# Decision Intelligence Platform

> DecisionIntelligencePlatform.com is an independent knowledge, architecture and implementation hub for enterprise decision intelligence (DI) — the discipline of engineering how decisions are made, executed and improved by combining data, analytics, AI, business rules and human judgment. It covers decision intelligence platforms, AI vs BI vs DI, decision autonomy and safety, and industry use in robotics and physical AI, manufacturing, healthcare, logistics, defense and oil & gas. Created by Yellowfirst (https://yellowfirst.com).

Key facts:
- Gartner defines decision intelligence as “a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made, and how outcomes are evaluated, managed and improved via feedback.”
- Gartner published its first Magic Quadrant for Decision Intelligence Platforms in January 2026.
- Core model used on this site: Signal → Context → Options → Evidence → Decide → Act → Learn.
- Five autonomy levels: Human, Augmented, On-the-loop, Automated, Agentic.
- Priority industries: Robotics & Physical AI, Manufacturing (including food & beverage), Healthcare, Logistics; also defense, oil & gas and cannabis.
- Types of intelligence: AI predicts and generates; BI reports what happened; CI explains customers; CX intelligence shows how the experience feels; SI (scenario intelligence) tests what-ifs; OI shows what is happening now; DI (decision intelligence) decides what to do, routes it to the right authority, executes it and measures the outcome. Guide: https://decisionintelligenceplatform.com/ai-bi-cx-di/
- The decision framework: five questions — Signal (what changed?), Context (what does it mean for us now?), Options (what could we do?), Evidence (why this one?), Decide (who has the authority?) — then Act and Learn. Guide: https://decisionintelligenceplatform.com/decision-framework/
- ROI of decision intelligence = decisions per year × improvement per decision × value of that improvement − program cost, measured against an agreed baseline with shadow mode or a holdout. Calculator: https://decisionintelligenceplatform.com/decision-intelligence-roi/
- Decision twins: digital twins built around a decision — heatmap, forecast, cost of waiting, owner. Live examples (processing plant, fighter aircraft, storage tank, bottling line): https://decisionintelligenceplatform.com/digital-twins/
- Robot mission profiles: reliable robots are specialists; each profile defines mission, environment, trained skills, exclusions, autonomy level and escalation. https://decisionintelligenceplatform.com/robot-profiling/
- The decision layer is technology agnostic: it sits on top of existing applications and runs on AWS, Azure, Google Cloud, on-prem, edge or air-gapped, with any AI model.

## Platform

- [Decision Intelligence Platform](https://decisionintelligenceplatform.com/decision-intelligence-platform/): A decision intelligence platform is software that turns important business decisions into explicit, governed models: it assembles context from existing systems, uses analytics and AI to generate and score options, applies policy to decide who may act, writes the chosen action back into operational systems, and audits the outcome.
- [Decision Intelligence ROI](https://decisionintelligenceplatform.com/decision-intelligence-roi/): The ROI of decision intelligence is the value of better decisions minus the cost of the program: annual value = decisions per year × improvement per decision × value of that improvement.
- [One Organization, Every Function](https://decisionintelligenceplatform.com/enterprise/): Enterprise decision intelligence connects the decisions of every function — operations, maintenance, fleet, finance, sales, customer service and the executive team — to one shared context.
- [Architecture](https://decisionintelligenceplatform.com/architecture/): A decision intelligence platform is a layer on top of your existing systems, not a replacement for them.
- [Data Integration](https://decisionintelligenceplatform.com/data-integration/): Data integration for decision intelligence connects enterprise applications, data platforms, industrial systems and documents where they live — using APIs, change-data-capture, streaming and industrial or healthcare protocols — resolves the same entities across systems, and writes approved decisions back into the systems of record.
- [Predictive Analytics](https://decisionintelligenceplatform.com/predictive-analytics/): Predictive analytics uses historical and real-time data to estimate what will happen — failures, demand, cash, risk or churn.
- [Dashboards by Persona](https://decisionintelligenceplatform.com/decision-dashboards/): A decision dashboard is a role-specific view that leads with the decisions a person owns — each shown as a decision card with the signal, recommended action, evidence, confidence, financial impact and approve or override buttons — and supports it with only the KPIs and predictive signals that matter for that decision.
- [Cloud vs On-Prem](https://decisionintelligenceplatform.com/cloud-vs-on-prem/): Choose your own cloud (AWS, Azure or Google Cloud) for the fastest start and elastic scale; on-prem or private cloud when data residency, existing GPUs or latency to plant systems matter; hybrid with edge runners when plants, depots or robots must keep deciding offline; and air-gapped for defense and classified or highly regulated environments.
- [Connected Intelligence](https://decisionintelligenceplatform.com/connected-intelligence/): We train enterprise AI by connecting every source into one decision context, not by pouring more raw data into a model.
- [Accelerators](https://decisionintelligenceplatform.com/accelerators/): Decision intelligence deploys in weeks instead of quarters because most of the platform is pre-built: industry use-case templates, connectors and APIs, a KPI and threshold library, decision logic, forecasting and optimization algorithms, AI guardrails, a decision API with write-back, a frontend kit with decision cards and digital twins, and infrastructure-as-code for any cloud.
- [Build vs Buy](https://decisionintelligenceplatform.com/build-vs-buy/): Buy off-the-shelf software for commodity processes where every company works the same way.

## Solutions

- [Solutions](https://decisionintelligenceplatform.com/solutions/): Decision intelligence solutions apply one shared decision layer to specific jobs: IoT and digital twins for physical assets, fintech for financial decisions, sales autopilot for revenue teams, customer support for service, and training for workforce enablement.
- [Digital Twin Examples](https://decisionintelligenceplatform.com/digital-twins/): A decision twin is a digital twin built around a decision rather than a visualization: it maps live condition data onto a 3D model of the asset, forecasts how each finding evolves, prices the cost of acting now versus waiting, and routes the recommended action to the person with authority.
- [Robot Mission Profiles](https://decisionintelligenceplatform.com/robot-profiling/): A robot mission profile defines one job precisely: the task, the operating environment (its operational design domain), the skills and data it is trained on, what it is explicitly not trained for, the autonomy envelope it may act within, and the conditions that escalate a decision to a person.
- [IoT](https://decisionintelligenceplatform.com/solutions/iot/): IoT decision intelligence connects sensor, machine and telemetry data — over OPC UA, MQTT, historians and edge gateways — detects anomalies and trends, combines them with business context such as schedules, spares and costs, and recommends or executes actions at the edge or in the cloud, with humans approving anything outside policy.
- [Digital Twin](https://decisionintelligenceplatform.com/solutions/digital-twin/): A decision-grade digital twin is a live 3D model of an asset or process with condition heatmaps from sensor, inspection and maintenance data, markers for each finding, and a fast-forward control that simulates the cost of waiting — so teams can compare options and approve the best one directly from the twin.
- [Fintech & Finance](https://decisionintelligenceplatform.com/solutions/fintech/): Decision intelligence for fintech and finance connects transaction, ledger, market and operational data to recurring financial decisions — credit approvals, fraud holds, collections, treasury moves and forecast updates — recommends actions with reasons, enforces policy limits and approvals, and logs every decision for audit and model-risk review.
- [Sales Autopilot](https://decisionintelligenceplatform.com/solutions/sales-autopilot/): Sales autopilot uses decision intelligence to recommend the next best action for each account and deal — who to contact, what to offer, which price and which delivery date — by combining CRM activity, product usage, pricing rules and real-time operations capacity, drafting the outreach for the rep to approve and learning from wins and losses.
- [Customer Support](https://decisionintelligenceplatform.com/solutions/customer-support/): Decision intelligence for customer support routes and prioritizes tickets, drafts grounded answers with citations, decides when to escalate, and triggers proactive outreach when an upstream decision — a delayed shipment, a plant outage, a claim change — affects customers, with agents approving sensitive actions.
- [Training & Enablement](https://decisionintelligenceplatform.com/solutions/training/): Decision intelligence for training and enablement puts procedures, expert reasoning and guided next steps inside the decisions people make every day, assigns work based on verified skills, captures how experts decide so that knowledge outlasts them, and uses approvals and overrides to find where people need coaching.

## Robot mission profiles

- [Humanoid Welding Robot](https://decisionintelligenceplatform.com/robot-profiling/humanoid-welding-robot/): A humanoid welding robot profile limits the humanoid to one skilled job — fillet and butt welds on ship hull blocks — inside a defined envelope of compartments, materials and positions.
- [Quadruped Inspection Robot](https://decisionintelligenceplatform.com/robot-profiling/quadruped-inspection-robot/): A quadruped inspection robot profile gives a legged robot one job: autonomous inspection rounds on a fixed route through a plant — reading gauges, detecting thermal hot spots, acoustic leaks and gas — across stairs, gratings and night shifts.
- [Mining Inspection Drone](https://decisionintelligenceplatform.com/robot-profiling/mining-inspection-drone/): A mining inspection drone profile limits a collision-tolerant drone to underground inspection: flying into stopes, ore passes and cross-cuts after blasting to map voids with LiDAR, locate ore-pass hang-ups and detect over-break or loose rock in GPS-denied, dusty conditions.
- [Autonomous Delivery Robot](https://decisionintelligenceplatform.com/robot-profiling/autonomous-delivery-robot/): An autonomous delivery robot profile limits an AMR to indoor runs on mapped floors — moving parts, totes, medications and samples between docks, lines, wards and labs — including elevator and automatic-door handoffs and people-aware speed.
- [Wall-Climbing Inspection Robot](https://decisionintelligenceplatform.com/robot-profiling/wall-climbing-robot/): A wall-climbing inspection robot profile limits a magnetic-wheeled crawler to integrity data collection: climbing ferrous steel tanks, boilers, pipes and hulls to map wall thickness with ultrasonic sensors, follow weld lines and plan full coverage.
- [Defense Humanoid Robot](https://decisionintelligenceplatform.com/robot-profiling/defense-humanoid-robot/): A defense humanoid mission profile limits a humanoid robot to forward logistics and hazardous-area support: carrying supplies and equipment across rubble and stairs into depots, damaged buildings and contaminated areas, inspecting equipment and scanning buildings with its sensors.

## Core concepts

- [What is Decision Intelligence](https://decisionintelligenceplatform.com/what-is-decision-intelligence/): Decision intelligence (DI) is the discipline of engineering how decisions get made: it combines data, analytics, AI models, business rules and human judgment into an explicit decision model, connects the chosen action to the systems that execute it, and measures the outcome so the next decision is better.
- [AI · BI · CX · DI · SI](https://decisionintelligenceplatform.com/ai-bi-cx-di/): AI (artificial intelligence) predicts and generates; BI (business intelligence) reports what happened; CI (customer intelligence) explains who customers are and what they will do; CX intelligence shows how the experience feels and what to fix first; SI (scenario intelligence) tests what-ifs; OI (operational intelligence) shows what is happening right now; and DI (decision intelligence) decides what to do, routes it to the right authority, executes it and measures the outcome.
- [Decision Framework](https://decisionintelligenceplatform.com/decision-framework/): The decision framework used across this site has five questions: Signal (what changed?), Context (what does it mean for us now?), Options (what could we do?), Evidence (why this one?) and Decide (who has the authority?).
- [DI vs BI vs AI](https://decisionintelligenceplatform.com/decision-intelligence-vs-business-intelligence/): Business intelligence tells you what happened, AI tells you what is likely to happen, and decision intelligence tells you what to do about it — then executes the action under clear authority and measures the result.
- [How to implement DI](https://decisionintelligenceplatform.com/how-to-implement-decision-intelligence/): Implement decision intelligence one decision at a time: pick a recurring, high-value decision with a clear owner, model it explicitly, assemble only the context it needs, set its authority level by policy, pilot it in shadow mode, then turn on execution and measure the outcome against a baseline before scaling to the next decision.
- [Anatomy of a Decision](https://decisionintelligenceplatform.com/anatomy-of-a-decision/): A decision has ten parts: a trigger, a question, the context it needs, the options available, the constraints that limit them, the objective used to compare them, the evidence behind the choice, the authority that approves it, the action that executes it, and the outcome used to judge it.

## Decision engineering

- [Decision Context](https://decisionintelligenceplatform.com/decision-context/): Decision context is the specific data, business rules, history, constraints and human knowledge that one decision needs — not all the data an organization owns.
- [Decision Safety](https://decisionintelligenceplatform.com/decision-safety/): Decision safety keeps AI-assisted decisions within acceptable risk by combining calibrated confidence thresholds, the ability to abstain, surfacing disagreement between sources, hard safety envelopes, preference for reversible actions, and escalation to accountable humans when any of these limits are reached.
- [Scenario Intelligence](https://decisionintelligenceplatform.com/scenario-intelligence/): Scenario intelligence generates the feasible options for a decision — always including doing nothing — and simulates each one’s outcome, uncertainty and trade-offs before anyone commits, using probabilistic forecasts, optimization and digital twins of the assets and processes involved.
- [Decision Autonomy](https://decisionintelligenceplatform.com/decision-autonomy/): Decision autonomy is the level of authority a system has over a specific decision.
- [Decision Execution](https://decisionintelligenceplatform.com/decision-execution/): Decision execution is the step that turns an approved recommendation into real change — a purchase order in ERP, a schedule in MES, a route in the fleet system, a task for a robot — using idempotent, confirmable, reversible actions that are logged against the decision that caused them.
- [Decision Observability](https://decisionintelligenceplatform.com/decision-observability/): Decision observability records every decision — its inputs, options, evidence, confidence, approver, action and outcome — and measures six things over time: decision quality, decision latency, confidence calibration, human intervention, compliance and economic impact.
- [Decision Ownership](https://decisionintelligenceplatform.com/decision-ownership/): Decision ownership assigns one named role accountable for each decision type — its model, policy, autonomy level and outcomes — even when AI recommends and systems execute.
- [Failure Modes](https://decisionintelligenceplatform.com/decision-intelligence-failure-modes/): Decision intelligence usually fails for non-model reasons: missing or stale context used silently, unclear ownership, autonomy granted too early, uncalibrated confidence, automation bias, no execution path, no outcome measurement, optimizing the wrong objective, ignoring ‘do nothing’, and no way to roll back.

## Industries

- [Industries](https://decisionintelligenceplatform.com/industries/): Decision intelligence is used wherever recurring, high-stakes operational decisions depend on data from many systems.
- [Robotics & Physical AI](https://decisionintelligenceplatform.com/industries/robotics-physical-ai/): Decision intelligence for robotics and physical AI governs what machines decide on their own and what they escalate: it fuses robot telemetry with plant, warehouse and safety context, recommends actions like reallocating tasks or pulling a robot for service, and enforces an autonomy envelope so robots act alone only inside limits a human owner has approved.
- [Manufacturing](https://decisionintelligenceplatform.com/industries/manufacturing/): Decision intelligence for manufacturing links MES, ERP, maintenance, quality and sensor data to the plant’s recurring decisions — when to maintain, whether to hold a lot, how to schedule, which supplier to use — recommends the best option with evidence, routes it to the accountable engineer or planner, writes the action back and measures the result on the P&L.
- [Food & Beverage](https://decisionintelligenceplatform.com/industries/food-beverage/): Decision intelligence for food and beverage manufacturing connects filler, blow-molder, inspection, labeler and utility signals with MES, ERP, quality and CIP schedules to decide what to fix, when, and whether to hold product — recommending actions that fit into planned stops, routing them to maintenance and quality for approval, writing work orders back, and measuring OEE, giveaway and compliance outcomes.
- [Healthcare](https://decisionintelligenceplatform.com/industries/healthcare/): Decision intelligence in healthcare speeds up the administrative and operational decisions around care — prior-authorization triage, eligibility and claims routing, care-gap outreach, member service and capacity — by assembling context from claims, EHR and contact-center systems, recommending the next best action with evidence, and keeping clinical decisions with licensed clinicians under HIPAA-grade governance.
- [Logistics](https://decisionintelligenceplatform.com/industries/logistics/): Decision intelligence for logistics handles the daily exceptions that break plans — weather, road closures, late loads, capacity gaps — by combining TMS, WMS, telematics and customer commitments, recommending reroutes, carrier swaps or holds with their cost and service impact, and executing them on-the-loop so dispatchers can override before anything commits.
- [Defense](https://decisionintelligenceplatform.com/industries/defense/): Decision intelligence for defense sustainment fuses inspection, sensor, maintenance and supply data onto a digital twin of each asset, recommends whether to fly, restrict, inspect or ground it with the cost of waiting made explicit, and keeps the decision with accountable maintainers and engineers.
- [Oil & Gas](https://decisionintelligenceplatform.com/industries/oil-and-gas/): Decision intelligence for oil and gas connects production forecasts, asset-integrity findings, field logistics and cash flow so each intervention — pulling a failing ESP, restricting a tank’s fill height, re-routing sand trucks or deferring a pad — is chosen on its combined operational and financial impact and approved by the accountable engineer or executive.
- [Cannabis](https://decisionintelligenceplatform.com/industries/cannabis/): Decision intelligence for licensed cannabis operators connects cultivation sensors, lab results, seed-to-sale tracking such as METRC, point-of-sale and finance data to recurring decisions — climate adjustments, harvest timing, yield and potency forecasts, lab-test holds, compliance reporting, inventory allocation across dispensaries and pricing — recommending actions with evidence and keeping compliance decisions with qualified staff.

## Reference

- [About](https://decisionintelligenceplatform.com/about/): DecisionIntelligencePlatform.com is created and maintained by Yellowfirst, an AI-first product studio and digital transformation consultancy based in Plano, Texas.
- [Contact & Demo](https://decisionintelligenceplatform.com/contact/): To book a decision intelligence demo, leave your full name and work email in the form on this page and, optionally, what you’d like to see.
- [Glossary](https://decisionintelligenceplatform.com/glossary/): This glossary defines the core vocabulary of decision intelligence — from AI, BI and DI to decision models, context health, autonomy envelopes, digital twins, physical AI and decision observability — in plain English.
- [FAQ](https://decisionintelligenceplatform.com/faq/): Decision intelligence is the discipline of engineering how decisions are made, executed and improved by combining data, AI, rules and human judgment.

## Common questions

- What is decision intelligence? The discipline of engineering how decisions are made, executed and improved by combining data, analytics, AI, business rules and human judgment. (https://decisionintelligenceplatform.com/what-is-decision-intelligence/)
- AI vs BI vs DI? BI reports what happened, AI predicts what is likely, DI decides what to do and executes it under clear authority. (https://decisionintelligenceplatform.com/decision-intelligence-vs-business-intelligence/)
- What is CX intelligence? Analysis of customer journeys, interactions and sentiment that shows how the experience feels and what to fix first. (https://decisionintelligenceplatform.com/ai-bi-cx-di/#cx)
- How do you measure AI ROI? Measure decisions, not models: baseline first, shadow mode, holdout, attributable outcomes only. (https://decisionintelligenceplatform.com/decision-intelligence-roi/)
- Who publishes this site? Yellowfirst — an AI-first product studio led by founder and CEO Karna Shukla. (https://decisionintelligenceplatform.com/about/)

## Optional

- [Full text of every page](https://decisionintelligenceplatform.com/llms-full.txt): all pages as plain text for AI assistants.
- [Sitemap](https://decisionintelligenceplatform.com/sitemap.xml)