You’ve heard about Agentforce and seen how it works, but how do you prepare your organization to effectively implement AI Agents for your unique data environment? Agentforce’s Atlas Reasoning Engine relies on access to accurate, relevant, and trusted data in order to build plans that Agents can execute on autonomously, making your data strategy an essential part of implementing Agentforce successfully. 

The Agentforce journey begins with Salesforce Data Cloud, which unlocks the power of generative AI to deliver outcomes without expensive model training. It creates a single platform to access and leverage all your enterprise data, seamlessly integrating structured and unstructured data into Salesforce with a library of connectors. Keep reading to learn why Data Cloud is an essential piece of the Agentforce puzzle, and how your organization can use it to transform your data strategy. 

Gain a complete understanding of your data environment

Mphasis Silverline is an early access Agentforce partner, and we’ve invested internally in both in our vertical expertise and critically thinking on the business side about how Agentforce brings new capabilities or replaces legacy capabilities. Married to that, we’ve also invested in internal resources to develop a center of excellence around Agentforce, Data Cloud, and AI in general within Salesforce. 

We have a framework that helps articulate a capability maturity model for businesses and verticals around the technical capability that they need to achieve to arrive at those business outcomes. How does one mature in their use of Data Cloud and Agentforce from day one. What about the next day? The next 30 days? 

1. We can come in, focus on a couple of use cases and get you up and running in Agentforce, but we are also looking toward the future to help our clients establish the foundational data-driven processes and learning models that will provide access to a broader base of data to autonomously infer and make decisions. 
2. We’re finding in practice that organizations don’t know the full scope or breadth of data that they have. They don’t know what elements they would need to enable these next-gen processes. We can help you make data analysis-driven decisions around the next things you can enable that would have the greatest impact. It’s the classic prioritization conversation, but rooted in analysis of your data environment. 
3. This analysis goes beyond just Salesforce. We have data scientists and experts that can help you understand and leverage the data across other solutions within your enterprise architecture, such as data lakes, data analysis environments, and other systems like SAP. 
4. Deciding what process you would like to have autonomous next after the first few can be a challenge because you’ll find that you will not be able to achieve it without being able to feed the platform with the right information. When we build this data picture, it is inclusive of all of those systems and how we source information from those systems to best make those decisions.

Modernize your data architecture

Much of the value of Agentforce is in enabling assistive agents in the front office that share insights and suggest actions, based on historical information along with real-time intent and behavior, to a human agent who guides the conversation and final action. While these assistive agents are helpful and part of a healthy AI strategy, they are still assistants and not decision-makers. We’ve also invested in developing practices and standards for autonomous, or ‘Agentic’, Agentforce agents that help with back-office work like development, IT, and the automation processes related to self-servicing and service desk. 

Let’s look at a bank that uses legacy on-prem core platforms to track all of their transactions. In order to take advantage of all of their data, the bank needs to modernize their architecture and bring it into the cloud. Data Cloud is the solution that brings all of that information together, harmonizing user profiles so that AI can take action and send notifications across systems and enable personalized experiences. 

The differentiator for Data Cloud is in the activation of the data throughout the Salesforce ecosystem. A challenge for some organizations is that they might have an IT team that is focused on developing similar solutions using their existing data intelligence like Snowflake or Databricks. The IT team is unwilling to give up control of that process, but they don’t understand the development effort between trying to activate that data across the organization using those platforms versus a Salesforce solution like Data Cloud that accelerates time to value. 

For example, marketing teams have an IT cost associated with generating a segment, and many are using a platform like Databricks to provide the segment and then manually piping it back to their marketing platform, where it sits in a queue until someone gets to it. If they want to make a change to the segment, the process repeats. Data Cloud allows marketers to see all of the data across the organization in a live segment so that they can tweak the parameters to see how that impacts their population numbers. Organizations become more efficient by cutting the time to generate that segment from weeks to minutes. 

Incorporate a horizontal integration layer

When we think about enterprise architecture structure, we have a central layer with Salesforce and then underpinning that we have Data Cloud. A specific capability within Data Cloud is its pipelines, which are linked to different data sources via hundreds of available connectors. 

These connectors are easily configurable, but they are also straight data pipes that lack the versatility to translate, transform, or take meaningful action on the data. Differentiated from that is having an integration platform like MuleSoft within your architecture. You need a horizontal element that can serve a lot of different data movement and data manipulation purposes, especially in large enterprises. 

Let’s look at a medical device organization that has high-value assets installed within facilities. They have IoT embedded at the edge and are monitoring, managing, and collecting data from these devices. This is all being aggregated in a data lake where they have likely applied some heuristic models, rote logic models, and AI models to understand what’s important from that mass of data and derive decisions or actions. The question then becomes how do you get this data into your broader architecture? 

The most enterprise thoughtful approach is to look at a horizontal layer that spans the entirety of your enterprise in something like MuleSoft. It provides API management and other capabilities that allow for the interconnection of data systems and solutions across your enterprise through a horizontal layer that is driving consistency around how you connect and how you both consume and serve data. In addition, that horizontal layer provides you capability to do translations, transforms, apply business logic, and more in the ingestion and distribution of data across the enterprise.

The integration layer in action

Let’s think about the full life cycle of an individual, in this case an insurance customer. They come to you for insurance services directly through your website in response to a marketing message targeting high net-worth individuals that could have high-value assets requiring specialized types of insurance. They’ve landed on your website and like the content of what they’ve seen, so they’ve reached out to inquire about more information. You respond to them and the conversation results in them wanting to secure insurance for a set of high value assets that they own. 

You go through that process within your CRM, but the account opening is going to affect a separate claims system that needs to understand who the customer is and their coverage. Business processes can be integrated across systems with the horizontal integration layer, which in this case could be partially AI-driven based on the type of individual and the products they might be purchasing or contracting for. 

That process can then carry over through the integration platform and be automatically created in your claims system. Some of those tedious activities in defining specific attributes of that transaction can be driven by AI to happen automatically. This is driven by Agents in Salesforce that understand what needs to be done, mediated and executed by the integration layer, and consumed by the claims system that then receives that information and sets everything up appropriately.

Start evolving your data strategy

Whether you’re looking for a more complete understanding of your data environment, considering modernization of your data architecture, or want to enhance your data integration, our data experts can help you develop the data strategy you need to make the most of Agentforce’s AI Agents. With the additional power of Mphasis’ vast data capabilities, we can guide enterprise organizations on every step of their AI journey, ensuring that they have the actionable data that will help them deliver exceptional customer service. Learn more about how our experts can implement Agentforce at your organization.