Key takeaways
- Value concentrates in decisions that are frequent, slow and costly to get wrong.
- Text-heavy processes are the best fit for the first wave.
- Uses that make final decisions about people should be addressed last.
- The same use case does not produce the same value in every organisation; context decides.
First wave: text and document-heavy processes
The fastest results come from processes whose input and output are text: drafting proposals and contracts, searching technical documentation, classifying and routing incoming requests, extracting actions from meeting notes, multilingual content production.
What these share is that the output is easy to verify. Because human approval can be given quickly, adoption is also quick.
Second wave: decision support and analysis
As data maturity grows the second wave arrives: demand forecasting, stock and pricing suggestions, churn risk, maintenance prediction, visual quality control.
Value here is larger, but so is the burden of data preparation and integration. Moving into this wave without the organisational trust earned in the first is usually difficult.
Areas that should wait
Uses that make final decisions about people — hiring rejections, performance ratings, disciplinary outcomes — carry high legal and ethical risk. AI should at most produce input here, never the decision.
The same applies to financial and operational decisions where errors cannot be reversed.
The prioritisation matrix
Score use cases on two axes: potential benefit (hours saved, error reduction, revenue impact) and implementation difficulty (data access, integration, privacy, process owner willingness).
High benefit–low difficulty is the first wave. High benefit–high difficulty goes on the roadmap. The low-benefit quadrant should be deferred however exciting it looks.