Agentforce, the GenAI Agent by Salesforce

Agentforce is a solution that uses advanced language models (LLMs or Large Learning Models) to understand the context of interactions and make autonomous decisions. The aim is to enable companies to automate many tasks without the need for predefined scenarios or constant supervision. This approach is particularly useful in customer relations environments requiring a high level of responsiveness.

Each agent can be customized according to your company's sector, domain and specific business processes, based on company data. For example, a customer service agent can automatically handle queries, resolve incidents and, if necessary, transfer more complex requests to human advisors. Similarly, a sales agent can engage prospects, handle objections or schedule appointments based on CRM information.

In terms of adaptability, Agentforce enables agents to be configured by function or sector. Companies can choose pre-trained agents to meet sector-specific needs, for example in retail / distribution, healthcare, or financial services, where specially pre-trained agents can be rapidly configured and operational.

Data quality is a key element in the success of any automation solution, especially Agentforce. For the virtual agent to be able to provide relevant responses and execute reliable actions, the data used must be complete, accurate, recent, and consistent across all sources. Incorrect or obsolete data can lead to errors or biases in interaction management, directly affecting the quality of service delivered.

Thanks to its native integration with Salesforce, Agentforce benefits from immediate access to CRM data, which is already structured and, theoretically, high quality. This reduces the complexity of time-consuming and costly data consolidation, reconciliation and quality assurance. What's more, integration with Data Cloud, Salesforce's tool for unifying and analyzing data from a variety of internal and external sources, reinforces this ability to use information from multiple Information Systems while ensuring that it is consistent and ready for use by agents, which is crucial to guaranteeing accurate, tailored responses to customers.

Although Agentforce offers a solution natively integrated with Salesforce, there are other AI-based virtual assistants, such as Dynamics 365 Copilot natively integrated with Microsoft's CRM solution, or Zendesk AI Copilot. These solutions can also manage customer interactions using chatbots or virtual agents. However, the capabilities of Einstein Copilot and its bots seem to be the natural choice for companies already invested in the Salesforce ecosystem, with an advantage in terms of rapid deployment and data reliability, where other independent solutions would require more extensive configuration and integration steps.

What's more, Agentforce offers considerable flexibility thanks to the Agent Builder, a low-code interface (with low coding requirements, usable by non-technical profiles) that enables companies to configure agents according to their specific needs. Tools such as MuleSoft (the Salesforce tool that facilitates the integration of information systems via APIs) also enable external systems to be connected for smoother data flow management, which can prove complex with some less integrated alternatives.

Implementing Agentforce, or a competing solution, requires an initial scoping and opportunity study that takes into account the complexity factors involved. First and foremost, data quality and availability play a central role. If the company's data is scattered across several IS or databases, or if it is obsolete, this can complicate implementation. Data cleansing, structuring and consolidation can represent a major undertaking before the agent's capabilities can be fully exploited.

Then there's integration with existing systems. While Salesforce and its tools like MuleSoft make this task easy, the complexity increases when you have to connect multiple external systems that don't naturally communicate with each other. This requires the technical skills of a data engineer or developer to configure APIs and ensure that data flows are fluid, scalable and consistent.

Another aspect to take into account is adaptation to the company's specific processes. Even with agents pre-trained by sector or function, it is often necessary to configure the agents to match the company's reasons for contact, types of request or internal business rules. This stage requires the definition of guardrails (the limits of action for virtual agents, to avoid abuse by customers or problematic hallucinations by bots), as well as the moments when the agent must escalate an alert or a request to a human.

Finally, data security is a key aspect to consider. Salesforce has implemented the Einstein Trust Layer, which ensures that sensitive corporate data is not shared with or viewed by third-party providers. This framework ensures that virtual agents can use language models while complying with strict security standards, particularly important in regulated industries.

In summary, Agentforce is a solution that offers companies intelligent automation of their interactions, with a notable advantage in terms of CRM integration and customer data management. However, its deployment can prove complex, not least because of the need to ensure irreproachable data quality and adapt the tool to internal processes. Although competing solutions exist, the native integration with Salesforce and the flexibility offered by Agent Builder and MuleSoft make it a solid option for companies that have already invested in the Salesforce ecosystem, to significantly improve the management of customer interactions and the operational efficiency of Sales and Customer Relations.

Sia Partners has created a Salesforce Center of Excellence to support Marketing, Sales, Customer Service and Customer Experience professionals in implementing Salesforce and optimizing its use. The aim is to enable sales teams, through the use of the platform, to realize the target customer experience, effectively deploy marketing strategies, drive profitable and sustainable growth, and deliver differentiated service experiences.

An analysis of your company's strategy, particularly in terms of customer relations, together with an analysis of your business needs, will enable you to choose the Salesforce functionalities best suited to your company. Sia Partners' Salesforce-certified consultants ensure their adoption, by carrying out diagnostics and formulating recommendations aimed at improving the customer and employee experience, and taking advantage of the new functionalities offered by the publisher (e.g.: generative AI, Data).

Sia Partners has developed expertise in the challenges faced by CRM and Customer Relations Centers, and in managing the activity and performance of CRCs. Depending on your needs, our consultants can support you at every stage of your project.

From the earliest stages of your project: 

• Flash diagnosis of your Data and AI maturity level for Customer Relations.
• Alignment of COMEX with the vision and use cases for Data and AI, including generative AI.
• Definition of your target operating model and co-construction of the generative AI roadmap: organization, processes, tools and governance at local and central level.

During the design phase: 

• Design of optimized target customer journeys and relational model between self-service, treatment by generative AI bots or human agents. 
• Specification of AI business requirements of your CRM, Marketing, Digital, Sales and Customer Service teams, functional and application mapping and preparation of the call for tenders. 
• Selection of the model or technical solution best suited to your business needs and organizational, data and IS constraints. 

Throughout the development and implementation of the target operational model: 

• Development of customized data models and products for your needs by our Data Scientists and Machine Learning Engineers.
• Project management for the transformation of the target operational model, coordinating your Business, Data and IS teams. 
• Change management, acculturation, and training teams to secure the adoption and value generated by Data and AI use cases.