How to Divide AI Tasks Between Salesforce & AI Models

#AI #Strategy #Planning #Salesforce

Author: Andy Forbes

The opinions in this article are those of the author and do not necessarily reflect the opinions of their employer.

As businesses increasingly adopt AI, a critical question arises: how should AI tasks be divided between Salesforce, a sophisticated CRM platform, and external AI models? Striking the right balance between these tools can unlock efficiencies, enhance customer interactions, and provide actionable insights, all while maintaining data integrity and maximizing return on investment.

Understanding Salesforce’s AI Strengths

Salesforce is purpose-built for managing customer data and driving business processes, making it a natural choice for handling CRM-specific AI tasks. Predictive AI tools in Salesforce allow businesses to leverage AI without the need for extensive customization. These tools are deeply integrated into Salesforce’s ecosystem, enabling seamless workflows, real-time insights, and compliance with organizational data security policies.

For instance, Einstein Prediction Builder can forecast lead conversion rates or customer churn using historical data. Einstein Next Best Action takes these insights a step further by providing sales or support teams with actionable recommendations tailored to each customer interaction. These capabilities allow organizations to optimize their existing CRM processes without external dependencies.

Because Salesforce AI is natively embedded, it excels in scenarios where data is structured, the workflows are clearly defined, and the insights are tied directly to Salesforce objects like leads, accounts, opportunities, or cases. This native advantage ensures minimal setup and maintenance, a shorter learning curve for teams, and a high degree of reliability.

The Role of External AI Models in Expanding Capabilities

While Salesforce’s AI tools are powerful, they are designed to address CRM-specific challenges. External AI solutions such as OpenAI, Google Cloud AI, or Azure AI extend the scope of what can be achieved, particularly for tasks requiring advanced customization, complex analysis, or integration with data outside the Salesforce ecosystem.

External AI shines in scenarios like unstructured data processing, where it can analyze open-text fields, customer emails, or documents for trends and sentiment. It is also ideal for tasks requiring cross-domain insights, such as integrating Salesforce data with external sources like ERP systems, marketing platforms, or public market trends. Additionally, external AI solutions are critical for specialized use cases like fraud detection, dynamic pricing strategies, and modeling that goes beyond Salesforce’s built-in capabilities.

For example, imagine your team needs AI to draft responses for multi-turn conversations in customer service. External AI trained on custom conversational models offers rich and context-aware responses. Similarly, if you are analyzing large datasets from Salesforce alongside third-party systems to identify industry trends, external AI can synthesize these inputs to provide more comprehensive insights.

Key Considerations in Dividing AI Work

When deciding whether a task should stay within Salesforce or involve external AI, several factors should guide your decision:

1. Data Sensitivity Sensitive customer data is often best kept within Salesforce, where robust security measures are in place. Salesforce ensures compliance with privacy regulations, such as GDPR and CCPA, making it a safe environment for storing and processing personal information. If external AI is needed, the Salesforce Trust Layer should be used to mask data.
2. Nature of the Task Tasks directly related to Salesforce objects and workflows—like lead scoring, opportunity prioritization, and customer service automation—are best handled natively within Salesforce. For broader tasks requiring non-CRM data or unstructured input, external AI may offer greater flexibility and capability.
3. Customization and Scalability If your use case requires significant customization, such as creating a proprietary model tailored to your business, external AI may be a better fit. External AI solutions hosted by hyperscalers are tuned for computationally intensive tasks or when processing massive datasets.
4. Integration Needs Salesforce is excellent for tasks in alignment to its ecosystem. However, if you need to integrate insights across multiple platforms—like blending CRM data with supply chain analytics—external AI may be necessary to bridge these domains.
5. Time and Resources Salesforce AI offers faster implementation and lower maintenance, making it ideal for organizations looking for quick wins. External AI, while more resource-intensive, delivers significant future potential for innovation when tailored to unique challenges.

A Practical Example

Consider a sales team aiming to enhance lead management and personalize communication. Within Salesforce, Einstein Prediction Builder can score leads based on historical CRM data, enabling the team to prioritize efforts. Einstein Next Best Action could suggest specific follow-ups, such as scheduling a call or sending a customized offer. However, external AI could analyze email sentiment and prioritize leads showing high engagement or generate nuanced email templates based on past behavior and external market data. In this scenario, Salesforce handles CRM-specific tasks, while external AI augments personalization and insight depth.

A Comparison of Salesforce and External AI

Creating a Feedback Loop

To maximize the effectiveness of both Salesforce and external AI, establish a feedback loop. For example, use Salesforce Einstein Generative AI Data Collection to capture feedback on AI-generated recommendations and feed this data back into the external AI to improve its learning. Ensure that all external AI outputs align with business rules defined by the organization, such as compliance regulations or customer interaction guidelines.

Conclusion

Dividing AI work between Salesforce and external solutions is about leveraging their strengths to complement one another. Salesforce should handle CRM-specific tasks where its native capabilities excel, while external AI can be deployed for broader, more complex challenges that require advanced customization or cross-domain integration. By making thoughtful decisions about where tasks should reside, businesses can build a scalable, secure, and innovative AI ecosystem that drives tangible results.

Have you tackled this challenge in your organization? Share your thoughts and experiences in the comments below—let’s continue the conversation on how AI can transform the way we work.

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