We participated in the Agentforce World Tours in São Paulo and Lisbon, hosted by Salesforce, and it was clear that the conversation was not centred on specific features. The market no longer discusses the relevance of AI; rather, it focuses on what needs to be ready for it to work.
This shift is significant. Previously, technology events were showcases of possibilities, with demonstrations and hypothetical cases. In São Paulo and Lisbon, operations, sales, and customer service executives sat down with architects and IT teams to discuss workflows, data, and obstacles. AI left the stage and took a seat at the decision-making table.
Before detailing the main lessons from the events, it’s important to contextualise them in relation to the realities of Salesforce projects in Brazil and Portugal. With this context in mind, we now share our main insights and perceptions based on what we observed and the conversations we had.
The Consolidation of the Agentic Enterprise
The concept of an agentic enterprise, in which AI agents work alongside people, systems, and processes to accelerate decision-making and execution, has moved beyond stage presentations to become a planning reference.
Companies attending the events already had mapped use cases, questions about governance, and doubts about integration. This indicates maturity, not just enthusiasm. For an AI agent to be truly useful, structured data, well-defined processes, and integration across the company’s touchpoints are required. Without these elements, the agent does not add value or improve operations.
Reliable Context as a Prerequisite
Context was a main highlight at both events. AI agents do not work well with fragmented data. Leads, accounts, history, and commercial status must be interpreted consistently in the instance; otherwise, the agent makes decisions with incomplete data, often without anyone noticing in time.
This issue isn’t new, but Agentforce exposes it faster. Bad data isn’t fixed by agents—it just makes problems more visible and urgent.
Reliable data is not a differentiator, but an essential prerequisite. Among the main lessons from the events is the realization that low-quality data increases operational risk, reinforcing the need for quality before automation.
Governance as a Factor for Scale, Not Just Compliance
The discussion about governance at both events moved from the regulatory field to the operational. Governance was not discussed as bureaucratic control, but as a condition for scaling safely: how to audit what the agent decides, how to define what it can or cannot execute, and how to measure whether the result is real or just activity.
Weak permissions and vague roles prevent trust in the agent, hindering team adoption despite technical quality.
Automating a bad process only worsens the problem. That’s why reviewing workflows, responsibilities, and decisions before Agentforce isn’t optional; it separates projects that deliver results from those that create rework.
The Impact on Customer Service, Sales, and Operations
Agentforce had the most impact in areas with high interaction volumes and complex context needs. Gains appeared in customer service, sales, and operations — where agents cut manual work, prioritise tasks, and accelerate workflows, freeing staff for higher-value work.
The key lesson from both São Paulo and Lisbon is clear: adopting agents is a cross-functional initiative. Success depends on leadership, operations, data, and change management working together. Companies that view it merely as a technical implementation risk are missing much of the technology’s potential.
What Needs to Be Ready Before Adopting Agentforce
Preparation for Agentforce starts with a diagnosis. The first step is to identify where AI can generate value with the least friction and greatest relevance. Not every process should be automated immediately. Some areas need review and standardisation before agents are received. Others are mature enough to move forward with specific workflows.
Involving teams early is critical; Agentforce brings value only when everyone is aligned on its use, automated tasks, and responsibilities. Without alignment, adoption faces resistance or remains isolated.
What Biz2People Brings to This Context
Biz2People connects people, processes, and technology on Salesforce. In Agentforce projects, we support diagnostics, workflow review, data integration, use case definition, and adoption monitoring. Each step requires both technical and business perspectives: agents demand both.
Key takeaway: Begin Agentforce adoption by thoroughly understanding your current foundation, identifying what needs improvement, and selecting mature, value-generating use cases. A detailed diagnosis distinguishes valuable strategies from ineffective ones.
→ Talk to Biz2People experts experts to understand the next step for your operation.
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FAQ
What is Agentforce?
Agentforce is a Salesforce solution that creates and operates AI agents connected to business data, processes, and workflows. The goal is to expand the capacity of customer service, sales, and operations, with agents operating in context and governance.
What is Agentforce used for in practice?
It assists teams with high-volume, quick-response, and context-dependent tasks—triage, follow-up, accessing history, and triggering workflows. Its value lies in reliable data and clear processes.
Does Agentforce replace professionals?
Agentforce aims to boost productivity by automating repetitive tasks, freeing professionals to focus on strategy, negotiation, and work that requires judgment or empathy.
What does a company need before adopting Agentforce?
You need quality data, CRM maturity, clear processes, defined governance, and readiness. Move only after diagnosing what’s ready and what needs organisation.
Why is data so important for AI agents?
AI agents rely on data. Fragmented, outdated, or inconsistent information reduces accuracy and increases risk.
How does Biz2People support projects with Agentforce?
Biz2People provides support with Salesforce consulting, diagnosis, workflow review, integration, case definition, and adoption monitoring. The goal is to integrate technology into operations, focusing on results and governance.
