Q&A

Following the discussion, we also addressed a number of insightful questions from participants, covering practical aspects of Data 360, Agentforce, and AI-driven marketing.

Below, you’ll find a summary of the key questions and answers from the session.

Q1: Identity resolution in Data 360 offers several different ways to unify your records. Can you create unified profiles from accounts and individuals?

A1: Yes, you can create identity resolution for unified profiles for both Individuals and Accounts.
If your data model includes both Individual and Account entities, you can run identity resolution for each and generate:

• A Unified Individual Profile
• A Unified Account Profile

Q2: Can you build unified profiles for households?

A2: If your objective is to create a household-level profile in Salesforce Data Cloud, you would not typically use identity resolution directly on households.
Instead, you would model households as a separate entity and then use Data Cloud capabilities to associate unified individuals with those households.

Q3: How can you ingest data from external systems such as SAP into Data 360?

A3: There are several ways to ingest data from external systems such as SAP into Salesforce Data Cloud (Data 360).
Data 360 provides over 270 pre-built connectors for various systems. Depending on your system, you may be able to ingest data directly using one of these connectors.

Additionally:
• SDKs are available for web and mobile app event data integration
• Ingestion APIs can be used to stream data or import bulk historical batches
• Zero-Copy Data Federation enables you to query data virtually from external data warehouses without physically replicating it

Q4: How do we maintain the authenticity of our brand voice whilst scaling agentic AI use cases?

A4: Agents can be grounded using brand-specific data, content and business context. Salesforce’s Brand Centre, announced at Connections, was designed specifically to help organisations maintain a consistent brand voice by providing agents with approved brand guidelines, messaging, tone and terminology.
This ensures that agentic experiences remain both intelligent and authentically aligned with the brand.

Q5: Since AI agents are not deterministic, how can we ensure they consistently perform as expected? Additionally, for AI-generated content in two-way conversational emails and SMS, how can we build trust that the content is accurate, relevant and on-brand?

A5: AI agents are not deterministic, so the objective is not to predict every response, but to control boundaries and validate outcomes. This is achieved by grounding agents in enterprise data, brand guidelines and guardrails.

Salesforce’s Agentforce Testing Centre enables agents to be tested against predefined scenarios prior to deployment, helping to ensure they behave as expected.

Agentforce Observability provides the tools required to measure, analyse and optimise agent performance.
For AI-generated emails and SMS, the same principles apply: brand controls, compliance checks, testing and ongoing monitoring help build confidence that the content is accurate, relevant and aligned with the brand.

Q6: How should organisations apply the “Start Small, Think Big” mindset when adopting Agentforce Marketing? What are the appropriate first steps, and how can you build towards broader transformation?

A6: Start with a focused, manageable use case, but think strategically to establish the right foundations. Select a use case that is impactful enough to demonstrate tangible business value whilst remaining achievable.
The most critical aspect of “Think Big” lies in the data foundation. Many desired marketing capabilities—such as personalisation, segmentation, journey orchestration, AI-driven recommendations and agentic experiences—depend on having trusted, connected and accessible customer data.
For marketers, this involves defining a vision for how customer engagement and campaign execution will evolve.

For technical teams, this means investing early in fundamentals, including:
• Designing a scalable data model
• Establishing identity and consent management
• Implementing data governance
• Building an integration architecture that can evolve over time

Focus on ingesting the data required for the initial use case whilst ensuring that structures, standards and architecture can support future use cases. Organisations that establish a strong foundation can scale more efficiently and unlock greater long-term value.

Q7: If Data 360 is already being used for sales use cases, how can we add marketing data and use cases without disrupting the existing sales data model?

A7: If Data 360 has been designed correctly, adding marketing use cases should not disrupt sales; rather, it should enhance it.
Data 360 is intended to function as a shared customer data foundation, rather than a platform dedicated solely to sales or marketing. The key is to design the data model, governance and identity strategy with future expansion in mind.

Q8: How can organisations predict and manage credit consumption as they expand Data 360 and Agentforce use cases across different business functions? For example, if credits were initially purchased for sales use cases, how can teams understand and govern consumption as marketing also begins using the platform?

A8: At present, there is no native model that automatically allocates Data 360 or Agentforce credit consumption specifically to “sales” or “marketing” in the same way costs are traditionally assigned to cost centres.
For Data 360, the Digital Wallet provides visibility into different usage types and resource categories, helping to identify which activities are driving consumption.

Salesforce has also expanded reporting capabilities for tracking consumption, including reporting by sources, prompts, agents and resource types.

Q9: How should we consider the role of Data 360 alongside our existing CRM, ERP, MDM and other data platform investments?

A9: Data 360 is not an MDM system and is not intended to replace systems of record.
Systems that create and own data—such as CRM, ERP, e-commerce, service or MDM platforms—remain the authoritative sources for that data.

Instead, Data 360 acts as a data harmonisation, unification and business engagement layer. Its role is to bring together data from multiple systems, resolve identities, create a more complete view of customers and accounts and make that information actionable within business processes.
What differentiates Data 360 from a traditional data platform is its ability to connect data directly to business outcomes. Whether supporting segmentation, personalisation, sales insights, service experiences, analytics, automation or Agentforce interactions, Data 360 helps operationalise data across Salesforce and other connected systems.