Why Smart Companies Are Doubling down on Salesforce AgentForce
Dreamforce ’24 was an outstanding event for Salesforce AgentForce.
I gained valuable insights into Salesforce strategy, engaged in productive client meetings, and, most importantly, had meaningful conversations with peers about what customers are seeking in Data Cloud and Gen AI.
For large companies with established data engineering capabilities and existing investments in business intelligence, what’s the value in further investing in Data Cloud and AgentForce? And for smaller companies, is AgentForce truly affordable?
So, what is AgentForce?
Salesforce’s solution empowers clients to build autonomous agents on top of their existing Salesforce platforms.
It goes beyond traditional bots with integrated GenAI capabilities and surpasses standard data warehouses by grounding responses in real-time data. The standout feature is the intelligent learning engine, Atlas, which identifies the next-best action based on ongoing interactions.
For me, the best part of AgentForce is its no-code/low-code functionality, enabling faster use-case deployment and significantly reducing time-to-market. Here is a summary of all the components that make up AgentForce:
Role: What job should they do?
Knowledge: The data an agent needs to be successful
Actions: The goals and agent can fulfil
Guardrails: The guidelines an agent can operate under
Channel: The applications where an agent gets work done
Why would AgentForce need Data Cloud?
ata Cloud, on the other hand, serves as a microcosm of broader business intelligence capabilities—typically offered by comprehensive platforms like Snowflake or Databricks—integrated directly within the Salesforce ecosystem.
It provides out-of-the-box extract, transform, and load (ETL) capabilities for data residing both within and outside of Salesforce organizations, laying the groundwork for advanced in-platform analytics.
The platform’s zero-copy capabilities address the persistent data duplication challenge, ensuring data integrity and security.
AgentForce relies on Data Cloud to deliver transformed business insights, enhancing response accuracy and relevance by incorporating contextually relevant information, regardless of data location. Here is a view of the microcosm:
Should larger companies adopt AgentForce?
Absolutely—and for a straightforward reason: speed to market.
For companies that prioritize building from the ground up and have substantial engineering resources, creating a fully customized AI capability in-house is achievable and may lead to standout, GenAI-driven innovations—you might be the next technology product unicorn startup on Gen AI.
However, for most companies where rapid market deployment is essential to maintain a high standard of user experience for both internal and external users, AgentForce offers a streamlined path to implementing GenAI use cases on the business front lines. This empowers agents to enhance sales and service team support with minimal lead time.
Should smaller companies venture into AgentForce?
Unequivocally, yes.
With 45% of the U.S. population already using Generative AI, these capabilities are becoming a necessity. It’s only a matter of time before customers and employees demand GenAI features in every platform they interact with.
While building in-house GenAI expertise and infrastructure can be costly—not to mention the ongoing operational expenses—AgentForce offers a compelling solution.
Its on-demand pricing model, combined with a no-code/low-code platform, significantly mitigates investment risks, while the faster time-to-market makes it an obvious choice for smaller companies.
What should the roadmap for Agentforce look like?
This is not an easy question to answer, but there are several clear and established approaches for enabling the organic adoption of this new technology.
To begin with, GenAI use cases focused on workflow automation represent the low-hanging fruit—such as automating customer interactions for service requests or automating lead qualification, for example.
Future phases should concentrate on creating entirely new workflows driven by GenAI: replacing manual, time-consuming processes (like navigating through countless clicks) with more efficient, streamlined solutions. Additionally, developing next-best-action use cases will be key in maximizing the potential of GenAI..
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