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.

On May 4, 2026, SAP announced two acquisitions in the same breath: Dremio, an Apache Iceberg-native agentic data lakehouse, and Prior Labs, a pioneer of Tabular Foundation Models. Neither acquisition is exotic. Together, they are contributing to the most coherent enterprise AI platform strategy any major vendor has shown this year.

Let me unravel what each company actually brings, why the combination matters, what it means for the competitive field, and — most importantly — what buyers and SAP customers should be doing right now.

The Problem SAP Is Solving

Before diving into the deals, let's formulate the problem addressed. SAP's CTO Philipp Herzig said it clearly: "Enterprise AI doesn't stall because the models aren't good enough; it stalls because the data isn't ready for AI agents".

That is not a marketing line. It describes a pattern analysts and practitioners see constantly: AI pilots perform in a sandbox and fail when they hit production. The reasons are familiar: data is locked in proprietary formats across a dozen systems, there's no consistent business context, ETL pipelines take months to build, and governance gaps make audit-ready AI decisions nearly impossible.

SAP has also faced an additional problem. The narrative about SAP is and always was that it works brilliantly if everything lives inside SAP and required considerable engineering if you want to connect it to anything else. In an enterprise world where the average organization uses dozens of SaaS applications, that story is a liability.

Both acquisitions address these problems directly from different angles.

Acquisition One: Dremio and the Data Layer

Dremio is an open-data lakehouse built on Apache​ Iceberg. That description undersells it. Dremio co-created Apache Polaris, the open catalog standard for Iceberg multi-engine interoperability, and Apache Arrow, the in-memory columnar format that is the plumbing of modern analytics. These are foundational open-source contributions. Dremio's customers include Shell, TD Bank, and Michelin; these are enterprises with complex, multi-environment data estates that needed exactly what Dremio offers: federated query across any data source, without copying data or running ETL.

The specific integration SAP is executing is significant. SAP Business Data Cloud will become an Apache Iceberg-native enterprise lakehouse. That means SAP and non-SAP data can coexist on the same open foundation without format conversion or data movement. A universal catalog built on Apache Polaris means every system — SAP and otherwise — can read and write using the same standards. The Dremio AI Semantic Layer adds consistent business context across all sources, so that an AI agent querying HR data from SuccessFactors and financial data from a non-SAP system is working from the same definitions, not two conflicting ones.

For SAP's agentic AI play, this is a big improvement. Joule agents need data to act on. If that data is fragmented, ungoverned, or requires human pre-processing before an agent can consume it, agentic AI becomes expensive proof-of-concept work rather than production value. Dremio's self-managing platform with automated clustering, compaction, query optimization, reduces the operational burden of keeping an AI-ready data estate running. The MCP integration Dremio already ships means any LLM or AI agent framework can access enterprise data without custom integration work.

What is production-ready versus pending: The transaction closes Q3 2026, subject to regulatory approval. The Dremio platform itself is proven at enterprise scale, so this is not an early-stage bet. The integration work to embed Dremio fully into SAP Business Data Cloud will take time after close. Buyers should expect a meaningful roadmap presentation at SAP TechEd 2026 rather than a shipping product today.

Acquisition Two: Prior Labs and the Intelligence Layer

Prior Labs is a different kind of acquisition. This is not a product acquisition. It is a research acquisition with a specific thesis: Large Language Models are the wrong tool for structured business data prediction.

The argument is technical but intuitive. LLMs are trained on text. They have a rudimentary understanding of tables and statistics. When you ask an LLM to predict payment delays, supplier default risk, or customer churn from tabular ERP data, you are using a tool designed for language on a problem that is fundamentally about numbers, distributions, and correlations within structured datasets. Tabular Foundation Models (TFMs) are purpose-built for exactly this domain.

Prior Labs' flagship model series, TabPFN, has been academically validated: published in Nature, top-ranked on TabArena, the leading benchmark for tabular models, and with over three million downloads as an open-source tool. The most recent TabPFN-2.6 matches the accuracy of a four-hour automated machine learning pipeline, produced instantly, in a single model. For enterprise use cases like predicting payment delays, supplier risks, upsell opportunity scoring, and churn probability, that is a meaningful capability gap versus what LLMs can offer today.

SAP will commit more than €1 billion over four years to scale Prior Labs into what it is calling a globally leading frontier AI lab for structured data, based in Europe. Prior Labs will operate as an independent entity and will retain its open-source strategy. The scientific advisory board will include Yann LeCun (ACM Turing Award winner, Advanced Machine Intelligence) and Bernhard Schoelkopf (Max Planck Institute for Intelligent Systems, ELLIS president). That is not a list assembled for press release credibility. Both are significant contributors to the field.

The European angle matters beyond headlines and marketing. Regulatory pressures on AI data handling, EU AI Act requirements for explainability, and data sovereignty concerns make a European-based frontier AI lab genuinely useful for SAP's core customer base. An AI model built and governed in Europe, on structured business data, with academic validation and an open-source foundation, addresses a set of enterprise objections that American hyperscaler AI labs cannot easily resolve.

What is production-ready versus pending: Prior Labs' existing models are available and proven at the research level. Their integration into SAP's commercial product stack, embedding TabPFN predictions into S/4HANA, CX, or SuccessFactors workflows, requires post-close engineering. The €1B investment is a four-year commitment, not a capability that appears in the next release cycle.

Why the Combination Matters: A Three-Layer Stack

Looking at these acquisitions together, SAP is assembling a three-layer stack for enterprise agentic AI:

Layer 1: Data access and governance: Dremio + SAP Business Data Cloud. Federated access to SAP and non-SAP data, open Iceberg-native foundation, AI-ready governance, no data movement required.

Layer 2: Business intelligence from structured data: Prior Labs' TFMs. Accurate predictions on tabular business data that LLMs cannot match; payment delays, supplier risk, churn, upsell scoring.

Layer 3: Agent orchestration: Joule + Business Technology Platform. SAP's existing AI agent and process automation layer, is now able to draw on a governed data foundation and specialized prediction capability.

The coherence here is unusual. Most enterprise AI announcements from large vendors are additive: another LLM integration, another AI assistant feature, another partnership with an LLM provider. This one is different. SAP is making a structural argument about what enterprise AI requires. Not just a better model, but the right kind of model running on clean, federated, business-context-aware data, and is acquiring the capabilities to back it up.

The Partnership Question: Databricks and Snowflake

This is where the Dremio acquisition gets complicated, and SAP customers with existing data platform investments need to pay close attention. In the 15 months before today's announcement, SAP built two high-profile partnerships around exactly the problem Dremio solves. Both are now in a different position than they were yesterday.

The Databricks situation

In February 2025, SAP launched SAP Databricks as a first-party service natively embedded in SAP Business Data Cloud. This is not a partner integration, but a component of BDC itself, paid via BDC Capacity Units. Databricks committed $250 million to support customer and SI success on the joint platform. It was positioned as the flagship data engineering and AI layer for BDC.

Dremio competes directly with this story. Where SAP Databricks brought data engineering, ML workloads, and Unity Catalog-governed analytics into BDC, Dremio brings an Iceberg-native lakehouse, federated query across SAP and non-SAP data, and an AI semantic layer. These capabilities overlap substantially.

The underlying table format tension makes this sharper. Databricks' platform is built primarily on Delta Lake, with Iceberg support added later. Dremio co-created Apache Iceberg's catalog standard, Apache Polaris, and is Iceberg-native by design. SAP's stated direction is to make Business Data Cloud Iceberg-native. If that commitment holds, SAP's own first-party data architecture moves toward the format Dremio built, and away from the format Databricks built on.

SAP has not addressed what happens to SAP Databricks after the Dremio close. The most plausible near-term outcome is that Databricks retains a role as the ML and AI model development workbench within BDC with Unity Catalog, model training, MLflow, Mosaic AI, while Dremio becomes the lakehouse storage and federated query layer. That is a narrower role than Databricks was sold as in February 2025, and it represents a meaningful change in the commercial argument for SAP Databricks. Customers who made BDC decisions partly on the strength of that integration should ask explicitly, in writing, what the integration roadmap looks like after the Dremio transaction closes. Remember Hybris? or SAP Marketing Cloud?

The Snowflake situation

Snowflake's position was structurally weaker before today. When SAP and Snowflake announced their partnership in November 2025, the difference in status was noted immediately: SAP Databricks was a first-party service inside BDC, while SAP Snowflake was a solution extension, an add-on. SAP's data and analytics leadership confirmed the distinction publicly. Snowflake was the third major data platform partnership SAP announced that year, after Databricks and BigQuery.

The Dremio acquisition makes Snowflake's position harder to defend commercially. Snowflake has bet heavily on Apache Iceberg adoption; it is one of the company's core strategic moves to remain relevant as the industry converges on open table formats. This Iceberg compatibility means SAP and Snowflake can still interoperate through an Iceberg-native BDC. But compatibility is not the same as commercial necessity.

The core proposition of the SAP-Snowflake partnership was: bring your existing Snowflake deployment, connect it to SAP Business Data Cloud via zero-copy BDC Connect, and get federated access to semantically rich SAP data without moving it. Dremio offers the same federation story natively, from inside BDC, without a separate Snowflake contract. For customers now deciding between SAP BDC + Snowflake and SAP BDC + Dremio as their non-SAP data federation layer, the math has changed.

There is also a timing question. The SAP-Snowflake BDC Connect integration was planned for H1 2026 general availability. Now that SAP has announced a native Dremio-based federation layer, watch whether that H1 2026 timeline holds or slips, and how the roadmap changes. I expect SAP's engineering prioritization to change.

What buyers with existing partner investments should do

Three hot questions for anyone already using or evaluating these partnerships:

Will Databricks' role inside BDC narrow from "first-party lakehouse service" to, e.g., "ML and model workbench"? If so, the ROI case for SAP Databricks changes, and customers who bought it as a data platform should model what Dremio covers versus what Databricks still delivers uniquely. Get that scoping conversation started now, before the Dremio transaction closes and SAP has to commit to a position.

Does the SAP-Snowflake BDC Connect rollout and ongoing development proceed on the original timeline? If SAP's native federation story via Dremio reduces internal urgency to complete the Snowflake integration, customers who built roadmaps around the Snowflake partnership will need to know. Ask immediately.

For both Databricks and Snowflake, the more important question is not whether the partnerships survive — they almost certainly will in some shape and form — but whether they survive with the same scope. SAP has a track record of gradually narrowing partner integrations once it builds equivalent native capability. Both Databricks and Snowflake should be scenario-planning for that conversation. And SAP customers should avoid being caught in the middle of it.

How This Changes the Competitive Map

None of SAP's major competitors is positioned identically. Each has a different set of strengths and gaps.

Microsoft has Microsoft Fabric as its unified data platform and Copilot as its AI layer. Fabric is a credible, well-funded platform, but it is Azure-native and more proprietary than an Iceberg-first architecture. Microsoft does not have a tabular foundation model research capability. The company's AI investments are primarily in OpenAI-based large language models, which face the limitations on structured data prediction that SAP's CTO named.

Salesforce has Data Cloud as its data unification layer and Einstein as its AI layer. Data Cloud is impressive for CRM-centric use cases and requires other data to be ingested into Salesforce's ecosystem. It is not a federated architecture. Salesforce's AI investments are focused on their customer data estate, not the broader structured data prediction problem. Compared to SAP's core industrial, manufacturing, and financial services customer base, that is a narrower scope.

Oracle is actually the most interesting comparison. Oracle has full-stack control (SaaS, PaaS, IaaS) and deep integration across Fusion applications and OCI. Like SAP, Oracle has a large base of structured operational data. But Oracle has not made a comparable move on the data layer (no Iceberg-native federated architecture commitment) or on the model layer (no TFM research acquisition). Oracle's agentic AI story is about embedding agents into Fusion workflows, not about predicting outcomes from structured data with specialized models.

Snowflake is worth watching as an indirect competitor. Dremio and Snowflake serve overlapping markets. Both are open-data lakehouse platforms. Snowflake has its own Iceberg support and Cortex AI layer. The SAP acquisition gives Dremio the enterprise ERP context and customer base that Snowflake competes for separately. Combined with SAP's process knowledge, the Dremio + SAP Business Data Cloud stack could become a strong alternative for Snowflake's enterprise analytics customers, particularly those running SAP.

The Open-Source Angle

Both acquisitions come with an explicit open-source commitment from SAP. That is not accidental and it is worth taking seriously.

Prior Labs' TabPFN has 3 million downloads and academic validation. SAP's stated intention to preserve the open-source strategy means the research community continues to develop and validate these models, which benefits SAP's commercial implementation. Dremio co-created Apache Iceberg's catalog standard Polaris and the Arrow query format. SAP's commitment to continue contributing to these projects matters for interoperability. It is what makes the "no vendor lock-in" claim credible.

For enterprise buyers, the open-standards story addresses a real concern: if you rebuild your data architecture around a vendor's AI platform, what happens in five years if you need to change vendors? An Iceberg-native, Apache Polaris-cataloged data estate is portable in a way that a proprietary format is not. SAP is betting that customers who build on open standards will stay, not that they have to stay.

What Buyers and SAP Customers Should Do

If you are an existing SAP customer: The most near-term action item is understanding what the Dremio acquisition means for your data estate. Specifically: how much of your business data lives outside SAP today? If the answer is "quite a lot", which it is for nearly every organization, then the Iceberg-native Business Data Cloud architecture becomes highly relevant to your AI readiness. Get on the roadmap conversation with your SAP account team now, before these capabilities are generally available.

If you are evaluating ERP vendors: SAP's TFM bet is a meaningful differentiator in the 2027-2028 timeframe, not immediately. But the evaluation question is important today: ask any ERP vendor you are considering what their structured data prediction story is beyond LLM-based AI. The question will either reveal a thoughtful answer or reveal that they haven't thought about it.

If you are not an SAP customer: Do not dismiss Dremio's trajectory because it is now inside SAP. The open standards commitment means Dremio-based data architectures remain viable in non-SAP contexts. Watch whether SAP's integration approach over the next 18 months preserves that independence or quietly narrows the platform to favor SAP data sources.

If you are a data platform decision-maker: The Dremio acquisition raises the strategic importance of your Iceberg adoption timeline. Organizations that have already standardized on Apache Iceberg as their table format will find the integration into Business Data Cloud straightforward. Organizations still on proprietary formats (Delta Lake, Hive, legacy warehouses) should factor this development into their modernization roadmap.

On timing: Both transactions are pending regulatory approval and are expected to close in Q3 2026. Neither capability is available in production today. Do not let this announcement accelerate or delay operational decisions that need to be made in the next quarter. Use the time to build internal alignment on data readiness strategy.

MyPoV

SAP has spent years getting credit for being the system of record for the world's largest enterprises, and criticism for being difficult to integrate with everything outside its own walls. These two acquisitions are a direct response to that criticism.

The Dremio play says: we will be the data layer for your entire estate, not just your SAP estate. The Prior Labs play says: we will build the AI that actually understands the data that runs your business, not just the AI that talks about it.

Neither of these is guaranteed to work. Post-acquisition integration is where most ambitious platform strategies starve. The open-source commitments are credible today but have to be proven under commercial pressure. The regulatory timelines are a fact of life. And the €1 billion Prior Labs investment is a four-year commitment in an AI research environment that moves faster than any multi-year plan can anticipate.

But directionally, this is the right problem statement and a good approach to solving it. The data readiness problem exists The LLM-for-everything assumption is flawed for structured business data. And the open-standards position is defensible in a way that proprietary data platforms are increasingly not.

The question SAP customers should be asking isn't whether these acquisitions make sense. They do. The question is how fast SAP can execute the integration without losing what makes both companies valuable: the engineering credibility of an independent data platform and the academic rigor of a research-first AI lab.

That execution question will be answered over the next 24 months. Start watching now.

The most consequential enterprise AI governance document published this year arrived in late April with surprisingly little fanfare. SAP's updated API Policy, version 4/2026, is a short document in plain English. The clause that is most interesting is Section 2.2.2. It restricts how autonomous and generative AI systems are permitted to interact with SAP APIs. Read literally, it has the potential to change the architecture of agentic AI projects across every SAP customer landscape.

Read carefully, it is also more interesting than the lock-in headlines suggest. The policy targets a specific category of AI behavior, not AI as such. It connects to commercial mechanics that go well beyond API stability. And the literal text, in its current form, will probably not survive the next two policy revisions intact. There is a lot to unpack.

I will walk through what the policy actually says, how the SAP-watching community is reading it, what the rest of the major enterprise vendors are doing in comparison, what counts as an "endorsed architecture”, and what customers and partners should be doing about it now. I’ll close with a view on whether the policy can stand the test of time.

What Section 2.2.2 actually says

The operative sentence is direct. “Except through and within the limits of SAP-endorsed architectures, data services, or service-specific pathways expressly identified and intended for such purposes, SAP prohibits API use for interaction or integration with semi-autonomous or generative AI systems that plan, select, or execute sequences of API calls”. The same paragraph also prohibits scraping, harvesting, or systematic large-scale data extraction.

Three things flow from that. First, only Published APIs, those listed on the SAP Business Accelerator Hub or in product-specific documentation, are usable at all. Internal, private, and reserved-namespace APIs are out. Second, published APIs must be used for their documented purpose. Third, any agentic use of those APIs has to flow through SAP-endorsed pathways. The policy explicitly reserves enforcement rights including throttling, suspension, and termination of access. It also explicitly prohibits circumvention through proxies, intermediary services, custom code, or impersonation.

That is the legal fence. The interesting question is what it means.

The five readings circulating in the community

The professional discourse on this policy has organized into roughly five interpretations, and most of them are simultaneously true.

The first reading is that SAP is closing the back door on undocumented APIs. For years, real projects depended on internal SAP endpoints that worked in practice but were never officially supported. Marian Zeis, who maintains the curated registry of SAP MCP servers and runs one of the more careful technical blogs in the community, told The Register that “the changes are more restrictive than the community expected” and that SAP is too slow to publish or improve templates, leaving real projects dependent on undocumented APIs to keep pace with what their use cases require..

The second reading is more commercial. As SAP CX architect Jorge Ocampos puts it directly, SAP is not objecting to Claude, GPT, or Gemini. It is controlling the path through which agents touch SAP data and SAP transactions (Spanish). That path is BTP, Joule, AI Core, the Generative AI Hub, SAP Build, Integration Suite, and Business Data Cloud. The same agent running outside this stack may be non-compliant; running through it consumes AI Units under SAP's new consumption-pricing model. Snap Analytics reaches the same conclusion from the data side: all roads now lead to BDC. That’s cynical, but probably accurate.

The third reading is the lock-in concern. The Register captured this most directly, and DSAG, the German-speaking SAP user group, made it formal. DSAG's board went on record demanding contractual clarity (German), transition timelines, transparent fair-use thresholds, and protection for existing integrations. Their basic position is that SAP cannot announce that the SAP Business Accelerator Hub and product documentation govern customer architecture without first making those documents formal contract components.

The fourth reading is more sympathetic. The policy does not kill AI on SAP. It targets a specific category that practitioners have started calling attached AI, agents that plan, select, and execute API calls against productive systems, as opposed to detached AI, which helps humans understand SAP, generate code, search documentation, or design data models without touching live transactions. Distinguishing the two is the most useful conceptual move available right now. Most coverage skips it.

The fifth reading is procedural. SAP has created compliance fog by not publishing an enumerated whitelist. The phrase "SAP-endorsed architectures, data services, or service-specific pathways expressly identified and intended for such purposes" is doing enormous work, and right now nobody knows exactly what is on the list. That ambiguity is uncomfortable when enforcement can include throttling and termination.

All five readings hold. The policy is technically defensible, commercially self-serving, contractually ambiguous, conceptually sound for its stated target, and procedurally underdeveloped. Customers and partners need to internalize all five at once.

The attached versus detached distinction

This is the single most important conceptual handle on the policy, and it is worth slowing down for.

Detached AI is what most people are using today. ChatGPT helps a developer read an SAP help page. Claude drafts an ABAP method based on documentation. GitHub Copilot in agent mode edits a UI5 application. A community MCP server lets a coding assistant pull SAP documentation into context. None of this touches a productive SAP system. None of it is targeted by Section 2.2.2.

Attached AI is different. A LangGraph agent reads open purchase orders from S/4HANA OData, decides which to escalate, drafts a follow-up email, and posts updates back. A Bedrock-based finance agent calls invoice APIs, validates against vendor data, and triggers a payment release. A custom MCP server exposes SAP business objects to a general-purpose Claude or GPT agent, which then plans and sequences calls to mutate records. This is what Section 2.2.2 is talking about, and this is what now requires an SAP-endorsed pathway.

The distinction matters because most of the panic is misdirected. The customer who is running Copilot for ABAP development is fine. The customer who has a non-SAP agent platform reaching into S/4HANA over OData to execute business workflows is not, unless that path is routed through Joule, the MCP Gateway, BTP, or BDC.

How this compares to what other vendors are doing

Across the major enterprise software vendors, every player is doing something to govern agentic API access. The interesting observation is how differently they are choosing to do it.

SAP regulates the pathway. Section 2.2.2 demands that agent traffic enter through approved architectures. Salesforce, with one important exception, regulates the result. Agentforce sits behind the Einstein Trust Layer with per-conversation pricing and an Acceptable Use Policy that limits automated decision-making with legal effect. The exception is Salesforce's tightening of Slack data terms last year, which restricted external AI tools like Glean from indexing Slack messages. That move is narrower than SAP's, but it points in the same direction.

Microsoft regulates the gateway. The Azure AI Gateway, Agent 365, and the Microsoft Agents SDK are explicitly framework-agnostic. Microsoft's documentation advertises support for OpenAI, Anthropic, LangChain, Copilot Studio, and AWS or Google-hosted agents. The control mechanism is identity, observability through Entra and Purview, and token-rate limiting. ServiceNow is similar in spirit. The December '25 ServiceNow release added A2A v0.3 with tested interop against Vertex AI, AWS Bedrock, and Azure AI Foundry, plus recursive-loop protection for agents that might trigger themselves. Oracle has gone resource-bound, with default tenancy limits of two agents and capped tool counts per agent. HubSpot has gone outcome-based, charging roughly fifty cents per resolved conversation. Zoho's MCP server is explicitly model-agnostic.

In other words, every vendor is choosing a control point. SAP is alone in choosing architectural restriction at this scope. That is not in itself wrong. It is, however, a competitive contrast that Microsoft, ServiceNow, and the hyperscalers will exploit aggressively in CIO conversations over the next two quarters.

What counts as an SAP-endorsed pathway

The policy does not list the endorsed pathways. The SAP Architecture Center, the AI Golden Path, and product documentation do, and the working inventory is reasonably stable.

For published API access, anything on the SAP Business Accelerator Hub, plus product-specific documented APIs across S/4HANA Cloud, SuccessFactors, Ariba, CX, Concur, Fieldglass, and BTP services, used as documented. For the agent runtime stack, AI Core with Kubernetes-namespace-based resource isolation, the Generative AI Hub for foundation-model access with prompt registry and content filtering, AI Launchpad, the SAP Cloud SDK for AI, the SAP Cloud Application Programming Model, Joule Studio in SAP Build, and the BTP Cloud Foundry and Kyma runtimes.

For action and process, Joule itself as the orchestrator, Joule Skills for deterministic operations, SAP Build Process Automation and Build Actions, SAP Document AI, and the Document Grounding Service. For execution boundaries, the MCP Gateway running within Integration Suite, which is what enforces tool allow-lists, per-tool authorization, and human-in-the-loop approval before any system change. Also the Intelligent Scenario Lifecycle Management framework for embedded AI inside S/4HANA, where the data never crosses the system boundary.

For integration and eventing, Integration Suite with API Management, Event Mesh and Advanced Event Mesh, Cloud Identity Services with App2App tokens, and the BTP Audit Log. For data, SAP Business Data Cloud as the strategic foundation, BDC Connect for zero-copy sharing into Databricks, Snowflake, Microsoft Fabric, and Google Cloud Platform, Databricks-in-BDC, Datasphere, the HANA Cloud Vector Engine with authorization-aware row-level security, and the Knowledge Graph. For interoperability, A2A as SAP's preferred external protocol, MCP used internally with community and official MCP servers emerging including a planned official ABAP MCP server in Q2 2026, and the Joule Agent Gateway for inbound agent consumption from Vertex AI, Copilot Studio, and Bedrock. The Agent Gateway is not yet generally available as of this writing, which matters for anyone being told to use it today.

The architectural pattern shift this implies is straightforward. The old pattern was Agent calls APIs calls SAP. The new pattern is Agent calls a governed SAP pathway calls published APIs and events and data products calls SAP. More mediation, more logging, more SAP architecture in the stack, almost certainly more SAP spend.

The three situations and what to do about each

Customers and partners fall into three buckets, and the compliance work differs for each.

If your AI agents are built by SAP and run on SAP technology, this is the lowest risk category. Joule, the Sourcing Agent, the Dispute Resolution Agent, embedded agents in SuccessFactors and Ariba, all of these are inside the intended architecture by construction. The work to do is operational rather than architectural. Track Assist consumption. Document write-action approvals for finance, HR, procurement, and master-data. Press SAP for transparent, predictable pricing of AI Core capacity, foundation-model token consumption, BDC data egress, and the fair-use thresholds DSAG has been asking about (German). SAP-built does not mean risk-free. It means policy-aligned.

If you are a partner or ISV building on SAP technology, the work is to prove your architecture against the policy. Build a compliance pack for every solution. Inventory every SAP API, endpoint, connector, event, and integration artifact. Show, for each one, the link to the SAP Business Accelerator Hub or product documentation. Map every API to its documented purpose. Classify the solution explicitly: does it include an AI system that plans, selects, or executes sequences of API calls? If yes, identify the endorsed pathway used. Define write-action approval thresholds for anything financial, HR-related, master-data-mutating, or supply-chain-critical. Capture audit traces for every agent action. Get written confirmation from SAP for any gray-zone design choice. Verbal assurance from your account team is not contractual.

If you are running AI agents built on non-SAP technology, you face the highest-risk situation, and it is the one where the policy bites hardest. The safer architectural pattern is to separate reasoning from execution. Let the external Bedrock, Vertex, Copilot Studio, or LangGraph agent reason on data grounded through BDC, Datasphere, or HANA Cloud Vector Engine. Let SAP-controlled services execute. Use A2A into Joule for actions, not direct API orchestration. Use IAS App2App tokens, not shared service accounts. Implement human-in-the-loop gates for finance, HR, procurement, supplier master, pricing, payments, inventory, and production. Stop using undocumented APIs. The policy explicitly prohibits using proxies, gateways, custom code, or intermediary services to circumvent these controls, so the technical workarounds are not just policy violations, they are explicitly flagged as such by name.

A consequence worth pointing out is that the policy interacts with SAP Digital Access licensing. An autonomous agent that creates 10,000 invoice documents through SAP, regardless of where the agent itself runs, owes Digital Access fees on those documents. Section 2.2.2 controls the path; Digital Access meters the documents. The two are coupled. Customers who treat them separately will be surprised on their next true-up.

Will the policy stand the test of time?

My thinking is that the spirit of the policy is durable but the literal text is not, and the gap between the two will close through clarification rather than enforcement.

The legitimate parts of Section 2.2.2 are uncontroversial. Anti-scraping language, throttling rights, anti-circumvention clauses, and the principle that published APIs shall be used for their documented purpose are consistent with how every major SaaS vendor protects shared infrastructure. As autonomous agents proliferate, vendors that do not tighten these controls will face genuine availability and security crises. The risk that an unsupervised agent creates for an ERP system is real. SAP is not wrong to insist on execution boundaries and identity enforcement.

The restrictive parts run into headwinds. Enterprise architecture is moving in the opposite direction, toward open multi-agent meshes built on standards like MCP and A2A that are explicitly designed to make API-gated walls obsolete. The autonomous-agent restriction is unenforceable in its strongest reading because SAP cannot reliably distinguish agent traffic from human traffic on the wire. Enforcement will collapse to volumetric throttling, which the policy already authorizes directly, and contractual audits triggered by complaints, both of which exist already. And the policy contradicts SAP's own open-platform messaging; CEO Christian Klein walked the message back on the investor call (starting minute 53), stating that this policy mainly refers to SAP’s domain know how and not customers’ data, within days of publication, and DSAG has formally surfaced the contradiction.

My prediction is that within the next months, SAP issues clarifying material, starting with an updated FAQ and then a v5 policy, that does three specific things. It explicitly grandfathers existing partner solutions and customer integrations that pre-date the new policy. It defines "SAP-endorsed architectures" as a maintained, versioned list with deprecation timelines. And it softens the autonomous-AI restriction to a fair-use throttling regime plus an explicit anti-circumvention clause, dropping the architecture-bounded prohibition.

The longer the literal text stands without that clarification, the more competitive damage Microsoft, ServiceNow, Salesforce, and the hyperscalers will inflict by framing themselves as the open alternative for any enterprise that does not want to route every agent action through Walldorf's runway. The risk of an Indirect Access redux, where SAP burns customer goodwill in audit disputes over agent traffic that customers thought was compliant, is certainly there. SAP burned years of trust in 2017 and 2018 over that issue. Customers still keenly remember and are wary.

Closing read

The policy is technically defensible, commercially self-serving, and strategically fragile in its current form. The fragility is curable, and SAP can, and should, cure it, and fast. Doing this requires three things SAP can actually control: publishing a clear, maintained whitelist of endorsed agentic architectures and pathways; certifying non-SAP-runtime agent patterns through A2A, BDC Connect, and the MCP Gateway so that customers do not have to put every agent inside BTP to be compliant; and making the endorsed pathways genuinely valuable rather than merely mandatory. The BDC Connect zero-copy sharing into Databricks, Snowflake, Fabric, and GCP is the working blueprint for what good looks like on the read side. The harder challenge is delivering the same quality on the write side, where Joule and the MCP Gateway need to become the best way to execute SAP transactions from anywhere, not just the only compliant way.

SAP made the perimeter grab. Now it has to earn it. The next two policy revisions will tell us whether the company understood that or not.

What Google Cloud Next 2026 actually told us about the titan pecking order

Google Cloud Next 2026 wrapped last week. The official version of the story is the one Google wanted you to read: 260 announcements, 1,302 customer use cases, the Gemini Enterprise Agent Platform, eighth-generation TPUs, a $750 million partner fund, an $240 billion Marketplace backlog. Big numbers. On-message keynote. Tidy "agentic era" framing.

The more interesting story is who showed up to validate it, and what Google actually built underneath.

Five of the seven enterprise titans I track walked into Las Vegas and announced expanded partnerships that all rest on the same architecture: Gemini Enterprise as the agent control plane, with the titan's product playing the role of premium ingredient. Salesforce. SAP. ServiceNow. Oracle. Adobe. Add Workday and Palantir Technologies to the picture, both adjacent to my titan list but visibly aligned in the same direction.

Two titans were not in the picture. Microsoft, because Copilot is the direct counter-position and Cloud Next is not Microsoft's stage. Zoho, because Zoho's stack does not need a Google motion and Zoho's buyer is not the same buyer.

Both absences matter. More about them a little later.

What Google actually built

Let’s start with the framing. Google did not just ship a model platform with new features. It repositioned Google Cloud from "AI development environment" to enterprise agent control plane. Vertex AI services and roadmap evolutions are now delivered through the new Agent Platform rather than as a standalone product. That is not a naming change, it's an entirely different playground.

The Agent Platform stack now visibly includes:

• Agent Identity for cryptographically secure agent authentication
• Agent Registry as the catalog of every agent and MCP server in scope
• Agent Gateway for traffic control and screening
• Agent Observability for production monitoring
• Agent Simulation for pre-deployment testing
• Agent Evaluation for measurable performance against benchmarks
• Agent Runtime with sub-second cold start
• Agent Inbox for human oversight of long-running agents
• Agent Studio as the low-code builder
• Agent Development Kit (ADK) across Python, Go, Java, with TypeScript

Sitting alongside this is the new Knowledge Catalog, which aggregates native context from partner data platforms and applications including Salesforce Data360, SAP, ServiceNow, Workday, and Palantir into a single accessible layer for Gemini agents.

That layer matters. It is the third leg of the orchestration story alongside interface (Gemini Enterprise app, Slack, Workspace) and runtime (Agent Platform). And physics teaches us that a third leg creates stability.

The Agent Marketplace and Agent Gallery surface partner-built agents directly inside the Gemini Enterprise app, with an IT-driven request-and-approval governance model. Open protocols carry the connective tissue: A2A, A2UI, and MCP, all positioned as neutral interoperability standards rather than proprietary lock-in.

This is the playground Google built. It is not a naming change. It is Google trying to redraw the enterprise AI battlefield.

Not by owning the CRM.

Not by owning the ERP.

Not by owning ITSM, HCM, marketing automation, or the system of record.

But by embracing them all.

It is Google Cloud repositioning from “AI development platform” to enterprise agent control plane.

Now look at who joined this merry party.

The seven partnership announcements, decoded one by one.

Salesforce

Agentforce Sales is in open beta inside Gemini Enterprise. Slack hosts Gemini Enterprise as a private preview app. Agentforce gets native Gemini reasoning through Atlas Reasoning Engine, with multimodal support across text, image, and video. Zero-copy access to Google Lakehouse is on the late-2026 roadmap. New BigQuery connectors for Salesforce Informatica IDMC are available now. Pepkor reportedly consolidated 64 million customer profiles down to 24 million using Salesforce Data 360 plus BigQuery, a 25 percent personalization reach lift. The framing from Srini Tallapragada is "agentic interoperability".

The translation is simple: Salesforce is letting Google become a distribution and work-surface partner for Agentforce, while Salesforce keeps the customer-data gravity and Atlas Reasoning Engine. Slack is the part of the deal that helps Salesforce most. Google Workspace and Gemini Enterprise are too big to ignore. This move is consistent with the recently announced Headless 360.

The tension that nobody named on stage is nevertheless there. If a buyer ends up with Agentforce on one side and Gemini Enterprise on the other, who governs the agents, where do they run, and which vendor gets paid for the orchestration? That fight is coming. Get yourself some popcorn!

SAP

SAP's announcement was imo the most strategically interesting of the week, and worth having a deeper look.

SAP Business Data Cloud (BDC) Connect for Google enables bidirectional zero-copy data sharing between SAP and BigQuery. Cortex Framework metadata in BigQuery grounds Gemini agents in SAP enterprise context. Joule Agents in SAP CX become deployable inside Gemini Enterprise. SAP Engagement Cloud picks up agentic capabilities for content development, marketing briefs, visual concepts, and collaborative multi-agent execution. Marketing is the first GA use case in H2 2026, with the model designed to extend across the SAP CX portfolio over time.

The headline from SAP itself describes Gemini Enterprise as "central hub for data integrations and multi-agent coordination”. On the surface, that is a vendor conceding the orchestration layer.

It is not. Read it again.

SAP is not handing over the operational core. SAP is making sure the Gemini agents that buyers run cannot meaningfully execute against enterprise data without going through SAP's very own grounding layer. Cortex Framework metadata in BigQuery is the move that matters. It means the semantic context for "what a customer record actually means in this enterprise" runs on SAP's side. Google gets the AI execution layer. SAP gets to stay the meaning layer.

That is SAP looking stronger, not weaker. The friendly stage handshake is going to turn into a knife fight in the field about where business logic lives. SAP appears to have positioned itself well for that fight.

ServiceNow

ServiceNow AI Control Tower integrates with Gemini Enterprise Agent Platform so that every agent and MCP server across both platforms appears in a single governed registry. Now Assist for IT Operations Management is available through Gemini Enterprise, focused on alert and incident management. Joint solutions ship in three industry domains: 5G autonomous network operations, retail predictive maintenance, and IT systems, all using ServiceNow agents and Gemini agents handing off through MCP and A2A. ServiceNow took home four 2026 Google Cloud Partner of the Year awards, including Agentic AI Innovation.

John Aisien positioned the agreement as "open, interoperable platforms, not walled gardens”. This framing is doing real work, and it is also strategically necessary. ServiceNow is the workflow titan most directly in Google's strategic crosshairs. Both companies want to be the orchestration layer above all systems. This partnership smooths the surface. Still, the strategic overlap is significant.

ServiceNow's strongest argument remains: "We already run the workflows, approvals, incidents, assets, service models, and operational context. Don't bolt orchestration on top. Run it where the work already lives". Google's counter is: "We can orchestrate across all of you, including ServiceNow". Both arguments are valid, and both are strong. Buyers will pick based on what they value more, neutrality across systems or depth inside the workflow platform that already governs work.

Partners today. Rival underneath. Both are true.

Oracle

Oracle's announcements were broader than the Database Agent that most coverage led with. The full set: Oracle AI Database Agent for Gemini Enterprise (currently in preview on Google Cloud Marketplace), a Managed MCP Server for Oracle workloads (also in preview), Database Center integration, Knowledge Catalog integration, GoldenGate integration, VPC Service Controls. Oracle AI Database@Google Cloud is now available across 15 regions with more to come.

Business users can query Oracle data in natural language without writing SQL. Identity propagates from Gemini Enterprise to the database via OAuth. Oracle's Deep Data Security enforces row- and column-level access at the database layer. Query processing stays inside the database, which Oracle frames as a security and latency benefit, and which has the side effect of keeping Oracle's data gravity intact.

Oracle is doing what Oracle has always done well. Make sure its database estate is unavoidable. Give Google enough access that the partnership is real. Do not pretend Oracle is going to own the AI front end. The Managed MCP Server, Knowledge Catalog hookup, Database Center, and GoldenGate pieces all point in the same direction: Oracle data stays central to AI execution regardless of where the agents are built; and the data stays in Oracle.

The database does not need applause. It needs to remain indispensable. Mission accomplished, I'd say.

Adobe

Adobe Marketing Agent for Gemini Enterprise lands in Google’s Agent Gallery. It connects natural language queries to Adobe's CX agentic capabilities, with insights on campaign performance, audiences, and journey monitoring, accessible from inside Gemini Enterprise. The integration is more lightweight than the others, which is consistent with Adobe's pattern.

Adobe benefits from showing up in the Gemini work surface, especially when marketing teams already live and breathe inside Workspace and Slack. But this is a distribution play, not a control-layer move on the level of SAP, Salesforce, Oracle, or ServiceNow. Adobe joins the gallery without conceding much architecturally and without claiming a piece of the orchestration plane. This is consistent with the Adobe Summit messaging.

Workday

Workday shows up through the Sana Self-Service Agent, which summarizes information from Workday and other sources and handles HR and finance tasks across hundreds of skills covering pay, time, and absence. Workday is also part of the Knowledge Catalog third-party context aggregation.

Workday is playing the employee-service and finance/HR productivity angle. It’s useful, sticky, high-volume in daily user activity. Compared with SAP and ServiceNow, it is narrower in operational control. Compared with Adobe, it is comparable in scope. Workday had a solid presence at this event, not a strategic re-positioning.

Palantir

Google says that Palantir is adding Gemini and BigQuery integrations for commercial customers, connecting models to critical AI workflows and operations. Palantir is also part of the Knowledge Catalog third-party context aggregation.

Palantir is the awkward guest at the titan table. It’s not a classic business application vendor but increasingly competing at the operational decision layer with Foundry and AIP. Google wants Palantir workloads close to BigQuery and Gemini. Palantir wants model optionality without losing AIP control. The integration is real and worth tracking precisely because Palantir does not usually settle for being an ingredient.

The pecking order this event produced

Most strategically advantaged: Google Cloud. It created the playground. The full agent control plane (Identity, Registry, Gateway, Observability, Simulation, Evaluation), the Knowledge Catalog, and the Marketplace make Google the layer everyone else runs on. Google’s risk is that it wants to be the enterprise control plane without owning the transactional cores that SAP, Salesforce, Oracle, ServiceNow, and Workday control. That requires relentless execution, not keynote poetry.

Most durable titan: SAP. SAP owns the operational core. The combination of BDC Connect and Cortex Framework gives SAP a stronger bridge into Google's AI without surrendering enterprise meaning. SAP's posture is "use Google's AI, but ground it in SAP business truth". That is the right defense and a subtle offense at the same time.

Most interesting tension: Salesforce. There's a great integration story today. The unresolved question is whether Agentforce and Gemini Enterprise will eventually compete for governance and orchestration once buyers are running both in production. And they will. Procurement will notice when both vendors invoice for the same workflow.

Most direct control-plane rival: ServiceNow. A visible partner. And an architectural competitor. The "open, interoperable" framing is correct in principle, and it is also the terminology a vendor uses when its core product overlaps strategically with the platform it just partnered with. The deciding factor for buyers is whether they want a neutral AI control plane above systems, or agentic execution inside the workflow platform that already governs work.

Most pragmatic: Oracle. No applause needed. The database stays indispensable. Managed MCP Server, GoldenGate, Knowledge Catalog hookup, VPC Service Controls all point in the right direction for Oracle. That’s pragmatic, and dangerous in the right way.

Useful but narrower: Adobe and Workday. Both gain Gemini Enterprise distribution reach. Neither announcement changes their strategic center of gravity. There is nothing earthshattering about them. They are domain wins, not control-plane bids.

Adjacent: Palantir. This is worth watching specifically because Palantir does not usually accept ingredient status.

The two missing names

Microsoft. Copilot exists exactly to defend the position Google is now contesting. Microsoft has spent two years building Copilot Studio, Microsoft 365 Copilot, Dynamics 365 agents, and an Azure-side AI tooling that competes head-on with what Google just announced. Microsoft was never going to show up at Cloud Next to validate Gemini Enterprise. Why would it?

The more interesting question is whether Salesforce, SAP, ServiceNow, and Oracle agents will sit as comfortably inside Copilot in twelve months as they now do inside Gemini Enterprise. Right now, the answer is no, and the gap appears to be widening. I expect Microsoft to respond at Build and Ignite. The main question then is whether the response will be "we have parity" or "we are bigger and we will route around you”.

Zoho works a different market segment. Zoho also builds its own AI stack. The company rarely participates in this kind of big vendor partnership theater. The absence is consistent and not so interesting when looked at in isolation. It becomes interesting, however, when paired with the observation that Zoho's mid-market and SMB-plus customers are largely outside the buying pattern Cloud Next 2026 is shaping. Two different conversations happening in two different rooms.

Open protocols, not walled gardens. Maybe.

I want to be careful about reading the "open, interoperable" framing.

A2A, A2UI, and MCP are all real, and they matter. ServiceNow's positioning is correct in principle. Salesforce kept Slack. Oracle kept the database. SAP kept Joule as the engagement layer in SAP applications. Adobe kept its CX stack untouched. Workday kept HR. Palantir kept AIP. None of these vendors handed over the asset they care most about.

But the registry is Google's. The gateway is Google's. The gallery, the runtime, the inbox, the identity model, the agent governance plane: all Google. Open protocols are not the same thing as a neutral platform. They are the price of admission to a platform that acts as a host.

The real question for the next twelve months is whether the protocols stay open enough that buyers can swap the host. If a customer can take A2A-compliant agents built around Gemini Enterprise and re-host them on Copilot Studio or AWS Bedrock AgentCore without rewriting most of the orchestration, the open framing holds. If swapping costs are high in practice, "open" is doing marketing work that the architecture does not back up.

I do not yet have any evidence either way. I expect to in the next two quarters with the first multi-vendor pilots moving into production.

What buyers should do this quarter

Force each titan to defend the front door. Salesforce will say Slack and Agentforce. SAP will say Engagement Cloud and Joule. ServiceNow will say ServiceNow. Microsoft will say Copilot. Google will say Gemini Enterprise. Make them defend the answer with specific cross-system workflows for agentic work. Note which vendor accepts being an ingredient in someone else's interface and which one fights for the seat.

Pin down the dates on zero-copy commitments. The Salesforce zero-copy with Google Lakehouse is late 2026. SAP BDC Connect is rolling out across 2026. Oracle's Managed MCP Server is in preview. Most of the headline-friendly capabilities are not in your tenant today. Build your 2026 plan around what you can run by Q3, not what is on a slide.

If you are a Microsoft shop, run a parallel evaluation. Bring in Copilot Studio. Ask whether Salesforce, SAP, and ServiceNow agents are first-class citizens inside it at the depth that Cloud Next demonstrated for Gemini Enterprise. Force Microsoft to demonstrate parity, not promises.

Treat the Agent Marketplace and Agent Gallery as procurement infrastructure. If your IT team adopts Gemini Enterprise as the agent procurement layer, that decision shapes which titans show up first in your future RFPs. Make this choice deliberately.

Test the governance question before you buy. If you end up with Agentforce, Joule, Now Assist, and a Gemini-Enterprise-built custom agent all running for the same business process, who governs them? Procurement will notice when you are paying twice for orchestration. Make a vendor own the answer in writing, and see to it that the SKUs do not overlap too much.

The unspoken partnership

The most interesting partnership at Next 2026 was the one nobody put in a press release. Five of seven titans, plus Workday and Palantir, plus a long list of consulting firms and ISVs, are all visibly aligning to ensure that Microsoft Copilot is not the only place enterprise AI gets done. None of them said that. All of their actions imply it.

That is the alliance worth watching.

I am still skeptical about how durable this alignment is once Microsoft responds, and buyer realities surface in the second half of 2026, and once we see what "open protocol" really means in production. All vendors will optimize for their own positions. They always do, and they need to. The orchestration question may stay answered for a year, or it may reopen the moment someone like Microsoft offers a credible alternative.

For now, Gemini Enterprise has the momentum. The titans showed up. The protocols are public. The Marketplace is live. The control-plane components are named. Google moved from "another model vendor" to "the agentic substrate the application titans run inside of”.

This is a very different conversation than the one we were having last year. It’s worth paying close attention to how it evolves and whether it is the right one.

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.

Before immersing myself into SAP Connect 2025, I had a number of questions that I would like to get answered during the event. These included the ones below and naturally focused on SAP’s CX and AI sides of the house. Some of them I got answered, some of them not, at least not explicitly.

• What is the plan to make SAP CX more prominent in the CRM/CX marketplace and what are main reasons that you see for customers preferring other CX solutions over SAP’s?
• What do customers say that they are missing in the CX suite?
• Where do you see the limits of agentic technology in the near to mid-term? Apart from adoption problems …
• And where do you see most potential for agentic AI going forward?
• What are adopted (agentic) use cases that concentrate on business transformation, gaining capabilities, uplift as opposed to “increasing efficiency”?
• How does SAP deal with the dichotomy between “human augmented by machine” and mass layoffs?
• SAP Consulting as well as SIs do face a need to change their business models away from billable hours. What do you recommend SIs do? How does SAP support them in this venture? How do you foresee the overall ecosystem change with an estimated increase of use and deployment of generative and agentic AI

But more about all this in a minute.

Of course, SAP took this event to announce a flurry of new capabilities across its suite of applications, AI, and technology, as evidenced in the long innovation guide and the theme-describing press release for the event, although I’d say that the event went well beyond “AI that partners with people, data that defies boundaries and applications that turn data into action”. To be sure, SAP Connect extended on the vision that got outlined during last Sapphire.

True to its name, SAP Connect was about connecting – or breaking, to use a stronger word – the silos that are imposed by different types of business software, predominantly, but not only, SAP’s business software and in extension the silos between business departments, The approach was to co-host conferences focusing on the different lines of business while providing the connecting tissue by means of cross references, supported by customer testimonials, in the keynotes.

And I want to say that this largely succeeded. Jon Reed wrote a great piece covering this using Heartland Dental as an example. Other customers like Migros or confirmed their success that got achieved through cross departmental collaboration. “Collaboration is a very, very key for service. We promote support cross-functional collaboration, that begins from idea, of course, but all colleagues from marketing, business and also colleagues from the stores but we work also together with other legal entities in our group, for example, of a work together, very tightly with the bank. And so, we work together with our financial colleagues in the context of practical credit card format is an integrated part of our loyalty program and so it's very important to get them common understanding and the common understanding is also based on a common technology. Baseline we work on a common data Lake. We have Cloud platforms, and this is also the key for a common understanding, for a 360-Degree of our customer view”. Similarly, Wella. Both of them part of the CX keynote.

Or, looking at supply chain processes, SLB, Red Bull and HP. All of them look at cross-functional cooperation as a critical success factor.

And these have only been some examples from the keynotes.

This is powerful support for the SAP E2E story. The only way to tell this even more powerful is to actually embed deep references into other lines of business within these key notes, e.g., closing the loop between demand (CX) and supply including finance. Basically, showing how the different SAP portfolios strengthen each other in a synergistical way.

This would emphasize on the power that the integrated capabilities that SAP commands have. After all, there are at best 2 to 3 companies that can dare telling a story like this: Oracle, to some extent Microsoft on an enterprise level, and Zoho with a bit more of a mid-sized focus for now. This is a tremendous opportunity for SAP, but one that requires not only having the capabilities – which SAP has – but also a messaging that does not disregard a crucial part of any business. An end-to-end story works only if it is supported by an equally strong story for each part of the business.

Ok, Connect works, but how about CX?

Having said this, let’s dig into a topic that is dear to my heart. For a long time, I have been saying that SAP treats its CX suite as a kind of Cinderella – the unwanted stepdaughter. The story being a fairy tale, it has a positive end. Does SAP’s CX suite now get the recognition it deserves?

Since last Sapphire, my view is slowly changing. CX appeared in Christian Klein’s Sapphire keynote, albeit not very prominently and had a more significant experience in Muhammad Alam’s keynote during SAP Connect, which you can revisit in Jon’s watch party with Josh Greenbaum and Bonnie Duncan Tinder that covers it extensively including valuable commentary. As said, giving CX a more prominent place is important, as it is a key ingredient of an E2E story and functionally an indispensable part of any business. There also have been significant announcements with a new loyalty solution, an engagement cloud, a digital service agent, WalkMe being made available for the CX solutions and an interesting revenue intelligence app on the SAP Business Data Cloud. My recommendation is to build on this momentum and to also have Christian Klein put more emphasis on CX going forward. No voice gets heard better and lends more credibility than the top dog’s CEO’s (sorry, Jon, I couldn’t resist using one of your techniques here). Having talked to the CP of  SAP CX, Balaji Balasubramanian, SAP CX Head of Product Strategy Riad Hijal and the new SAP CX CMO Jessica Keehn, I see the outlines of a plan going forward. I am keen to observe it come to fruition (and perhaps help, too?). One thing is for sure, during Sapphire and also during SAP Connect, there was an emphasis on becoming category leader where SAP invests. And there surely are considerable investments into SAP CX.

What about AI?

As part of the press release for this event as well as throughout keynotes, Q and A’s and individual discussions with SAP executives, there is one consistent theme: “AI that partners with people”, or in other words humans are getting help from technology instead of being replaced. This is a very laudable messaging although it is doubtful that a vendor, even one as powerful as SAP, is able to prescribe customers what to do with its technology. It is the buyers’ choice whether they follow this philosophy … or not. Alam and other executives repeatedly emphasized that their objective for using their own AI technology is not replacement of people. Instead, they aim at increasing their organizations’ outcomes by making their team members more productive. SAP COO Sebastian Steinhäuser presented some impressive numbers during his keynote. Apparently, SAP made its flywheel of apps, data and AI work for itself. From a limitation point of view, SAP bets big on the Business Data Cloud that is to deliver clean, consistent, reliable data as the foundation for well-working and collaborating agents that “help execute complex workflows within a specific function”.

For SAP, as well as its competitors, the challenge remains to not fall into the trap of explaining major layoffs as a result of the efficiency gains through their AI. Doing so, would destroy the narrative of AI creating business results through being a helper, not replacement, of people, and thereby the credibility of the tellers of this story.

And what about partners?

SAP has always been strong about ecosystem and partnerships. Given, that AI is all about platform, it makes an ecosystem play even more important. However, the ecosystem changes. IP becomes ever more important, especially over integration services. These get increasingly supported by AI, which requires SI’s to rethink their business models. They need to change from IP that “lives” in the heads of consultants and enables billable hours to IP that is encoded into software and AI. This requires them to adapt to survive and to continue to thrive. SAP encourages them to do this change and to create their AI infused solutions. To be frank, I expect the importance of SI’s in the SAP ecosystem to reduce in favor of ISVs. In future, implementations will require less effort and will have a higher degree of automation. Consulting will be again what it was, becoming more strategic again and less about the nitty-critties about implementing the desired solution. Not having had the chance to go in depth into a detailed discussion, this is probably a topic for another article or a few LinkedIn posts.

Famous last words

I think that SAP came in strong during this conference. The flywheel tells a sound story, and SAP is uniquely positioned to make this vision a reality. Are there pitfalls along the way? For sure. Will SAP trip into some of them? Probably. Will it matter? Likely not. For SAP, as well as for other businesses the game to play is adapt and overcome. And this is something that SAP can do. The company has shown it numerous times.

The remaining proving ground remains CX. There is considerable investment, competitive, in some areas even leading functionality. What is missing is, credibility. As one colleague this week told me “As far as I am concerned, SAP doesn’t have CX solutions”. Ouch. That needs to change.

The past weeks certainly brought a lot of news, with SAP Sapphire and Salesforce's surely strategically timed announcement of acquiring Informatica, ranging at the top. I have covered both in recent articles.

The enterprise software landscape is crackling with energy, and Artificial Intelligence (AI) is certainly the star of the show. It isn't anymore about AI as a mere feature; it's about AI as the strategic core of enterprise software. Two recent announcements underscored this shift: SAP's ambitious AI-centric vision that was unveiled at its Sapphire 2025 conference, and, arriving hot on its heels, Salesforce's agreement to acquire data management titan Informatica for $8 billion. Both signal an intensified battle for AI supremacy, where trusted, enterprise-wide data is the undisputed new monarch. Of course, SAP and Salesforce are not the only ones duking this one out.

SAP's Sapphire Vision: An AI-Powered, Integrated Enterprise

At its Sapphire 2025 event in Orlando, SAP laid out a sweeping vision for an AI-driven future. The central themes resonating from Sapphire were "AI everywhere" and "the AI flywheel", with intelligence deeply woven into its integrated suite of applications and powered by the SAP Business Data Cloud as a strong, unified data layer.

Joule, SAP's AI copilot, is slated to become "omnipresent," extending its reach not only across the entire SAP ecosystem but also into third-party applications. It's designed to proactively assist users and launch autonomous "Joule Agents" to automate a wide array of workflows. SAP has ambitious plans to significantly expand its library of these specialized agents. Underpinning this is the SAP AI Foundation that includes Joule Studio for agent development, a Knowledge Graph for data exploration, and an AI Hub for observability and governance. This shall effectively create an "operating system for Business AI".

The critical fuel for this expansive AI engine is the SAP Business Data Cloud (BDC). Conceived as a unified data fabric, BDC integrates SAP Datasphere, Databricks (via a key partnership), SAP BW, and SAP Analytics Cloud. Its mission is to harmonize disparate SAP and non-SAP data streams, thereby preserving the all-important rich business context inherent in this data through the SAP Knowledge Graph. BDC represents SAP's strategic answer to providing the "nonnegotiable data foundation" that is essential for Business AI to work reliably.

Complementing its AI and data strategy, SAP is vigorously promoting a "Suite-as-a-Service" model. This involves best in class "second to none" line-of-business-specific Business Suite packages (e.g., for Finance, Supply Chain, CX) designed to offer managed integrations and promote extensibility via the SAP Business Technology Platform (BTP), all while enforcing a "clean core" architecture. This is SAP’s strategic push to encourage cloud migration, particularly through RISE with SAP and GROW with SAP, arguing that an integrated suite provides superior intelligence when AI is the objective.

Salesforce's $8 Billion Data Play: Towards an AI-Ready Infrastructure

Just as the industry was digesting SAP's comprehensive Sapphire pronouncements, Salesforce unleashed its own blockbuster: a definitive agreement to acquire Informatica for approximately $8 billion. Slated to close in early fiscal 2027 (calendar year 2026), this is far more than a typical M&A deal. It's a calculated, strategic power-play designed to create what Salesforce CEO Marc Benioff describes as "the ultimate AI-data platform — trusted, explainable, and built to scale".

Informatica brings a rich portfolio of enterprise-grade data integration, quality, governance, metadata management, and Master Data Management (MDM) services into the Salesforce fold. The plan is to deeply embed these capabilities across Salesforce's ecosystem, supercharging flagship offerings like Agentforce, Data Cloud, Tableau, and MuleSoft. Informatica's ETL capabilities, albeit competing with Mulesoft, are a nice bonus. The explicit aim is to establish the most complete, agent-ready data platform in the industry, providing the critical foundation for AI agents to operate safely, reliably, responsibly, and at scale. This is addressing a crucial gap in Salesforce's data infrastructure and a continuation of the company's hard pivot to become an AI-first company, moving beyond its CRM origins. It’s also a clear acknowledgment that for the grand vision of enterprise AI, CRM data in isolation is insufficient; a holistic, enterprise-wide data perspective is essential.

However, this bold gambit is not without potential pitfalls. A key concern is whether Informatica can maintain its prized neutrality — its reputation as the "Switzerland of data"—now under the Salesforce umbrella, especially as Salesforce increasingly competes with other major cloud and application vendors. And then, there is Informatica's own far-from-complete cloud transition, which adds a layer of complexity to its integration. Additionally, Salesforce's track record with integrating large acquisitions has seen its share of challenges, making a careful migration of Informatica’s assets and ecosystem paramount. 

The New Battleground: Data & AI Dominance 

Salesforce's acquisition of Informatica, announced shortly after SAP's Sapphire event, throws the contrasting philosophies of these two enterprise software titans into even sharper relief. While SAP's announcements were not a direct response to a then-unknown deal, the acquisition undeniably alters the competitive terrain SAP was addressing.

Divergent Data Strategies: Salesforce is buying comprehensive data management capabilities through Informatica, prioritizing speed and breadth to quickly bolster its AI-data platform. SAP, in contrast, is primarily building its Business Data Cloud organically, aiming for deep semantic understanding and contextualization of the vast quantities of enterprise data that already flows through its core systems. Salesforce gains immediate access to broad, enterprise-grade data tools; SAP seeks to leverage its deep incumbency and process knowledge for contextually rich data insights.

Agentic AI Ambitions: Both vendors are making massive bets on agentic AI. Salesforce's Agentforce, now set to be powered by a significantly enhanced data backbone via Informatica, aims to inject greater intelligence into its CRM and associated cloud offerings. SAP’s vision for Joule and its expanding ecosystem of Joule Agents targets the automation and optimization of processes across its entire enterprise suite – encompassing finance, supply chain management, HR, procurement, and customer experience.

Impact on CRM & ERP: The Salesforce-Informatica combination promises a more potent Customer 360, capable of unifying customer data from a wider array of enterprise sources to deliver more personalized experiences.5 SAP, meanwhile, is focused on embedding AI deeply within its ERP core (S/4HANA) and bolstering its Customer Experience (CX) solutions, all fueled by the Business Data Cloud and its integrated suite strategy. In essence, one company wants to get out of the CRM "niche" while the other one wants to strengthen its moat and get back into this very sizable niche.

The primary challenge for Salesforce will be the seamless integration of Informatica's technology and culture, alongside the crucial task of maintaining Informatica's perceived neutrality in a competitive market. For SAP, the onus is on accelerating widespread cloud adoption among its vast customer base and demonstrating that its Business Data Cloud is not only powerful but also genuinely open and agile enough to meet modern enterprise needs.

Navigating the AI-Driven Future

This intensified competition and rapid technological evolution demand proactive engagement and strategic foresight. Here are key considerations:

Data Is King (Now More Than Ever): Both Salesforce's acquisition and SAP's BDC strategy hammer this home: a robust, well-governed, high-quality data foundation is the absolute bedrock of any successful AI initiative. Now is the time to critically assess your organization's data readiness.

Brace for Accelerated Innovation & Competition: The pace of change is quickening. Expect faster product development cycles from these two and other vendors and potentially more market consolidation as platform players race to build out their end-to-end data-to-AI stacks. This will likely lead to better solutions but also increased complexity in vendor selection and management. It also makes platform decisions more crucial than ever.

Agentic AI Is on the Horizon: Prepare for a paradigm shift where AI evolves from an assistant to an autonomous actor capable of executing increasingly complex business tasks. This has implications for process automation, governance structures, and workforce planning.

The Suite vs. Best-of-Breed Debate Intensifies: SAP's "Suite-as-a-Service" model and Salesforce's increasingly comprehensive platform bring this long-standing debate back to the forefront. Carefully weigh the allure of deep integration against the risks of vendor lock-in, and meticulously scrutinize claims of openness and interoperability.

For SAP Customers—The Cloud Imperative Looms Larger: SAP's most advanced AI capabilities are architected for its cloud-native platforms. The message from Sapphire is clear: modernizing to S/4HANA Cloud with a "clean core" is increasingly positioned as essential to fully leverage SAP's innovation roadmap.

Trust, Governance, and Ethics Are Paramount: As AI systems become more powerful and pervasive, a focus on robust data governance, ethical considerations, data security, and transparency is more important than ever to make them work.

The Road Ahead

The race for enterprise AI dominance is well and truly underway, and the stakes are getting ever higher, for vendors and buyers alike. Salesforce's acquisition of Informatica is a bold, data-centric offensive maneuver. SAP, through its comprehensive Sapphire announcements, has clearly articulated its own integrated, suite-driven path to an AI-powered future. The coming months will be interesting as they will reveal how these colossal strategies unfold, how effectively they are executed, and ultimately, how they will reshape enterprise software. For businesses navigating this dynamic environment, agility and informed decision-making will be key to harnessing the transformative power of AI.

The news

SAP just held its annual Sapphire event in Orlando, FL. It is totally under the theme of uncertainty and how technology, in particular SAP’s technology, can help businesses steer through poorly charted waters, to use a nautical metaphor. Uncertainty is caused by evolving regulatory situations, tariffs and shipping delays with their impact on the supply chain, waning consumer confidence in the light of all of this, and of course, the big gorilla in the room: AI. SAP’s response to this is the “SAP Flywheel”, which consists of three components

• Applications, of which SAP commands the broadest portfolio amongst all business applications vendors
• Data, which all these applications create, which in turn gives SAP extensive access to semantically rich business data
• AI, which analyses all this data, makes it actionable and, in turn feeds it back to the applications, closing the loop to establish the flywheel.

SAP demonstrated how this works in a scenario that showed how a C-suite consisting of a CFO, CRO, COO and CHRO use the integrated SAP suite with embedded AI to rapidly respond to new tariffs, managing compliance, assessing financial impact, developing strategies, adjusting supply chain plans, and aligning people strategy, all based on unified data.

Key announcements are based on the concept that AI is changing how businesses, and therefore its business applications, operate. Supporting this, is the purpose of SAP’s business AI, which has its foundation in SAP’s Business Technology Platform, BTP. A centerpiece of this change in how businesses – and users – operate is Joule, which is embedded (or will be soon) in all SAP applications and being expanded into non-SAP applications with the help of SAP’s recent WalkMe acquisition to support tasks. On a process level, SAP announced the availability of and the fast delivery of a big number of new Joule Agents. To support businesses and users, all this is underpinned by the AI Agent Hub which is powered by LeanIX to control and govern the deployment of agents.

On the partnership side, SAP announced cooperations with

• Palantir to develop new real-time analytics use cases,
• Adobe, which builds an intelligent application to combine SAP ERP Data with Adobe Experience Cloud data to enable more accurate financial planning and marketing activities
• Not Diamond, to eliminate prompt engineering by automating it (NB: gen AI is eating its own children already)
• And most notably, Neurobotics and Nvidia to connect Joule Agents to the physical world by combining it with (humanoid) robots.
• Functionally, SAP committed to aggressive innovation in all application domains,  including Finance, Spend, Supply Chain, HCM, and Customer Experience, aiming to be best in class, “second to none”, as Muhammad Alam put it.

The bigger picture

AI, in particular agentic AI is in every vendor’s messaging, these days. This is an inevitable consequence of the hype that we are still in. AI-based technology may or may not help businesses navigate the treacherous waters that the current economic uncertainties are, but then all business action creates data, and AI is able to speed up the process of making sense out of this data and to come to informed decisions. This is the most visible reason why many vendors now change the narrative to also include a “data cloud” and/or a “data fabric” as a fundamental part of their software stack.

The other big topic, connected to the first one, is the “death of SaaS” and the degradation of ERP systems to mere data repositories with the business logic being overseen, orchestrated and executed by interacting AI agents. This is essentially the narrative of businesses that are strong on agentic and/or workflow but weak on ERP and it will be interesting how this narrative pans out in the next months.

My point of view and analysis

Let’s start with the keynote itself. I always have a challenge with artificial handovers between speakers or repeating the term “amazing” in a kind of unimpressed tone of voice, and frankly, we have seen more than our share of both in the approximately 2 hours that the keynote lasted – which in itself is pretty much on the long side. Don’t believe me? Watch the slightly edited version on YouTube for yourself (btw, why is there a need to edit it at all? This only reduces authenticity). From a delivery side, I think that Dr. Philipp Herzig performed best, showing mostly credible excitement. And delivery is a thing. Look at other keynotes of enterprise software vendors, like Salesforce or ServiceNow. I agree with Paul Greenberg and Brent Leary, when they say that the keynote is the one opportunity that a company has to excite the attendees. The other thing that struck me is the nearly total absence of topics like sustainability (mentioned in brief as part of the product name Sustainability Tower) or other non-monetary values that have been expressed by SAP in earlier years. While this is understandable to an extent, it raises the question whether these values have been merely lip service. To be sure, SAP is in good company with this – which doesn’t make it better.

Functionally, nearly everything revolved around AI and AI agents in one shape or another. Like most vendors, SAP positions the AI as a helper to employees, developers and consultants, that drives their productivity. SAP’s objective is thirty percent, which I consider low for some SAP user groups – like developers and consultants. Still, I caution about positioning of a tool as a helper and how it is going to be used by buyer organizations. This is something that is beyond any vendor’s control.

Still, I think that SAP has what it takes to help businesses become far more efficient. The innovation behind all that SAP showed during Sapphire is nothing short of mindblowing. And, it is credible, too, as it all relies on business data, which are abundantly available via the whole, often SAP driven, enterprise value chain. The data platform, given by the SAP Business Data Cloud and SAP Graph, can bring the unified data that is necessary for effective agent deployments to life.

More interestingly, SAP launched a full-fledged counterattack on the “SaaS is dead” and “the ERP is only a data repository” narratives of some vendors by demonstrating the ubiquitousness of its own agent, Joule and emphasizing on application logic being just that – logic – combined with already built-in privacy, security, integrations, compliance etc., that are on top of the data and give the necessary business context. Emphasizing on this while showing the ability to orchestrate Joule agents across platforms is quite a powerful message. Yes, orchestration is necessary in a process automation world that is increasingly dominated by agents (whether this is useful or not). With SAP demonstrating this capabilities, other vendors need to have very good arguments if they want to sell an agentic automation platform that has the same already built in.

SAP’s ambition to be best in class in all application domains is probably already achieved in many areas. One of them where it is not, is CX, although SAP put a huge investment into its new Sales- and Service Clouds, which technologically probably are the most modern apps around. And then, there is commerce, where SAP seemingly has lost its edge and marketing that was notably absent during the keynote – apart from the partnership with Adobe. Summing it up from a CX perspective – SAP has a long way to go before being recognized as a key CX player, let alone being “second to none”. And this recognition starts with C-level executives speaking more about it, which is another chance that SAP missed out on during this keynote. The CRO persona in one demo was about analytics and how management can be supported, which is not what CRM/CX is – or at least should be – about. And everything else was about finance, HR and supply chain.

Having said this, SAP’s CX solutions probably are amongst the best – but what is that worth if SAP doesn’t proudly position them? In addition, not doing this weakens the – else incredibly strong – end-to-end story that SAP justifiably tells. So, the easiest way to bolster this story is to leave the impression that CX is SAP’s Cinderella behind by incorporating more of it in the messaging.

All in all, SAP delivered quite a strong positioning a suite vendor. The moat around its ERP solution got far stronger, also considering commitments around becoming easier to deal with. The remaining piece seems to be to prove how SAP is or becomes “second to none” in the CX market or a subset of it.

Cloud Wars’ Bob Evans recently did an excellent and very interesting interview with SAP CEO Christian Klein about SAP’s priorities, which include integrating generative AI with SAP Business AI “to address complex business challenges an drive holistic transformation by optimizing processes like quote to cash”.

Klein repeatedly referred to end-to-end (E2E) and SAP’s great library of E2E processes that gives the essence of or at least a standardized framework for the value streams within a business.

Not surprisingly, and correctly so, Klein also repeatedly emphasized the value of AI and, in particular, generative AI, to create customer value. This happens via Joule’s ability to orchestrate different agents across the value chain, i.e., different E2E processes. Joule is SAP’s Ai assistant. He also emphasized on the value of the suite and on the importance to “in the core business” not run with “agents of 100s of different tech companies”. This is where “the suite is winning”.

Evans writes that SAP had “significant growth in applications, outpacing competitors. Klein attributes this to SAP’s suite approach, which provides a comprehensive solution for core business processes. He talks about the importance of integration and extensibility, allowing customers to choose the best solutions for their needs.” This is technically a correct statement. I am fairly sure that SAP will report another outstanding year on January 28, 2025. In the first three quarters of FY 2024 SAP certainly outpaced the cloud business applications competition, including Salesforce.

However, there is a caveat to it. This growth is largely attributable to S/4HANA cloud.

Don’t get me wrong, doing this is no mean feat. SAP profitably grows while transforming from an on-premises vendor to a cloud and AI vendor of business applications. This is big as it essentially means that the company cannibalizes its own cash cow on the way.

Klein also positions SAP as an enabler of holistic business processes and an E2E solution provider. Again, this is correct. Heck, I have been inside SAP for more than a decade and am observing the company even longer since then. And for the record, I have nothing than admiration for this awesome company - well, apart from a bit of sadness perhaps.

Why sadness you ask?

MyPoV

SAP has the smarts and the muscle to pull off and execute on an enterprise applications strategy that Christian Klein describes it in the interview with Bob Evans. Even more so in what I would call the dawn of AI. SAP is actually one of the very few companies on this planet that has the ability to pull off a stunt like this, especially while also finalizing its own transformation to a cloud (and AI) vendor.

However, SAP suffers from a major blind spot and that is its less than credible effort on the demand side of the E2E processes. SAP says that the suite is winning because of the AI side, yet it belittles the CRM/CX part of this suite.

Cases in point: With hybris, SAP acquired in 2013 the then leading B2B and B2C ecommerce platform. At this time and for some more years, both the B2B- and the B2C variants marked the top right end of the corresponding Gartner Magic Quadrants. Now, they are barely within the leader quadrant. I will not discuss the value of these reports here. The other one is the biannual IDC report on CRM market share. In the summer 2024 report, SAP held the 5th spot. Now, I hear you saying fifth ain’t bad, and certainly it sounds good. Until you remember that they have been second and you look at the current third place: Adobe. Why is this staggering? Well, Adobe is great in marketing and and good at e-commerce. Now, one can dispute two things here: One can say that e-commerce is not part of CRM and the value of the report. Again, no point for discussion here. 

The Emarsys acquisition with the subsequent announcement of sunsetting the SAP Marketing Cloud directly after massive investments into making its Marketing Cloud B2B ready. Well, Emarsys, as the vast majority of other marketing solutions is a B2C solution, albeit a good one. However, there is no migration path, but a reimplementation. In addition, SAP’s core clientele is B2B. Or, how about Salescloud V2 without a migration path from v1? Again, I do not say that these decisions have been wrong, but they shine a light.

Some more personal examples: In 2017 and 2018 SAP had the good idea to hire some salespeople from Adobe, to push its marketing solution. One of them told me that he had a hard time getting to talk to his former client and prospect contacts “because SAP doesn’t have a marketing solution”. Well, it had … To close off with this topic, around 10 years ago, SAP managed to oust itself out of the CRM market in Australia and New Zealand. Why? Strategy: SAP CRM was unsellable at that time. The cloud products weren’t quite ready by then. When they were ready, salespeople weren’t quite incentivized for selling them. The result, when this was finally corrected? See above. 

Why do I bring all this up?

A solid strategy in the CRM and CX markets looks different. And with that, there is a gap in the E2E story that Klein tells. A big one. And this gap extends into the strategy.

Why? Glad you asked. Let me explain.

The brief version is that it needs a lot of demand side aka CRM/CX data to train and then inform the AI. It is good to have an abstraction layer on top of other vendor’s data structures but that’s not enough as it complicates the software stack, which makes it more expensive, which reduces its value. SAP has all it needs and just needs to take more advantage of it.

Again, I hear the resounding ‘Why? SAP has a very strong position in the very attractive ERP market, which it needs to defend and extend’. This is true, while arguable the CRM and CX market meanwhile is bigger and faster growing than the ERP market.

However, if I compare ERP with the castle that SAP defends, its defensive system has a couple of holes while this castle is under ‘attack’ from different angles.

The simple truth is that SAP needs a strong and credible customer facing systems strategy and systems to stay credible and successful in the enterprise software market. Or else, there is the risk of becoming a niche vendor. Yes, you heard it right, I just called the ERP market a niche of the enterprise software market.

Now, what are these angles that I talked about?

• There are tier 1 vendors that offer a similarly broad and wide functionality while having more credibility in the CRM and CX market. These vendors are Oracle, and to a lesser extent, Microsoft.
• There are tier 1 vendors that, on top of delivering credible enterprise software, also have a strong infrastructure play. These are Oracle and Microsoft.
• There are tier 1 vendors that come from SAP’s open CRM and CX flank and offer a land-and-expand strategy via their AI. These are Salesforce, and to a lesser extent, Adobe.
• There are tier 2 vendors that offer a similar functional breadth - not necessarily depth. Some of these are NetSuite, ServiceNow or Zoho.

All of the above mentioned vendors, and some more that I did not mention here, have strong platforms, ecosystems, and LC/NC capabilities. More importantly, they offer quite strong AI capabilities. 

And AI platforms are the next battleground in this iteration of the Clash of the Titans. That’s why belittling CRM and CX software is a problem. Today, more than ever, enterprise software has become a platform game. And SAP is inviting strong and aggressive competitors in its house, its customer base, by leaving this corner open. From then on platform ownership at the customers means a fight for the data, i.e., an AI competition. It is not likely that customers want to run multiple different AI platforms, especially if they are integral parts of the business functionality - or worse, the business functionality is made integral to the AI platform, which is similar to what Satya Nadella suggested by reducing ERP to a CRUD layer on the database.

In this situation, the decision to harmonize on one of the available platforms lies only one little step away. After all, the introduction of AI is all about efficiency at this time. Why pay twice for very similar capabilities while using only one?

And it is unlikely that all these decisions are in favor of SAP.

The good news is that this challenge can be addressed quite easily by SAP.

Stop belittling your own CRM and CX chops

Again, SAPs E2E and AI stories are generally strong! Still, they can be made more credible and convincing.

I suggest three fairly simple actions that can be initiated by SAP to strengthen the own position

• The C-suite needs to talk more about CRM and CX to create a signal. Quote to Cash is great. Everyone believes that SAP’s ERP solutions can do this well. How about using more examples from Market to Order? It is not enough when the head of the CX division rings the CX bell. This is just noise.
• Work more and more consistently with the analyst and influencer community. This process, admittedly, seems to have started. Still, the perception of SAP amongst this community is between not good and abysmal. Turning this community helps turning market perception. 
• Evaluate and then offer more CRM core functionality as integral parts of S/4HANA. The more relevant CRM is in S/4HANA, the less need there is for SAP customers to shop elsewhere. The difficulty lies in finding the right mix to not cannibalize the CX division.

There we are. I said it. Am I wrong? Happy to discuss! Tell me what you think!