A good AI use case can be explained without naming a model.
- 01
Start with a recurring task and the current problem, not a list of AI products.
- 02
Check whether search, rules or conventional automation solve the task more reliably.
- 03
Assess value and risk together; higher consequences require stronger review and approval.
The problem comes before the technology.
Many initiatives start with a product demo and then look for a task. That creates interesting experiments but weak priorities.
Start with recurring friction, missing information, waiting time and avoidable error. Only then decide whether AI is the right approach.
- Name the task and users
- Measure frequency and effort
- Identify sources and systems
- Define a better outcome
Not every knowledge problem needs an agent.
Fixed rules are usually better served by conventional automation, while simple retrieval may only need better search.
AI becomes useful when content must be interpreted, combined or adapted. Even then, assistance may be a better start than autonomous action.
- Search
- Workflow
- Copilot
- Bounded agent
Time saved is not the whole business case.
Consider volume, current effort, expected improvement and the full cost of implementation and operations.
Quality, lead time and revenue impact can matter as much as labour savings.
- Volume and effort
- Quality or revenue effect
- Implementation cost
- Ongoing platform cost
- Cost of errors
Test real cases and allow a stop decision.
Use a defined user group, real tasks and a fixed timeframe.
After four to eight weeks, decide whether to scale, change or stop.
- Representative test set
- Named owners
- Quality and cost measures
- Explicit decision point
Is AI a fit for this task?
- 01
Is the current problem clear?
- 02
Is the task frequent enough?
- 03
Is the required information available?
- 04
Can quality be verified?
- 05
Is error impact manageable?
- 06
Is there an operational owner?
- 07
Is there a scale or stop decision?