Build or Buy AI: Make the Right Call for Your Enterprise

Build only when the AI capability is core to your business IP and gives you a competitive advantage, you already have clean organized data, and you have in-house data science talent. For everything else—email generation, case summaries, sentiment analysis—buy a solution.

Prompt engineering shapes model behavior through instructions and examples in the prompt without changing the model's weights. It is fast and sufficient for most Salesforce use cases. Fine-tuning retrains the model on your labeled data, changing its weights for domain-specific precision. It requires ML expertise and significant ongoing maintenance.

Key risks include prompt injection, data leakage of PII to third-party models, hallucinated outputs presented as fact, and sensitive data appearing in model responses. Mitigate by masking data before it leaves Salesforce, using a gateway architecture with access controls, and keeping a human in the loop.

Your Salesforce org calls an external AI model via API through a gateway layer. The gateway handles prompt engineering, data extraction and masking, and sends anonymized data to your chosen cloud provider. The response is de-masked and presented back to the user, with all access governed by Salesforce permission sets.

Model drift occurs when the characteristics of your data change over time, causing a previously well-performing AI model to produce degraded outputs. Implement observability by periodically running known inputs against expected outputs, and version-control your prompts like production code.