Financial Services
Financial services dashboard for client service and risk visibilityA practical AI enablement case study.
Client service, risk, and productivity dashboard
What changed from problem to rollout
Client service teams were managing high-volume requests with limited visibility into cycle time, risk flags, and where AI could safely reduce administrative load.
We designed a dashboard that organized service work by urgency, risk category, owner, and aging, with AI summaries constrained to approved customer-service and internal-operations use cases.
Enablement focused on compliant prompt use, review checkpoints, exception handling, and manager dashboards that measured adoption without bypassing human judgment.
- 1Categorize one week of client requests by type and count how many are routine (address changes, document requests, status checks) versus judgment-based.
- 2For routine requests, draft response templates and let AI personalize them, with a person approving every send. This is the lowest-risk starting point in a regulated firm.
- 3Write down your review-before-send rule explicitly: which request types require human approval, who approves, and what they check.
- 4Track aging on requests for one month before changing anything, so you have a baseline to measure against honestly.
Compliance review added time to every workflow we launched, and that is the cost of doing this correctly in financial services. The teams that succeeded treated their compliance group as a design partner from week one rather than a final gate.