Every free-text suggestion from 10 interviews, clustered into canonical use cases by job-to-be-done, de-duplicated, and ranked by a blend of value, feasibility and demand. Open a row to drill into the value breakdown, enabling tooling, prerequisites, and the anonymised employee evidence behind it.
Auto-draft acknowledgements, status updates and process-explanation emails.
Compress long claim threads and case histories into a 2-minute brief.
Suggest replies to common inbound policyholder questions.
Extract risk/submission fields from broker packets straight into CRM and underwriting tools.
Turn a bullet-point decision into a compliant, on-tone customer letter.
Answer 'what does the wording / guideline say' without scrolling PDFs.
Check a packet for completeness and list exactly what's missing.
Classify incoming claims/complaints by severity and route to the right queue.
Speed up SQL/R/Python data-prep, reconciliation and validation scripting.
Turn first-notice-of-loss emails and attachments into a structured, pre-filled claim record.
Flag when an estimate exceeds limits or figures don't reconcile.
Surface the right answer mid-call instead of searching the KB while the customer waits.
Auto-generate the wrap-up note / case log from the conversation.
Draft empathetic, compliant complaint responses that hold tone under pressure.
Answer broker queries by pulling policy/commission status across systems.
Cluster complaints to surface recurring root-cause themes.
Draft model-methodology docs, variance commentary and committee narratives.
Generate quote letters and cover notes from the underwriter's agreed terms.
Pull the underwriting-relevant facts out of long, unstructured medical reports.
Function (rows) × use-case category (columns). Each cell is coloured by AI-opportunity density — addressable hours weighted by how strongly employees asked. Hover a cell for the underlying count. The hot corner is where to start.
The same ranked use cases, sequenced by feasibility into three waves. Quick Wins are high-feasibility, low-regulatory-risk plays to ship first; Build needs integration and controls; Transform carries the heaviest data/regulatory lift (e.g. health-data or model-governance) and is staged last.