infotech
Before you begin
Three things to keep in view.
- Start with the information required for a specific decision or task.
- Make definitions, data quality and access ownership explicit.
- Evaluate the first use case before extending the data programme.
Choose a useful outcome
Begin with a decision or task that could benefit from better information. Define the intended user, the setting and the difference the solution should make. A focused outcome makes both delivery and evaluation more manageable.
An outcome should describe what changes for a user. For example, a service colleague may need a dependable answer from maintained knowledge, while a planner may need a better view of operational records. Those tasks have different information and evaluation needs. Naming the user and the decision makes the data work easier to prioritise.
Put it into practice: Write down the user, task and decision the information must support.
Know what information the solution needs
Identify relevant data sources, business definitions and access requirements. Look for gaps, inconsistent records and information that is out of date. Addressing these issues can improve existing reporting as well as future AI use cases.
Review a representative sample rather than assuming the source is ready because it exists. Check whether definitions are consistent, important fields are complete and the history reflects the task you want to support. Record gaps with their business impact. This gives the team a practical order for cleanup and integration work.
Put it into practice: Validate source quality with realistic examples.
Make ownership explicit
Assign responsibility for data quality, access and maintenance. Decide how information will be reviewed and corrected. Technical architecture and business accountability need to work together.
A data owner should be able to explain the information, approve meaningful changes and help resolve quality issues. Make that responsibility visible across departments. When several systems hold similar records, agree which one is authoritative and how differences are reconciled before an analytical or AI experience depends on them.
Put it into practice: Name the owner of the definition, not only the technical source.
Plan how people stay involved
Consider where a person should review an answer or approve an action. Set clear expectations about the system’s role and establish a route for feedback. Useful AI fits into an operational process people understand.
Decide how a user can inspect supporting information, challenge an answer and move the task to a person when needed. Test those paths with the same attention as the successful demonstration. The purpose of the design is to make the tool useful within the workflow, including the moments when the information is incomplete.
Put it into practice: Test what happens when the answer cannot be trusted or completed.
Build and evaluate in stages
Choose a bounded first use case and evaluate it against agreed criteria. Use the results to improve the solution and inform the next investment. A roadmap should reflect what you learn.
A first release can reveal that the main constraint is a missing definition, an unsuitable source or an unclear process. Treat that as useful evidence. Update the roadmap around what has been learned and make the next investment proportionate to the benefit the business can now explain. A wider platform should support a visible sequence of useful outcomes.
Put it into practice: Use evaluation evidence to shape the next stage.
Explore Microsoft guidance
Make it specific to your business.
Bring a process, a priority or a decision you are working through. Our team can help you explore the Microsoft capabilities and implementation choices that fit.
Start a conversation