Dashboards that do not drive action
Plenty of reports exist, but decisions are still made on instinct.
04 / Hybrid Intelligence
Decision Intelligence Solutions
Organisations make the same important decisions again and again: how much to stock, which cases to prioritise, where to send crews, which customers to contact. Decision intelligence improves those decisions by bringing the right data, predictions, rules and scenarios together at the moment of choice.
At a glance
- Service
- Decision Intelligence Solutions
- Pillar
- Hybrid Intelligence
- Typical starting point
- Decision mapping workshop
- Typical first phase
- 3 to 5 weeks for decision design; solution in 10 to 16 weeks
- Key technologies
- Machine Learning, Data Engineering, Artificial Intelligence
Durations depend on scope, data readiness and integration needs.
What is Decision Intelligence?
Short answer
Decision intelligence is a discipline that designs and supports specific organisational decisions by combining data, analytics, predictive and optimisation models, business rules and human judgement in a structured way, with feedback on outcomes used to improve future decisions.
Traditional analytics produces dashboards and leaves people to work out what they mean. Decision intelligence starts from the other end: what decision is being made, by whom, how often, with what options, constraints and consequences? Only then does it ask which data and models would help.
A decision intelligence solution might forecast outcomes for each option, apply constraints and policies, run what-if scenarios and recommend a choice with its reasoning. The accountable person reviews, adjusts and decides, and the outcome is recorded so the system learns which recommendations worked.
Business problems
What problems does Decision Intelligence solve?
These are the situations where organisations most often find this service worthwhile.
Inconsistent decisions across teams
Similar situations are handled differently by different people or regions.
Too many options to evaluate manually
Complex trade-offs, such as allocation or scheduling, exceed what people can compare.
No learning from past decisions
Outcomes are not linked back to decisions, so the same mistakes recur.
Slow planning cycles
Scenario analysis takes days of spreadsheet work.
Decisions made with incomplete information
Relevant data exists in other systems but is not available at decision time.
Use cases
Where can Decision Intelligence be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Supply chain | Inventory replenishment and allocation recommendations with scenario testing. |
| Field operations | Prioritising maintenance jobs and assigning crews based on risk and capacity. |
| Customer management | Next-best-action recommendations for retention and service outreach. |
| Public services | Prioritising inspections, grievances or interventions using risk indicators. |
| Finance | Collections prioritisation and credit limit decisions within policy. |
| Workforce planning | Staffing decisions using demand forecasts and scheduling constraints. |
Who should consider it
- Operations and planning leaders with recurring high-impact decisions
- Organisations with analytics but little impact on decisions
- Teams handling complex allocation or prioritisation
- Public bodies needing consistent, explainable prioritisation
How AARHIT approaches it
How we deliver Decision Intelligence
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Decision mapping
We define the decision, decision makers, options, constraints, information used, frequency and how outcomes are measured.
Output: decision model and success measures.
Stage 2 of 6
Data and signal assessment
Relevant data sources and signals are identified and assessed for availability and quality at decision time.
Output: data inventory for the decision.
Stage 3 of 6
Model development
Forecasting, risk scoring, optimisation or simulation models are built to evaluate options.
Output: validated decision models.
Stage 4 of 6
Rules and policy integration
Business rules, regulations and policies are encoded so recommendations respect them.
Output: rules layer with owner-managed configuration.
Stage 5 of 6
Decision interface
Recommendations, reasoning, alternatives and scenarios are presented where decisions are made.
Output: decision support interface.
Stage 6 of 6
Outcome tracking
Decisions and outcomes are recorded and analysed to improve models and rules.
Output: feedback loop and performance reporting.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Analytics | Data warehouses, semantic layers, business intelligence tools |
| Prediction | Forecasting and risk models using gradient boosting, time series and deep learning |
| Optimisation | Linear and integer programming with OR-Tools, Gurobi or open solvers |
| Simulation | Scenario and what-if modelling, Monte Carlo methods |
| Rules | Decision tables and rules engines managed by business owners |
| Interfaces | Web applications, embedded recommendations in existing systems |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- More consistent decisions across people and locations
- Better use of data and forecasts at the moment of choice
- Faster scenario planning
- Documented reasoning for audit and review
- Continuous improvement from outcome feedback
How long does implementation generally take?
Typical timeline
Decision mapping and data assessment typically take 3 to 5 weeks. A first decision intelligence solution usually takes 10 to 16 weeks, depending on model complexity and integrations.
What determines complexity?
- Number of options and constraints
- Availability of outcome data
- Real-time versus periodic decisions
- Number of decision makers and locations
- Regulatory requirements for explanation
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Define outcomes carefully
If the measure of a good decision is wrong, the system will optimise for the wrong thing.
Keep policy separate from models
Business rules should be adjustable by owners without retraining models.
Record why people override
Overrides reveal missing information or model weaknesses.
| Risk to consider | How we manage it |
|---|---|
| Optimising the wrong objective | Outcome definitions agreed with leadership and reviewed after deployment. |
| Opaque recommendations | Reasoning, key factors and alternatives shown with each recommendation. |
| Feedback loops that reinforce bias | Monitoring outcomes across groups and periodic model review. |
| Data unavailable at decision time | Designing models around information genuinely available when decisions are made. |
FAQ
Questions about Decision Intelligence
What is decision intelligence?
Decision intelligence is an approach that designs and supports specific decisions by combining data, predictions, optimisation, business rules and human judgement, and learns from the outcomes of past decisions.
How is decision intelligence different from business intelligence?
Business intelligence reports what has happened, usually in dashboards. Decision intelligence focuses on a particular decision, evaluates options and recommends actions, with people making the final choice.
Does decision intelligence make decisions automatically?
It can automate low-risk, well-understood decisions, but in our designs consequential decisions remain with accountable people, who see recommendations, reasoning and alternatives.
What data is needed?
Data describing the situation at decision time, the options available, relevant constraints and, ideally, historical outcomes of past decisions. The decision mapping stage identifies exactly what is needed.
Can decision intelligence handle uncertainty?
Yes. Forecasts can include ranges, and scenarios can test decisions under different assumptions so decision makers understand the risks of each option.
Next step
Considering Decision Intelligence for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.