Client Intake to Proposal in 90 Minutes: A Mock Advisory Case Study

Snapshot

Firm profile: Sydney-based advisory practice, 22 staff, 6 client-facing consultants
Core issue: slow intake, delayed scoping, inconsistent proposal quality
Project window: 8 weeks from discovery to live rollout
Primary objective: reduce time from inquiry to proposal while improving consistency

The Problem

The team was losing momentum in the first 48 hours of every new inquiry.

• New leads arrived via website forms, email threads, and referral messages.
• Discovery details were captured differently by each consultant.
• Scope summaries were often incomplete, so proposal drafting restarted multiple times.
• Senior team members spent high-value time rewriting first drafts.

The outcome was predictable: slow turnaround, inconsistent proposal quality, and avoidable internal friction.

What We Implemented

1. AI Intake Triage

An intake assistant normalized incoming inquiries into a structured brief:

• client type and urgency
• service category and likely scope band
• key risk/compliance flags
• missing information checklist

2. Scoping Prompt Framework

We created a guided scoping workflow so consultants could validate assumptions quickly instead of drafting from scratch.

3. Proposal Draft Generation

Using the approved scope brief, AI generated first-draft proposals with:

• service summary
• delivery phases and assumptions
• timeline and milestones
• fee structure template language

4. Human Review Layer

No proposal was sent without consultant review. AI accelerated drafting; humans controlled quality and commercial judgment.

Results (Mock Metrics)

• Inquiry-to-first-draft time: 2.5 days to 90 minutes
• Proposal revision cycles: 3.2 average to 1.4 average
• Senior rework time: reduced by 46%
• On-time proposal delivery: 61% to 93%

These are representative outcomes for a well-scoped first-phase implementation, not guaranteed benchmarks.

Why It Worked

• The team standardized inputs before automating outputs.
• AI handled repeatable structure; consultants handled strategy and nuance.
• Review checkpoints prevented low-quality or off-brand proposals.
• The rollout focused on one revenue-critical workflow first.

Implementation Notes

If this were a live engagement, phase one would typically include:

• discovery workshop and process mapping
• policy and data-handling guardrails
• pilot workflow with 1-2 service lines
• success metrics baseline and post-launch tracking

Next Step

If you'd like, we can run this exact workflow against your process and show where AI can cut turnaround time without reducing quality.