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.

The News

On February. 25, 2026, Salesforce announced a pricing and metrics update. During the company’s Q4 FY2026 earnings call, CEO Marc Benioff, together with CMO Patrick Stokes, unveiled the Agentic Work Unit (AWU). Positioned as a metric to quantify the labor performed by autonomous digital systems, Salesforce defines an AWU as one discrete task accomplished by an AI agent.

According to Salesforce, this discrete task represents the exact moment "raw intelligence is converted into real work". It is not a fixed unit but measured as a processed prompt, a completed reasoning chain, or an invoked tool. Salesforce explicitly designed the AWU to move the industry conversation away from the raw consumption of Large Language Model (LLM) tokens. As Benioff noted, tokens only measure "how much an AI talks," whereas the AWU is intended to measure actual business execution.

The scale of this rollout is massive. Salesforce reported that its platform has already processed over 19 trillion AI tokens, translating them into 2.4 billion Agentic Work Units, with 771 million AWUs delivered in the fourth quarter alone. This new metric serves as the underlying foundation for Salesforce's evolving Agentforce monetization strategy.

The bigger picture

Following a nearly 18-month period of pricing triangulation, which included a $2.00 per conversation model and a $0.10 per action "Flex Credit" model, Salesforce is leveraging the AWU to track system utilization, even as it wraps enterprise purchasing in familiar, unmetered per-user license agreements starting at $125 per user per month.  

To understand the significance of the Agentic Work Unit, one must view it through the lens of a broader industry crisis: the so-called "SaaSpocalypse" and the looming threat of the seat cannibalization trap. For two decades, the Software-as-a-Service business model has been dominated by seat-based licensing. However, as agentic AI systems mature and promise to be capable of executing multi-step workflows autonomously, they inherently reduce the need for human software operators. If an AI agent resolves 84% of tier-one support tickets without human intervention, the enterprise requires fewer human support seats. I have repeatedly written about pricing models, e.g., here, as part of my CustomerThink column.

This dynamic has forced the software industry into a frantic transition toward usage-based and attempts at outcome-based pricing models. Usage-based pricing, popularized by cloud infrastructure providers like AWS and data platforms like Snowflake, charges customers based on system consumption, e.g., compute seconds or data processed, or, these days, tokens consumed. While this protects the vendor's margins and aligns with their variable cloud GPU costs, it shifts the financial risk of system inefficiency entirely onto the buyer. The vendors essentially play the role of Pontius Pilate and wash their hands in innocence.

Conversely, agile AI disruptors and customer service incumbents are aggressively pioneering true outcome-based pricing, where the billable event is delayed until a verified business success is achieved. For instance, Intercom's Fin AI agent charges a strict $0.99 per successful resolution, while Zendesk recently started a $1.50 per automated resolution model. In these models, if the AI fails to resolve the customer's issue, the customer pays nothing. Correspondingly, the vendor has skin in the game and needs to be interested in its software actually delivering value.

Salesforce’s introduction of the AWU represents a kind of a middle ground. Industry analysts like Constellation Research’s Liz Miller observe that the AWU acts as a placeholder for the agentic era, much like clicks and likes functioned in the early days of online and social media. It is still a usage-based consumption metric masquerading as an outcome metric. The industry is currently witnessing a tug-of-war: legacy giants are deploying metrics like the AWU to track utilization and justify high enterprise license costs, while pure-play AI vendors intend to leverage outcome-based pricing as a competitive weapon to steal market share by guaranteeing and demonstrating return on investment.

My point of view and analysis

As someone who has spent years helping organizations unlock their potential through digital transformation initiatives, I look at the Agentic Work Unit highly skeptical. When evaluating generative and agentic AI investments, the critical measure is the ability to deliver measurable business results, not just technological activity. In this context, the AWU represents a fundamental conflation: it equates doing work with achieving outcomes, which simply is not true.

By defining an AWU as a discrete task, such as invoking an API or triggering a workflow, Salesforce has created a metric that measures machine exertion rather than enterprise value. In the realm of autonomous systems, an AI agent can execute thousands of discrete tasks, burn through immense computational resources, and work incredibly hard while achieving absolutely nothing of commercial consequence.

Working hard on the wrong thing still doesn’t deliver results.

To fully grasp why measuring discrete AI tasks is a poor proxy for value, consider the analogy with an unoptimized database query vs. an optimized one in cloud data warehouses. A highly optimized SQL query returns a vital dataset in seconds for pennies. Conversely, a poorly written query forces the database engine into massive data scans. It might be running for hours and consuming plenty of CPU and memory resources. From the vendor's billing perspective, the system successfully performed the discrete scanning tasks it was instructed to execute. However, it results in a massive consumption bill for the customer. The business gains little value, as much of it is harvested by the vendor; even worse, if the result is wrong. Yet the financial penalty is severe.  

The Agentic Work Unit operates exactly on this flawed economic principle. Autonomous AI agents are still highly susceptible to unique failure modes, e.g., the infinite reasoning loop. If an agent encounters an ambiguous prompt or lacks solid memory tracking, it may repeatedly call the same tool or query the same database in an endless cycle due to perfection bias. While engineers desperately build so-called Meta-Reasoners to halt this wasted computation, the AWU metric actively monetizes it. If a confused agent loops fifty times before timing out, it has successfully generated fifty AWUs delivering zero result. The customer is actively billed for the machine's confusion.

Furthermore, agentic workflows suffer from compounding hallucinations, or epistemic debt. If an agent hallucinates a false premise in step one, it will still confidently execute subsequent tools based on that fabrication. By the time the workflow concludes, the agent may have triggered dozens of AWUs across multiple enterprise systems, corrupting data and requiring costly human remediation.

Ultimately, a dashboard celebrating 2.4 billion AWUs gives the illusion of massive productivity, but it is a vanity metric. If those tasks were merely redundant internal data reshuffling or failed reasoning loops, the actual profit multiplier of the organization remains unchanged. An Agentic Work Unit quantifies motion, but motion is not progress. Until AI pricing models mature to align the cost of digital labor with the verified delivery of business outcomes, enterprises must continue to treat effort-based metrics like the AWU with extreme skepticism.

Just my $.02. What do you think?

The digital commerce industry has spent the last twenty-five or so years optimizing a single, unit of measurement: the session. We built cathedrals of conversion rate optimization (CRO), obsessed over pixel-perfect hero images, and deployed armies of "customer success" bots that were little more than glorified FAQ routers. We tracked users from the moment they landed on the homepage, watched them struggle through navigational hierarchies, and celebrated when 3% of them actually bought something.

Anywhere else, a 97% failure rate would be grounds for executive termination. In e-commerce, it was the benchmark for success.

We can safely say that the era of the session comes to an end, thanks to conversational and then agentic commerce, which put the "homepage" on life support. What comes more and more into the foreground is the intent, whichis what the session was supposed to help derive. And crucially, the entity expressing that intent is increasingly likely to be a machine, not a human.

What we are seeing now is the transition from browser-based commerce, where humans operate interfaces, to agentic Commerce, where AI agents operate APIs. This isn't just a channel expansion like conversational commerce; it is a fundamental inversion of the retail power dynamic. In the browser era, the retailer controlled the environment. In the agentic era, the customer (or their proxy) controls the context. This is quite similar to what happened in the 2000s with the advent of social media. And it will likely be countered by vendors as fast as the power shift back then, e.g., using GEO instead of SEO.

The demise of the search box

Since the rise of Google, the search box was the primary interface for intent. According to findings by Bain and Company, it gets increasingly exchanged by generative AI. 30 to 45 per cent of US consumers already use generative AI for product research and comparison with 17 per cent stating that they start their (holiday online) shopping with ChatGPT, Perplexity and co. This basically says that the traditional search box is becoming obsolete and replaced by the prompt. While this doesn’t look dramatically different, the use is different. Instead of punching in some disjointed keywords, we can now use real phrases to express complex intents, like "I need a sustainable gift under fifty dollars for a coworker who loves cooking, feels premium, and arrives by Friday".

A traditional e-commerce engine likely chokes on this request. It sees "cooking" and shows a spatula, or maybe a pot? It misses "sustainable" because that data is buried in a PDF product description, and it misses "feels premium" because that is a sentiment, not an attribute. An AI agent can parse these constraints and orchestrate a query across the catalog and across vendors.

If a commerce architecture cannot serve this kind of "headless" request from an AI agent with the same fidelity as a human visiting a homepage, it is effectively closed for business because it is still optimizing for SEO when it should be optimizing for GEO (Generative Engine Optimization), which is the art of making your data palatable to an LLM.

To make this work, it needs two key ingredients: Discoverability, which needs to be changed from search centric to language centric. For this, it needs protocols, which are emerging, the Agentic Commerce Protocol ACP from Open AI and the Universal Commerce Protocol UCP from Google, the former more checkout centric and the latter mor centering around interacting entities. All this needs to support the gamut from simple agent to site to brokered agent interactions that include a buyer agent, a seller agent and a broker agent.

The other one is far more basic: interoperability, which is the domain of the model context protocol and the agent 2 agent protocol.

Until around the second half of 2025, OpenAI had plugins, Google had Actions, and none of them talked to each other. Recognizing that fragmentation kills adoption, and that it needs a kind of a “USB port” for commerce, the industry got to its senses and moved toward standardization with the formation of the Agentic AI Foundation under the Linux Foundation.

So, one can say that the basics are sorted now.

The discovery portion is still up for grabs.

This is also the portion where it will be decided whether the customer stays in control or not.

However, …

Salesforce, Cimulate, Microsoft, Algolia and the Discovery Imperative

There is lots of movement in this area. Algolia partnered with Microsoft, Salesforce acquires Cimulate, other vendors build on own agents, protocols and strength. All of them rely on strong ecosystems.

Every single one of them, plus probably some more, are likely to play an embrace, extend, extinguish strategy to gain the upper hand in this emerging market. EEE is basically about achieving vendor lock-in by polluting standards: vendors don’t win by building a better product on a level field but by changing the field so rivals can’t interoperate without copying their proprietary stuff. Having said that, not every extension is evil. Extending a standard can be legitimate innovation if it’s standardized back upstream or remains interoperable. HTTP cookies are an example for this. Extending a standard becomes EEE when the extensions are used, especially by a dominant player, to make competitors incompatible and to shift the ecosystem onto proprietary rails.

What does this mean for the enterprise buyer?

As usual, the path forward is fraught with traps, which makes it important that retailers maintain immediate control of their end points and do not rely on intermediaries. Discovery is migrating off your site, into agent surfaces. So, do not buy the "Agentic Suite" just because a vendor bought a startup last week.

Some recommendations:

Stop thinking in channels and become channel agnostic, or headless, if you will. Your commerce logic from pricing, inventory, to catalog, must be decoupled from the presentation layer. If your Add to Cart function is tied to a JavaScript button on an HTML page, an AI agent cannot trigger it. Even your customer that comes via WhatsApp, will not be able to trigger it. You need an API-first architecture where the "channel" is just an implementation detail.

Structure Your data to embrace GEO. Your catalog is probably a mess. An agent doesn't care about your "whimsical fall vibes" marketing; it cares about structured attributes. Implement a PIM that supports vector embeddings and maybe publish an /llms.txt file on your domain. Invite the agents in; don't make them scrape.

Implement agent-ready security. If an agent is buying, who is checking the ID? We are already seeing prompt injection attacks, where malicious inputs hijack autonomous agents. Adopt the Agentic Commerce Protocol (ACP) to ensure you aren't holding the bag for a rogue agent.

Measure the right things. Measuring NPS doesn’t matter for bots. It just doesn't have feelings. Measure goal completion rate (GCR) and similar. If you are optimizing for session time, you are optimizing for a ghost.

Prepare for MyTerms. As I've written previously, the consent banner might have found a successor. We are (hopefully) moving toward IEEE 7012-2025 ("MyTerms"), where the user’s agent negotiates privacy terms with your site automatically. If you don't support this machine-to-machine negotiation, you might be avoided by the privacy-conscious agent.

We are leaving the era of the operator and entering the era of the orchestrator. The new front door is invisible. It is an API call from a user's agent to your agent. For the unprepared retailer, this may very well be an extinction event. For the channel-agnostic retailer, it is the moment where scale can decouple from headcount.

Welcome to 2026. The agents are here. Try not to let them automate your chaos.

The news

On June 23, 2025, Salesforce announced Agentforce 3, the third iteration of its Agentforce platform. Agentforce 3 is a major upgrade to Salesforce’s digital labor platform. It gives customers the visibility and control that is needed to scale AI agents that already have proven useful at many companies. The release covers several additional capabilities.

Salesforce has introduced the Agentforce Command Center, an observability console designed for managing AI agents. This tool allows businesses to track and scale AI agent activities. It is built into Agentforce Studio and includes features for monitoring performance metrics such as latency and error rates through live analytics.

The update also brings native support for the Model Context Protocol (MCP), which enables Agentforce to connect with any MCP-compliant server without requiring custom coding. The platform's Atlas architecture has been enhanced to improve latency, accuracy, and resiliency. Support for additional Large Language Models has been added, including Anthropic's Claude Sonnet via Amazon Bedrock, with future support planned for Google Gemini.

Agentforce 3 includes over 200 pre-built industry actions, with half of these being new additions. The agents are now capable of performing web searches and providing citations for the information they retrieve. The platform's availability has been expanded to Canada, the U.K., India, Japan, and Brazil, adding support for six new languages.

More than 30 new partners have been added to the AgentExchange marketplace. These include companies such as AWS, Box, Cisco, Google Cloud, IBM, PayPal, and Stripe. Salesforce has also introduced new pricing options, including per-user plans for its Sales, Service, and Industry Clouds that provide unlimited usage of actions for employee-facing agents.

The bigger picture

Agentic solutions are the biggest thing since sliced bread in the industry right now. They are even more overhyped than Metaverse and Blockchain have ever been. Still, the number of case studies and use cases that get published is rising fast. This is a sign of buyer interest, increasing adoption and ultimately shows that agentic AI is not a solution in search of a problem but can address actual business challenges. However, these solutions need to be implemented in a thoughtful and planned approach to harvest their benefits. This expressively also requires a hard look at pricing, which can easily dig deep into the gains achieved by productivity increases, increased efficiency, or other gains. The other hugely important topic is the change management that is required. The implementation of agentic AI has the smell of impending lay-offs. This is bad for employee morale and therefore a topic that needs to be actively addressed by vendors as well as by buyers.

My PoV and analysis

With this release, Agentforce has arrived in the Enterprise. The main missing link is the still emerging A2A support. After focusing on the agents themselves, followed by their development and deployment in the previous releases, this release is about agent management and scalability, including scaled development through the Salesforce ecosystem.

Without a good, if not complete, built-in observability, Agentforce was still more or less a tool for rapid prototyping of and experimentation with AI agents. This is not bad in itself, but not enough for an enterprise scale product. Still, it helped tremendously by getting mindshare and establishing Salesforce as a category leader, which deserves kudos. In puncto interoperability, SAP has beaten Salesforce to the punch with its Joule Everywhere announcement during the yearly Sapphire event. Interoperability and A2A capabilities will be the next battleground for platform domination in which the business applications behemoths, including Microsoft, find themselves.

What I find quite notable, even laudable, is how strong Salesforce (or SAP for that matter), emphasize upon agents cooperating with humans, both “sides” working on what they are doing best, instead of agents replacing humans. Unluckily, especially Salesforce sends mixed signals with CEO Marc Benioff making statements like “up to 50% of the work at his enterprise software company is now being done by AI” during an interview with Bloomberg. In the light of Salesforce (and other tech companies) laying off employees in the thousands or tens of thousands because of efficiency gains through the use of AI and AI agents, this is more than just a bit contradictory. This clearly suggests that the messaging should be cleared. Words must match actions, especially with a topic that is as sensitive as this and considering the huge visibility that Salesforce has.

Using Agentforce as customer zero certainly helps with proving that the product works and with gaining more credibility. This is especially in the light of the fast deployment of the system. As time to value is important, this proof point and the fast delivery of prebuilt industry specific agents plus the Agent marketplace are crucial for continued success in a market that moves extremely fast.

Last, but not least, I am looking forward to seeing two particular sets of functionalities. The first one is a true agent to agent workflow that includes external systems and agents, not only Salesforce provided ones. The second one is even more interesting in the light of the need to move fast. It is one thing to offer a Command Center that helps administrators and users to understand what is going on, what is more important is the ability to immediately suggest and generate solutions for identified problems. This is where the real efficiency gains can be harvested.

As usual, a good part of the announcement is for functionality that is still in pilot or beta stage, in particular the MCP andA2A support as well as the Command Center.  

Having said all this, Salesforce has done it again. The company evolved a solution to become enterprise ready from its first humble days during Dreamforce. Congratulations for this. And let’s see how the competition changes the playing field.

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

On May 27, 2025, Salesforce announced that it has reached a definitive agreement to acquire Informatica for about $8bn.

According to the press release, “bringing together Informatica’s cloud-native capabilities — including its extensive data catalog, data integration, governance, quality and privacy, metadata management, and MDM — with the Salesforce platform will unlock new capabilities for Salesforce’s enterprise data stack, delivering a complete solution to the challenges of AI at scale”.

The acquisition is planned to enhance Salesforce’ data foundation which is critical for deploying agentic AI. “The combination of Informatica’s rich data catalog, data integration, governance, quality and privacy, metadata management, and Master Data Management (MDM) services with the Salesforce platform will establish a unified architecture for agentic AI — enabling AI agents to operate safely, responsibly, and at scale across the modern enterprise”.

The bigger picture

For agentic AI systems – or agents in general – to operate efficiently, it needs two things: data and data.

Data from a vendor’s own systems as well as data from external systems that can get harmonized and accessed/used by the software agents. SAP during its annual Sapphire event just made exactly this point by showcasing how its Joule family of agents can use data from SAP and non-SAP applications.

As there is no vendor to rule them all (thanks to J.R.R. Tolkien for this inspiration) there is an importance on again two things: (zero copy) data integration and data management including its governance. These seemingly not-so-sexy capabilities are sorely lacked by many application vendors but, again, thanks to AI, increasingly necessary. Again, AI, especially autonomous AI systems, cannot be truly reliable, let alone trustworthy, without a solid platform of very well understood data. This is also evidenced by one of the core messages of the press release. Informatica shall “strengthen Data Cloud’s leadership as a Customer Data Platform (CDP), ensuring data from across the organization is not just unified but clear, trusted, and actionable”.

My point of view and analysis

Usually, one says that the third time is the charm. Salesforce did it in a second attempt, after failing in April 2024. The good news for Salesforce is that the wait saved the company around $3bn.

In my eyes, this acquisition closes a few gaps in the Salesforce software stack. Salesforce gains Informatica’s capabilities in data integration, data quality, metadata and masterdata management. And yes, I deliberately include integration, although Salesforce’s emphasis probably lies in the other topics. After all, strong data management and data governance are key preconditions for being able to run software agents. It simply needs a strong data platform. So far, Salesforce, in spite of promoting its own data cloud, wasn’t really there. So, this is a very good match for Salesforce, also considering that Informatica is considered a leader in many competencies, including Data and Analytics Governance, Data Quality, and, yes, integration.

Why is integration important? After all, Salesforce has Mulesoft. However, I see Mulesoft as somewhat clunky compared to Informatica’s more real time integration capabilities. The Informatica platform simply is more complete, which in the light of agentic software being in the need to access data from different sources in a consistent way, is paramount.

As for Informatica, the company certainly has lost some of its mojo, as evidenced by a significant decline in growth and at best moderate profitability. These are caused by several factors that I rather leave to people who are more qualified than I am. But one thing is for sure. Informatica can surely benefit from the well-oiled marketing machine that Salesforce is.

So, overall, this is a good acquisition for both sides. Salesforce gets something that it was sorely lacking – an own strong data management and -governance platform with increased integration capabilities as a bonus. Informatica gets a new lease of life.

What is remaining is the hard work of finishing Informatica’s cloud transformation and the deep integration of its capabilities into the Salesforce stack.

Congratulations to both for this merger that, I am sure, was in the making for a long time.

The News

On December 17, 2024, Salesforce announced Agentforce 2.0 after introducing Agentforce 1.0 during the company’s Dreamforce event. With it, it repositions Agentforce as a “Digital Labor Platform” that is capable of supplying businesses with an infinite workforce. This way, they shall be able to address internal challenges like labor shortages, fixed capacity, stalled productivity, or burnout. In addition, they increase their ability to work with increasing customer demands like no patience, their wish for personalization and empathy and with a knowledgeable expert, etc.

“Agentforce 2.0 is the newest version of Agentforce and the first digital labor platform for enterprises — a complete AI system for augmenting teams with trusted, autonomous AI agents in the flow of work. This new release introduces a new library of pre-built skills and workflow integrations for rapid customization, the ability to deploy AI agents in Slack, and advancements in agentic reasoning and retrieval augmented generation (RAG) - enabling teams to scale their workforce with a custom Agentforce capable of handling complex, multi-step tasks with even more precision and accuracy.”

Agentforce 2.0 comes with a library of prebuilt skills, which are jobs that digital agents can perform. These skills do not only cover Salesforce software but also cover partner software and, with the help of Mulesoft, any other system. The agent builder, that is part of Agentforce 2.0 enables the building of new agents/skills using natural language. New agents can be built using the skill library or custom logic.

Agentforce 2.0 is live and will get additional capabilities throughout Q1/2025.

The Bigger Picture

AI has hit an inflection point. We came from rule-based chat bots, added assistants and are now focusing on agency. This is possible because technology improved, the ability of grounding inferences into company data improved and furthermore, the ability of technology to show empathy by also employing what I would call world knowledge (after all, this is what an LLM is trained on) can be combined into responses to queries. Add to that the increasing desire of (business) users to not be forced into a machine-enforced user interface but to interact in a human way, and there is something that is very exciting. I am a technologist, after all.

At the same time, there is undoubtedly a need for more automation in business operations. Customer demands are increasing – shareholder demands are, too. So is the pressure on employees. Pretty much every senior manager (and their dog) is talking about a shortage of skilled labor, which is a notion that I do not fully buy into as there is also a still substantial unemployment. Given that, saying that there is a skill shortage is akin to saying that who is unemployed is unskilled or lazy. There are also many lay-off rounds which cause a drain of skills from companies. So, these people cannot be re-skilled as well?

To me, this dichotomy sounds to me like we are in the need of having some deep ethical discussions about the value of (human) labor vs. digital labor.

But back to Agentforce.

My PoV and Analysis

Agentforce 2.0 is BIG. Big not only, because it promises to deliver a whole new level of automation to businesses, but also because it is the way for Salesforce to extend its reach out of the “CRM” niche and to support the whole enterprise. Salesforce wants to achieve this by essentially making Agentforce the user interface to all business applications, not only Salesforce applications. The vehicle to achieve this is Slack, supported by MuleSoft.

At the same time, Salesforce boldly creates a new market category. No other significant company has taken a stance like this before.

This is a significant repositioning, or should I say pivot, including a direct attack on all other enterprise software vendors and I am now waiting to see their responses.

Salesforce itself uses Agentforce to power its customer service and to improve its customer service efficiency. According to its own data, this already now results in an automated handling of more than 80 per cent of all inquiries and into a reduction of escalations to humans by 50 per cent. Zendesk already during its Relate event last May predicted customer service automation rates of 80 per cent and above. Looks llike there is an increasing number of instances where this prediction has become reality.

What neither the article about the success of Agentforce in Salesforce customer service nor Mark Benioff said during the announcement is what happened to the human agents to which cases are no more escalated. Are they tasked with “more valuable” work or did they get replaced by Agentforce. I hope for the former, but my fear is the latter.

Agentforce, undoubtedly, hits a nerve. In the short time since Dreamforce, Salesforce signed up more than 1,000 customers of which several hundred are already live. I’d count this as a huge success although I’d love to hear more about the use cases that these customers deployed, what their success metrics are and how they fare along these metrics.

Let me put this into a bigger context here again. Whether skilled labor shortage is a perceived or a real problem, Agentforce allows businesses to focus on shareholder value by replacing humans with Agents. And I think that they will. The term “Digital Labor Platform” already strongly hints into this direction although Benioff repeatedly emphasized on “humans supported by agents”. Still, the conversation moves into the direction of agency fast. And, the vast majority of the announcement is about efficiency, I did not hear much about businesses being able to do entirely new things, offer entirely new services that they couldn’t offer before.

In summary, it is hard to overestimate the potential impact that a technology like Agentforce has, especially in the light of Agentforce being a work in progress. Why is this? Because Salesforce managed to combine a set of powerful technologies into a coherent whole.

Still, I’d really like to err on the side of caution and would like to see the business metrics and successes used by Agentforce customers.

Lastly, as said above, I see a high need for a strong ethics discussion. Agentforce invites businesses ta automate business processes instead of rethinking them. I strongly urge potential buyers to not fall into this trap. Low hanging fruit are just that: low hanging. Identify where the real value lies in processes and data, and apply AI from there. Remember the adage that a fool with a tool remains a fool.

Congrats to Salesforce for bringing a bold vision to life. It is now up to all of us to use this sensibly.

The News

Yesterday, December 3, 2024, Salesforce reported its Q3 / 2025 numbers.

The company reported a total GAAP revenue of $9.44B US, which is a year-over-year change of eight percent and also slightly above the guidance of up to $9.36B that it gave after Q2. In addition, Salesforce raised the lower end of its full year FY25 guidance by $100M to $37.8B. What is also visible is the continued focus on operative margin and operating cash flow, which are up by 280 basis points and 29 percent, respectively.

These numbers exceeded ‘market expectations’, with the corresponding impact on the Salesforce share price.

"We delivered another quarter of exceptional financial performance across revenue, margin, cash flow, and cRPO,” said Marc Benioff, Chair and CEO, Salesforce. “Agentforce, our complete AI system for enterprises built into the Salesforce Platform, is at the heart of a groundbreaking transformation. The rise of autonomous AI agents is revolutionizing global labor, reshaping how industries operate and scale. With Agentforce, we’re not just witnessing the future—we’re leading it, unleashing a new era of digital labor for every business and every industry."

“We continue to drive disciplined profitable growth with third quarter GAAP operating margin of 20.0%, up 280 basis points year-over-year, and non-GAAP operating margin of 33.1%, up 190 basis points year-over-year,” said Amy Weaver, President and CFO of Salesforce. “To date, our total capital returns have surpassed $20 billion and we remain focused on driving shareholder value.”

The Bigger Picture

Salesforce has managed to become the biggest vendor of enterprise software in only 25 years. This is nothing short of amazing, especially, when considering that this market has been changing and is continuing to change fast and that Salesforce started out as a player in a small niche of this market. Add to that who has been there already. Since its humble early days, the company pivoted a couple of times, from a single application company to a multi-application company, from best of breed to best of suite, to platform company, to AI company.

Which is straightforward, as AI, combined with conversational interfaces, are the current battleground of the enterprise software market. Companies that are able to establish themselves with these pivotal technologies, have a great chance at becoming quite sticky. It is one thing to migrate a database from one vendor’s system to another’s.

It is a totally different ballgame to migrate proprietary models that are ingrained in a software stack.

My Point of View and Analysis

In my eyes the quarter’s Salesforce numbers, in contrast to what Benioff boldly stated, are not exactly overwhelming. They are in line or below the growth figures of other major vendors.

 Yet, Salesforce is telling a very compelling story.

Salesforce is in another iteration of the clash of titans, this time it is not about dominance in the wider CRM market, now it is about AI and about the whole enterprise. It looks like Salesforce has found a way to make it into ‘core enterprise software land’ by virtue of its Data Cloud and Agentforce. It is basically attacking the vendors that support the full enterprise value chain via its data platform, its ecosystem, and AI.

The compelling story is the story of the rise of a new market of “digital labor” and, of course, Salesforce being the “largest supplier of digital labor” in this market. Personally, I find the moniker ‘digital labor’ dangerous as it, in contrast to contemporary wording, does not convey the message of ‘human augmented by machine’. It is a pivot away from it, albeit a straightforward one.

In fact, it is the story of Agentforce that allegedly fundamentally changes how businesses can operate. It is the story of AI agents doing a lot of the work that will not be done by humans anymore for various reasons; these reasons may include work being dull or dangerous, a lack of ‘skilled labor’ or mere linear thinking in terms of efficiency. According to Benioff, “productivity is no more tied to workforce growth but to this intelligent technology that can be scaled without limits”. The intelligent technology being Agentforce, that has turned into a platform, along with the whole company Salesforce. The problem with bold statements like this is that there are no real proof points yet. Agentforce is live only for a few weeks at the time of this writing. Yet, I agree that this market is “so much bigger than the data management market”. This is largely because the data management market is a prerequisite, a part of the foundation of the agent market – as evidenced by Salesforce’s failed attempt to acquire Informatica and the subsequent acquisition of Own.

While Benioff cites some success stories, these are still the same as the ones used during Dreamforce.

Yes, Agentforce works off the huge Salesforce databases, and yes, there are guardrails in place, but still … call me a pessimist, but as long as there are still too many incidents of language models, I would prefer if vendors like Salesforce openly addressed this topic along with possible ways to overcome it. I’d also like to see a few more implemented use cases along with their value.

Other vendors are working with less exuberant messaging. Maybe, this is part of Salesforce’s success.

Now, it is time to deliver.