, cost thresholds by agent and model, and audit logging applied uniformly, whether the agent shipped with the product or was authored in-house. Each customer sets the rigidity, from letting agents auto-transition stages to requiring a human at every gate.

How the competition does it

Most competing approaches to agent-building fall into one of three patterns:

• an agent designer wired tightly to a fixed data model, as with Salesforce's Agentforce and ServiceNow's AI Agents;
• a horizontal builder paired with a separate CRM, as with Microsoft's Copilot Studio and Dynamics 365; or
• a pro-code toolkit that still needs the engineering capacity it was supposed to eliminate.

Each carries a trade-off: opinionated designers constrain any customer whose process diverges from the vendor's reference, horizontal builders mean stitching two governance models together, and pro-code toolkits demand the scarce engineers they promised to free up. Creatio's pitch is that collapsing the AI CRM, the data model, the process engine, and the AI authoring layer into one product, governed from one console, sidesteps all three.

Analysis

The architectural claim holds up on inspection: governance, authoring, and the AI CRM itself sit in one architecture rather than three, which is a structural condition most agentic CRM vendors talk about, but few actually deliver.

The Unlimited Enterprise pricing model is the more interesting bet, however. It shifts the conversation from seats to execution at a moment when every competing consumption model bends upward exactly as AI adoption succeeds. The caveat is that Creatio's own default is AI credit-based consumption, so the unlimited promise and the metered tier still need reconciling. Whether it holds as genuinely unlimited at scale is the open question I would flag for any multi-year commitment.

The weaker spots are predictable for a company this size. Brand recognition in the upper enterprise and the North American mid-market still trails the legacy CRM incumbents, and delivery runs through that partner network, where outcomes vary with partner maturity. Neither is disqualifying, but both belong in a buyer's due diligence.

The AI CRM category itself is still being defined, so the more durable test isn't feature count. It's whether this architecture and this commercial model survive contact with deployments larger than the reference customers cited above.

Want the full picture, including the complete five-agent scenario, the competitive comparison, and the SWOT? My full report is available for download here.

Every so often a vendor conversation earns the word "useful," and this one flirts with it. On CRMKonvo #309, Pega's Matt Healy walked into a room of skeptics and, refreshingly, did not try to sell AI as pixie dust. He sold governance. Let me explain why that is the interesting part, and where the pitch still needs a second look.

First, the setup, because it is absurd. According to Matt, ninety-five percent of Fortune 500s still run a mainframe in some capacity. Roughly thirty thousand organizations are still on Lotus Notes. Healy mentions a government claims system running on hardware funded by a grant from the JFK administration, and a separate agency contracting retirees back out of retirement homes to keep the thing alive. This is the installed base that every "AI-native transformation" slide ignores.

TL;DR

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The graveyard problem, now with agents

Modernization has been on the CIO agenda since CIOs were invented. What changed, is that frontier AI does not mix with data and processes trapped inside sixty-year-old systems. But even worse: if AI lets you build faster, it also lets you fill Alan Trefler's famous "application graveyard" faster than ever. The Lotus Notes graveyard of the 2000s simply reopens as an agent graveyard, or a Claude graveyard; take your pick of tombstone.

Healy does not dodge this. He cites the now-familiar numbers on AI-generated code: roughly eight times more duplicated blocks, double the code churn, and about 1.7 times the vulnerabilities versus human-written code. Convenient for a platform vendor to quote? Absolutely. Wrong? Not so much. His conclusion is the sensible one: what works for a hacker building a toy on a weekend does not survive contact with regulated, mission-critical scale.

"Model, don't code," or low-code wearing an AI hat?

This is the core message. Do not let AI generate an application from the foundation up. Use AI to translate business requirements into a model of the business: the processes, the decisions, the rules. Then let a platform run that model consistently, so ninety percent of the application behaves the same across hundreds of apps and only ten percent is specialized.

Revolutionary? Not quite: this is model-driven development, wearing an AI hat. The new and genuinely valuable move is using AI early: analyzing legacy systems, gathering requirements, researching regulations, and doing it grounded on curated best practices rather than, in Healy's words, "who knows where out there on the internet." That is where the productivity actually lives.

Predictability is the whole ballgame

Businesses want predictability, and probabilistic models are by definition not predictable. Healy's stance is the adult-in-the-room part of the episode. Keep orchestration and governance deterministic; confine agents to tasks like summarization, document handling, content generation, and research; and check their work. Crucially, produce visual, explainable models rather than millions of lines of machine-translated code that no auditor can read. When only about ten percent of enterprises have compelling AI in production, and the two roadblocks are cost and risk, "explainable and deterministic" is not a nice-to-have. It is the entire permission slip.

The pricing tell

Healy spells it out. Token-based pricing measures how much thinking the model does, which is not tied to value at all, and this incentivizes vendors to make the model think more. Pega's, as well as other vendors’, counter is outcome-based pricing: pay per claim, or a percentage of your cost per claim, and burn as many tokens as you like. This is the most buyer-aligned idea in the whole conversation. One caveat is of course that outcome pricing is also a lock-in and margin play, and the real negotiation lives in how you define a "claim" or a “resolution” and what today's baseline cost supposedly is.

The three-month miracle

Then the headline claim. An insurance was quoted by a system integrator seven years and roughly $25 million for a lift-and-shift from COBOL to Java. Pega plus it’s AWS tooling, we are told, produced a working application in three months.

The lift-and-shift critique is dead right: machine-translating COBOL into Java that nobody can read just translates old debt to new debt in the cloud. But "a working application based on their mainframe in three months" is an extraordinary claim resting on a single vendor-chosen reference that also happened to speak at Pega's own event. Before anyone budgets around that number, define "working." Which of the seven systems? What stayed on the mainframe? Who maintained it the day after go-live? And note the honest piece Healy volunteers: mainframe-zero is a fantasy. High-volume, low-latency payment processing stays put. The play is to extract the customer-facing, longer-running workloads and leave the transactional core alone.

The debt you cannot see is architectural

Which brings us to the point that vendors would rather skip: Gartner's observation (behind paywall)  that technical debt is increasingly becoming architectural debt. That distinction is important, because swapping COBOL for something modern is a code problem, while re-cutting your solution architecture is a business-continuity problem. You cannot simply stop the enterprise, rebuild the plumbing, and switch it back on. Healy's answer is reasonable if unglamorous: plan top-down for where the business must be in one, three, and five years, let AI do the bottom-up archaeology of what you actually have, and meet in the middle. His genuinely useful framing is that this rationalization work, the sort of thing that used to eat six to twelve months of enterprise-architecture effort, can now be compressed into roughly two weeks. That is the compression worth paying for. Just do not let the same speed refill the graveyard with agents nobody governs.

Before you sign anything: three notes for CX buyers

Buy the archaeology, scrutinize the miracle. The lowest-risk, highest-certainty value is in using AI for legacy analysis, requirements gathering, and regulation research. Fund exactly that. Treat "three months, one platform, done" stories as scope-defined case studies, not as your project plan. Ask what "working" means, what was left running on the mainframe, and who owns maintenance after the confetti settled. A demo is a promise; a reference is a data point; neither is your architecture.

Make explainability and determinism contractual, not aspirational. For regulated CX, the probabilistic parts belong at the task level, boxed in and checked, while the process and governance stay deterministic and auditable. Insist on visual, explainable models. If your vendor cannot show a regulator how a decision was reached, understand that you own that risk, not them. "The AI decided" is not a defense you want to offer an auditor.

Price for outcomes, own the baseline. Outcome pricing beats a token meter hands down, so push for it. Just remember the leverage sits in the definitions: what counts as a claim or resolution, what today's cost really is, and what happens when volumes move. Bring finance and a hard-nosed controller into the room early, not after the first invoice. And govern the citizen-developer and agent sprawl from day one; the alternative is watching your Lotus Notes graveyard reopen under new management.

An unusually grounded hour. Healy sells discipline, not magic, and that alone puts this ahead of most vendor pitches. Just read the three-month case study with your glasses on.

TheStreet ran a piece this week arguing that, of Microsoft's two Copilot announcements, the shift to usage-based pricing matters more to investors than the DeepSeek flirtation. That read is correct. It is also the tell.

Here is what Microsoft actually did. Copilot Cowork, the agent that reaches across Microsoft 365 to run multi-step work on your data, is coming off the flat per-seat add-on and moving onto consumption billing the company calls "Copilot Credits." Charles Lamanna, who runs Copilot, told Axios the product could not be offered on an unlimited-use basis. The users he pointed to are the ones doing hundreds of tasks a week. He called them "way productive." And then he said the part vendors normally keep off the slide: their costs go very high.

So the most productive users are the expensive ones. Hold that thought, because the whole argument lives there.

What "good for investors" is really saying

A pricing model earns the label "good for investors" when three things are true. Revenue starts to track cost-to-serve. Revenue scales with consumption instead of sitting flat per seat. And the vendor stops eating the margin on its heaviest users. All three are true here. None of them is a statement about whether a customer got value.

That is the gap I want to sit in for a minute.

Usage-based pricing meters an input. Tokens, compute, credits, whatever the unit. The customer does not buy tokens because they want tokens. They want a finished report, a resolved ticket, a reconciled spreadsheet. The token count is the cost of producing the outcome, not the outcome. And the relationship between the two is loose at best.

TSIA put it plainly in its May analysis of AI pricing: usage does not equal value, and consumption models often fail to reflect actual business value. That is not a critic talking. That is a research firm whose audience is the vendors building these models.

Agents make the coupling worse, not better

A chatbot answers and stops. An agent keeps going. It reads files, calls tools, checks its own work, hits a wall, tries again. Each of those steps burns compute, and the steps are what get metered. Every retry, every verbose detour, every loop the agent runs to second-guess itself adds to the bill. The customer pays for all of it.

Now ask yourself the hard question. Does a workflow that took the agent three retries and a long chain of self-checks deliver more value than the same workflow done cleanly in one pass? Of course not. It delivers the same outcome and costs more. Under seat pricing, that inefficiency was the vendor's problem. Under usage pricing, it is line-itemed onto the buyer's invoice.

The billing platform Flexprice, which sells the plumbing for this, says it out loud to its own customers: retries, loops, and background jobs are friction, not value, and usage-based pricing only works when customers gain something real as the meter climbs. Their warning to vendors is the buyer's whole case.

The people who get punished are the people who bought in

We do not have to guess how this lands, because GitHub Copilot already ran the experiment. On June 1 it moved to token billing. The median user barely noticed. The pain landed on the top five to ten percent, and it landed hard: community projections of bills jumping ten to fifty times, one developer modeling a move from roughly $29 a month to nearly $750, another claiming $50 to $3,000. TechCrunch called it the end of Copilot's golden age.

Look at who those heavy users are. They are not abusers. They are the people who took the vendor's three-year advice to use the tool for everything, built agentic workflows around it, and made it part of how they work. The pricing change penalizes exactly the depth of adoption every vendor claims to want. And the old safety net, where running out of premium budget dropped you to a cheaper model so you could keep working, is gone. What is gone, too, is the cost ceiling.

Lamanna's "way productive" power user and GitHub's top-decile developer are the same person. The model charges most to the customer who is succeeding most. Reward and penalty have swapped places. The reward now goes to the vendor.

The structural problem, which is bigger than the bill

Now the second half, and this part is more important than any individual invoice.

Think about where accountability for value sits in each pricing model. With outcome-based pricing, the vendor gets paid when a result lands and not before. Fin's (formerly known as Intercom) Fin charges 99 cents per resolution, billed only when the customer confirms the AI actually solved the problem. Under that model, every failed attempt costs the vendor. So the vendor has a direct, financial reason to make the agent efficient, accurate, and sparing with compute. Their margin depends on it.

Usage pricing inverts that incentive. The vendor is paid for activity regardless of whether the activity worked. An agent that burns more tokens, retries more often, and reasons more verbosely produces more revenue, not less. I am not claiming Microsoft will deliberately bloat Cowork to pump credits. I am saying the financial pressure that used to push toward lean, effective agents has been switched off; and switched-off incentives have a tendency of showing up in the product eventually.

The demand side pulls in the same direction. There is a name for it now: tokenmaxxing, the workplace habit of treating AI usage as a proxy for productivity, where people get judged on how many token they burn rather than on what they shipped. Built In's writeup is blunt about it: the habit rewards visible activity, not results. So stack the three forces. Buyers under pressure to run up consumption as a status signal, a vendor that meters by consumption, and an agent that inflates consumption on its own. Everything drives the meter up. Nothing points it at the outcome.

That is the real cost of the model. It moves the vendor one step further from owning the question of whether you got value, and it hands that entire question to you. The vendor essentially plays Pontius Pilate. The buyer now runs FinOps for AI. You set the budget caps. You write the spending policies. You read the consumption dashboard. You type /cost to see what a task burned. Microsoft, to its credit, is shipping all of those controls, and they are better than the ones GitHub fumbled out the door. But notice what they are. They are tools for the customer to govern value. They are not the vendor guaranteeing it.

The honest counterargument

I would be doing the same vendor-spin thing I just criticized if I left out the other side.

Flat pricing for agentic tools is inherently unsustainable. The economics are upside down: the model subsidizes the heaviest five percent and overcharges the lightest fifty. Metered billing is the rational fix for that, and for a low-volume or experimental buyer it is a better deal than paying a fat seat fee to barely use the thing. Aligning price with cost-to-serve is a good thing. It is just a vendor virtue, not a customer one, and the trick to watch is anyone presenting the first as if it were the second.

Cheaper models do not solve the coupling problem. A fine-tuned DeepSeek on Azure lowers the unit price of the metered thing. It does not make the metered thing track value. You are paying less per token for a number that still has a loose relationship to your outcome. And who knows how many additional tokens a potentially inferior model burns.

Where this lands

On my orchestration battleground, Cowork is the M365 layer that coordinates work across your apps and your Graph. Pricing that orchestration by consumption reframes it from a capability you own into a utility you rent by the drink. That is a substantial shift in who carries the risk when an orchestrated workflow goes long, and it is the buyer.

So, on the null hypothesis. Is usage-based pricing good for customers? Largely no, and for the reasons the question assumed. The metered unit is loosely coupled to value, agents widen that gap rather than closing it, the model bills the most engaged users the most, and it relocates the entire burden of value accountability from the vendor onto the buyer. Good for investors and good for customers are not in alignment here. On this one, they partly trade off.

Three things to do if you are buying.

Model your power users, not your average. The average user will not break your budget. The fifteen people who actually adopted the thing will, and they may very well be the ones delivering your return.

Make the vendor define the unit before you sign. If a task can cost anywhere from a few credits to a few hundred depending on how many times the agent talks to itself, that is not a price, it is a range. And you'll end up at the upper end, trust me. Ask vendors to commit to a per-outcome cost and watch how fast the conversation gets vague.

Push for outcome terms on anything that has a definable outcome. Resolution, completion, ticket closed. If the vendor will only price the effort and not the result, they are telling you something about how confident they are in the result.

The interesting question is not whether usage pricing is here. It is. The question is whether buyers will accept a model where the vendor is paid the same whether the agent nails it on the first try or flails through ten, or whether the market pushes back toward paying for outcomes the way Fin does. Or Zendesk. Or Hubspot. Or others. I do not know which way that goes. But the vendor whose margin improves when its agent works harder is not, structurally, the vendor most motivated to make the agent work better.

Microsoft Build 2026 produced two announcements that, read together, describe something more interesting than the usual conference launch cadence: a plausible scenario in which enterprise AI stack decisions made in the next 12 months could become significantly harder to reverse. The operative word is "could". Several pieces of the announced architecture are not fully shipping yet. But the direction is clear.

The News

Microsoft delivered two related announcements at Build 2026.

The first came from Jay Parikh, EVP of CoreAI: the model is not the differentiator; the system governing it is. Microsoft's answer is a six-step loop. Agents are built in GitHub, contextualized with Microsoft IQ, which grounds them in enterprise data from Microsoft 365, core business systems, knowledge bases, and the web, run in Foundry, governed via Agent 365, and continuously improved through a hill-climbing optimization cycle. Agent 365, combined with Entra, Purview, and Defender, catalogues every agent in the estate, regardless of where it was built, and lets IT enforce policy across all of them.

The second came from Mustafa Suleyman, CEO of Microsoft AI: seven new MAI models built from scratch, with no distillation from third-party models. MAI-Thinking-1, the flagship reasoning model at 35 billion active parameters, benchmarks at parity with Anthropic's Claude Sonnet 4.6 on software engineering tasks at significantly lower per-token cost. MAI-Code-1-Flash is integrated natively into GitHub Copilot. MAI-Transcribe-1.5 claims leading accuracy across 43 languages at five times the speed of competing models. Image and voice models complete the family.

Alongside the models, Microsoft introduced Frontier Tuning: enterprises can train MAI models on their own workflow data using reinforcement learning environments. The model stays in the customer's Azure tenant, is trained on their operational traces, and owned by them. Microsoft's cited example: a model tuned to McKinsey's standards matched GPT-5.5 performance at roughly ten times lower cost.

The Bigger Picture

These announcements arrive midway through one of the more competitive contests in enterprise software: the race to become the orchestration layer for AI agents at scale.

Every major platform vendor has staked a claim. Salesforce closed its Q1 2027 with Agentforce at more than $1.2 billion ARR, up 205% year-over-year. ServiceNow repositioned at Knowledge 2026 as the AI Control Tower for Business Reinvention, an orchestration layer governing every agent, model, and action across the enterprise regardless of origin. SAP's Sapphire 2026 introduced the Autonomous Enterprise vision, with Joule orchestrating more than 200 agents across finance, procurement, supply chain, HCM, and CX. I wrote about this before here and here. Futurum Research, assessing the field days after Build 2026, identified Microsoft, Salesforce, and ServiceNow as the three early leaders, with orchestration and governance increasingly determining who wins.

The contest runs on two battlegrounds: interface control, which platform surfaces agents to users, and orchestration, which layer coordinates agents across systems and governs their behavior. Microsoft's Build 2026 argument is that it holds the strongest hand on both, because of one asset its competitors lack at the same scale: identity infrastructure. Azure runs in 95% of Fortune 500 environments. Entra is already the identity backbone across those estates. Governance-by-default, because it runs at the identity layer, is architecturally stronger than governance bolted on afterward.

ServiceNow's counter is strong. Its AI Control Tower is explicitly vendor-agnostic, covering AWS, GCP, Azure, SAP, Oracle, and Workday from a single policy point. The acquisitions of Armis and Veza give it a capability to map AI agent identities alongside human identities in a unified access graph that most of the market lacks today. And it combines control plane arguments with domain knowledge. SAP takes a different position altogether by not claiming to be the identity layer, but the authoritative business context layer, with Anthropic's Claude embedded in Joule for reasoning across HR, procurement, and supply chain. In brief, SAP is all about domain knowledge.

In summary, there are 3 main camps. Vendors that say that the systems and domain knowledge are key, others that claim independence and the group that positions itself in between, claiming both capabilities.

The MAI model launch adds to the equation in two ways. Since April 2026, when Microsoft and OpenAI amended their partnership to end Microsoft's exclusive IP license, Microsoft has had the freedom to develop its own Azure models. Build 2026 marks the first major shipment of that strategy. And it puts a native model option inside a platform that already governs identity, compliance, and access, at benchmarked parity, and at lower cost. One analyst framed it sharply: Microsoft is not ending OpenAI's presence, it is making that presence look optional. The same shift applies to Anthropic.

My PoV and Analysis

The Frontier Tuning architecture is the most underappreciated part of the announcement. Microsoft's marketing says "no vendor lock-in" because your model weights stay in your tenant. That is technically correct and commercially misleading at the same time. A model trained on your institutional workflow traces, tuned to your decision patterns, is deeply embedded in your operational context. That makes it incredibly sticky, creating, you guess correctly, lock-in. Migration does not mean exporting a file; it means retraining from scratch on a different platform. For most enterprises, that cost never gets budgeted. None of that makes it a bad decision. Institutional specialization is precisely what makes the model valuable. But buyers should know what they are choosing. The McKinsey benchmark Microsoft cites is an interesting data point, but not an independently audited one.

The identity-layer governance argument is solid, with one qualification. Entra's governance of human identities is mature. Governance of AI agent identities, like service principals, managed identities, tool-calling permissions at multi-agent complexity, etc., is newer and less proven. Agent 365 is GA, but its depth against real multi-vendor agent estates running across Azure, GCP, and on-premise systems has not been tested at production scale. ServiceNow can fairly argue that its AI Control Tower, designed from the ground up for agent governance rather than extended from human identity management, is currently better suited to that specific problem.

The timing question is important, too. Salesforce has 29,000+ and growing Agentforce deals. ServiceNow is offering AI Control Tower free for a year as a market stimulus. SAP shipped Joule Work with MCP and Agent-to-Agent protocol support. These are platforms in production motion. Microsoft's architecture is more comprehensive than any single competitor, but in some of its more important pieces, it is still catching up on deployment velocity.

On the models: MAI-Thinking-1 at Sonnet 4.6 parity is strong, and building it without third-party distillation is importatnt for enterprise IP hygiene. But benchmarks are a snapshot. Anthropic has shipped Claude Opus 4.6, 4.7, and 4.8 since the start of 2026 alone. Parity today does not guarantee parity in six months. And it is parity to Anthropic’s mid-tier model.

What Build 2026 consolidates is a combined structural position no other titan holds: the identity layer, the developer platform, the productivity offerings, and now owned models across all primary modalities. SAP has process depth and models but not the identity layer. Salesforce has CRM data depth and interface momentum but no developer platform or owned models. ServiceNow has governance credibility and IT workflow depth but no productivity solution and no owned models. Microsoft now has all four.

Whether the Frontier Tuning plus identity governance combination creates the kind of enterprise AI commitment that changes stack consolidation decisions over the next 18 months is the open question. If it does, the most exposed competitors are ServiceNow, whose governance position gets squeezed by an identity-native argument, and the model vendors Anthropic and OpenAI, whose enterprise contracts require a deliberate additional procurement choice rather than sitting as the obvious default.

Neither is displaced. Both face a changed market dynamic.

The platform decision and the model decision used to be separable. They may not be for much longer.

Every single week, I sit through pitches from enterprise software vendors boasting about the next iteration of their " AI-powered sales pipeline optimization platforms". They promise to auto-magically turn cold leads into closed contracts while minimizing human intervention. That sounds great on a slide deck designed to pump the stock price before an earnings call. In reality, however, these systems are automating an architectural flaw that has plagued B2B organizations since, well, forever: the linear sales funnel.

Let me be clear here. The classic sales funnel is not an asset; it is a structural failure. It assumes a predictable, straight line where marketing captures raw interest, tosses a lead over a wall to a sales development representative, who then passes it to an account executive to close the deal. Once the contract is signed, the customer disappears from the pipeline, and is handed off to an underfunded customer success department that operates like a glorified complaints department. This system assumes that buying journeys have a finite endpoint.

The B2B buying journey does not end when a contract is signed. By treating marketing, sales, and service as isolated phases with independent processes and technology stacks, enterprise organizations create massive amounts of friction. Norbert Schuster, a veteran B2B strategist who joined us in the latest episode of the CRMKonvos podcast, summarized this beautifully when he described the classic setup as the "Currywurst-Pommes effect". Individually, a sausage or a plate of chips is acceptable; combined, they become something functional. Yet, in most organizations, marketing automation platforms and CRM instances do not communicate well. They sit side by side as poorly connected line items on an IT budget, completely unaware of the buyer's actual context.

TL;DR

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Exposing the Mess with a Digital Spotlight

AI is not a strategy. Anyone who tells you that deploying an LLM-based agent will fix your declining customer acquisition metrics is selling vaporware. AI is an amplifier. It takes whatever processes, data models, and organizational habits you already have and executes them at massive scale and speed. If your data hygiene is abysmal, your processes are broken, and your teams are actively fighting over lead ownership, AI will simply automate that chaos and deliver bad results faster than any human ever could.

The reality is that customer behavior has moved beyond the internal structures of the average enterprise. Schuster uses a great concept to describe this phenomenon: the "Sunday-Monday gap". On Sunday evenings, B2B buyers act as modern digital consumers. They use highly integrated, AI-driven platforms to book travel, order food, and manage their lives with single-click simplicity. On Monday morning, they walk into a corporate environment and are forced to deal with siloed databases, manual handovers, or sales reps who rely on a sheet of paper because the corporate CRM is too cumbersome to use.

This gap means that buyers are conducting the vast majority of their research completely hidden from your tracking scripts. They are reading peer reviews, participating in private communities, and leveraging generative AI tools to evaluate vendors long before they ever fill out a form on your landing page. When they finally do show up, they are not looking for a generic corporate brochure or an automated five-email nurturing sequence. They expect an immediate, context-aware interaction based on the specific business problems they are trying to solve.

Moving Beyond Handovers to Loops

To fix this, organizations must replace the linear funnel with an integrated revenue engine. This requires an mental and architectural shift from handovers to loops. In a traditional siloed structure, a marketing manager’s responsibility ends when a lead achieves an arbitrary lead-score threshold and becomes a "marketing qualified lead". This metric is entirely self-serving. It measures internal activity rather than external buyer readiness.

A true revenue engine changes the core question. Instead of asking who owns a lead at any given moment, a cross-functional revenue team asks a much more relevant question: what information or interaction does this specific buyer require to make their next business decision? This requires a new architecture. It acknowledges that the buying process is cyclical, encompassing landing, expanding, and maximizing lifetime value. If a customer signs an initial agreement, they immediately enter a new phase of the journey focused on cross-selling, upselling, or platform adoption.

If your organization still separates "hunters" who bring in new business from "farmers" who manage renewals, you are likely losing massive amounts of revenue at the handover point. Most account management teams operate reactively. They wait for a renewal date to approach or for a client to log a support ticket before initiating a meaningful conversation. There is no predictive lead management, no presales automation, and no continuous content alignment designed to show how a client can derive more value from their initial investment. The energy simply evaporates the moment the initial deal closes.

Rebuilding the Organizational Fabric

If you look at the underlying cause of this, it is rarely the software. The issue is organizational design. Marketing and sales teams routinely operate with completely separate key performance indicators, entirely distinct vocabularies, and incompatible views of the ideal customer profile. Marketing celebrates record-high lead volumes, while sales misses its quarterly numbers and blames marketing for delivering low-quality data.

To overcome this structural divide, organizations need to bring these functions under a unified leadership structure, such as a Chief Revenue Officer who owns the entire customer journey from initial discovery through long-term retention. This ensures that data definitions are shared, technology stacks are integrated natively, and incentive structures are aligned around total revenue generation rather than departmental vanity metrics.

We can look to a sport like rowing to visualize how this works when executed correctly. An eight-person rowing crew requires total synchronization. Every single rower must move with identical timing, guided by a single coxswain who maintains the direction and tempo. If one side of the boat pulls harder than the other, or if individuals decide to row to their own rhythm, the boat loses momentum and veers off course. This is exactly what occurs when marketing, sales, and customer success operate as independent silos. They pull in different directions, burn through budgets, and ultimately fail to deliver a coherent customer experience.

Corporate Realities for Enterprise Tech Buyers

Enterprise buyers face a difficult environment when evaluating sales and marketing technology. The market is saturated with “platforms” claiming to solve every revenue problem with automated intelligence. To avoid buying expensive software that fails to deliver a return on investment, decision-makers must keep some fundamental realities in mind:

Reject the Illusion of Out-of-the-Box Integration

Every software vendor claims their marketing automation tool integrates seamlessly with your existing enterprise CRM. In practice, these integrations are often shallow API connections that sync basic contact fields while completely failing to transfer behavioral context or historical intent signals. If your sales representatives cannot see which whitepapers a prospect read, which private community threads they interacted with, or how they used your digital tools directly within their primary CRM view, your systems are not integrated. Do not sign a contract until you have audited the data model compatibility under real-world conditions. Deep architectural alignment is what transforms disconnected software into a functional revenue engine.

Prioritize Process Clarity Over Automated Intelligence

Deploying a generative AI email outreach tool or an automated lead-scoring algorithm on top of a broken data architecture will only accelerate your pipeline problems. AI is highly effective at finding patterns and executing repetitive tasks, but it possesses zero strategic judgment. If your teams do not have a clear, shared definition of a qualified buying signal, or if your customer data is trapped in disconnected silos, an AI tool will simply generate masses of generic messages that alienate your target audience. Before investing in AI capabilities, invest time in identifying your core buyer journeys and cleaning your data infrastructure. Software cannot optimize a process that does not exist.

Maintain Absolute Control Over the Human Relationship

Automated workflows, automated nurturing sequences, and AI-generated content can handle initial research inquiries and routine data entry effectively. However, they can’t own the relationship with the buyer. B2B purchasing decisions are high-risk corporate investments made by real people who are risking their internal reputations and budgets. These buyers do not want to be processed by a series of automated emails or generic conversational bots. They require empathy, technical expertise, and personal accountability from their vendors. Use technology to remove administrative burdens from your sales and service teams, but ensure your human professionals remain the primary point of contact for every meaningful decision. Enterprise revenue engines are built on human trust.

Technology is merely the infrastructure that supports it.

If you only read the press releases, Zendesk Relate 2026 told a strong, clean story. The era of the chatbot is over. Welcome the Autonomous Service Workforce. Resolution replaces deflection. Outcome-based pricing is the new norm. Specialization beats generalist orchestration.

That’s strong. Really strong.

If you also watched the customer panel, listened to the day-two keynote, and had the chance of having analyst one-on-ones, you got a richer story. One in which the strategic bets are well-placed, the customers describe a more nuanced reality than the slogans, and three specific refinements over the next twelve months that would turn a strong position into a durable moat.

I came home quite positive. Here is why, and where I think the next twelve months are important.

What Zendesk announced and why it lands

The headline product story was the Autonomous Service Workforce: a network of specialized AI agents working alongside humans, orchestrated through what Zendesk now calls the Resolution Platform and improved continuously by the Resolution Learning Loop. Agent Builder gives customers a no-code interface to build bespoke agents. The Copilot suite expanded to four personas: Agent, Admin, Knowledge, Analyst. Voice AI handles 60+ languages mid-conversation. Employee Service AI agents from the Unleash acquisition live inside Slack and Teams. Knowledge Graph spans SharePoint, Google Drive, Notion, Guru, Contentful and Document360. Model Context Protocol support is bidirectional. Quality Score evaluates every interaction.

This is quite a handful.

Two of these messages are more powerful than the others. The first is resolution over deflection. Zendesk charges only when a resolution is verified by a second AI evaluation model; outcome-based pricing as the natural commercial expression of the philosophy, and a model Forrester has been telling vendors to move toward for the past year. The second is specialization over generalization. The argument is that 19 years of CX data, billions of  service interactions, and an opinionated service stack beat horizontal platforms using commoditized LLMs.

It is a strong argument. It is also working. Zendesk reported 130% year-over-year AI ARR growth, 20,000 active AI customers out of an 80,000 base, and more than 1,500 competitor replacements in 2025. Salesforce's own May 2026 State of Service survey shows agentic AI adoption in service jumping from 39% to 66% in twelve months. This is independent confirmation that the market is genuinely re-platforming, not just re-branding, and that Zendesk's growth sits inside a rising tide.

What customers told us and what it confirms

The customer panel completed the story. Stacy Niven of Direct Supply, Dena Fuentes of Emburse, Sam Bellach of Lyra Health, Jessica Hsieh of Levi's, Elymae Cedeño of Bumble, and Rob Giglio of Canva each added a dimension the headlines could not.

First, data foundation is more important than vendors usually admit. Stacy described Direct Supply's multi-year rebuild. Half of orders have been manually touched, processes worked in spreadsheets, an internally developed chatbot they walked back on because the product data was bad. Sam Bellach put it plainly: AI is only as good as the data feeding it. Salesforce's research confirms this: 59 to 72% of service professionals name data readiness as the top AI blocker. The Zendesk message would land even more cleanly if it acknowledged this work upfront. The customer panel, by being candid about it, did the job the brand did not need to.

Second, customers want more human connection in the AI era, not less. Jessica Hsieh cited research that 61% of CX leaders see live volumes rising. Elymae Cedeño at Bumble was emphatic that in a trust-and-safety product, humans are foundational. Levi's deploys AI for "where's my stuff" so human agents can be reserved for judgement and empathy. This is a tailwind for Zendesk's design philosophy — human-as-architect, AI-as-tool — and it argues for sharpening the messaging around that strength, not against the strategy itself.

Third, the outcome-pricing model has earned its lead, and the field will likely catch up over the next year. Sam Bellach, who is on Zendesk's Customer Advisory Board,  pushed back on the rigidity in what she describes as a candid debate. This debate is about the chicken-and-egg problem of spending ahead of proven RoI, the lack of mid-contract convertibility between agent-seat and resolution spend, and the ambiguity in what counts as resolved.

Forrester's Q2 2026 Conversational AI Wave found only one vendor scored above 3 of 5 on pricing flexibility. The fact that Sam is comfortable having that debate in public is a signal in itself. Zendesk leads the category and is co-designing the next version with its best customers.

Fourth, the most interesting moment of the week. Rob Giglio's part of the day-two keynote was structured around his recent frustration with someone else's deflection bot; he half-named "a name that sounds a lot like Zierra”. His thesis is that deflection causes churn, while resolution drives loyalty. Salesforce's State of Service report approvingly features Smarsh's 68% call deflection as "a phenomenal win". Zendesk is apparently on the right side of a still-unsettled industry debate, and Giglio's anecdote made the case more vividly than any product slide could.

The orchestration position is right. It just needs one more slide

Zendesk's Chief Product Officer Shashi Upadhyay was deliberately precise when I asked about orchestration. Zendesk wants to orchestrate every service interaction, they close the learning loop on every service interaction, and they do not pretend to orchestrate sales or marketing or the rest of the company. That is the honest answer. Salesforce, ServiceNow, SAP, Microsoft and Adobe are all pitching cross-system orchestration, with Google Cloud now positioning on top of them. Zendesk wisely declines that fight, interestingly using the same argument that SAP does against ServiceNow: You cannot govern what you cannot understand.

This strategic position is correct. What the messaging needs is one additional slide saying *we orchestrate service interactions; we hand off to your meta-orchestrator at these named integration points*. This single piece of clarity would turn a defensible boundary into an attractive value proposition. CIO buyers who currently hear "platform" and wonder whether to default to the suite would have a clear reason to choose the specialist for service while keeping their meta-orchestrator for everything else. The position is built. The slide is the missing piece.

The autonomy framing has room to grow into the brand

Salesforce measures 40% autonomous resolution today. Gartner optimistically projected 80% by 2029. The trajectory points exactly where Zendesk has bet. Independent analysis suggests today's genuine autonomy figure across the industry is closer to 20-30%, because much of what is marketed as agentic is nothing more than rebranded chatbot functionality. Zendesk's actual product reality of supervised agentic, with humans correcting, retraining and approving, is materially better than that field average, and is also the design that operationally safe service AI requires today.

This is a real strength, and it deserves equally real framing. "Supervised agentic resolution" or "agentic service workforce" would probably describe the product more accurately and would shift the conversation away from the autonomy bar to the supervised-agentic bar, which is a bar Zendesk easily clears. It is one of those cases where I think that a slightly more conservative brand line might be both more credible and more competitive.

The learning loop is the next big story

In the analyst one-on-ones I asked how Zendesk ensures the Resolution Learning Loop is learning in the right direction. If the system optimizes for what counts as a verified resolution under the current rubric, what stops it drifting toward easy-to-verify outcomes at the expense of harder ones? What stops the rubric from being gamed?

The answer covered the basics: multi-LLM grading, customer dispute mechanism, "a little conservative" on what counts as resolved. That is a solid, customer friendly foundation. What would turn it into a competitive advantage is a public, documented governance posture covering drift detection methodology, rubric versioning, human review cadence, adversarial test cases, audit visibility. Once that exists, the Resolution Learning Loop stops being a feature and becomes a moat that nobody else in the field is anywhere near ready to match. This is the most under-told story in Zendesk's deck.

My point of view

Three bets are working, three twelve-month refinements are available. The refinements: one more orchestration-boundary slide, a slightly more accurate autonomy line, and a public learning-loop governance posture, are all communication and documentation projects, not architecture ones. The strengths are outcome pricing years ahead of the field, a real data moat, an integrated platform, and partner ecosystem leverage, are durable, defensible, and can get stronger.

For buyers, the practical lessons are important, regardless of which vendor wins your shortlist.

Fix your data foundation before going agentic! Every successful customer at Relate 2026 did this first.

Demand outcome-priced contracts and negotiate flexibility into them. Design for supervised agentic, not autonomous. Stress-test demos on the hard cases.

Treat change management as a primary project. The 5-10% edge cases determine real-world performance. And the customers who built for those cases are the ones now reporting the strongest results.

Zendesk has built something real. The next twelve months are about telling that story as clearly as the customers are already telling it.

Kudos to Zendesk!

SAP Sapphire 2026 was a major platform announcement, a competitive shot at ServiceNow, a coherent acquisition story across Reltio, Dremio and Prior Labs. It featured an Anthropic partnership that puts Claude at the center of the SAP Business AI Platform. For anyone who cares about customer experience, it was also a missed opportunity dressed up as ambition.

If you watched only the keynote, you concluded SAP barely talks about CX. Klein did finance with JP Morgan. Herzig demoed pharma pricing. Industry AI showcased RWE wind turbines. The named flagship was the Autonomous Close Assistant. CX got line items.

That reading is incomplete. Here is what actually happened for CX at Sapphire 2026, what it means competitively, and what SAP and SAP CX customers should do about it.

What SAP actually shipped for CX

On the same day as the keynote, Balaji Balasubramanian, SAP's CX President and Chief Product Officer, published a substantive announcement listing ten named Joule Assistants for CX. Marketing gets Content and Campaign Assistants. Commerce gets Merchandising, Shopping and Order Management Assistants. Sales gets Sales, Deal Qualification and Deal Closing Assistants. Service gets Case Management and Service Management Assistants.

The supporting announcements are the part most analyst coverage missed. A Google partnership brings Gemini into SAP CX, plus adoption of the open Universal Commerce Protocol. Vercel handles storefront development. SAP Unified Payment runs on Adyen, with Checkout.com and PayPal configurable. Expanded Parloa and Amazon partnerships cover voice and digital service. A new SAP Commerce Cloud, cloud ERP edition targets mid-market. Two Industry AI scenarios for CX: Autonomous Revenue Growth Management and Unified Commerce.

All of it planned for general availability in Q3 2026.

For a category the keynote treated as a sub-bullet, that is a substantial product agenda. SAP Commerce Cloud has been a Gartner Magic Quadrant Leader for Digital Commerce for eleven consecutive years and the customer base is real. Adidas, Coca-Cola, Allianz, large utilities and banks all run on it.

The Cinderella problem

Despite all this, the keynote did not put CX in the same tier as Finance, Supply Chain or Industry AI. No flagship CX customer on stage opposite JP Morgan. No Autonomous Service Resolution Assistant matched to the Autonomous Close. No CX product head with mainstage time. The CX story subsists in a blog post by the CX CPO. The Autonomous Close gets prime time in the Sapphire keynote.

This is the Cinderella problem. SAP CX has the product. SAP CX has the customers. What SAP CX lacks is executive air cover. The product team shipped. The C-suite did not promote.

The signal is clear. Adobe Summit features Anil Chakravarthy championing CX Enterprise Coworker. Marc Benioff personally champions Agentforce Service at Dreamforce. The market – and analysts, too – reads those vendors as doubling down on CX. When Klein does finance and supply chain and CX gets a blog post, they read SAP as deprioritizing CX. The signaling problem makes the purchasing budget harder to defend, even where SAP CX has the better product.

The E2E story breaks at the customer interface

The bigger structural problem is what this does to SAP's own Autonomous Enterprise pitch. SAP says the autonomous enterprise runs on Joule across finance, supply chain, procurement, HR and customer experience. That is the E2E argument. It is a strong argument. It claims SAP can orchestrate cross-application, cross-department processes because SAP owns the system of record and the agentic platform on top of it.

Like each chain, it breaks at the weakest link. CX is this weakest agentic; the pitch breaks right there. And it breaks where it hurts most. Customers do not experience companies through CFO close cycles. They experience companies through service tickets, sales conversations, commerce checkout flows and marketing engagement. An enterprise that can compress financial close from weeks to days but still routes every service case to a human is not autonomous. It is back-office automation with a CX problem.

The Cinderella treatment of CX hurts SAP more than SAP appears to recognize. The cost is not just lost CX deals. It is the credibility of the Autonomous Enterprise narrative itself.

Where SAP CX sits competitively

Adobe is the most credible CX competitor in the marketing and commerce arenas. CX Enterprise Coworker launched at Summit on the same NVIDIA OpenShell runtime SAP uses, with multi-model interoperability across Anthropic, AWS, Google, Microsoft and OpenAI. AEP Agent Orchestrator, Brand Concierge, Real-Time CDP and the Magento commerce stack form a consistent CX agentic story. Adobe does not compete in ERP, so the two only collide in marketing, commerce and customer data. Adobe is winning the visibility fight there easily.

Salesforce with Agentforce 360 plus Operations is at $540M ARR with Service Agent, Personal Shopper and Buyer Agent shipping is the 800-pound-gorilla. Headless 360 makes Salesforce CX accessible through any MCP front end. Salesforce wins front-office. SAP wins back-office. They collide in customer-to-cash.

Microsoft Dynamics 365 Copilot for Service and Sales are generally available. Microsoft Agent 365 hit GA on May 1 at $15 per user per month. SAP wins ERP depth. Microsoft wins productivity surfaces and developer mindshare.

ServiceNow Autonomous CRM processes 100M+ customer cases monthly. AI Control Tower with 30 enterprise connectors positions ServiceNow above the application layer. This constitutes a real threat to SAP Service Cloud in enterprise customer service, particularly where ServiceNow ITSM already runs.

Mid-market matters more than enterprise watchers want to admit. HubSpot Breeze has shipped Customer, Prospecting, Content and Data Agents for years. Zoho Zia ships 100+ pre-built agents at $40 per user per month. SugarAI rebranded around precision selling and ERP signals last month. Creatio went seat-free with Unlimited on May 1. Freshworks Freddy and Zendesk AI Agents are GA. The new SAP Commerce Cloud, cloud ERP edition signals SAP wants to compete here. The pricing model will be the test.

What the platform actually does for CX

The platform announcements help SAP CX in tangible ways. Reltio gives Service Cloud a customer golden record across SAP and non-SAP systems with MCP support. Dremio lets agents reason on commerce and customer data without moving it. Prior Labs brings frontier tabular foundation models for the propensity scoring, churn prediction and lifetime value modeling LLMs are bad at. Free Joule Studio through 2026 lets SAP CX customers build custom agents without the per-seat AI surcharges Salesforce stacks on top. AI Agent Hub on LeanIX governs SAP and non-SAP CX agents at no charge.

No doubt, these are useful pieces. But they are not enough to offset the signaling problem.

Three recommendations for CX buyers

Audit the ten Joule Assistants for your modules with GA dates, not roadmap dates. All ten are planned for Q3 2026. Plan is not GA. Push SAP for module-specific commitment dates before signing renewal or expansion contracts. The free Joule Studio through 2026 is a real negotiating lever and a good opportunity to evaluate SAP as a credible CX alternative.

Run a real CX agent bake-off across the relevant vendors. SAP Service Management Assistant against Salesforce Service Agent. Adobe Brand Concierge against SAP Content Assistant. Test agent quality on your actual customer data, using your actual intent. The winner is the one whose agents resolve your cases or convert your shoppers, not the one with the best slide deck.

Take the CX orchestration plane decision deliberately, and not by accident. If your non-SAP CX estate is substantial (Salesforce Sales Cloud, Adobe Experience Manager, ServiceNow Customer Service Management, Zendesk, Freshworks, HubSpot), the question of where CX agent governance lives is now architectural. SAP AI Agent Hub, ServiceNow AI Control Tower, Salesforce Agent Fabric and Microsoft Agent 365 are not interchangeable. Test which one actually governs your non-SAP CX agents in production today. Loyalty is not a strategy.

Three recommendations for SAP

Ship an Autonomous Service Resolution Assistant or another relevant CX agent with the same visibility as the Autonomous Close. These agents exist. The flagship treatment does not. The Autonomous Close got JP Morgan, a named customer arc and Klein keynote time. CX deserves a named flagship agent with a named customer reference. Adidas, Coca-Cola, Allianz are sitting right there. Use them. The cost of doing this is a quarter of marketing investment. The cost of not doing it is another lost year as Cinderella does not get found by her prince, aka the customer.

Put the CX CPO on the next Sapphire mainstage with a named customer, or two. Balaji Balasubramanian wrote a substantive blog post. This is good, necessary – but insufficient. The mainstage signals priority. Service Cloud, Commerce Cloud, Emarsys and Customer Data Cloud are competitive products with reference customers and Magic Quadrant placement. They need keynote time, not blog time. SAP's bench can match Adobe's Chakravarthy and Salesforce's Benioff. The question is whether the C-suite decides to.

Decide whether CX is core or peripheral to the autonomous enterprise pitch, and commit visibly. Either invest with budget, headcount, roadmap parity and executive time equal to Finance and Supply Chain, or stop including CX in the E2E story. The Cinderella position helps no one. As said before, an autonomous enterprise that can close books in days but routes every service case to a human is not autonomous. It is back-office automation with a CX problem. Either fix the CX side of the story or shrink the story to fit what is real.

My PoV

SAP Sapphire 2026 shipped more for CX than the keynote let on. Ten named Joule Assistants. Partnerships with Google, Vercel, Adyen, Parloa and Amazon. A mid-market commerce edition. Two Industry AI scenarios. The product is more substantive than the press and analyst coverage suggests.

What is missing is the executive championing. The product team did its part. The C-suite did not do theirs. That asymmetry is the structural problem Sapphire 2026 did not fix, after I had some hope last year.

Let’s see when Cinderella finally gets to dance.