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.

The Ghost of Cluetrain

In 1999, the Cluetrain Manifesto famously declared that "markets are conversations." It was an inspiring, romantic notion that promised to democratize commerce, wresting power from faceless corporate monoliths and handing it back to a sovereign consumer. Fast forward to today, and that conversation has been thoroughly co-opted. What was supposed to be a bilateral dialogue has devolved into an automated, highly-optimized monologue. The emergence of agentic AI, which features autonomous software agents supposedly operating on our behalf, promises a return to that original democratic vision. But let us be honest: is this actually a revolutionary shift, or is it just another iteration of vendor-controlled slop designed to monetize our decisions before we even make them?

The dream of conversational commerce was simple: technology enables humans to speak to other humans at scale. Instead, the vendor community realized that humans are expensive, inconsistent, and prone to demanding fair treatment. The corporate response was to replace them with IVR systems, chatbots, and automated messaging. These tools were never designed to foster actual conversations; they were designed to create efficient deflection barriers. Now, we are told that generative AI and agentic systems will change all this by acting as our personal proxies. But will it come true?

TL;DR

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Else, be my guest and continue to read.

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The Illusion of Agentic Agency

During our recent CRMKonvo with Dan Miller, founder of Opus Research, we wrestled with this paradox. We have been apocaloptimists when it comes to conversational AI, marveling at the technology's ability to improve our lives while ignoring its potential as a tool for corporate surveillance. The simple truth is that the economic incentives of surveillance capitalism remain unchanged. When a vendor provides you with an "autonomous assistant" to help you shop, that assistant is not working for you; it is a digital Trojan horse. It is programmed to maximize the vendor's margins, steer you toward high-commission partners, and dynamically adjust prices based on your historical data. They call it serving you better; in reality, it is just more sophisticated extraction.

This is where the asymmetry of power becomes glaringly obvious. The consumer enters the arena with a simple objective: to find a quality product at a fair price. The vendor enters with predictive algorithms, historical CDPs, and agentic bots designed to extract the maximum possible lifetime value from that specific consumer. When these two forces meet, it is not a conversation; it is a “negotiation” where one party has access to the other's entire cognitive blueprint. If your personal shopping agent is hosted, managed, or trained by the same corporate infrastructure it is supposed to negotiate against, your agent is effectively a double agent.

The Guardrail Paradox and the Friction of Safety

One of the most fascinating aspects of our discussion centered on the concept of guardrails. In theory, guardrails are designed to protect users, prevent systemic bias, and ensure compliance. In practice, they are a friction point. If you are a malicious actor, or a vendor looking to maximize short-term profit, you do not want guardrails; building and maintaining them requires computational and human effort. Consequently, the path of least resistance is to deploy systems with minimal oversight and dealing with the possible fallout later. When they put restrictions in place, they often reduce legitimate user choices instead of protecting the user.

This creates a bizarre scenario where the consumer is locked in a digital playpen, restricted by strict guardrails on what their agent can ask or do, while the vendor's algorithms roam free in the wild west of data exploitation. The guardrail paradox is that by trying to make AI safe, we often make it useless for the consumer while doing absolutely nothing to stop the vendors from using unbridled models to leverage market dynamics. It is an asymmetric conflict: the defensive side must comply with every rule, which are set by the offensive side.

The 'Trusted Agent' in a Corporate State

Senator Warner and others have proposed regulatory frameworks that would authorize approved entities, be they banks, credit card issuers, or the vendors themselves, to host "trusted user agents." This is a farce of epic proportions. How can anyone believe that a vendor-hosted agent will prioritize the consumer's interests? The Martech community has spent decades building systems to capture, analyze, and exploit user data. To expect these entities to host an objective, consumer-first agent is akin to asking the fox to protect the chicken coop.

Such proposals do not democratize AI; they institutionalize the power dynamic favoring the vendor, dressed up in the shiny new clothes of trusted agentic technology. The vendor-hosted agent will inevitably suffer from a conflict of interest. It will prioritize the products that yield the highest margin, mask competitive alternatives under the guise of "simplifying choice," and feed our preferences back into the corporate data lake. True consumer agency cannot exist within a closed corporate ecosystem. It requires independent, decentralized, and locally run models that answer to no one but the individual user.

Pay-to-Play Algorithms and the Opacity of LLMs

Let us look at a concrete example of how this plays out in the real world. Generative AI led to the discipline of GEO (generative engine optimization) to ensure being highlighted in search feeds and assistant recommendations. This is the reality of a black box. When you ask a modern LLM for a product recommendation, you have absolutely no way of verifying why it chose a particular vendor. There is no transparent ledger of recommendations. It is entirely possible that the recommendation you receive is the result of an agreement between the LLM provider and a corporation.

As long as these models remain opaque, any promise of objective personal assistance is a marketing myth. The algorithms are trained on data that is already heavily skewed by advertising dollars and SEO manipulation. Therefore, when an agentic bot uses it, it is recycling corporate propaganda, presenting it as unbiased advice. This is not artificial intelligence; it is automated salesmanship. To combat this, we need absolute transparency in how recommendation engines operate, including a public ledger of all corporate sponsorships and algorithmic biases that influence the output. A tall order.

The Scalability Farce of Manual Compliance

Even if we establish clear privacy guidelines, such as the right to be forgotten or standard opt-outs like in the GDPR, e.g., implemented using the IEEE My Terms standard, the enforcement mechanism is broken. If a consumer requests that their data be deleted or excluded from training sets, how do they verify compliance? They cannot. If you send a compliance request to a trillion-dollar tech company, that request likely lands on the desk of an understaffed compliance team using a manual process to scour databases, call transcripts, and unstructured chat histories. This does not scale. It is impossible for these enterprises to manually comply with millions of granular privacy requests.

The vendor's SOP will be to say they complied. Yet, once your data has been ingested into an LLM, it is practically impossible to "un-train" that model on your information. The data becomes an inseparable part of the algorithmic weights. Therefore, any regulatory framework that relies on retroactive compliance is a toothless tiger. We must shift the battleground from retroactive deletion to proactive, systemic prevention.

VCONs and the Architecture of True Data Sovereignty

If we want consumer agency, we must shift the paradigm. This is where technologies like Virtual Conversations (vCon) become critical. A VCON is a standardized, secure digital container that houses the transcript, audio, and metadata of a conversation. Crucially, instead of relying on a vendor's pinky-promise to respect our privacy, the data itself is encapsulated with its own governance rules. This is a step toward true data sovereignty, but it requires a massive cultural and technical shift.

Conclusion: Taking Back the Loop

The term "human-in-the-loop" is frequently used to describe safe AI integration. But as agentic AI evolves, we are moving toward a world where humans are removed from the loop, replaced by autonomous agents transacting with other autonomous agents. If we do not demand models that genuinely operate on our behalf, we will find ourselves shut out of our own decision-making processes. This is time to stop being passive consumers of AI convenience and start being active architects of our digital autonomy.

Pragmatic Playbook for Enterprise CX Buyers

Enterprise buyers are currently being bombarded with vendor pitches promising that agentic AI will magically solve their customer experience woes. If you are a buyer and concerned about ethical AI use, here is your survival guide to avoid making an expensive, possibly brand-damaging mistake:

Prioritize Architectural Integrity Over Hype: Do not be seduced by an agent's ability to generate natural-sounding excuses. Demand to see the integration map. If the agent cannot access your back-office CRM and ERP data securely and deterministically, it is not an agent; it is a glorified chatbot with a larger vocabulary.

Mandate Strict, Verifiable Data Boundaries: Ensure that your customers' data is never used to train a vendor's public LLM. If the vendor cannot guarantee and prove that your proprietary customer interactions are kept in a secure, isolated RAG environment, walk away. Your customer data is your competitive moat; do not give it away to train your competitor's next model.

Implement 'Agent-in-the-Loop' Safeguards: Autonomous agents are highly efficient at going sideways before they go south. Never deploy an agentic system in a customer-facing role without a deterministic routing mechanism that instantly escalates complex, emotional, or high-value interactions to a well-trained human agent, complete with full conversational context.

Insist on Standardized Metadata and vCon Support: Prepare for a future of decentralized data. Your architecture should support standard containers like vCons to ensure that as consumers demand greater control over their conversational data, your systems can comply programmatically rather than relying on manual, unscalable processes.

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 news

At its PegaWorld conference in Las Vegas on June 8, 2026, Pegasystems announced Pega Infinity 26, which it says will be available in Q3 2026. The principal change is commercial: Pega is moving away from per-token pricing for its AI agents toward a flat charge per completed "case," which it defines as a task carried out from start to finish, such as a customer changing an order, a loan approval, or a claim. Pega frames the move as removing what it calls the "AI token tax".

The pricing change rests on an architecture Pega calls Predictable AI. Reasoning-heavy AI work is concentrated at design time, when workflows are authored in Pega Blueprint and the new Infinity Studio. At runtime, a lighter-weight model identifies the user's intent, selects a pre-approved workflow, and executes it step by step; where an individual step requires a language model, for example to parse a document or summarize a prior interaction, that step is given bounded instructions rather than open-ended latitude. Pega gives two reasons: more consistent outcomes, because agents follow approved workflows rather than re-reasoning each request, and more predictable cost, because the heavier processing happens only once during design rather than on every transaction.

The architecture is not new to this release. Pega introduced Predictable AI Agents in May 2025 and integrated them into Pega Infinity '25, which reached general availability in December 2025. Infinity 26 primarily adds the outcomes-based pricing model, alongside a companion announcement that exposes Pega processes as Model Context Protocol (MCP) servers, allowing third-party agents from Anthropic, OpenAI, Google, and AWS to call them under Pega's governance controls. The release cites no named customer, quotes analyst Liz Miller of Constellation Research. The "more than 20x" savings figure comes from Pega's AI Token Cost Calculator and is qualified as applying "depending on workflow complexity and scale".

The bigger picture

Two industry currents explain the timing of this announcement.

The first is pricing. The customer-service software market has spent the past year and a half moving away from per-seat and per-token models toward charging for outcomes. Intercom Fin charges $0.99 per resolution. HubSpot cut its customer agent to $0.50 per resolved conversation in April. Zendesk runs around $1.50 per automated resolution on committed volume and has been selling outcome-based pricing since 2024. Salesforce launched Agentforce at $2.00 per conversation, a unit so loose that only roughly 8,000 of its 150,000-plus customers adopted it, which forced a pivot to per-action Flex Credits and Agentic Work Units. Sierra, Decagon, and Ada all sell per-outcome on custom enterprise contracts. Gartner, in a March 2026 forecast, projects that the cost of running inference on a trillion-parameter model will fall more than 90% by 2030, while noting that those provider-side savings will not fully reach customers and that agentic models consume between 5 and 30 times more tokens per task than a standard chatbot. Not all of it will reach the buyers, though. The unit price of thinking is falling while the number of units per task climbs, which is the squeeze every vendor in this market is now pricing against. Pega's per-"case" charge belongs to this trend, with its unit defined differently from a customer-service "resolution": a case spans a back-office task such as a loan approval or an insurance claim run end to end, rather than a single support interaction.

The second current is a deep disagreement across the industry about how much freedom an AI agent should have at runtime. One camp ships prompt-based tooling and lets agents reason and plan at each step, treating flexibility as the key point. Another constrains agents to pre-approved workflows and treats unbounded runtime reasoning as a liability, especially in regulated processes. Pega sits firmly in the second camp, and its CEO has said publicly that competitors asking users to write prompts are setting themselves up for trouble. The context underneath the argument is not trivial. A widely cited 2025 MIT study from its NANDA initiative found that roughly 95% of enterprise generative AI pilots produced no measurable return on the profit line, which the authors attributed less to model quality than to a "learning gap" in how organizations integrated the tools. This is the line the market is arguing about right now, and the vendors have started to pick sides.

My point of view and analysis

Start with the part Pega frames as leadership. On price, Pega is not leading, it is catching up, and the per-"case" charge is the same outcome-based move the customer-service vendors made first, just dressed for a different room. Credit where it is due, however, because the chosen unit is better than most: a completed back-office case is harder to game than a support "resolution" and maps to work a CFO already values. That is a real distinction. It is also a modest one, and it is not a first.

On the architecture, Pega's CEO is not entirely wrong about the risk he is arguing against. Letting a model improvise its way through a regulated claims process is asking for trouble, and the graveyard of failed genAI pilots is full of companies that could not audit what their agents did. The trouble is that the cure and the original promise of agentic AI pull in opposite directions.

Here is the question I cannot get my head around. There is real value in customer interactions that follow a rote path, and a great deal of work is exactly that; so Pega serving the rote case cheaply and consistently is a good thing, period. But the value of an agentic system was supposed to be the other case: the request that does not fit the workflow as designed, the genuinely novel situation. Pega's architecture is built to do the opposite of reasoning through those at runtime. So how does the system know it can safely run the rote workflow if it never reasons through the case at the outset? Pega's answer is the lightweight intent query that does the routing, which means the only runtime intelligence in the loop is intent classification, and classification is itself probabilistic and perfectly able to misroute. A request that matches no workflow then has three exits: forced onto the nearest approved path, escalated to a human, or handed to Blueprint to generate a workflow on the fly. However, that third option is the one Pega spends the whole pitch warning against, because runtime generation in a regulated process is precisely what it calls dangerous. You cannot headline determinism and keep on-the-fly generation as the safety valve without owning the contradiction.

There is a distinction underneath all of this. Deterministic guardrails wrapped around a probabilistic system set the boundaries of acceptable action without collapsing the space inside them. The agent still reasons; it simply cannot climb the fence. Pega is doing something else. At runtime, the approved space is the entire space. There is no reasoning inside the fence, because the fence is the answer. That is not an agent operating within guardrails. It is a workflow engine with a probabilistic front desk. For loan approvals and claims that may well be the right trade, and it should simply be named as one. The industry spent two years insisting agents would handle the unscripted long tail, and Pega's bet is that the long tail is where you get hurt, so it designed the long tail out. They may be right about the risk while conceding the promise without saying so. This is BPM, Pega's home turf since 1983, with an AI intake layer on the front. Calling it agentic is generous.

So here is what I would do before believing the deck. Ask Pega for one named production customer, on the record, who has run this at scale and watched the cost curve flatten, because a calculator output is not a reference you can phone. Then get the definition of a billable "case" in writing, including what happens when the workflow misroutes, fails, or escalates to a human, because "resolution" was always a vendor-defined word and "case" is no different, and that ambiguity surfaces on the invoice rather than in the contract. Finally, ask the uncomfortable one: what share of your real request volume does not map cleanly to a pre-approved workflow today, and what does Pega do with that slice? If the answer is "a human takes it" or "Blueprint writes a new one live," you are buying a very capable workflow engine, which may be exactly what you need, as long as you buy it with your eyes open.

The token critique landed because it is true, and the architecture is sensible for the work Pega is aiming at. I am just not convinced the market asked for agents that are forbidden from thinking the moment a request gets interesting, and I would like to know whether buyers are actually asking for this or whether the industry has decided the long tail was a bad idea all along.

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!

Three titan announcements in two weeks reveal what enterprise software vendors are actually fighting over in 2026, and it is not agents.

If you have been tracking enterprise AI announcements through 2025, you have been watching a race about agent counts. How many prebuilt agents. How many industry-specific use cases. How many customer stories. Agents were the marketing, the demo, the SKU. A year of the same playbook.

Something shifted in April 2026.

Inside a two-week window, Salesforce, SAP, and ServiceNow each published an announcement that, at first glance, looks like more of the same agent theater. Salesforce launched Headless 360 at TDX 2026 and the Agentforce Experience Layer. SAP pushed a simplified-architecture argument alongside a persistent agent memory layer on BTP. ServiceNow rolled out Context Engine and, on its SPM community blog, Fred Champlain published an essay reframing governance itself as "strategic decision debt”.

Different products. Different audiences. The same structural move.

All three titans just walked one layer down the stack.

Read individually, each announcement is a product release. Read together, they are a category shift. The competition is no longer about who has the best agent. It is about who owns the substrate those agents operate on. And each titan is staking a different piece of it.

The Pattern Nobody Is Naming

Strip the vendor branding from all three sets of material and the structural claim is identical:

“Your agents are only as good as the layer underneath them. The data they ground on, the logic they inherit, the memory they carry, the permissions they respect, and the decisions they represent. That layer is what we sell.”

These three vendors are by no means the only ones making this shift. They just did it in a remarkably short period, and on stages loud enough to frame the category.

The pitch is more sophisticated than the 2025 version. Agent count was a volume game, easy to parody and easy to commoditize once every vendor had a hundred prebuilt agents. Substrate is harder to commoditize, harder to rip out, and (not surprisingly) easier to price at a premium once customers have built architectural dependencies on it.

Each titan is claiming a different piece of the substrate. None of the claims overlap cleanly. All of them expand the vendor's footprint.

Salesforce: The Interface and Intent Layer

Salesforce made the boldest move. Headless 360 exposes every platform capability as API, MCP tool, or CLI command, which means external coding agents (Claude Code, Cursor, Codex, Windsurf) get live access to an org's data, workflows, and business logic. Agentforce Vibes 2.0 ships with open agent harnesses supporting both Anthropic and OpenAI SDKs. Developers no longer need to work inside Salesforce's own IDE.

Is the new? Not quite; API-first architectures exist for quite some time. And they are a best practice.

However!

The accompanying Agentforce Experience Layer (AXL) is the delivery side. Build logic once in Salesforce. Deliver the same agent response into Slack, Teams, mobile, ChatGPT, WhatsApp, a customer portal, or any third-party surface, with the UI rendering automatically adapted to each channel. Permissions inherit from the Salesforce platform.

This part is new.

The subtext is the real story. For twenty-seven years, Salesforce's primary interface was the browser. Headless 360 is an explicit statement that the browser has become optional. VentureBeat's framing of the Salesforce answer to "does a company still need a CRM with a graphical interface?" was a blunt no, and that is exactly the point. Joe Inzerillo, Salesforce's president of enterprise and AI technology, said Headless 360 lets agents operate directly on the platform's business logic and datasets "rather than relying on separate integrations or user interfaces”. Read together, Salesforce is telling buyers it wants to remain the system underneath, even when the user never opens a Salesforce tab.

Not everyone loves it. The "Context, Work, Agency, Engagement" framing can create the ultimate vendor lock-in architecture, and the pricing is conspicuously silent. Headless 360 is included in platform licenses today. That is a statement about today. Salesforce's historical pattern is to introduce capability in the base tier and later wrap premium SKUs around it. CIOs should be asking the pricing question before making the architectural commitment.

SAP: The Data and Process-of-Record Layer

SAP is running a different play. It is not trying to be the interface layer. It is trying to be the gravity well.

The simplified-architecture argument is a rejection of the 2024 playbook, which basically said: sprinkle Joule on top of S/4 and be AI-ready. The current SAP pitch, across the Clean Core guidance, the Reltio acquisition, the Business Data Cloud strategy, the SAP-RPT-1 foundation model for structured data, and the new agent memory layer on BTP, is a single argument: your AI is only as trustworthy as the ERP data underneath it, and most of the world's transactional data lives in SAP.

The agent memory layer deserves a deeper look. Persistent memory is where consumer AI assistants finally became useful. ChatGPT remembering preferences, Claude carrying project context across sessions. Enterprise agents have historically been stateless, forcing users to re-prime the same context on every session. SAP's answer to this problem is to build memory as a BTP service, grounded in HANA Cloud Vector, with short-term, long-term, and reflective memory tiers governed by enterprise policies (retention, right-to-be-forgotten, audit trail).

Not a plug-in. A layer.

The SAP story has one recurring weakness, though: pace. DSAG's Technology Days 2026 in Hamburg, which drew more than 3,000 participants, delivered a consistent message from users. More clarity. Less architectural theater. Customers want SAP to ship faster and integrate more smoothly, not add more conceptual layers. The "simplified architecture" framing is partly defensive. It is a tacit acknowledgment that the SAP AI stack has become overwhelming to prospective buyers and to existing customers trying to execute.

ServiceNow: The Governance and Decision Layer

ServiceNow made the most conceptually ambitious move of the three. And it did so without a single major product announcement on the day.

Fred Champlain's piece on the SPM community blog introduces "strategic decision debt" as a category. The argument: the accumulated weight of unmade, unclear, or inconsistent portfolio-level decisions is what actually prevents enterprises from turning AI capability into AI outcomes. It is not a technology problem. It is a governance problem. And, Champlain argues, the governance layer is what ServiceNow sells.

The product scaffolding around the argument is substantial. Strategic Portfolio Management. Enterprise Architecture. The newly announced Context Engine, built on ServiceNow's Service Graph and Knowledge Graph, which captures the "why" behind decisions alongside the "what." AI Control Tower for governing agent behavior. TrustCloud and ComplianceCow, both of which received ServiceNow investment, shipped AI-native risk and compliance apps directly on the platform earlier in the week, reinforcing the partner-network moat.

The piece that matters most is the language. If "strategic decision debt" becomes a term CIOs use in quarterly reviews, ServiceNow owns the vocabulary, which means it owns the sales motion. No other titan has been publishing framework-level essays this quarter. Salesforce is publishing product pages. SAP is publishing architecture diagrams. ServiceNow is publishing a hypothesis about why enterprises are stuck and is offering its product portfolio as the answer. That is analyst-grade positioning, and it is rare from a vendor.

The Two Battlegrounds

I look at all these titan moves through two lenses.

• Interface control: who owns how users and agents access business applications.
• Orchestration: who owns the layer that coordinates work across systems.

This set of announcements maps cleanly on either lens.

Salesforce is the aggressive play on interface control. Own the access, and you own the orchestration that follows. AXL is the clearest multi-surface interface-layer bet any titan has made so far. SAP's interface-control play is softer, still routing interactions through Joule and its own surfaces. ServiceNow, interestingly, is not fighting for the interface at all. It is interested in being the backbone under whatever interface the user happens to be using.

On orchestration, the roles invert. Salesforce orchestrates experiences across channels, and, excluding what MuleSoft does, is quieter on orchestrating workflows across non-Salesforce systems. SAP orchestrates processes across SAP and non-SAP via BTP, Integration Suite, Advanced Event Mesh, and now master data via Reltio. ServiceNow makes the most conceptually interesting move by extending orchestration into the decision flow itself. Context Engine plus Service Graph plus Knowledge Graph is orchestration applied to how decisions get made, not just how tasks get executed.

Three titans. Three different pieces of the substrate. No direct overlap. Significant expansion of footprint for each.

The Titans Who Skipped This Quarter

Reading these three announcements in sequence raises an interesting question. Where are Microsoft, Oracle, Adobe, and Zoho?

Microsoft in particular is the puzzle. Copilot, Fabric, Dataverse, Foundry, Power Platform. Every component needed to tell the same substrate story is already on the Microsoft roadmap or already shipped. The gap is the narrative. Microsoft has the pieces, but Satya Nadella's team has not bundled them into a coherent layer-down argument the way Salesforce and ServiceNow have. If Build 2026 does not fix that, Microsoft cedes the architectural high ground on substrate for yet another quarter, while three of its main competitors keep compounding.

Oracle's AI Data Platform push is similar to SAP's BDC play but has not surfaced an equivalent integrated narrative. Adobe remains anchored to content and CX. Zoho continues its integrated-suite, lower-price playbook with less architectural theater, which is arguably the right move for Zoho's segment and consistent with its philosophy. It keeps the company out of this conversation, though, and that is a choice with consequences.

What Buyers Should Actually Do

The three recommendations from my LinkedIn post on this hold, and they deserve elaboration.

Stop evaluating AI features in isolation

A feature list is a snapshot. The substrate is what survives the next 18 months. Ask every vendor you are evaluating which layer of the substrate they claim to own, analyze whether the claim is architecturally coherent or three product pages stapled together, and what happens to your architecture if the vendor executes on that claim versus if they don't. Features come and go. Architecture commitments do not.

Ask the pricing question now, not later

Headless 360 is included in Agentforce 360 platform licenses today. SAP's agent memory layer is part of BTP today. ServiceNow's Context Engine sits inside existing product lines today. None of these vendors has announced whether they will keep the substrate capabilities in the base tier indefinitely. The historical pattern says no. Build your architectural dependencies with pricing clarity, not without it. And build the architecture in a way that those dependencies do not become impossible to unwind later. After all, today’s pricing clarity might be tomorrow’s pipe dream.

Treat "memory," "context engine," and "experience layer" as three costumes for the same problem

All three titans are building a substrate for agents to reason over. The vocabulary differs. The underlying capabilities: persistent cross-session state, grounded enterprise context, consistent multi-surface delivery are the same, just with different strengths and weaknesses in each implementation. Write down the capabilities your agents need. Map each vendor's product to these capabilities.

Do not let vendors map you to their product pages.

Three Things to Watch

Whether Microsoft responds at Build 2026 with a bundled substrate narrative, or lets Copilot keep carrying the whole story alone.

Whether SAP's Reltio integration actually ships as the promised trusted-data spine for Joule Agents or becomes another BTP component that customers must stitch together themselves.

Whether Salesforce's "Trust Moat" language around AXL holds up in enterprise deployments, where the every agent needs consistent permissions across Slack, Teams, ChatGPT, a customer portal, and more. If it does, lock-in critique loses force. If it does not, the critique becomes the dominant analyst read.

The Question That Matters

All three titans have moved one layer down, coming from different angles. The logic is sound. The architectural ambitions are serious. The open question is whether three companies each trying to own a different piece of the substrate produces three coherent platforms, or three partial platforms that leave buyers integrating the substrate themselves.

Twelve months from now, we will know whether April 2026 was the moment the agent conversation matured, or the moment it splintered.

I am curious whether CIOs are reading these three announcements as compatible stories, or as three competing bids for the same piece of architectural real estate.

Welcome to the second quarter of 2026. The dust of the generative AI explosion seems to have finally settled, so actual business realities can be seen. For the last few years, the enterprise software market has been drowning in vendor promises of AI magic. Now, companies are waking up to the hard truth. AI is no longer a futuristic promise; it is a budgetary line item with concrete expectations. As our guest Clint Oram accurately pointed out in our CRMKonvo sit-down, businesses are actively hunting for 20 to 40 percent productivity gains from their knowledge workers. But are these gains real, or just another SaaS vendor hallucination? The market is scrambling to figure out what actually works and what is just expensive hype.

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 …

While the underlying LLMs have become core components of daily workflows, the execution at the enterprise level remains often fraught with mediocre strategies. At the same time, we are seeing a profound shift in how work is accomplished with the help of AI. This year will be defined by a massive, societal scramble to understand if, and if so, how, this technology supports the bottom line of the companies using it. Let us see if there is actually any substance there, or if we are just increasing vendor revenues. The focus must shift from adoption at any cost to architectural integrity, and it already does in some areas.

Vendors love to sell you a tidy vision of a fully autonomous future, but the reality on the ground is different and far messier. Integration into legacy systems is painful. The data architectures required to make these systems hum are often neglected in favor of flashy superficial updates. We must rigorously question every new "feature" that hits the market and understand. Its value for us.

The Customer Experience Disaster

Perhaps the most glaring failure in the current AI landscape is the impact on customer experience. Companies are desperately trying to cut costs by replacing human agents with AI bots, and the results are regularly embarrassing, even infuriating. Clint shared a chilling anecdote about interacting with an AI screening agent over the phone. He described the interaction as an Interactive Voice Response system on steroids. It was a frustrating wall erected between the customer and a human being. The AI could comprehend the words, but it lacked the fluid capability to navigate a non-scripted conversation. Deflection, not service.

When organizations deploy AI merely to deflect customers rather than serve them, they are missing the entire point of a modern CRM strategy. This is not innovation. This is just a cheaper, faster way to annoy your buyers. Well, that’s kind of an innovation, too; but probably not a desirable one. The market must understand that AI replacing human agents is still failing miserably. The technology simply cannot yet handle the nuance of human frustration. Conversely, AI augmentation of human agents is where the real value lies for time being. When AI works in the background to provide context to a live agent, the customer wins. We must stop treating AI as a cost-cutting guillotine and start treating it as an enablement engine.

Context Blindness and the SDR Spam Machine

The root cause of these failures is what Clint terms "context blindness". LLMs are incredibly articulate, but they are incredibly stupid without specific, grounded data; even with that, it is still worth to follow the trust-but-verify principle. If you drop an AI into a workflow without connecting it to your CRM, CDP, or a robust knowledge graph, it will confidently generate useless responses. The industry is finally realizing that localized context is the missing link.

This is acutely visible in the sales area. Inbound AI Sales Development Reps (SDRs) are performing reasonably well at qualifying leads. Tools like Regie.ai and Win.ai are successfully routing inbound interest. However, outbound AI SDRs are an unmitigated disaster. They disappoint prospects with formulaic, obvious AI spam. Buyers immediately recognize the lack of human nuance and discard these messages. You cannot automate relationships with generic prompts. The tools exist, but companies are burning through their prospect lists with low-quality, automated outreach. It is the new version of spam, and it is destroying brand equity. To fix this, vendors must prioritize architectural integration over generative party tricks.

Seniority Beats Juniority: The Unlikely AI Masters

Here is the most fascinating observation from Q1 2026. The so-called digital natives are losing the AI race. We assumed the younger generation would master AI effortlessly. Instead, older, experienced professionals are extracting vastly more value from generative tools. Why? Because effective AI usage requires deep domain expertise. You have to know what questions to ask, and even more importantly, you must have the experience to judge whether the AI's output is correct or just plausible nonsense.

Clint points out that delegating tasks to AI is identical to managing a junior employee. You must give precise instructions, provide context, and meticulously review the work. Senior managers who know how to delegate are thriving. They are building complex slide decks and strategic documents in minutes instead of weeks. Seniority beats juniority in the AI era because wisdom and context cannot be downloaded. The prompt is only as good as the professional typing it. But remember, not having juniors today means not having seniors tomorrow.

The Exhaustion of Accelerated Productivity

Finally, we cannot ignore the human cost. The pace of technological change is causing massive AI burnout. Professionals are generating high-quality work at breakneck speeds, leading to a relentless, continuous stream of high-impact decision-making. The mental fatigue of constantly managing AI agents and making rapid-fire strategic choices is exhausting the workforce. You are no longer doing the rote work; you are just making decisions all day long. And this will increasingly distance you from your domain expertise, which means that decisions may be taken at an increasing level of uncertainty.

This also means that the conversation around a four-day workweek might no longer be an HR perk; it might become a physiological necessity. When you remove the friction of content creation, you are left with the intense cognitive load of continuous evaluation. We are burning out our best people by forcing them to operate at the speed of a machine. The balance must be restored before the productivity gains collapse under the weight of human fatigue.

Reality Check for Enterprise AI Buyers

Let us cut through the vendor noise and establish ground rules for buying AI in 2026. If you are an enterprise buyer looking to inject AI into your customer experience architecture, stop buying hype and focus on reality. Here are three crucial learnings and recommendations.

Integration Realities Require Starting Small.

Do not attempt to boil the ocean with a massive AI rollout. As Clint advised, you must start small, think big, and move quickly. Identify a highly specific friction point in your business, such as territory planning, and deploy a targeted AI solution like BoogieBoard to solve it. Technology implementations stall when organizations lack a clearly defined problem. Stop buying AI just to have AI. You need a targeted business case. Force your vendors to prove their worth on a micro-scale before expanding.

Data Quality Triumphs Over Generative Hype.

Your shiny new LLM is entirely useless without context. Stop obsessing over foundational models. Instead, focus on your internal data structures first. Curing context blindness means feeding your AI localized, curated data through retrieval-augmented generation (RAG) and structured knowledge graphs. If your CRM data is garbage, your AI will simply generate garbage at unprecedented speeds. Context is the only thing that separates a useful tool from a hallucinating liability. Do not let vendors convince you that their AI will magically organize messy data.

Third: The Human-in-the-Loop Necessity.

Do not replace your human workforce with cheap AI alternatives. The technology cannot replicate the empathy and strategic judgment of a human being. Focus entirely on AI augmentation. Use AI to feed context to sales representatives at the exact right moment, rather than using it to spam prospects with outbound garbage. Keep the human in the loop to handle complex escalations. Your customers deserve a premium experience, and humans are still the only ones who can help them have it.

We have been lying to ourselves since, well, basically since forever. We placed customer support agents into a padded room called the "contact center," handed them a ticketing system, and told them to keep the angry people away from the rest of the business. We tracked average handle times; we cheered when a routing algorithm saved a fraction of a second; and we pretended that managing an interaction was the same thing as solving a problem. Deflecting an issue was the holy grail.

That era is over. The walls of the contact center have been blown wide open, and the debris is currently raining down on the CRM and operations landscapes. The market is shifting from asking the question "who can capture the ticket best?" to "who can actually resolve the problem fastest?” Which is an entirely different category of question. And far more meaningful.

And as Cameron Marsh from Nucleus Research so accurately pointed out in our recent CRMKonvo, that is a much nastier, much more complex place to compete.

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 feel free to do both …

The Illusion of the "Smart Ticket"

Let’s just be absolutely clear from the start: nobody wants a ticket. A ticket is simply a formalized receipt of failure. It is documented proof that a product broke, a service failed, or a user interface was too clunky to navigate.

For years, many vendors, including specialists like Zendesk, Freshworks, and others have built success around making those tickets prettier, easier to handle and pass around. And companies like Five9, Genesys, Verint and too many more to count happily managed the interaction center. But when Salesforce forcefully entered the CCaaS conversation with new voice and AI capabilities, they fired a warning shot across the bow of every standalone service vendor. Salesforce is betting that because they own the underlying customer data and the core business workflows, they should own the resolution. They are not selling a better queue; they are selling an operations layer.

The response from the service-first vendors is equally bold. They argue that because they capture the initial intent and handle the front-line friction, they are ideally positioned to resolve the issue before it ever touches a core system.

But the time-honored truth is that buyers do not really care about these architectural turf wars. Customers do not care if your platform uses a sophisticated LLM or a massive RAG implementation. They care about one thing: when something breaks, who fixes it? If your AI simply routes a ticket more efficiently to a human who still has to manually process a refund across three legacy systems, your AI is nothing more than expensive window dressing, or as Cameron aptly put it, “automation with a better branding”.

Smelly Data and the LLM Mirage

Which brings us to the most uncomfortable reality of the current enterprise software hype cycle: the data problem. Every vendor is currently parading their AI Agents and "AI Studios" around like magic wands that will instantly get things done while vaporizing operational costs.

But as we discussed on the show, almost every organization suffers from what we fondly call smelly data. They have decades of poorly categorized customer records, duplicate entries, and contradictory knowledge base articles. Pointing a state-of-the-art LLM at a mountain of unwashed data does not give you artificial intelligence; it gives you a very fast, very confident artificial idiot.

Garbage in equals garbage out. It is a cliché because it is true. The organizations that are actually seeing a return on their AI investments are not the ones buying the flashiest tools. They are the ones putting in the hard and unglamorous work of normalizing their data models, cleaning up the data and integrating their core systems. If you do not have a pristine data foundation, your generative AI will simply generate more work for your human employees to clean up while chasing your customers down rabbit holes.

The "Suite vs. Best-of-Breed" Debate Reignited

For the last decade or so, the enterprise software pendulum swung heavily toward "best-of-breed." We bought point solutions for every micro-problem and relied on brittle APIs to hold the Frankenstein monster together.

The trend slowed a few years and nowadays AI is starting to violently push the pendulum back toward the suite, or at least a platform. When you deploy an AI agent to resolve a customer issue, that agent needs instantaneous, read-and-write access to lots of data, often including billing, shipping, inventory, and customer history. Every single integration point is a potential point of failure. Every seam between different software vendors is a place where context gets lost and the AI starts to hallucinate.

As Cameron noted, the burden of proof has shifted. It is no longer up to the suite vendors to prove they are better. It is up to the best-of-breed vendors to prove that their necessary integrations will not slow down the AI or destroy the seamless execution of a workflow. If an integration creates lag or drops context, the ROI of the entire project collapses. The suite wins not because it has the best individual features, but because it has the fewest broken bridges while being good enough to do the job.

The True Predictor of Success: It Is Not the Tech

Here is the most interesting takeaway from Cameron and Nucleus Research evaluating dozens of successful AI implementations: the specific technology stack rarely predicts the success of the project. Whether a company chose Salesforce, Zendesk, or a bespoke solution, the common denominator of success was the human element.

The companies that succeed have a stellar relationship with their vendor's Customer Success Management team. They admit what they do not know, they ask for help with workflow design, and they partner with their vendors rather than treating them like mere order-takers. Conversely, the CSM teams admit where their software might not be the one to go for. It’s a two-way street. The technology is just the engine; the vendor relationship is the steering wheel. If you buy a Ferrari but refuse to talk to the mechanic, you are going to crash.

Reality Check: Three Imperatives for Enterprise CX Buyers

If you are a CIO, CXO, or IT leader currently evaluating AI for your service operations, you are swimming in an ocean of marketing fluff. Vendors are promising to cut your headcount in half while doubling your customer satisfaction. To avoid making a catastrophic and expensive mistake, here are three critical learnings you must apply to your buying cycle today.

Stop Buying "Deflection" and Start Buying "Resolution"

Deflection is a vanity metric. Often, a high deflection rate simply means you have made your IVR or chatbot so incredibly frustrating to use that the customer gave up and went to a competitor. Do not reward vendors for preventing customers from reaching you.

Instead, force vendors to prove their "fully resolved" rate. Demand to see how their AI handles a complex workflow end-to-end without a human ever touching it. If the AI can only handle password resets and order status checks, it is just a basic automation script wearing a tux. You are paying an "ego tax" for a label. Demand actual resolution.

Clean Your Pipes Before You Buy the Pump

Do not spend a single dollar on an AI agent if you have not invested time and resources to clean your data. The most sophisticated LLM in the world cannot resolve a billing dispute if your billing data is housed in an on-premise server from 2012 that only updates in batches every 24 hours and doesn’t even offer an API.

You need to establish a rigorous knowledge management practice. You need clean, structured data and clear workflow documentation. If you skip this step, your AI implementation will fail, your ROI will evaporate, and you will spend the next two years blaming the software for a problem that you created on your own.

Calculate the True "Human-in-the-Loop" Maintenance Cost

Vendors love to show ROI models based on how many tier 1 support agents you can eliminate. What they conveniently leave out of the spreadsheet is the cost of the highly skilled engineers, data scientists, and workflow managers you will have to hire to babysit the AI.

An AI system requires constant tuning, monitoring for hallucinations, management of drift, and edge-case management. You might eliminate fifty low-cost roles, but you will need to hire five very expensive experts to maintain the system. The need for humans in the loop further reduces the systems autonomy and hence “efficiency”. If your ROI calculation does not account for this shift in personnel costs, you are presenting a work of fiction to your board. Focus on the payback period, don’t forget about ongoing maintenance costs, and never buy a black box that your own team cannot audit.