Breaking Analysis with Dave Vellante

Salesforce’s next growth opportunity may come from customers spending less time in its interface—and having agents do more of the work.
Coming out of Dreamforce 2026, we believe that is the shift worth exploring. The progression from command lines to graphical interfaces, browsers and mobile is entering another phase: an agent can generate an interface around the task, inside Claude, Slack or another client. The customer no longer has to start with the application’s screen. The starting point becomes a customer outcome.
But generating an interface is not the same as understanding the business. That is where our System of Intelligence (SoI) framework comes in. The system of engagement (SoE) connects people and agents. The system of intelligence supplies the business context. And the system of agency (SoA) turns that understanding into action. Salesforce’s larger ambition is to connect these elements through what it calls an enterprise AI harness: the data, business knowledge, workflows and controls that let models do useful work across systems. This goes well beyond answering questions.
The customer evidence from our good friends at Qualitate, suggests an exciting opportunity…but the data also shows a company in transition. Qualitate’s latest channel checks find that most organizations interviewed remain in Agentforce pilots or proofs of concept. Yet among customers who have modeled or experienced the financial impact of headless access, 75% expect Salesforce spending to increase—not decline. Working outside Salesforce’s interface could actually mean doing more work on its platform.
That creates both an opportunity and a challenge. More consumption can generate more revenue, but it does not automatically generate more customer value. Pricing must make sense as pilots scale. And if customers increasingly work through their preferred AI environment, Salesforce must demonstrate why its business context and execution capabilities remain an essential underpinning of the customer experience. The point is, the interface can move elsewhere, meaning the next purchase may not automatically stay within Salesforce.
Welcome to Breaking Analysis #327. Salesforce After Dreamforce…How $CRM (the company) can grow beyond it’s own interface. In this episode, George Gilbert joins us to map Salesforce’s post-Dreamforce direction into our System of Intelligence framework. We combine his firsthand observations with new Qualitate customer research to examine headless access, Agentforce maturity, competition from AI-native agents, and the changing relationship between pricing and business outcomes.

The cloud shared responsibility model was initially not well understood by many customers. In fact, early adopters often believed that simply having data in the cloud meant that Amazon, or a SaaS vendor were responsible for safeguarding it. Amazon had to educate its customers and partners that security and compliance duties were split between the vendor and the client organization. In short, the vendor was responsible for securing the cloud resources but you, the buyer, were responsible for securing what you put inside the cloud; based on your policies, priorities and budget. We believe a similar but much more consequential dynamic is unfolding with respect to agentic AI. Specifically, Cloud computing divided responsibility by infrastructure layer. Agentic AI distributes authority across a chain of models, platforms, clouds, partners and customers. Our premise is the industry now needs a shared accountability model for the decisions, actions and outcomes that chain produces.

In short - The cloud shared-responsibility model told customers who secures what. The agentic shared-accountability model must define who can do what, who can stop it, who can prove what happened and who pays when it goes wrong.
Welcome to episode 326 of Breaking Analysis. Beyond Shared Responsibility - When AI Acts, Who owns the Blast Radius. 

In this Breaking Analysis, Principal CUBE Research Analyst Krista Case and I explain why the agentic era demands a new accountability model. We’ll draw on learnings from last week’s CrowdStrike Fal.Con event, where the post Mythos moment and the OpenAI/Hugging Face “accident” were front and center. We’ll also draw on other new datapoints, including conversations with CISOs at Fal.Con and Palo Alto Networks’ earnings print from last week, to unpack what we’ve defined as a new AI accountability model. We’ll also test this new model against our Sovereignty framework, developed by Amit Govrin and assess sovereignty in the context of business recovery. We’ll explore the sequence of events that leads up to the ultimate question of who pays when something goes wrong?

Breaking Analysis with Dave Vellante

Breaking Analysis with Dave Vellante and Amit Eyal Govrin

Breaking Analysis with Dave Vellante.