What is creating the friction?
For a brokerage where speed-to-response and clean information affect conversion and service, leaders cannot see what is stuck or why. The visible signal is leaders learning about problems through escalations instead of queue signals. In insurance, that friction is amplified by submission, policy, claims, and service work where accuracy and traceability matter.
The first useful question is not "Which AI tool should we buy?" It is "What should happen from the first request to a reviewer receiving the right evidence and an explicit decision path, and who owns each handoff?"
What does a practical first workflow look like?
Design the first version around a reviewer receiving the right evidence and an explicit decision path. Capture the request, associate relevant information from email and documents, assign an owner, record status, and make exceptions visible. Keep scope to one workflow type or team so baseline and adoption signals stay clear.
- Instrument queue age, state, owner, and exception reason.
- Define the trigger and the review-ready outcome.
- Preserve source context and capture required information once.
- Assign ownership, fallback ownership, and the next action.
- Route ambiguous cases to human review.
Where can AI assist?
For review preparation, useful assistance may include assembling relevant context, summarizing changes, and highlighting exceptions. AI should assist a defined task, show enough context for review, and have a safe fallback when confidence is low.
Do not use an AI output as an unreviewed decision in a consequential workflow. The appropriate controls depend on the data, decisions, permissions, retention, and regulatory context of the company.
How should a team measure the change?
Baseline the current workflow before implementation. Track the measure closest to a reviewer receiving the right evidence and an explicit decision path, plus time to first response, percentage of work with an owner, manual touches, rework, queue age, exception rate, and adoption. For this problem, start with leaders learning about problems through escalations instead of queue signals and turn one signal into a weekly operating review.
What should happen next?
A 30-minute discovery chat is the right first step when the workflow is recurring and important enough to improve. Bring one example, the systems involved, the people who do the work, and what "better" would mean. Herculean can then identify whether the right next step is no project, a free short pilot, a paid pilot, or recurring AI engineering support.