Short answer
Decision intelligence is the discipline of engineering how decisions are made, executed and improved by combining data, AI, rules and human judgment. The answers below cover definitions, platforms, comparisons with AI and BI, governance and industry use.
What is Decision Intelligence
What is decision intelligence in simple terms?
Decision intelligence is a way of making important business decisions better and faster by combining data, AI and human judgment in a clear, repeatable process — and then checking whether each decision actually worked.
Is decision intelligence the same as AI?
No. AI produces predictions or content. Decision intelligence uses AI as one input, alongside rules, context and human authority, to decide what action to take and to execute and measure that action.
What is an example of decision intelligence?
Rerouting 2,400 units to a second supplier when the first one slips, with the system gathering order and inventory context, simulating options, showing its evidence, getting a buyer’s approval, raising the purchase order and measuring the result.
Who coined the term decision intelligence?
The discipline was shaped by decision-modeling researchers such as Lorien Pratt and Mark Zangari; Google adopted the term in 2018 for its applied data science work, popularized by Cassie Kozyrkov.
What industries use decision intelligence?
Manufacturing, robotics and physical AI, healthcare payers and providers, logistics, energy, defense, financial services and retail all use decision intelligence for recurring, high-stakes operational decisions.
Read the full page: What is Decision Intelligence →
Decision Intelligence Platform
What does a decision intelligence platform do?
It models decisions explicitly, gathers the context each decision needs, uses analytics and AI to recommend options with evidence, applies policy to decide who may act, executes the action in operational systems and audits the outcome.
How is a decision intelligence platform different from a BI tool?
A BI tool shows what happened. A decision intelligence platform recommends and executes what should happen next, with authority, evidence and a feedback loop.
Do I need to replace my ERP or data platform?
No. Decision intelligence is typically deployed as a layer on top of existing ERP, MES, EHR, CRM and data platforms, reading context from them and writing actions back.
Is there a Gartner Magic Quadrant for decision intelligence platforms?
Yes. Gartner published its first Magic Quadrant for Decision Intelligence Platforms in January 2026.
How long does it take to implement?
A first production decision can typically be piloted in about 90 days when it is scoped to one recurring decision with an owner, existing data and a measurable outcome.
Read the full page: Decision Intelligence Platform →
DI vs BI vs AI
Is decision intelligence better than business intelligence?
They do different jobs. BI reports what happened; decision intelligence uses BI metrics and AI predictions to recommend and execute actions. Most organizations need both.
Is decision intelligence a type of AI?
Decision intelligence uses AI but is broader: it also includes business rules, context, human authority, execution and feedback. It is a discipline and a software layer, not a single model.
What is the difference between decision intelligence and decision support systems?
Classic decision support systems present information to a human. Decision intelligence also models the decision, applies authority policy, executes the action and learns from the outcome.
Can BI tools do decision intelligence?
Some BI tools add alerts and AI summaries, but without explicit decision models, authority control, write-back execution and outcome tracking they remain insight tools, not decision intelligence.
What is the difference between AI, BI and DI?
AI predicts, BI reports, DI decides. Decision intelligence combines the other two with context, policy and execution to turn insight into governed action.
Read the full page: DI vs BI vs AI →
How to implement DI
How long does it take to implement decision intelligence?
A first production decision can typically be delivered in about 90 days if it is scoped to one recurring decision with a clear owner, existing data and a defined outcome metric.
What is the best first use case for decision intelligence?
A decision made weekly or daily, with real money at stake, existing data, one clear owner and reversible actions — for example maintenance timing, supplier reallocation or claim routing.
Do we need a data lake first?
No. Decision intelligence reads the specific context a decision needs from existing systems. A data platform helps but is not a prerequisite.
Who should own a decision intelligence program?
Each decision needs a business owner. The program is usually sponsored by operations or the COO, with data, IT and risk as partners.
How do you measure decision intelligence ROI?
Compare the outcome metric of decisions made with the system against a pre-launch baseline, and track decision latency, override rate and calibration.
Read the full page: How to implement DI →
Anatomy of a Decision
What is decision modeling?
Decision modeling is the practice of writing down a decision’s trigger, inputs, options, constraints, objective, authority and outcome so that it can be reviewed, automated, audited and improved.
What is DMN?
DMN (Decision Model and Notation) is an Object Management Group standard for modeling decision requirements and decision logic in a way that business users and software can share.
What are the elements of a decision?
Trigger, question, context, options, constraints, objective, evidence, authority, action and outcome.
Which decisions should be automated first?
Frequent, operational, reversible decisions with clear constraints and a measurable outcome.
Read the full page: Anatomy of a Decision →
Decision Context
What is decision context?
The specific data, rules, history, constraints and human knowledge a particular decision needs, with each input’s freshness and completeness known.
Why not just give AI all our data?
More data increases cost and noise and still does not guarantee the critical input is present and current. Decision-specific context is cheaper, safer and easier to audit.
What is context health?
A score showing how complete and fresh the inputs required by a decision are, so the system and humans know how much to trust a recommendation.
How does context engineering relate to RAG?
Retrieval-augmented generation is one way to deliver context to a language model. Context engineering decides what to retrieve for a given decision and how to handle gaps.
Read the full page: Decision Context →
Decision Safety
What is decision safety in AI?
The controls that keep AI-assisted decisions within acceptable risk: confidence thresholds, abstention, disagreement handling, safety envelopes, reversibility, rate limits and human escalation.
What is the difference between human-in-the-loop and human-on-the-loop?
In-the-loop means a human approves before the action. On-the-loop means the system acts within a window while a human monitors and can override.
When should an AI system abstain?
When the situation is outside its known domain, when critical context is missing, or when sources conflict beyond a set tolerance.
How do you make AI decisions reversible?
Prefer actions such as holds, drafts and staged changes; require idempotent write-back with rollback; and require higher authority for irreversible actions.
Read the full page: Decision Safety →
Scenario Intelligence
What is scenario intelligence?
Generating feasible options for a decision and simulating their outcomes, uncertainty and trade-offs before commitment.
How is scenario intelligence different from scenario planning?
Scenario planning explores long-range futures for strategy. Scenario intelligence evaluates concrete options for a specific operational or tactical decision, often in near real time.
How do digital twins support decisions?
They model an asset’s current condition and let teams fast-forward degradation or test interventions, turning simulation into a visible decision tool.
What are P10, P50 and P90?
Percentile outcomes of a probabilistic forecast: a 10% chance of being below P10, a 50% chance of being below P50 and a 90% chance of being below P90.
Read the full page: Scenario Intelligence →
Decision Autonomy
What are the levels of AI decision autonomy?
Human, augmented, on-the-loop, automated and agentic.
What does human-on-the-loop mean?
The system acts within a time window or envelope while a human monitors and can override before consequences land.
When should a decision be fully automated?
When it is frequent, bounded by policy, reversible, well calibrated on real outcomes and has exception escalation.
Can autonomy be revoked?
Yes. Drift, incidents, novelty or policy changes should automatically move a decision back to a lower autonomy level.
Read the full page: Decision Autonomy →
Decision Execution
What is decision execution?
The controlled write-back of an approved decision into the operational systems that carry it out, with confirmation, rollback and audit.
What is the insight-to-action gap?
The delay and loss of reasoning between noticing a problem in data and actually changing something in an operational system.
Can decision intelligence write to SAP or other ERPs?
Yes, through APIs or integration layers, usually starting with drafts for human approval and moving to direct write-back for automated decisions.
How do you undo an automated decision?
By designing every action with a rollback or compensating action and tracing each change to its decision ID.
Read the full page: Decision Execution →
Decision Observability
What is decision observability?
Logging every decision end to end and measuring decision quality, latency, calibration, human intervention, compliance and economic impact over time.
How is decision quality measured?
By comparing each decision’s realized outcome with the expected outcome and the outcome of alternatives, aggregated over many decisions.
What is confidence calibration?
The match between stated confidence and actual success rates — 80% confident decisions should succeed about 80% of the time.
Why track human overrides?
Overrides and their reasons reveal where models, context or policies are wrong and are the fastest path to improvement.
Read the full page: Decision Observability →
Decision Ownership
Who is accountable when AI makes a decision?
The named decision owner for that decision type remains accountable, even when AI recommends and systems execute.
What are decision rights?
The defined authority to make, approve, override or change a decision and its policy.
Should a committee own a decision?
No. Committees can advise, but one role should be accountable for each decision type.
How often should decision owners review outcomes?
On a fixed cadence matched to decision frequency — weekly for high-volume operational decisions, monthly or quarterly for tactical ones.
Read the full page: Decision Ownership →
Failure Modes
Why do AI decision projects fail?
Most fail because of missing context, unclear ownership, premature automation, lack of an execution path or no outcome measurement — not because the model was inaccurate.
What is automation bias?
The tendency of people to accept automated recommendations without sufficient scrutiny, especially under time pressure.
How do you prevent AI decisions from going wrong?
Score context health, assign owners, earn autonomy in shadow mode, calibrate confidence on real outcomes, and make actions reversible and observable.
Read the full page: Failure Modes →
Industries
Which industries use decision intelligence the most?
Manufacturing, logistics, healthcare, financial services and energy are established users; robotics and physical AI is the fastest-growing area because software decisions now move physical machines.
What is the best decision intelligence use case to start with?
A frequent operational decision with money at stake, existing data, a clear owner and reversible actions — such as maintenance timing, claim triage or exception rerouting.
Read the full page: Industries →
Robotics & Physical AI
What is physical AI?
Physical AI refers to AI systems that perceive and act in the physical world through robots, vehicles, drones and machines. It is also called physical intelligence or embodied AI.
How does decision intelligence apply to robotics?
It governs fleet-level decisions — task allocation, service timing, escalation, acting on inspection findings — combining robot telemetry with operational context and enforcing autonomy envelopes.
What is an autonomy envelope?
The explicit set of conditions under which a robot or fleet may decide on its own; anything outside it escalates to a human owner.
Does decision intelligence replace robot safety systems?
No. Certified safety functions remain below the AI layer. Decision intelligence plans within them and escalates anything that approaches their limits.
Can decision intelligence manage humanoid robots?
Yes. The same pattern applies: humanoids execute tasks, while the decision layer allocates work, manages service and governs which tasks they may accept without approval.
Read the full page: Robotics & Physical AI →
Manufacturing
How is decision intelligence used in manufacturing?
To decide maintenance timing, quality holds, schedule changes, supplier reallocation, inventory policy and energy optimization — combining MES, ERP, maintenance and sensor data with human approval and write-back.
What is a plant digital twin?
A live model of a plant’s assets and processes that shows current condition and lets teams test decisions — like maintenance timing — before committing.
Is decision intelligence the same as predictive maintenance?
No. Predictive maintenance predicts failure. Decision intelligence decides what to do about it given production plans, spares, windows and cost, then executes and measures the decision.
Does it work for process manufacturing and oil and gas?
Yes. Process plants have high-consequence decisions on rotating equipment, fouling, integrity and energy that suit decision intelligence well.
Read the full page: Manufacturing →
Healthcare
How is decision intelligence used in healthcare?
For prior-authorization triage, claims routing, eligibility answers, member outreach, contact-center next best action, care-gap prioritization, patient flow and denial prevention.
Does decision intelligence make clinical decisions?
No. It prepares, prioritizes and explains; licensed clinicians make clinical determinations.
Can decision intelligence speed up prior authorization?
Yes. It can check documentation completeness, match requests to policy criteria and route straightforward cases faster while sending complex ones to clinical reviewers with the evidence assembled.
How does decision intelligence handle HIPAA?
By accessing only the minimum necessary information for each decision, logging every access and decision, and keeping protected data inside governed systems.
Read the full page: Healthcare →
Logistics
How is decision intelligence used in logistics?
For exception rerouting, carrier selection, dock and yard scheduling, inventory positioning, customer promising and supplier reallocation.
What is the difference between route optimization and decision intelligence?
Route optimization computes routes. Decision intelligence decides which exception to act on, weighs cost and service, applies authority and executes and measures the decision.
What does on-the-loop mean for dispatchers?
The system executes after a short window unless a dispatcher overrides, keeping humans in control without slowing every decision.
Read the full page: Logistics →
Defense
How is decision intelligence used in defense?
In sustainment and readiness: deciding whether to fly, restrict, inspect or ground assets, when to pull maintenance forward and how to allocate parts, using digital twins and maintenance data.
What is an aircraft digital twin?
A live model of an individual aircraft that maps inspection and sensor findings onto its geometry and forecasts how condition and readiness will change.
Oil & Gas
How is decision intelligence used in oil and gas?
For well interventions, choke optimization, asset-integrity decisions, capital-program timing and field logistics, with production and financial forecasts connected.
What is a storage tank digital twin?
A live model of a tank that maps inspection readings such as wall thickness onto its geometry and forecasts when fill restrictions or repairs become necessary.
How does production forecasting connect to finance?
The production forecast becomes the volume input to revenue and cash-flow forecasts, so operational decisions can be evaluated on financial impact.
Read the full page: Oil & Gas →
Architecture
Is the decision intelligence platform cloud agnostic?
Yes. It runs in your AWS, Azure or Google Cloud account, on Oracle Cloud or private cloud, on-prem Kubernetes, at the edge, or fully air-gapped.
Do we need to move our data to a new platform?
No. It connects to data where it lives through connectors, federated queries and change-data-capture, and writes decisions back through system APIs.
Which AI models does it support?
Any model through a model gateway — Anthropic Claude, OpenAI GPT, Google Gemini, Meta Llama, Mistral, cloud-hosted versions on Bedrock, Azure OpenAI or Vertex AI, or your own models.
How does it integrate with SAP, Oracle, Salesforce or Epic?
Through standard APIs (OData, REST, JDBC, HL7 FHIR), event streams and embeddable decision cards that appear inside those applications.
Read the full page: Architecture →
Connected Intelligence
How do you train AI on our company data?
By connecting sources into a shared context and knowledge graph, grounding each decision in the relevant slice, composing several model types, and learning continuously from approvals, overrides and outcomes rather than one-off training runs.
Do you fine-tune a large language model on our data?
Usually not first. Grounding with retrieval and a knowledge graph gives better, auditable results. Fine-tuning or smaller task models are added where the feedback data shows they help.
What is a knowledge graph in decision intelligence?
A connected model of your assets, orders, people, rules and documents and how they relate, so the platform can follow cause and consequence across systems.
How does the AI improve over time?
Every approval, override and measured outcome becomes a labeled example that improves thresholds, rankings and models in the next cycle.
Read the full page: Connected Intelligence →
Accelerators
How fast can decision intelligence be deployed?
A first decision typically connects data in the first week, runs in shadow mode by about week three, executes approved actions by about week six, and reaches production around day 90, depending on data access and security review.
What is pre-built in the platform?
Use-case templates, connectors and APIs, decision logic, algorithms, KPI and threshold libraries, AI guardrails, backend services, frontend components including digital twins, and cloud infrastructure-as-code.
What programming languages does it use?
Decision logic and models are written in Python and SQL with YAML policies; the frontend uses React and three.js; infrastructure uses Terraform and Helm.
Do we need a data lake first?
No. The platform connects to data where it lives. A lake or warehouse helps but is not a prerequisite.
Read the full page: Accelerators →
Food & Beverage
How is AI used in food and beverage manufacturing?
To catch fill-level drift, quality and seal failures, schedule CIP and changeovers, balance lines and manage utilities — recommending actions with evidence that maintenance and quality teams approve.
What is a bottling line digital twin?
A live 3D model of a bottling line that shows the condition of the filler, blow molder, labeler, inspection and packing equipment, and lets teams fast-forward to see the cost of waiting.
Does decision intelligence help with food safety compliance?
It supports HACCP, FSMA and GFSI programs by preparing hold-and-release decisions with evidence and logging every decision; release authority stays with qualified people.
Read the full page: Food & Beverage →
About
Who runs DecisionIntelligencePlatform.com?
Yellowfirst, an AI-first product studio in Plano, Texas, founded by Karna Shukla.
Is this site independent?
The educational content is written to be vendor-neutral. The site is created by Yellowfirst, which also offers decision intelligence design and delivery.
One Organization, Every Function
Can one decision intelligence platform serve multiple departments?
Yes. Each department gets its own decisions, dashboards and permissions, but all read from shared entities, forecasts and policies — which is what keeps them consistent.
What is a CXO decision dashboard?
An executive view that shows the few decisions that need leadership attention today, with the evidence, financial impact and recommended action — rather than dozens of KPIs.
Where should we start across functions?
Pick one decision that crosses functions, like maintenance timing that affects production, sales promises and cash, and connect those roles first.
Read the full page: One Organization, Every Function →
Dashboards by Persona
What is the difference between a BI dashboard and a decision dashboard?
A BI dashboard reports metrics. A decision dashboard leads with recommended actions, their evidence and impact, and lets the owner approve or override in place.
Can decision cards appear inside our existing tools?
Yes. Cards can be embedded in Teams, Slack, email, mobile and inside applications like SAP, Salesforce or ServiceNow.
Read the full page: Dashboards by Persona →
Data Integration
Do we need a data warehouse before decision intelligence?
No. The platform connects to data where it lives. A warehouse or lakehouse helps for analytics but is not a prerequisite.
How does it connect to industrial equipment?
Through OPC UA, MQTT, historians such as AVEVA PI, and edge gateways — read-only by default.
Can it write decisions back?
Yes — as draft or approved work orders, holds, tasks or updates, through each system’s API, with an audit trail.
Read the full page: Data Integration →
Predictive Analytics
What is the difference between predictive and prescriptive analytics?
Predictive analytics estimates what will happen; prescriptive analytics recommends what to do about it. Decision intelligence adds execution and learning from outcomes.
What is predictive modeling?
Building statistical or machine-learning models that estimate future outcomes — such as failures, demand or risk — from historical and real-time data.
Is predictive maintenance the same as decision intelligence?
No. Predictive maintenance estimates failure; decision intelligence decides what to do given production, spares, cost and authority, then executes and measures.
Read the full page: Predictive Analytics →
Cloud vs On-Prem
Is on-prem AI less capable than cloud AI?
Not necessarily. Open-weight models run well on your own GPUs; cloud offers elastic scale and managed models. Many enterprises combine both.
Can the platform run air-gapped?
Yes, with offline model packages and signed updates for disconnected networks.
Which clouds are supported?
AWS, Microsoft Azure, Google Cloud and Oracle Cloud, as well as on-prem Kubernetes, OpenShift and VMware.
Read the full page: Cloud vs On-Prem →
Build vs Buy
Is it cheaper to build or buy AI software?
Buying is usually cheaper at the start for standard workflows. For high-usage, differentiating decisions, owned software often costs less over time because per-seat and usage fees and renewals keep rising, while custom build cost is largely fixed.
Who owns the IP in custom AI software?
It depends on the contract. For work by an outside developer, include a written IP assignment; software generally does not qualify as work made for hire by default under U.S. law.
Why do custom AI projects fail?
Research on 2025 enterprise AI pilots found most never reached production, often because they were not integrated into real workflows. Partnering with experienced builders and starting from a pre-built platform reduces that risk.
What is the best of both worlds?
Custom decision logic you own, built by an experienced partner on a pre-built decision layer, running in your cloud.
Read the full page: Build vs Buy →
Solutions
Which decision intelligence solution should we start with?
The one with the most frequent, costly decision and data you already have — often maintenance timing, sales next-best action or support triage.
Read the full page: Solutions →
IoT
What is IoT decision intelligence?
Turning sensor and machine data into governed decisions and actions, rather than only dashboards and alarms.
Can decisions run at the edge?
Yes. Lightweight runners execute time-critical decisions locally and sync with the cloud when connected.
Digital Twin
What is a digital twin heatmap?
A color overlay on a 3D model showing condition or risk by location, generated from sensor, inspection and maintenance data.
What 3D formats can be used?
Most CAD and 3D formats can be converted to web-friendly glTF, the format used by the live twins on this site.
Read the full page: Digital Twin →
Fintech & Finance
Is AI allowed for credit decisions?
Yes, within regulatory requirements such as explainability and fair-lending rules. Decision intelligence keeps reason codes and human review for exceptions.
How does finance connect to operations?
Through the shared decision layer: when a plant outage or delivery slip is decided, the cash and revenue forecasts update automatically.
Read the full page: Fintech & Finance →
Sales Autopilot
What is a sales autopilot?
An AI system that recommends and drafts the next best action for each account and deal, with the rep approving before anything is sent.
Does it replace sales reps?
No. It removes busywork and gives reps better timing and context; relationships and commitments stay with people.
Read the full page: Sales Autopilot →
Customer Support
Will AI replace support agents?
It handles routine triage and drafting so agents focus on complex, high-value conversations; agents keep control of sensitive actions.
How is it different from a chatbot?
A chatbot answers questions. Decision intelligence also decides routing, escalation and proactive outreach based on what is happening across the business.
Read the full page: Customer Support →
Training & Enablement
How does decision intelligence help training?
It delivers the right procedure and expert guidance at the moment a person makes a decision, and learns where people need coaching from their outcomes.
Can it capture retiring experts’ knowledge?
Yes. Expert approvals, overrides and the reasons behind them become searchable, teachable knowledge.
Read the full page: Training & Enablement →
Cannabis
How is AI used in cannabis cultivation?
To keep grow-room climate in the target range, forecast yield and potency, time harvests and detect problems early — combined with compliance and retail data for end-to-end decisions.
Does it work with METRC?
Decision intelligence can read seed-to-sale data from track-and-trace systems like METRC through their available integrations to reconcile inventory and flag discrepancies.
Is this only for cultivation?
No — it spans cultivation, manufacturing, distribution and retail, wherever the operator is licensed.
Read the full page: Cannabis →
Contact & Demo
How long is a demo?
About 30 minutes, tailored to your decision and industry.
Is there a cost for a demo?
No.
Read the full page: Contact & Demo →
Robot Mission Profiles
What is a robot mission profile?
A precise definition of one job for a robot: the task, operating environment, trained skills, explicit exclusions, autonomy level, escalation rules and learning loop.
Why not train one robot for every task?
Because reliable real-world autonomy today comes from robots tuned to specific tasks in specific settings. Narrowing the mission makes the robot dependable and its limits explicit.
What is an operational design domain (ODD)?
The set of conditions — environment, surfaces, weather, speeds, people — under which a robot is designed to operate safely. Outside it, the robot stops or escalates.
How does decision intelligence help robot fleets?
It assigns the right robot to each job, enforces autonomy envelopes, escalates to the right person and uses outcomes to decide what to train next.
Read the full page: Robot Mission Profiles →
Humanoid Welding Robot
What is a humanoid welding robot?
A human-shaped industrial robot trained to perform welding in spaces designed for human welders, such as ship hull blocks and confined compartments.
Why use a humanoid instead of a welding arm?
Arms and gantries need fixed, open workcells. Shipyard welds happen in cramped, changing compartments that a human-shaped robot can enter.
Which companies are building humanoid welders?
Persona AI is developing humanoid welding robots with HD Hyundai for shipbuilding, with a prototype targeted for the end of 2026 and field testing from 2027.
Read the full page: Humanoid Welding Robot →
Quadruped Inspection Robot
What does a robot dog do in a plant?
It walks scheduled inspection rounds, reading gauges and detecting thermal, acoustic and gas anomalies, then sends findings to operators.
Can quadruped robots work in explosive areas?
Yes — ANYbotics offers ANYmal X, an Ex-proof version of its inspection robot designed for hazardous areas.
Why not let the robot fix what it finds?
Manipulating live equipment needs a far larger training and safety case. Keeping the profile to sensing makes it reliable today.
Read the full page: Quadruped Inspection Robot →
Mining Inspection Drone
What is a mining inspection drone used for?
Surveying stopes, ore passes, cross-cuts and old workings underground — mapping voids, locating hang-ups and checking blast results without sending people in.
How do drones fly underground without GPS?
They use LiDAR and visual-inertial odometry to map and localise, with collision-tolerant cages for tight spaces.
Who uses drones in underground mines?
Glencore’s Kidd Mine in Ontario uses Flyability’s Elios 3 daily for ore-pass, stope and raise-bore inspections.
Read the full page: Mining Inspection Drone →
Autonomous Delivery Robot
What does a hospital delivery robot carry?
Medications, lab specimens, supplies and linen between pharmacy, labs and wards, often in locked compartments that only authorised staff can open.
Can delivery robots use elevators?
Yes — hospital and plant AMRs integrate with elevators and automatic doors, though elevator capacity at peak times is a key constraint.
What is an intralogistics AMR?
An autonomous mobile robot that moves materials inside a facility — between docks, lines, stores, wards or labs — using a map rather than fixed tracks.
Read the full page: Autonomous Delivery Robot →
Wall-Climbing Inspection Robot
How do wall-climbing robots stick to steel?
Most industrial crawlers use permanent magnetic wheels on ferrous surfaces, which is why non-ferrous surfaces sit outside their profile.
What do climbing inspection robots measure?
Mainly wall thickness with ultrasonic sensors, producing dense maps of corrosion and wall loss on tanks, boilers, pipes and hulls.
Who builds wall-climbing inspection robots?
Gecko Robotics is a leading example; its TOKA robots climb industrial assets and feed its Cantilever data platform.
Read the full page: Wall-Climbing Inspection Robot →
Defense Humanoid Robot
What can defense humanoid robots do today?
Early field trials used them mainly for logistics in dangerous areas. Payload, battery life, waterproofing, balance and jamming remain real limits.
Should a humanoid robot make use-of-force decisions?
This profile says no: weapons, targeting and any use-of-force decision are excluded by design and always escalate to a human commander.
What happens if a defense robot loses communications?
It follows a pre-approved safe behavior — hold, retreat or return — and the event escalates to its commander for review.
Read the full page: Defense Humanoid Robot →
Digital Twin Examples
What is a digital twin?
A live digital representation of a physical asset, process or system, updated with real data so you can see its condition and simulate what happens next.
What is a decision twin?
A digital twin designed around a decision: it forecasts how findings evolve, prices acting now versus waiting, and routes the recommended option to the person with authority.
What data does a digital twin need?
Condition data (sensors, inspections, robots, drones), asset history from maintenance systems, the operating plan, and the limits and policies that define when to act.
How is a digital twin used for predictive maintenance?
It maps condition findings onto the asset, estimates remaining life, and recommends the cheapest safe window to act — before an unplanned stop.
Do I need a perfect 3D model to start?
No. Start with the decision and the data. A simplified model with accurate findings beats a photorealistic model with none.
Read the full page: Digital Twin Examples →
Decision Framework
What are the steps of a decision-making framework?
Signal, Context, Options, Evidence and Decide — then Act and Learn to execute the choice and improve the next one.
How is this different from a dashboard?
A dashboard answers the first question at best. The framework carries the decision all the way to an accountable action and a measured outcome.
Can AI answer all five questions?
AI helps most with signal detection, context assembly, scoring options and evidence. Who decides is set by policy, based on consequence and confidence.
Where should we start?
Pick one frequent, high-value decision with a clear owner, write its canvas, and run it in shadow mode against a baseline for a few weeks.
Read the full page: Decision Framework →
Decision Intelligence ROI
How do you calculate the ROI of AI or decision intelligence?
Multiply the number of decisions per year by the improvement per decision and the value of that improvement, subtract the full program cost, and compare against an agreed baseline.
How long does decision intelligence take to pay back?
It depends on the decision. High-frequency, high-value decisions such as maintenance timing or supplier exceptions often pay back within months; measure it with a holdout rather than assuming it.
What costs should be included?
Platform or licenses, build and integration, data work, change management and training, and the ongoing run cost.
Why do AI projects fail to show ROI?
They measure model accuracy instead of decision outcomes, skip the baseline, or never connect the recommendation to an action someone owns.
Is the calculator a quote?
No — it’s an illustrative model with assumptions you replace. A real business case uses your baseline data and a pilot.
Read the full page: Decision Intelligence ROI →
AI · BI · CX · DI · SI
What is the difference between AI and BI?
BI reports what happened using historical data; AI predicts what is likely or generates new content. BI answers “what and where”, AI answers “what next”.
What is the difference between BI and DI?
BI informs a person who then decides elsewhere. Decision intelligence models the decision itself — options, evidence, authority — executes it and measures the outcome.
What is CX intelligence?
Customer-experience intelligence analyzes journeys, interactions and sentiment to show how the experience feels and which fixes matter most.
What is SI in business?
On this site SI means scenario intelligence — simulating what-ifs and comparing options before committing. The acronym is also used for sales intelligence.
What is operational intelligence?
Real-time analytics on live operations — what is happening right now across lines, fleets or sites — usually with alerts.
Is decision intelligence a type of AI?
It uses AI, but it is broader: it combines AI with rules, optimization, human judgment and governance to make and execute decisions.
Which intelligence should a company start with?
Start from the decision you need to improve, then use whichever intelligences it needs. Most enterprises already have BI; the gap is usually DI.
What does AIBICXDI stand for?
AI, BI, CX and DI — artificial intelligence, business intelligence, customer-experience intelligence and decision intelligence: the four layers most enterprises combine.