From Prediction to Action: How to Turn AI Outputs Into Decisions

Summary

Salesforce addressed an operational challenge where sellers faced roughly 12,000 dashboards and over 20 applications outputting machine learning predictions without clear next steps. The engineering team reframed machine learning outputs as raw signals rather than standalone answers. To bridge the gap between assessment and action, they built a Next Best Action layer that combines model signals, business logic, and contextual institutional knowledge into actionable recommendations. They integrated this layer with an AI agent using Model Context Protocol (MCP) tool contracts, enabling dynamic discovery and explicit handling of missing data. Finally, rather than introducing a separate dashboard destination, the agent serves on-demand recommendations directly inside Slack where sellers already collaborate.

Takeaways

1. 1

   Treating model outputs as signals rather than answers requires combining them with business logic and contextual institutional knowledge to generate actionable recommendations.

2. 2

   Model Context Protocol provides explicit schemas and contracts that allow AI agents to dynamically determine needed tools at runtime and recognize the absence of data rather than guessing.

3. 3

   Delivering AI recommendations via a pull-based interface in existing workspaces like Slack prevents the cognitive overhead of adding new dashboards or unsolicited notifications.