Start with the work, not the model
A useful use case describes who must do what, with which data, what result is acceptable and what happens when the system is wrong. “Use AI” is not an objective; reducing classification time, finding information or assisting a response can be.
The first question is which part requires interpretation, prediction or generation. If clear rules are enough, a deterministic workflow is usually easier to control.
Signals of a good use case
AI fits work involving language, documents, images or many signals that are difficult to encode as rules. It still needs accessible data, an evaluable result and a way to manage uncertainty.
- recurring activity or request volume
- available and authorised data
- criteria for quality and risk
- human review proportionate to error impact
When AI is not the right choice
Do not introduce a model when rules already solve the process, the volume does not justify integration and maintenance, or there is no way to tell whether the output is correct.
It is also risky when a plausible but incorrect answer has uncontrolled consequences. Improve the data, process or interface first.
- too little, inconsistent or inaccessible data
- error tolerance incompatible with probability
- no process owner
- an objective expressed only as “do something with AI”
How to measure value
Define a baseline before implementation: time per task, error rate, requests handled, response time or quality assessed by people. Include integration, verification, access management and model usage costs.
A useful prototype is a limited test with representative data, evaluation criteria and a clear negative-outcome path.
How MightyPixel approaches it
MightyPixel analyses the process and system where AI would live, comparing classic automation, search, rules and AI where interpretation is required.
Technology follows the use case: the aim is a measurable, observable and correctable capability.
In summary
A technical decision is useful when it clarifies the next step, the risks and how to verify the result. The most complex technology is not necessarily the right one; the choice should fit the process and its evolution.
Explore: AI for businessFrequently asked questions
How do we know whether a process fits AI?
It needs recurring work, available data, a component difficult to express with rules and measurable criteria for the output.
When is AI inappropriate?
When simpler rules solve the problem, data is insufficient or the error tolerance is incompatible with a probabilistic result.
How is ROI measured?
Compare a process baseline with real results, including integration, verification, operations and model costs.