Agent force is here - Are you ready to harness the power?

Agent force is here? - Are you ready to harness the power?

Salesforce introduced Agentforce at Dreamforce with great fanfare and excitement. But what does it actually mean for the average business user?

At first glance, it might seem like everything is changing—and yet, nothing is. Let me explain.

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Agentforce represents a natural evolution in the AI maturity cycle and its adoption within the industry. It is set to become a leader amidst similar initiatives from various vendors. In fact, I’ve seen people exploring similar ideas.

This is set to create significant opportunities within the Salesforce ecosystem for both customers and partners. I foresee the potential for hundreds of specialized, purpose-built AI agents becoming available on the AppExchange to address unique challenges. This could serve as a powerful catalyst for innovation. While some technocrats may debate the merits of AI versus generative AI, I believe this has the potential to be a game-changer. Multiple virtual assistants could enable employees to focus on critical tasks, boosting productivity across organizations. What’s even more exciting is that Agentforce will be able to integrate seamlessly across platforms like Workday.

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At the same time, from a business perspective, nothing fundamentally changes. To fully harness the power of this technology, you still need to focus on the basics and execute effectively. It will be good to get started on foundational activities to take advantage of the technology in the near future.

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Data Quality: As the saying goes, “Garbage in, garbage out.” The quality and accuracy of the data you feed into your AI systems are crucial. This process requires careful curation and attention to detail, which can be time-consuming and tedious. However, the sooner you begin, the better positioned you’ll be to fully benefit from AI when the technology reaches its full potential

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Data Strategy: To unlock the full potential of advanced features, like those demoed with Atlas, adopting Data Cloud is essential. With corporate data scattered across multiple locations and various organizations pursuing their own data warehouse strategies, it's critical to engage your enterprise architects and system integrators to align on a cohesive data strategy. Data Cloud offers native integrations with platforms like BigQuery and Snowflake, and its no-copy data strategy makes it particularly promising. However, the devil is in the details. As more application vendors roll out their own built-in AI solutions, the demand for accessible, curated data will grow. Establishing a clear AI-data strategy now is crucial to avoid future complexities and ensure seamless integration.

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Data Security: Salesforce is well-regarded for its robust data compliance and encryption capabilities (though some features may come at an extra cost). They ensure customer data is explicitly separated from AI model training. Additionally, techniques like data masking and anonymization can be employed when using AI to analyze or automate processes, safeguarding sensitive information.

In today's world of heightened data security, it’s crucial to involve your InfoSec team early on. This will help establish clear guidelines, remove potential roadblocks, and fast-track approvals. By doing so, your application teams can proceed more efficiently, avoiding repeated engagement and delays.

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Use Cases: At Dreamforce, everyone I spoke to had multiple potential use cases in mind for Agentforce, though there’s still uncertainty about its full capabilities. As more Systems Integrators (SIs) get trained and engage with customers, this will become clearer. I recommend reaching out to your Salesforce Solution Engineers and having your architects run Proof of Concepts (POCs) to experiment. This will help distinguish between the hype and what’s actually achievable. While the technology is still maturing, early use cases could focus on knowledge generation, sending personalized summary emails, and eliminating mundane tasks.

Remember, this isn't a magical new solution—it's a natural evolution in technology.

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Foundations: Beneath the surface, a solid foundation is still critical. You need a reliable co-pilot, effective prompt engineering, strong NLP, well-structured Flows, and optimized Apex code and API integrations. These components must be scalable, robust, and deliver high fidelity. Conducting a health check on your development team's knowledge and existing code is essential. The more you automate, the more potential there is for frustration if the foundation isn’t solid. Prioritize this groundwork while the industry continues to refine the "holy grail" of automation.

These are my thoughts and first impressions, and I will share them as I learn more. What are your thoughts, and what use cases do you think it can solve? Please comment

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