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

If you want to watch the full CRMKonvo, please go ahead here (optimized for smartphones) or here (optimized for tablets/computers).

Else, be my guest and continue to read.

Or do both …

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.

For years, the enterprise software complex has sold us on a beautiful fairytale: the single source of truth. We were told that if we just poured enough capital into our CRM systems, and if we just badgered our front-line sales representatives enough to log every transactional interaction, absolute operational clarity would emerge. Now, the enterprise technology industry has found its next silver bullet: generative artificial intelligence. Every major software vendor is frantically bolting an AI copilot, a generic conversation summarizer, or an automated opportunity scoring engine onto their legacy applications. They promise that these shiny additions will magically transform messy, unlogged data into executive-grade operational insights. But let us be completely clear here: it is mostly marketing fluff designed to protect legacy vendor stock prices rather than solve foundational architectural bottlenecks.

The recent conversation on CRMKonvo with the co-founders of Brief Executive Intelligence cuts straight through this generative AI hype. Larry Augustin, Clint Oram, and Zac Spreckett are not starry-eyed AI tech evangelists; they are battle-hardened industry veterans who built SugarCRM and spent decades in the enterprise application trenches. Their core thesis is as brutal as it is interesting: CRM platforms were natively architected for front-line reps, not for the executives who actually manage the strategic direction of an organization. Bolting a generic large language model (LLM) onto a legacy database framework does not fix the fundamental structural deficiencies of that historical ledger. It merely allows corporate environments to generate summaries of incomplete information faster than ever before.

TL;DR

If you want to watch the full CRMKonvo, please go ahead here (optimized for smartphones) or here (optimized for tablets/computers).

Else, be my guest and continue to read.

Or do both …

Legacy Software Architecture and the Customer-Centric Trap

To understand why current corporate AI initiatives are stalling, we must evaluate the structural foundation of legacy software. Systems of record are fundamentally passive; they are organized around a specific external entity, which in the case of CRM software is the customer. They excel at capturing transactions retrospectively, functioning as a historical record of what your account teams managed to input between active sales calls. This framework works reasonably well for rear-view reporting, but it breaks down when an executive needs to make real-time asset allocations or make time-sensitive decisions.

When vendors attach an AI assistant to a standard CRM framework, that artificial intelligence remains severely restricted by the constraints of the underlying data model. The copilot can surface information about an active customer account—assuming your reps actually took the time to manually input it—but it maintains zero architectural visibility into what is occurring across the rest of the enterprise infrastructure. It possesses no conceptual awareness of internal product pivots hammered out in Slack, the roadmap modifications documented in engineering tools, or the critical budget parameters negotiated in email chains. The bolted-on AI layer is effectively blind to the context of the executive function itself; it optimizes for an isolated department while leaving the senior leadership team completely in the dark regarding cross-functional reality.

The Rampant Enterprise Epidemic of Decision Amnesia

This deep architectural gap manifests in what Clint Oram labels "decision amnesia." In modern corporate environments, organizational activity happens at a dizzying pace. Generative utilities have made it incredibly simple to mass-produce content, which in turn accelerates the sheer volume of daily communications an executive must filter. This structural acceleration creates a striking paradox: modern enterprises are communicating more than ever and are understanding less. Critical corporate decisions are finalized in frantic chat threads, impromptu video calls, and rapidly buried message strings.

Without an enterprise framework that treats these decisions, commitments, and strategic corporate goals as native, first-class data objects, these crucial operational elements simply evaporate into organizational noise. SAP calls this framework to avoid decision amnesia the company memory.

The long-term cost of this decision amnesia is staggering; entire corporate leadership teams spend hours re-litigating the exact same strategic issues they supposedly resolved weeks prior because no internal platform recorded the precise reasoning behind the original alignment, nor the precise agreement. Enterprises find themselves trapped in an operational loop, acting like corporate hamsters spinning a wheel fueled by an endless stream of AI-generated communication exhaust. Organizations become highly active, deeply exhausted, and yet structurally stagnant, moving nowhere.

Beyond Storing Artifacts: The Era of Continuous Understanding

This brings us to a critical architectural distinction: the difference between merely storing data artifacts and actively maintaining an ongoing understanding of work. Traditional software applications are exceptional at storing passive artifacts: a saved document file, a logged call note, or an archived email chain. But a massive collection of independent data artifacts does not equal true institutional knowledge. Without interpretation it remains mere data. Expecting a human executive to manually synthesize thousands of scattered communication artifacts into a coherent operational picture is a guaranteed recipe for immediate corporate burnout.

The alternative approach requires a technology architecture built around a persistent knowledge graph paired with targeted language models. Instead of waiting for a user to actively execute a search query in a blank text box, an executive-grade system must continuously monitor the operational tendrils of the enterprise infrastructure. It must automatically parse ongoing communications, extract underlying corporate commitments, map those vectors against explicit corporate goals, and maintain an ongoing semantic representation of corporate reality. This is not about building a better data indexing engine or expanding an LLM context window. It is about establishing a foundational technology layer that inherently understands how a corporate entity operates.

Proactive Intelligence Versus the Search Box Obsession

Most current corporate AI tools are completely reactive; they sit quietly in a side panel until an executive types a specific prompt into a search interface. But as any seasoned enterprise leader will tell you, an AI search query is only as good as the question you know to ask. If you are completely blind to a developing operational crisis or a slipping cross-functional dependency, you will never think of typing it into your AI copilot. Reactive software infrastructure keeps corporate leadership in a perpetual defensive posture, scrambling to address systemic vulnerabilities after they have already degraded the bottom line.

True executive-grade technology must pivot entirely toward proactive intelligence. The underlying software must understand your current corporate context – the strategic partners you are meeting with, the business accounts that are drifting, the internal commitments coming due – and actively surface relevant insights to you before you realize a gap exists. If you are preparing for an investor call or a board presentation, you should not be spending the prior evening frantically querying disparate data silos to compile a status brief. The platform should already have mapped the operational trajectory and prepared you for the discussion. This is the difference between a simple digital assistant and an enterprise intelligence layer that actively protects your focus and accelerates human execution.

Enterprise AI Buying Strategies: A Guide for CX Leaders

For corporate leaders navigating the chaotic market of enterprise AI, avoiding expensive mistakes requires rigorous architectural skepticism. Consider these three core recommendations.

First, audit the underlying data architecture beyond the copilot hype. When legacy vendors show AI that creates quick summaries, check where that information originates. If the AI merely queries a siloed database, it will never provide cross-functional context. Demand a unified knowledge graph that synthesizes disparate communication channels like email, calendar, and chat. Do not pay a premium for a thin conversational interface over bad data.

Second, prioritize proactive intelligence over reactive search utilities. A system that requires users to constantly query a prompt box is a system that fails them. Evaluate software based on its ability to surface insights autonomously using immediate context. Ask vendors how their platform alerts leadership to misaligned goals or slipping project timelines without requiring manual configuration. Eliminate the administrative burden of searching for information.

Third, insist on absolute data privacy and security at the user level. Executive context contains sensitive enterprise data like financial trajectories and board reports. A generic cloud solution that pools data or exposes it to manual vendor reviews is an unacceptable liability. Ensure a security model where data is encrypted individually with unique keys, preventing vendor access to corporate intelligence.

The contemporary B2B sales landscape is currently drowning in its own engineering achievements. For the past decade, the holy grail of outbound sales development was scale: how many touches could an automated sequence tool squeeze out of a Sales Development Representative (SDR) per day? The answer was always "more". With the mainstream infiltration of generative artificial intelligence and LLMs, the marginal cost of creating more text collapsed to zero, well, almost. Predictably, this did not produce a renaissance of enlightened business communication; it merely triggered an existential crisis in the recipients’ mailboxes.

TL;DR

If you want to watch the full CRMKonvo, please go ahead here (optimized for smartphones) or here (optimized for tablets/computers).

Else, be my guest and continue to read.

Or do both …

When any entry-level sales rep can prompt a system to instantly parse a prospect's digital footprint and draft a customized icebreaker, personalization is no more a competitive differentiator. As Ganesh Iyer of ASPR AI succinctly observes, personalization is officially the new spam. It has morphed into a meaningless background drone: a highly polished, entirely hollow manifestation of lazy marketing that enterprise decision-makers have naturally trained their brains to screen out completely.

The structural mistake is confusing personalization with relevance. A cold email congratulating a Chief Revenue Officer on their recent round of series-B funding feels automated, even if an LLM wrote it dynamically.

Why?

Because one hundred other vendors are hitting the exact same spot with identical messages. Genuine relevance requires more: it needs a deep, mechanical understanding of the prospect's actual current internal operational challenges. If a vendor can trace that the target organization has aggressively hired forty specific field reps over the past quarter, the conversational entry point shifts entirely away from marketing boilerplate toward actionable operational triage. Relevancy and temporal accuracy outpace linguistic personalization every single time. The real battleground isn't text generation: it's contextual timing.

The Structural Collapse of the Predictable Revenue Stack

For years, the B2B tech sector operated on a highly segmented, assembly-line model of sales development. The SDR nursed the lead, the Account Executive (AE) closed the contract, and the Customer Success Manager (CSM) prevented churn. It was a model optimized for the natural data limits of human beings. However, this classic three-tier architecture is breaking, driven by agentic workflows.

If an AI system can flawlessly execute list building, basic multi-channel sequencing, generic follow-ups, and baseline qualification without a human lifting a finger, the traditional foundational tier of the sales funnel falls apart. The entry-level SDR role as a brute-force pipeline loader is essentially dead on arrival. We are moving rapidly toward a consolidated lifecycle rep: a unified architectural sales role where the boundaries between SDR, AE, and CSM blur into a singular, highly strategic asset.

This displacement will also reorganize corporate talent pipelines. Historically, the SDR role was the training ground where future enterprise closers learned the ropes and earned their stripes. If that tier is entirely automated, organizations must completely rethink where their future strategic sellers come from. The future belongs not to the volume-driven pipeline chaser, but to the business analyst who knows how to leverage AI to handle the tactical grunt work while they focus on strategic trust engineering. AI will act as a cognitive amplifier for junior reps, significantly compressing the time it takes to achieve full quota competency. The software becomes an operational coach in the loop, providing real-time navigation through complex corporate buying dynamics.

The Data Sewer: Why CRMs are Facing an Existential Crisis

The core bottleneck of any enterprise AI strategy remains data integrity. The enterprise tech stack is littered with the remains of failed automation projects that assumed that an advanced algorithm could magically transform chaotic data inputs into pristine business intelligence. In the current world, CRM databases are notoriously dirty, often resembling a digital graveyard of outdated records, half-logged interactions, stale opportunities, and mismatched fields.

Sellers despise manual data entry; and why wouldn’t they? Expecting a high-performing enterprise seller to meticulously log pipeline updates or cleanse customer profiles is a process design flaw. As a result, CRMs have historically functioned as passive, historical content repositories rather than dynamic execution engines.

To remain structurally relevant, the modern CRM architecture must bypass manual human data collection as much as possible and move directly to the execution layer. The software must autonomously harvest the natural digital exhaust of the business motion: parsing emails, meeting transcripts, and contract exchanges to dynamically build its own contextual knowledge graph. After all, he communication stream contains the absolute highest concentration of tribal business intelligence.

Furthermore, you cannot simply dump un-cleansed, un-normalized data sets into a generic foundational model and expect strategic outcomes. If you put a mountain of dirty data into a larger enterprise container, you do not get corporate perfume; you merely get a bigger, more expensive repository of smelly garbage, and that faster. The real value lies in the extraction layer: structuring raw corporate exhaust into clean schemas so that localized LLMs can parse it with high precision to determine real buying intent, deal clarity, and structural risks.

The Trust Frontier and the Rise of Bot-to-Bot Bargaining

A fascinating architectural divergence is appearing that is based entirely on transactional deal value. In the low-velocity, high-volume world of B2C transactions, a high degree of automation is a must; the risk is low, and efficiency is the main metric of success. However, in complex, high-value enterprise B2B selling, the mechanics of purchases are closely tied to human accountability.

When a corporate buyer signs off on a seven-figure enterprise infrastructure implementation, they are not just purchasing a feature set; they are placing their own professional reputation and career on the line. If a critical system experiences a catastrophic operational failure, it is not a digital agent that stands before an executive board or takes personal accountability for a remediation SLA. Buyers implicitly demand a physical human being accountable: a real stakeholder they can look in the eye and hold responsible. High-value enterprise commerce will always be anchored in human trust – at least in the foreseeable future.

Simultaneously, we are entering the era of bot-to-bot filtering. Buyers, overwhelmed by the sheer volume of AI-generated noise, are starting to deploy inbound AI filters to actively parse, summarize, and gatekeep their mailboxes as a self-defense. The sellers’ agents craft a perfectly optimized, contextually personalized outreach sequence, only for the buyers’ agents to aggressively intercept it, strip out all the rhetorical marketing fluff, and reduce it to a blunt three-bullet-point operational summary for the decision-maker, if they don’t dispose of the mail altogether.

When algorithms are actively selling to algorithms, the traditional sales funnel collapses into a game of signal isolation. The only messages that will successfully pass the algorithmic gatekeepers are those that precisely align with validated pain points. The flashy copywriting, the emotional hooks, and the artificial conversational mechanisms become obsolete. The sales motion gets stripped down to pure, unadulterated structural relevance.

Architectural Safeguards for the Modern CX Buyer

Enterprise technology buyers are currently standing on the edge of a potentially incredibly expensive mistake: buying into generative AI hype cycles without auditing their underlying data architecture. To successfully navigate this transition without incinerating corporate capital, buyers must anchor their strategy in realities rather than vendor press releases.

Audit the Data Sewer Before Deploying the Engine

Do not buy an enterprise-wide generative AI layer if your underlying CRM is an un-mitigated disaster. An agent will not fix broken data collections: it will merely hallucinate inaccurate business conclusions at unprecedented speed and scale. Prioritize vendors that focus on autonomous extraction and normalization of natural communication channels over those offering shiny text-generation interfaces.

Enforce Strict Accountabilities in High-Value Flows

Clearly isolate your low-risk efficiency workflows from your high-value trust motions. Attempting to fully automate complex, multi-stakeholder enterprise buying journeys with digital agents, let alone standalone digital agents is an operational risk you do not want to take. Ensure your AI tools are strictly engineered to act as cognitive co-pilots for high-context human reps rather than attempting to replace human accountability entirely.

Prepare for the Bot-to-Bot Reality

Optimize your procurement and vendor selection processes for pure, structured relevance. Recognize that executive teams will inevitably use algorithmic gatekeepers to block out marketing noise. Look for sales tools that focus deeply on temporal precision and hard operational indicators rather than tools designed to maximize outbound communication volume. Volume is officially a dead metric.

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.

We have reached a point in the hype cycle where "AI" is being sprinkled on enterprise software like a seasoning on a cheap steak: it masks the poor quality of the underlying meat but doesn't make it more nutritious. In the latest CRMKonvo, Bhawani Shankar and the CRMKonvo team tore into the reality of what it actually takes to make "Agentic AI" work in a Customer Experience (CX) environment.

The analysis? Most enterprises are trying to drive a Ferrari without wheels.

Bhawani used this metaphor that I find particularly apt: the AI model is the shiny red car that gets the CEO excited; but the data is the wheels, the engine, and the fuel; and they come as options. If you buy the car without ensuring the wheels are attached and the tank is full of high-octane, verified data, you aren't going anywhere. You are just sitting in an expensive garage making engine noises.

TL;DR

If you want to watch the full CRMKonvo, please go ahead here (optimized for smartphones) or here (optimized for tablets/computers).

Else, be my guest and continue to read.

Or do both …

The Death of the "System of Record"

For decades, we have worshipped at the altar of the "System of Record." The goal was simple: get the data into the CRM. It didn't matter if the data was messy, duplicated, or six months out of date; as long as it was "in the system", leadership was happy. But as Bhawani correctly pointed out, we need to be moving from a system of record to a system of context.

In the old world, a marketing campaign was a monolithic beast planned over months. In the new, agentic world, context changes by the hour. If your AI agent is trying to help a customer but is basing its "reasoning" on a record that doesn't include the tweet they sent ten minutes ago, the click history of the past 5 minutes or the failed login attempt just now, the agent is useless. It isn't just about having the data; it is about having the connected data in a "Golden Context”.

The industry likes to use the term Master Data Management (MDM), but let’s just agree that this is becoming legacy terminology, and that fast. MDM implies a static, central truth. Agentic AI requires something more fluid. It requires an environment where the agent itself can find and connect data points across silos without a human having to manually build a brittle connector for every single API.

The Governance Paradox

Which brings us to the important concept of "dynamic governance." To an analyst, “something fluid” sounds like a polite way of saying "no governance”. Governance, by definition, is supposed to be the set of rules that keep things stable. If the rules are constantly moving, do you actually have a governed system?

The reality is that traditional, top-down, "thou shalt not" governance is dead. The stick does not work anymore. One could say that it never worked anyway; sales teams are stereotypically ungovernable because they are driven by incentives, not policy manuals. They rather ask for forgiveness later than for permission beforehand.

Bhavani’s solution is one I can get behind: incentive-linked, embedded governance. If the system makes it easier for a salesperson to hit their number by providing them with agentic support, they will use it. If the agent automatically transcribes a meeting and populates the CRM, the "governance" of data entry happens as a byproduct of a tool that actually helps the employee. Then, the system serves them, and not the other way round.

However, the human element remains the biggest hurdle. You can cook up the most sophisticated agentic sandwich in the world, but if the humans don't trust the data coming out of it, they will revert to their old spreadsheets and shadow IT "caves”.

Shadow Data: The Enemy Within

We often talk about "shadow IT”, but the real killer for AI is "shadow data”. These are the Excel sheets living on desktops, the manual exports from 2019 that are still being used for "reporting”, and the tribal knowledge that never gets digitized.

If you want to speed up your AI strategy, Bhavani’s advice is simple and straightforward: kill the shadow data. Anything that is not visible, verified, and connected is an impediment to an agent's ability to learn, hence an organizational impediment. An AI agent is only as good as the context it is offered. If half of your customer's context is hidden in a CSV file on a retired manager's hard drive, your "intelligent" agent is effectively flying blind in one eye, if not both.

Three Pillars for the Enterprise AI Buyer: A Reality Check

If you are currently sitting through vendor presentations where a 24-year-old in a slim-fit suit is promising you "Agentic CX Transformation”, here are three things you need to make sure of before you sign the check.

Demand "Golden Context," Not Just "Clean Data"

Stop asking if the data gets clean, and kept so, and start asking if it is connected. An agent doesn't just need a "clean" address; it needs to know why the customer changed that address, what they bought immediately afterward, and if they sounded annoyed on the last support call. If the vendor cannot show you how their AI bridges the gap between your static system of record and your moving system of context in real-time, they are just selling you a fancy search bar.

Verify the Incentive Alignment

Before deploying any AI tool, ask: "How does this make the user’s life easier in the first fifteen minutes?" If the tool requires more manual data entry or tagging to work, it will fail. It just will not be used, regardless of sticks or carrots. The best governance is invisible. You want a system that captures data as a byproduct of work (like meeting transcriptions or automated email logging) rather than one that treats data entry as a separate chore. If the AI doesn't give the employee an immediate win, they will starve it of the data it needs to survive, let alone help them.

Kill the Silos Before You Feed the Agents

Do not attempt to "fix" your data silos with AI. Fix the silos so that AI can function. Bhawani suggests an agentic assessment of your data landscape to identify where the "shadow data" is hiding. If you feed an AI agent data from five disconnected silos, it will hallucinate five different versions of the truth. Clear out the "caves" of manual data and lead everything into a visible, connected stream. Only then will your Ferrari actually have the wheels it needs to move.

The age of the static database is over. The age of the contextual agent is here. Just make sure you aren't the one paying for a shiny red car that is permanently parked.