AI use cases
AI solutions for your area of responsibility
See which challenge to start with, what data it needs and how to bound a pilot before scaling.
Discuss your project →Start with the task
The same model does not solve manufacturing, retail and public-service challenges in the same way. Context determines sources, permitted actions, explainability and human oversight. We define the decision or workflow first, then choose the architecture.
- A specific user and workflow
- Available and permitted data sources
- Quality criteria and automation boundaries
- A pilot that can be validated
A shared foundation
Industry scenarios differ, but all depend on documented sources, aligned metrics, governed access and dependable integrations. A focused pilot can reveal the minimum data preparation needed without redesigning the entire environment.
Human control
AI can retrieve, summarise, classify and propose an action. Decisions with legal, financial, safety or employment consequences remain with an accountable person. Boundaries are defined before launch.
Fit before scope
Is this the right starting point?
Use these criteria for an initial orientation. The final recommendation follows a review of your context, data and constraints.
A good fit when
- A process or decision that needs improvement is identified
- An accountable sponsor and result user are available
- A measurable outcome or quality criterion can be defined
Resolve this first when
- The goal is simply to buy unspecified AI without a defined challenge
- A guaranteed impact is expected before data are assessed
- No one is accountable for accepting the result
A verifiable first stage
What you can use to make the next decision
The exact scope is agreed before work begins. These are typical decision artefacts, not promised business results.
AI use-case matrix
Challenges compared by value, data availability, risk and ease of verification.
Pilot boundary
One process with defined users, sources, human oversight and stop conditions.
Decision criteria
Quality measures and a process to scale, refine or stop the initiative.
Five questions · no datasets or credentials
Industry use cases
Choose your sector
AI document assistant
Retrieval, fact extraction, classification and draft preparation with human review.
Learn more ↗02Management reporting
Aligned measures, variance explanations and recurring reports from governed data.
Learn more ↗03Data migration for AI
Move history and reference data into structures suitable for analytics and AI.
Learn more ↗04Knowledge search for public institutions
Role-aware retrieval across regulatory, methodological and internal documents.
Learn more ↗05Manufacturing
Knowledge, quality, maintenance and production analytics.
Learn more ↗06Retail
Assortment, enquiries, product content and sales analytics.
Learn more ↗07Logistics
Documents, exceptions, routes and operational visibility.
Learn more ↗08Finance
Reconciliation, documents, metric explanations and governed assistants.
Learn more ↗09Communities and regions
Requests, programmes, resources and management reporting.
Learn more ↗10Government institutions
Documents, knowledge, registries and analytics with role-based access.
Learn more ↗Frequently asked questions
Frequently asked questions
Do we need a large volume of data?
Not always. Document retrieval depends more on completeness, freshness and permissions. Forecasting requires sufficient history and stable recording rules.
How should we choose the first use case?
Choose a repeatable task with accessible data, a clear process owner and a practical way to validate quality.
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