Dreamforce 2026 has been a show of big claims, big screens, and an even bigger vision for how AI will reshape enterprise work.
Salesforce has announced AIforce, a new interface strategy designed to bring CRM context and actions into Claude, Slack, Lightning, and custom experiences. It has expanded Agentforce, added new customer-service capabilities, announced Contact Center as a Service, introduced Koa with NVIDIA, and made a case for its platform as the governed foundation of an “agentic enterprise.”
It is a lot for CX leaders to absorb.
But speaking to CX Today at Dreamforce, Liz Miller, VP and Principal Analyst at Constellation Research, cut through the product sprawl with a more practical interpretation of the week’s announcements.
“Honestly, the story is an easy button.”— Liz Miller, VP and Principal Analyst, Constellation Research
That does not mean Salesforce is making agentic AI simple. The technology, data, governance, and operating-model questions remain formidable. But Miller’s point is that Salesforce is trying to reduce the time and specialist expertise required to turn an AI idea into a working enterprise capability.
“It’s about being able to stand up agents, stand up skills in days rather than years,” she said.
That is the promise. The harder question for CX leaders is whether the pursuit of faster deployment will lead to better customer outcomes—or simply faster automation of processes that were already broken.
As CX Today reported from Dreamforce, Salesforce wants to take CRM out of Salesforce by making customer data, business rules, permissions, and approved actions available in multiple AI interfaces. Its wider agentic CX strategy goes further, positioning agents as operational workers capable of resolving issues rather than simply answering questions.
Miller’s analysis helps explain what that means in practice.
Start With the Work, Not the Interface
Salesforce has spent much of Dreamforce talking about interfaces. AIforce is intended to let users work through Claude, Slack, Salesforce Lightning, or a custom-built experience. Meanwhile, the company’s headless architecture is designed to let organizations use Salesforce data and business semantics without being restricted to its traditional user interface.
For CX teams, that can sound like another technology decision to make.
Miller’s advice is to start somewhere else: the work itself.
“You have to think about work first.”— Liz Miller, VP and Principal Analyst, Constellation Research
Different enterprise functions have different operating realities, she argued. A marketer, a seller, and a contact-center agent do not use the same tools, or work in the same interface. Yet they should draw on a consistent taxonomy, shared metadata, and trusted organizational data.
That is the central AIforce proposition. A seller who prefers to work in Claude could retrieve Salesforce context there. A contact-center agent should not be forced into Slack simply because Slack is a strategic Salesforce surface. They should receive what they need in the agent desktop where they already work.
“They don’t want to swivel chair between tabs,” Miller said of contact-center agents. “They don’t want to have to learn another interface.”
For CX leaders, this is an important distinction. The future of agentic CX is not necessarily one universal interface. It is trusted customer context and approved actions delivered in the places where employees and customers can use them effectively.
Headless CRM Is Not Just a Developer Story
Salesforce’s “headless” message could easily be mistaken for a developer-led architecture play. But it has larger CX implications.
It means an organization can use Salesforce as the governed data, workflow, and action layer while designing a different front end around the specific needs of customers, service employees, managers, or field teams.
That range was visible across Salesforce’s Agentic Enterprise City. Brands demonstrated ready-made Agentforce experiences, custom interfaces, and new front ends created with AI-assisted development techniques.
For Miller, the significance is that businesses can begin to rethink how work gets done, rather than simply place AI inside the interfaces they already have.
“This is the opportunity to try that.”— Liz Miller, VP and Principal Analyst, Constellation Research
That should come with a warning. A custom interface does not automatically create a better customer journey. It must still be grounded in accurate data, clear business rules, reliable permissions, accessible design, and an escalation route when the agent cannot safely proceed.
The risk is that organizations focus on the novelty of AI-generated interfaces while overlooking the operational work beneath them. A slick new service experience will not resolve an order issue if inventory data is wrong, entitlement rules are unclear, or the employee receiving an escalation cannot see what the agent has already done.
Koa Is About CRM Context, Not Just Another Model
One of the most notable announcements of the week was Koa, Salesforce’s new CRM reasoning model, developed with NVIDIA.
Salesforce says Koa is designed for complex, multi-step CRM work, including next-best-action recommendations in sales and customer-service scenarios. The company has also said the model was trained on synthetic data, rather than customer data.
Miller’s view is that Koa should not be understood as a model-for-model’s-sake announcement.
“Koa understands the function of CRM. It understands the functionality, it understands the nuance and the difficulty which has been operating these CRM strategies within these technologies.”— Liz Miller, VP and Principal Analyst, Constellation Research
That is the logic behind a CRM-specific reasoning model. General-purpose models may be capable of producing fluent answers, but enterprise CX depends on more than language. It requires an understanding of the customer, the service history, the product or asset, the entitlement, the applicable policy, and the approved next action.