--- title: From Prediction to Action: How to Turn AI Outputs Into... description: Salesforce engineers describe an architectural pattern for turning raw machine learning outputs into actionable recommendations rather than more dashboards.... canonical: https://daily.dev/posts/from-prediction-to-action-how-to-turn-ai-outputs-into-decisions-dq1u6hyfr twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: From Prediction to Action: How to Turn AI Outputs Into Decisions | daily.dev og:description: Salesforce engineers describe an architectural pattern for turning raw machine learning outputs into actionable recommendations rather than more dashboards.... og:url: https://daily.dev/posts/from-prediction-to-action-how-to-turn-ai-outputs-into-decisions-dq1u6hyfr og:image: https://api.daily.dev/og/posts/dQ1U6hyFR.png og:image:alt: From Prediction to Action: How to Turn AI Outputs Into Decisions og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # From Prediction to Action: How to Turn AI Outputs Into Decisions **[Salesforce Engineering](https://daily.dev/sources/salesforceeng)** · 9 min read · 3 upvotes · 0 comments ## Summary Salesforce engineers describe an architectural pattern for turning raw machine learning outputs into actionable recommendations rather than more dashboards. The core idea is treating model predictions as 'signals' that must be combined with business logic and contextual/tacit knowledge to produce a usable recommendation. They built a Next Best Action layer that fuses these three ingredients, exposed it to AI agents via Model Context Protocol (MCP) with explicit tool contracts, debated centralized versus decentralized MCP ownership (concluding architecture should follow data/capability ownership), and delivered recommendations inside Slack (where sellers already work) using a pull-based 'Plan my day' interaction instead of another dashboard or notification stream. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the difference between an AI signal and an AI decision in a recommendation system? A signal is a raw model output, like a churn risk score of 0.83, that identifies what is happening but requires additional interpretation before someone can act. A decision or recommendation combines that signal with business logic (thresholds, priorities, operating rules) and contextual knowledge (institutional and field expertise) to produce an action, the context it applies to, reasoning, and expected impact. _Engineers designing recommendation layers can find more architecture breakdowns like this on daily.dev._ ### How does Model Context Protocol help AI agents use business recommendation systems? MCP acts as a contract between an AI agent and a recommendation layer by exposing callable tools with defined input schemas and return types, letting the agent determine at runtime which tools it needs. If a tool returns nothing, the agent recognizes the absence rather than fabricating a result, and it can decline a request if the needed capability isn't in its tool surface. _Teams wiring agents into internal systems can track MCP patterns and pitfalls on daily.dev._ ## Similar posts on daily.dev - [DO → AI → Design: The Framework Turning Data Into Decisions People Actually Trust](https://daily.dev/posts/do-ai-design-the-framework-turning-data-into-decisions-people-actually-trust-7v0alpq94) · Medium · 0 upvotes · 0 comments - [Bridging the Gap - How We Made AI Agents 10x Developers in Our Organization](https://daily.dev/posts/bridging-the-gap---how-we-made-ai-agents-10x-developers-in-our-organization-9xldqiaaq) · DEV · 4 upvotes · 0 comments - [Building Your AI Work Factory](https://daily.dev/posts/building-your-ai-work-factory-dzibwosow) · Atlassian · 0 upvotes · 0 comments - [Beyond Code Generation: AI for the Full Data Science Workflow](https://daily.dev/posts/beyond-code-generation-ai-for-the-full-data-science-workflow-hqrcj0p03) · Towards Data Science · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#career](https://daily.dev/tags/career), [#ai-agents](https://daily.dev/tags/ai-agents), [#mcp](https://daily.dev/tags/mcp), [#salesforce](https://daily.dev/tags/salesforce) [View this post on daily.dev](https://daily.dev/posts/from-prediction-to-action-how-to-turn-ai-outputs-into-decisions-dq1u6hyfr) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"From Prediction to Action: How to Turn AI Outputs Into Decisions","url":"https://daily.dev/posts/from-prediction-to-action-how-to-turn-ai-outputs-into-decisions-dq1u6hyfr","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/from-prediction-to-action-how-to-turn-ai-outputs-into-decisions-dq1u6hyfr"},"datePublished":"2026-08-24T23:43:51.672Z","dateModified":"2026-09-14T08:58:14.135Z","description":"Salesforce engineers describe an architectural pattern for turning raw machine learning outputs into actionable recommendations rather than more dashboards....","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/cd683134481435fa766a5826559adf36?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/cd683134481435fa766a5826559adf36?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Salesforce Engineering","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Salesforce Engineering","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/513375817ec24d97b9cdb5efd16f87f2","url":"https://daily.dev/sources/salesforceeng"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/from-prediction-to-action-how-to-turn-ai-outputs-into-decisions-dq1u6hyfr","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":3},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,career,ai-agents,mcp,salesforce","timeRequired":"PT9M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Salesforce Engineering","item":"https://daily.dev/sources/salesforceeng"},{"@type":"ListItem","position":3,"name":"From Prediction to Action: How to Turn AI Outputs Into Decisions"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/from-prediction-to-action-how-to-turn-ai-outputs-into-decisions-dq1u6hyfr#faq","mainEntity":[{"@type":"Question","name":"What is the difference between an AI signal and an AI decision in a recommendation system?","acceptedAnswer":{"@type":"Answer","text":"A signal is a raw model output, like a churn risk score of 0.83, that identifies what is happening but requires additional interpretation before someone can act. A decision or recommendation combines that signal with business logic (thresholds, priorities, operating rules) and contextual knowledge (institutional and field expertise) to produce an action, the context it applies to, reasoning, and expected impact. Engineers designing recommendation layers can find more architecture breakdowns like this on daily.dev."}},{"@type":"Question","name":"How does Model Context Protocol help AI agents use business recommendation systems?","acceptedAnswer":{"@type":"Answer","text":"MCP acts as a contract between an AI agent and a recommendation layer by exposing callable tools with defined input schemas and return types, letting the agent determine at runtime which tools it needs. If a tool returns nothing, the agent recognizes the absence rather than fabricating a result, and it can decline a request if the needed capability isn't in its tool surface. Teams wiring agents into internal systems can track MCP patterns and pitfalls on daily.dev."}}]} ```