Idea Mining
Idea mining is a structured way to find AI scenarios in processes, pain points, audits, working groups, and operational data. Its purpose is not to collect as many ideas as possible, but to identify initiatives that can move through the conveyor toward measurable impact.
Where to look
| Source | What to look for |
|---|---|
| Manual operations | Repeated actions, copying data, preparing text, reconciliations |
| Queues and delays | Steps waiting for an expert, document, check, or decision |
| Errors and rework | Frequent returns, manual fixes, inconsistent interpretation |
| Expert decisions | Classification, recommendation, prioritization, review |
| Documents and knowledge | Search, summarization, answers over policies, draft preparation |
| Customer requests | Repeated questions, request classification, operator assistance |
Session flow
- Prepare process context, metrics, known pains, and existing initiatives.
- Map the current process: inputs, steps, roles, systems, decisions, data, outputs.
- Identify AI application points: reading, search, classification, drafting, prediction, automation.
- Triage ideas by value, data, feasibility, and risk.
- Convert strong ideas into initiative cards and record why weak ideas were rejected or postponed.
Triage questions
| Dimension | Question |
|---|---|
| Value | Which metric changes, and who owns the impact? |
| Data | Are the required data or documents available? |
| Feasibility | Is there a suitable AI product or delivery route? |
| Risk | Which data, decisions, and users are affected? |
Output
- collected idea list;
- initial value/data/feasibility/risk assessment;
- initiative candidates;
- rejected or postponed ideas with reasons;
- owners for the next steps;
- tasks to clarify data, impact, product, or risk.