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

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

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

What "good for investors" is really saying

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

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

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

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

Agents make the coupling worse, not better

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

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

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

The people who get punished are the people who bought in

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

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

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

The structural problem, which is bigger than the bill

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

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

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

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

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

The honest counterargument

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

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

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

Where this lands

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

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

Three things to do if you are buying.

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

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

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

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

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

The News

Microsoft delivered two related announcements at Build 2026.

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

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

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

The Bigger Picture

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

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

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

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

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

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

My PoV and Analysis

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

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

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

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

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

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

Neither is displaced. Both face a changed market dynamic.

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

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 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.

This will be a rant, but a rant with roots in the belief that employee experience fosters customer experience.

As it should be well-known by now, Microsoft fired around 9,000 employees in July, after doing some smaller layoff rounds already in 2025, totaling more than 15,000 employees according to the TechCrunch comprehensive list of 2025 tech layoffs. This continues a trend from 2023 and 2024.

Now, I get it. Companies need to be and stay profitable, reorganize and, if needed, lay off employees. As such, there is nothing wrong with this.

Where things start to get wrong is when at the same time two other things can be observed: strong and successful growth in revenues as well as profits, and very generous payments to the C-suite.

Microsoft is immensely successful. For a long time now, we see record revenue and profits every quarter, which means strong growth. Looking at the most recent Q4/25 numbers, this is poised to continue and made Microsoft the second $4 trillion company after Nvidia.  

Consequently, Satya Nadella as the company CEO, receives an enormous total compensation ($ 79.1M in 2024). To compare this number, the average total compensation of a Microsoft employee is $220k with a median compensation of around $192k. This is a factor of 400. And no, this is not me being jealous.

Microsoft’s written down corporate values are respect, integrity, and accountability.

What triggered this text is the dissonance between all this that is also evident in Satya Nadella’s mail to the employees titled “Recommitting to our why, what, and how” that got published on the corporate blog on July 24, 2024.

So, there are 9,000 people laid off – not let go, a term that Nadella luckily didn’t use this time – while the company and the C-suite enjoy record earnings. If I were one of these 9,000 people, reading this letter, reading about a “seeming incongruence” and about a decision “weighing heavy on me”, I would feel taken for a ride.

There is no “seeming incongruence” but a real one. The incongruence is that top management is increasingly jaded and disconnected from where the success of the company is created: where the median compensation is about a quarter of a per cent of the CEO’s. Yet, these are the people who get sacrificed. Health insurance? Gone. Stock options? Gone. Future? Precarious.

The stock markets – or rather the investors – celebrated this layoff round as another success, bumping up the share price. And here is where the problem lies. The management is not answering to the employees but to investors who hunt short term profits. Employees appear on the liability side of the balance sheet, they are mere “resources”. Mind you, resources get consumed and then disposed of. That this is the thinking comes across in little things. “I want to express my sincere gratitude to those who have left.” Technically, they have left, but not voluntarily. Why not admit it? “Progress isn’t linear. It’s dynamic, sometimes dissonant, and always demanding. But it’s also a new opportunity for us to shape, lead through, and have greater impact than ever before.” Just that there are only few who really benefit from it.

Let’s look a little further.

The why

“When Microsoft is succeeding, the world around us must succeed too as a company it’s how we earn our social permission to operate.” This thinking is inside out. The success of Microsoft should rather be defined by making the world around successful. And Microsoft is undoubtedly successful. However, the success of the “world around us” is not a byproduct of a company’s success but its reason. The social permission to operate is given for its commitment to make the society it is embedded in more successful. I wish that more so-called leaders would accept that. How will the 8 billion people that Nadella talks of be able to “summon a researcher, an analyst or a coding agent at their fingertips” if they, increasingly get disenfranchised and cut of from the “success” of the few?

The what

The priorities are clear: “security, quality, and AI transformation. Security and quality are non-negotiable”. And they are good. But are they followed through? You might remember the disastrous Storm-0558 security breach in 2023 and the 2024 report by the Cyber Safety Review Board. The CSRB “identified a series of Microsoft operational and strategic decisions that collectively pointed to a corporate culture that deprioritized enterprise security investments and rigorous risk management” (highlight by TW). After this incident, Nadella named security a priority. In 2025 we saw a major Sharepoint breach that lasted for two weeks before being disclosed and in between numerous other ones. Yes, Microsoft is probably in the crosshairs of many bad actors, but this string of breaches does not indicate security being a top priority. Who suffers from it? Customers. And employees. Executives? Not so much, although  a part of their performance-based pay is tied to cybersecurity performance and Nadella himself asked for a reduction of is incentive payout – which got agreed to – just that his payout got increased, too.

So, the priorities are clear – and who gets held accountable, too. And it is not the board …

The how

Culture is important. A growth mindset, too. Just that a growth mindset far too often correlates with an elbow mentality and egoism. The interesting part in this section of Nadellas letter is that there is no mention of the written down corporate values, which are respect, integrity, and accountability. Instead, he preaches competition and inside-out thinking “we can go win together, and change the world in the process” instead of winning because of changing the process.

What all this creates is not a culture of “we” and collaboration, it is a culture of fear. Fear of one’s ideas being usurped by a manager or colleague and fear of being the next one to be axed.

I see a great company, great not because of its managers and executives, but because of its people. I also see a company that is its own worst enemy. Microsoft could do better – by living up to its values and not treating employees as resources. This is not integer, nor is it respectful. And on top of it, it would make employees more productive, as they do not need to worry about their future. Employee experience helps improving the customer experience.

End of rant.

What do you think?

SaaS is dead! It will be replaced by agentic systems that replace coded business logic by AI agents that autonomously interact to bring said business logic to life, just smarter. Satya Nadella said it - or at least something in these lines, if I believe all the pundits around. His words lit up the Internet. And Satya Nadella being the CEO of a 3 trillion dollar company is the ultimate fount of truth and wisdom, when it comes to business applications. Is he not?

So, what should we take from his statements? After all, the words of the CEO of one of the top 3 valuable companies on this Earth carry some weight.

Let me start straight.

I call BS!

SaaS, first of all, is a delivery model of logic that also had some implications on vendors‘ business models and their approaches to pricing. For a variety of good and not so good reasons this delivery model succeeded vs. the prevalent model of on-premises software. Some of the more important reasons have been “no lock in by vendors”, “only pay for what you use”, “reduction of own infrastructure cost”. Of course, there are more. All of them being true – or not so much. One thing is for sure, SaaS led to a considerable centralization of compute resources. Hyperscalers emerged. Vendors took over the management of the application stack for their clients. It is very hard to envision that this gets reverted any time soon, even in a world with increasing trust issues and a good argument for edge computing.

What SaaS is not, or only marginally, is a way of creating business logic. Of course, software architecture changed to support multi tenancy vs. single tenancy, but this is rather an internal reason to make this delivery model more efficient for vendors. Clients do not care about single- or multi-tenancy, as long as their requirements are met. Just to be clear, multi tenancy, and with it, SaaS, first of all is a means to make software delivery and maintenance more efficient for the vendors. The benefits of this efficiency gain may or may not be forwarded to customers.

What Nadella suggests is a totally different way to build application logic. Instead of formulating it by coding and/or rule systems that automate a process, he envisions a network of specialized artificial agents that collaborate flexibly to achieve the desired outcome. Sounds cool and modern, doesn’t it?

Just that it ain’t work.

For several reasons.

Chief of them are accuracy, reliability and predictability. While coding and rule-based systems deliver predictable and repeatable – i.e., auditable – outcomes, the current breed of artificial agents does not. Instead, they are probabilistic and very likely deliver good results, which is often not good enough. And this is not fully mitigated by technologies like RAG. And in the vast majority of situations, businesses and their managers prefer, even need predictability. How well agents based on different foundation models fare in “consequential real-world tasks” is impressively evaluated in a research study titled “The AgentCompany”. The researchers found that even the best model (Claude 3.5 Sonnet) manages to complete only 24 per cent of the total of 175 tasks. I’d say that this is quite sobering. Research conducted by the Australian National Science Agency on the usefulness of Microsoft’s Copilot vs. expectations shows mixed results, too. Now, one can say that it doesn’t really matter whether a human employee or an AI agent makes the mistake as the result is the same. I, myself, use this argument a lot. But in this situation, we are talking about trading a predictable outcome for a probabilistic one.

Then, there is cost. Running large language models, let alone train them, is incredibly costly. Why would a CFO or his/her CEO in their sane mind replace their expensive systems with even more expensive systems? These systems being AI based cannot be the justification, cool or not. OpenAI is losing $5 bn in 2024 on revenues of $3.7 bn. Microsoft is planning to invest $80 bn into AI-ready data centers in its fiscal year 2025 alone. It would be naïve to assume that these are done out of charity and that there is no intention to create revenues out of these investments. So, the only reasons for doing so, are the agent-based system delivering far superior capabilities or saving even more money somewhere else.

But Nadella is right, too!

SaaS does not live up to many of its promises. We still do see considerable lock-in, we see pricing that is geared towards vendors and, in my opinion, worst of all, we see considerable “siloisation” of business logic, which led to disjointed data and, in consequence, broken processes. In addition, codified business logic has a high cost of change. Additionally, SaaS software traditionally offers less scope for customization than on premises software did.

In this sense, SaaS, or business software in general, needs an overhaul, although one can argue that few of these problems are SaaS specific and that it is in no way sure that agentic systems will resolve them.

Agentic systems require a data harmonization to work. Whether they access a multitude of different databases or just one, directly or via an abstraction layer doesn’t matter. The bottom line is that there is a considerable drive towards quality data.

Architecting systems based on autonomous agents that interact with each other has the potential of making applications truly smart. Theoretically, they can adapt their behavior automatically based upon changing customer/user requirements. Easier adaptation of systems is the holy grail for some time. Microservices and composable architectures are good examples for this force.

The necessary technologies are there and “only” need to be combined.

Staying close to architecture, agentic systems are platform systems, i.e., they strongly prefer a single (family of) vendor(s). For connected agents to work properly, they need a common language that all of them understand – aka an interaction protocol. This is entirely possible, but mandatory to avoid rising integration cost.

Pricing is different, non-technical, topic. Yours truly and other people have written about this topic. Here a good one by Phil Wainewright. Agentic software offers the possibility to achieve outcome orientated pricing and to align vendors’ and their customers’ interests. However, as Phil writes succinctly “Throughout the history of Saas, people have been talking about outcomes-based pricing, but while it sounds attractive on paper, it's really hard to deliver in practice.” Still, pricing models will change as there is no coupling between agents and users anymore in agentic systems.

Let me add another point. The interaction with software is traditionally built not around how humans interact but around the limitations of how computers – machines – can interact. I do not say that GUIs and point-and-click are all bad, but still, they are a band aid to enable the effective and efficient interaction with software. With the advent of NLP, NLU, NLG and LLMs, these limitations do not really exist anymore. Instead, humans can now interact with computers in human ways and are no more confined to the application window. This is game changing.

So, what now? BS or not?

In summary, one needs to read Nadella through a Microsoft lens. Agentic systems offer the possibility of drastically simplifying the (business) software stack by migrating a vast variety of different tasks into different types of software agents that work in orchestration. Doing so, is certainly changing the SaaS world into something that looks very different from now. Microsoft as a technology vendor supports nearly all of these tasks to an extent. Apart from his click-bait type of terminology he has a point or two.

But is SaaS dead? By no means.

Can a world as Nadella sketches it evolve? Sure as!

It only needs three ingredients to make this new world of SaaS happen:

• A bold vendor that offers considerable business functionalities via working agentic software
• At attractive pricing
• That convinces enterprising businesses that it is well worth the plunge

Exciting times ahead!

The News

This has been an exciting weekend for the generative AI industry. On Friday November 17, OpenAI announced that the company fired its figurehead CEO Sam Altmann and appointed Chief Technology Officer Mira Murati as interims CEO in a surprise move. The press release states that Altmann “was not consistently candid in his communications with the board.”

Surprised was apparently not only Sam Altmann, but also the till then chairman of the board Greg Brockman who first stepped down from this position and subsequently quit OpenAI. Investors, notably Microsoft, found themselves blindsided, too – or flat footed depending on the individual point of view. Satya Nadella was compelled to state that Microsoft stays committed to the partnership with OpenAI in a blog post that got updated on November 19, 11:55 pm.

All hell broke loose.

Microsoft shares took a significant hit.

A number of additional senior OpenAI personnel quit. Both, Altman and Brockman, voiced the idea of founding another startup together.

Microsoft CEO Satya Nadella flew to San Francisco to negotiate a reinstatement of Altmann. It initially seemed that this would be going to happen, along with the complete board stepping down and being replaced by Silicon Valley tech executives.

Apparently, this did not work out.

The result is that Altmann, Brockman, and some other former senior OpenAI staff are now Microsoft employees, with Altmann becoming the CEO of a new advanced AI research unit.

Also on Sunday, Emmett Shear, former CEO of Twitch, was appointed new interims CEO at OpenAI.

Meanwhile, more than 500 of OpenAI’s employees, including former interims CEO Murati threatened to quit OpenAI and join Altmann at Microsoft, which apparently “assured us that there are positions for all OpenAI employees” in the new unit that Altmann shall lead. 

The bigger picture

Generative AI is deemed as one of the most promising technologies around. While it is not exactly new, and OpenAI is by far not the only company working on it, it has been OpenAI’s success to make it mainstream in a very short period of time by virtue of ChatGPT. 

It is also one of the most feared technologies around with potentially far- and wide-ranging social and economic impacts. The topic of ethical usage of AI is currently a very hot one. 

And OpenAI was founded with the core mission of ensuring that artificial general intelligence benefits all of humanity. In fact, it started as a non-profit organization and turned into a “capped” for-profit organization in 2019. This is already indicative of the rift between fast paced innovation and governance. This rift is clearly visible in OpenAI. Ray Wang dubbed this “balance between too much governance and not enough innovation” at OpenAI in his brief but scathing analysis in his LinkedIn newsletter. What he means with innovation, is actually commercialization.

My point of view and analysis

Wow, “not consistently candid in his communications”. It is not often that a company CEO is called a liar by his/her board of directors. Whether this accusation is true or not, the fast-paced process of ousting Altmann shows that it is in between difficult and impossible to find a balance between “creating safe AGI that benefits all of humanity” as OpenAI’s homepage proudly announces, and fast commercialization. AI, in particular generative AI, is an extremely expensive technology – and it is not likely that investors care too much about the rest of “all of humanity”. 

The main problem with the aforementioned balance between governance and innovation/commercialization is that there is always one player that leans more towards innovation/commercialization. 

AI, AGI even more so, is a platform game. And platform games favor big players. One is either top three – ideally top – or irrelevant.

Given that background, one can say that the current business model of OpenAI can be considered a failure. The chasm between a non-profit board that is tasked with preserving the “nonprofit’s mission, governance and oversight” and a “fundamental governance responsibility to advance OpenAI’s mission and preserve the principles of this charter” and a desire and need to raise and make significant money proved to be just too deep and too wide.

As a result, OpenAI turned from being the 800-pound-gorilla into a lame duck – at least temporarily. This creates a limbo.

The future of OpenAI will depend on whether the charter will be changed and the board steps down or not. And if it does, OpenAI will emerge as a different company.

As a consequence of this limbo, the good number of capable competitors that I do not need to name, have a small window of opportunity that they can capitalize on to dethrone OpenAI in the perception game. The window is small, as in particular, Microsoft is in a very comfortable position now. OpenAI re-emerges from this limbo as a weakened player that needs to take up steam again.

This is the best case for OpenAI.

Worst case: OpenAI is toast.

In any case, Microsoft is the big winner of this situation. The investors certainly seem to be of this opinion, too. MS shares took a nosedive on Friday and started with good gains on Monday. Microsoft continues to have the massive workloads of OpenAI on Azure. Microsoft has the unique opportunity to hire a massive amount of AI talent, namely the majority of OpenAI’s current staff – if said staff chooses to accept this offer. And Microsoft now has a prime seat when it comes to outright owning OpenAI, with a price tag that is likely to be significantly below last week’s purported valuation of up to $90bn US.

I wouldn’t be surprised if acquisition talks are already ongoing.

The following article is an excerpt of a White Paper by Customerization’s Kira Tchernikovsky. Kira is the co-founder and CMO of this Canadian consulting company that focuses on helping SMBs stand out through superior business automation.

You can download the full white paper here.

Customer experience (CX) is how a customer feels about a company over time. Creating great CX is about positive emotions, building trust, and leaving a lasting impression with customers.

Orchestrating excellent customer experience is essential for building brand loyalty and increasing customer retention. It's also becoming increasingly important as more and more clients. B2C and B2B, choose to do business with companies who personalize interactions and prioritize customer satisfaction.

While larger businesses have the resources to invest in Customer Experience (CX), small businesses (and by small we mean <200 employees) face unique challenges in providing an outstanding CX.  

Here are a few common challenges for small businesses:

• Limited resources: Small businesses have limited financial resources and less employees to dedicate to a comprehensive customer experience solution. This makes it difficult to compete with larger businesses that have more resources to invest in customer experience initiatives.
• Lack of expertise: Small businesses may not have in-house expertise in areas such as customer experience design, research, and analytics. Therefore, it is not easy for SMEs to develop and implement an effective customer experience strategy.
• Limited data and insights: Small businesses may have limited access to customer data and insights, which in its turn, prevents them from objectively understanding customer needs, preferences, and pain points. How can they then build effective customer interactions and experiences?
• Limited technology resources: Small businesses often do not have the budget to invest in the latest technology solutions, such as personalized messaging or mobile apps, to enhance CX.

Nevertheless, when small businesses use technology and effective guidance for their customer experience initiative, they can overcome these challenges and turn them into advantages that help them grow their business.  

The importance of CX

Customer experience (CX) refers to customers' overall perception of a business based on their interactions with the company. CX has become increasingly important in recent years as customers expect more personalized and seamless experiences. In fact, 88% say experience matters as much as products, according to Salesforce's State of the Connected Customer report.

A study by Forrester Research found that companies that prioritize customer experience see a 5.1x revenue growth compared to those that don't. While the exact ROI will depend on the specifics of each business, research has shown that companies that prioritize customer experience tend to outperform their competitors in terms of revenue growth and customer retention.

So, where is this revenue growth coming from? A good customer experience leads to your customers spending more. In fact, 86% of buyers are willing to pay more for a great customer experience.

The global customer experience management market was valued at USD 10.65 billion in 2022 and is expected to expand at a significant compound annual growth rate CAGR of 15.4% from 2023 to 2030. This market growth can be attributed to the mounting importance of understanding customer behavior and their preferences, which drives various brands and organizations to implement customer experience strategies, such as regularly communicating and engaging with customers, developing a long-term program, and utilizing automation, to provide the best service performance to customers in real-time. 

The Challenges of Providing a Great Customer Experience for Small Businesses

While large businesses are investing significantly in customer experience because they have the resources (both financial and personnel), small businesses do face challenges in providing a Customer Experience at the same level. Let’s describe the key challenges:

• The first challenge is limited resources. Despite their size, small businesses need to invest in technology and personnel to provide a great CX. They also have limited data on their customers which makes personalization difficult.

• The second challenge is time. Small business owners often have to juggle multiple responsibilities, so they may need more time to focus on CX. Without time to prioritize CX, there can be a lack of consistency across the CX, negatively impacting the customer's perception of the business.

• Finally, small businesses often need more expertise in CX. They may need help knowing where to start or what strategies to use to improve their CX. Not knowing what to do next may cause a lack of confidence in implementing CX initiatives. This lack of confidence or direction often leads to analysis/paralysis or even head-in-the-sand behavior where companies pretend the opportunity cost isn’t hurting the business.

On the bright side, corporate environments may have more bureaucracy and layers of decision-making, which can slow down the implementation of CX initiatives. In contrast, SMEs may have more flexibility and agility to implement changes quickly but may lack the scale and resources of a larger company.

What Are The Key Components Of Customer Experience (CX)? 

Customer experience (CX) is built of several key building blocks that are relevant for any company, independent of size, industry, or geography. These building blocks are:

Customer journey mapping: CX begins with understanding the customer's journey from the first touchpoint to the last. Mapping out this journey helps to identify the specific steps the customer takes, any pain points they experience, and record opportunities for improvement. The company needs to get the understanding of what the journeys are in order to be able to improve the layout and connectivity of the touch points.

Customer feedback: Gathering feedback from customers is essential for understanding their needs and preferences, and for identifying areas where the company can improve. 

This can be done through surveys, customer support processes, social media, or other feedback channels.  

The ‘loop’ refers to the circular process of gathering feedback, acting upon what you’ve learned, and then asking for feedback again. 

Because this journey is circular, it describes an ongoing process that never really ends, but that should result in continuous improvement.

Personalization: Customers expect a personalized experience that is tailored to their specific needs and preferences. This requires companies to collect and analyze customer data, and to use that data to provide customized recommendations, offers, and interactions.

Omnichannel presence: Customers expect to be able to interact with companies through a variety of channels, including email, phone, chat, social media, and in-person. It's important for companies to provide a seamless experience across all these channels so that customers feel like they are dealing with one cohesive entity.

Research out of Adobe found companies that have the strongest omnichannel customer engagement strategies see 10% growth year-over-year. Plus, they increase order value by another 10% and improve close rates by 25%.

Employee engagement: Employees play a critical role in delivering a great customer experience. They need to be engaged, motivated, and well-trained in order to provide the level of service that customers expect. Employees also need the right tools at their disposal, and most importantly, to be empowered to act for the customer.

Continuous improvement: CX is not a one-time project, but an ongoing effort to improve the customer experience over time. This requires companies to collect and analyze data on an ongoing basis, and to use that data to make iterative improvements to their operations.

Overall, the key building blocks of CX are customer journey mapping, customer feedback, personalization, omnichannel presence, employee engagement, and continuous improvement. By focusing on these building blocks, companies can create a customer-centric culture that prioritizes the needs and preferences of their customers at every touchpoint.

The Future of Customer Experience for Small Businesses

The future of CX for small businesses will likely involve using emerging technologies such as artificial intelligence, machine learning, and conversational AI, as well as business processes that support customer interactions to drive revenue. 

These technologies help small businesses provide a more personalized and immersive CX. In reality, most small businesses will also need a guide and implementation experts to help them over the finish line.  

From understanding the current state of CX in the business to documenting goals, expert assistance around the business processes that need to change is a must-have. In addition, guidance on the best technology for a small business can be a game-changing experience.  

A guide is the secret sauce to making a great customer experience. It is not a one-time accidental piece of happiness but a well-thought-out strategy for maximizing revenue and increasing customer success. Small businesses should continue to focus on providing a seamless and consistent CX across all channels.

Recommendations for Small Business Owners

Small business owners can improve their Customer Experience by:

• Linking CX to business outcomes: Small businesses should tie the customer experience they want to create to the goals they have for their business.

• Investing in CX: Small businesses should prioritize CX and allocate resources accordingly.

• Collecting customer data: Small businesses should collect and analyze customer data to personalize the CX.

• Using automation: Small businesses should use automation to streamline the CX and free up time for other tasks.

• Focusing on consistency: Small businesses should focus on providing a consistent CX across all channels.

From the pre-internet dawn of segment-of-one marketing to the customer journey of the digital era, personalized customer experiences have unequivocally become the basis for competitive advantage. 

Interested in more? Download the white paper!