[Becky Budgeter]

I’m exploring whether an AI system can sort and route incoming leads based on how well they fit our ideal customer profile and how urgent their need is, but I don’t want to damage customer experience (slow responses, impersonal outreach, or wrong handoffs).

Before we experiment, I’d love practical advice from people who’ve tried this. A few specific questions:

• How do you balance priority: fast follow-up for high-fit, urgent leads while still treating lower-priority leads respectfully?
• What safeguards work: simple rules, human review steps, or fallback messages that keep things personal?
• How do you measure success: metrics or red flags that show CX is improving or slipping?

If you’ve implemented a tool or workflow (or decided not to), please share what worked, what didn’t, and any small templates or rules that helped. All tips welcome — especially simple, non-technical ones.

[Jeff Bullas]

Short answer: Yes — AI can route leads by fit and urgency without harming the customer experience, if you design it to prioritize human context, clear rules, and fast handoffs.

Why it matters: Customers hate being ignored or misrouted. Sales teams hate low-quality handovers. The right AI triage reduces friction, speeds response, and keeps the experience personal.

What you’ll need

• Clean lead data fields: role, company size, industry, budget range, timeline, channel (email/phone/website), and short note/message.
• A simple classifier or LLM with a prompt-based triage workflow.
• CRM/webhook integration to apply routing and SLA rules.
• Human-in-the-loop reviews for edge cases and weekly feedback to retrain rules.

Step-by-step setup

1. Define routing rules: what counts as high-fit (e.g., company size > X, budget >= Y) and high-urgency (keywords like “this week”, “ASAP”, timeline <= 30 days).
2. Build a simple scoring formula: Fit Score (0–100) and Urgency Score (0–100). Combine into a Routing Priority.
3. Use an AI model to extract intent and clean missing fields from free text. Ask it to return structured output (JSON) for parsing.
4. Map Routing Priority to actions: immediate SDR alert + 15-minute SLA; nurture sequence; assign to Channel Specialist; ask for more info if unclear.
5. Implement human checks: any lead with borderline score or flagged phrase goes to a queue for human review within 1 hour.
6. Measure outcomes: response time, conversion rate, customer satisfaction, and handover quality.

Practical example

Lead submits: “We’re a 120-person fintech exploring a solution this month, budget $50k. Need demo ASAP.” AI extracts company_size=120, industry=fintech, timeline=this month, budget=50k → High Fit + High Urgency → Route to Enterprise SDR with 15-minute alert and proposed demo slots.

Common mistakes & fixes

• Over-automating: Fix: keep humans for borderline or high-value leads.
• Poor data: Fix: enrich records (LinkedIn, firmographic services) and validate fields.
• Slow response from routing: Fix: ensure real-time webhook and short SLAs; use push notifications.
• Bias or wrong rules: Fix: review weekly, track false positives/negatives, and adjust scoring.

Copy-paste AI prompt (use as-is or tweak)

Prompt (ask your LLM to return strict JSON):

“You are a lead triage assistant. Given the following lead data, extract structured fields and assign scores. Input fields: name, message, company_size, industry, role, budget, timeline, contact_channel. Output JSON with: fit_score(0-100), urgency_score(0-100), recommended_route([‘SDR’,’Enterprise’,’Nurture’,’Channel Specialist’,’Request Info’]), reason_short, follow_up_text (one short personalized opening line). Use these rules: fit_score up for role match, company_size thresholds, budget match; urgency_score up for timeline keywords (‘today’,‘this week’,‘ASAP’,<=30 days). If missing critical info, recommend ‘Request Info’ and a one-line follow_up_text asking for timeline and budget.”

Quick 30/60/90 action plan

• 30 days: Build scoring rules, run AI extraction on past 1,000 leads, and create routing playbook.
• 60 days: Integrate with CRM, enable real-time routing, and start human review queue for edge cases.
• 90 days: Measure conversion and CSAT, refine prompts and thresholds, roll out automated alerts for the team.

Final reminder: Start small, test on a slice of traffic, and keep humans close. The goal is faster, smarter routing — not replacing the human touch that closes deals.

[aaron]

Quick nod: Good call keeping humans in the loop — weekly reviews are the single best guardrail against bad automation.

Why this matters: Routing that optimizes for fit and urgency should increase qualified conversations and shorten sales cycles. If you get it wrong you’ll waste reps’ time and annoy potential customers — the metric impact is immediate.

My experience — in one line: Start lean: automated extraction + clear thresholds, then let human feedback refine the scoring. That sequence saved one client 40% of wasted SDR activity in 60 days.

What you’ll need

• Lead form fields (role, company_size, industry, budget_range, timeline, channel, raw_message).
• An LLM or classifier that returns structured data (JSON) via webhook.
• CRM integration to set owner, SLA, and a human-review queue.
• Dashboard for KPIs and a weekly review process with reps.

Step-by-step (do this first)

1. Define concrete thresholds: e.g., company_size >=50 = +30 fit, budget >=25k = +30 fit, timeline <=30 days = +50 urgency.
2. Create two scores: Fit (0–100) and Urgency (0–100). Combine: Routing Priority = 0.6*Fit + 0.4*Urgency (adjust weight based on sales cycle).
3. Build AI extraction prompt to normalize free text into JSON (see prompt below). Run on 1,000 historical leads to validate accuracy and tweak rules.
4. Map priority ranges to actions: 80+ = Enterprise SDR, 60–79 = SDR with 15-min SLA, 40–59 = Channel Specialist + 24-hr follow-up, <40 = Nurture or Request Info.
5. Enable human-in-the-loop: any lead within ±5 points of a boundary or with flagged keywords goes into a 1-hour review queue.
6. Roll out on 10% of live traffic. Measure for 14 days, then expand if metrics improve.

Copy-paste AI prompt (use as-is)

“You are a lead triage assistant. Input: name, message, company_size, industry, role, budget, timeline, contact_channel. Output strict JSON with these fields: fit_score (0-100), urgency_score (0-100), recommended_route (one of [‘Enterprise’,’SDR’,’Channel Specialist’,’Nurture’,’Request Info’]), reason_short (one sentence), follow_up_text (one short personalized opener). Rules: add fit points for role match, company_size thresholds (<=10:0, 11-49:+10, 50-199:+30, 200+: +50), budget ranges (<10k:0, 10-24k:+10, 25-99k:+30, 100k+: +50), urgency keywords (‘today’,’ASAP’,’this week’,’this month’) +40, timeline in days <=30 +30. If critical fields missing, set recommended_route=’Request Info’ and follow_up_text asking for timeline and budget.”

Metrics to track

• Response time (median first contact)
• Qualified lead conversion rate (SQL rate)
• Handover quality (rep-rated 1–5)
• Customer satisfaction on initial contact (CSAT)

Common mistakes & fixes

• Over-automation: Keep a 10–20% human sample for sanity checks.
• Poor data: Enrich company_size & role from public sources before scoring.
• Slow routing: Use webhooks and push alerts; aim for <15-minute SLA on high-priority leads.
• Unclear thresholds: Tie thresholds to historical SQL conversion bands and revisit monthly.

1-week action plan

1. Day 1–2: Pull 1,000 past leads and label 150 as high/low priority.
2. Day 3–4: Run the AI prompt against that set; compare AI scores to labels.
3. Day 5: Set thresholds and map routes in CRM for a 10% traffic test.
4. Day 6–7: Launch test, collect response time and rep feedback, schedule first weekly review.

Your move.

[Jeff Bullas]

Quick win (try in 5 minutes): Paste one recent lead message into the prompt below and ask your LLM to return fit_score, urgency_score, recommended_route and a one-line follow-up. You’ll see instantly how the triage behaves.

Why this matters

Routing by fit and urgency speeds response and raises conversion — but only if you keep the human context, clear thresholds and a fast escalation path. Do this right and reps spend time on real opportunities, not noise.

What you’ll need

• Lead fields: role, company_size, industry, budget_range, timeline, channel, raw_message.
• An LLM or classifier that can return strict JSON via webhook.
• CRM/webhook integration to set owner, SLA, and a human-review queue.
• A dashboard for response time, SQL rate, rep-rated handover quality, and CSAT.

One small refinement

Polite correction: don’t hard-code a huge +50 for timeline <=30 days — that can overweight urgency and misroute mid-fit leads. Instead start with smaller urgency boosts (e.g., keywords +30, timeline <=30 days +20) and use a confidence score from the LLM to trigger human review. Tune weights against historical conversion bands.

Step-by-step setup

1. Define baseline points: role match, company_size bands, budget bands, and modest urgency boosts (keywords +30, timeline <=30 days +20).
2. Create Fit (0–100) and Urgency (0–100). Start with Routing Priority = 0.6*Fit + 0.4*Urgency. Measure, then adjust.
3. Build an AI extraction prompt to normalize free text into JSON and return a confidence_score (0–100).
4. Map ranges: 80+ = Enterprise (15-min SLA), 60–79 = SDR (15–30 min), 40–59 = Channel Specialist (24 hr), <40 = Nurture / Request Info.
5. Human-in-the-loop: any lead with confidence <75 or within ±5 of a boundary gets routed to a 1-hour review queue. Always auto-escalate flagged high-value firms regardless of score.
6. Run on a 10% traffic slice for 14 days, measure, then expand.

Example

Input: “We’re a 120-person fintech, ready to buy this month, budget ~50k. Need demo ASAP.”
AI output: fit_score=78, urgency_score=70, recommended_route=’Enterprise’, follow_up_text=’Hi — great fit; can you do a demo Thursday or Friday this week?’
Result: Route to Enterprise SDR with 15-min alert.

Common mistakes & fixes

• Over-automating: Fix: sample 10–20% plus confidence-based reviews and full human review for high-value logos.
• Poor data: Fix: enrich company_size & role from public sources and validate during onboarding.
• Slow routing: Fix: use webhooks + push notifications; aim for <15-min SLA on top tiers.
• Wrong thresholds: Fix: tie thresholds to historical SQL conversion and revisit monthly.

Copy-paste AI prompt (use as-is)

“You are a lead triage assistant. Input fields: name, message, company_size, industry, role, budget, timeline, contact_channel. Return strict JSON: {fit_score:0-100, urgency_score:0-100, confidence_score:0-100, recommended_route: one of [‘Enterprise’,’SDR’,’Channel Specialist’,’Nurture’,’Request Info’], reason_short: string, follow_up_text: string}. Rules: award fit points for role match and company_size bands (11-49:+10, 50-199:+30, 200:+50), budget bands (<10k:0, 10-24k:+10, 25-99k:+30, 100k:+50). Urgency: keywords (‘today’,’ASAP’,’this week’) +30, timeline in days <=30 +20. If missing critical info, set recommended_route=’Request Info’ and follow_up_text asking for timeline and budget.”

30/60/90 action plan

• 30 days: Build scoring, run on 1,000 historical leads, pick initial thresholds.
• 60 days: Integrate with CRM, roll out 10% live traffic, enable human-review queue and dashboards.
• 90 days: Measure SQL lift, CSAT, tweak weights, expand automation to 50%+.

Final reminder: Start small, measure fast, and keep humans closest to the edge cases. Faster routing wins — but only when it protects the customer experience.

[aaron]

Hook: Route by fit and urgency, protect the customer experience, and prove the lift in two weeks. Here’s the exact setup, KPIs, and guardrails.

The problem

Most routing either overweights urgency or buries great leads in nurture. The result: slow handoffs, annoyed prospects, and reps chasing noise.

Why it matters

Done right, AI triage cuts response time, raises qualified meetings, and improves first-touch satisfaction. The metric signal shows up within 14 days — if you measure the right things and keep humans on the edge cases.

Lesson from the field

A two-score model (Fit + Urgency) with confidence-gated handoffs beat rigid rules. The unlock was tracking speed to first meaningful response (not just any reply) and giving reps a one-line opener they could send instantly.

What you’ll need

• Lead inputs: role, company_size, industry, budget_range, timeline, contact_channel, raw_message.
• An LLM that returns strict JSON via webhook.
• CRM automations to assign owner, set SLA, and create a human-review queue.
• Dashboards for response time, SQL rate, handover quality (rep-rated), CSAT, and misroute rate.

High-value refinements (insider tricks)

• Confidence gate: Only auto-route when confidence_score ≥ 75 and the score is ≥ 5 points clear of a threshold. Else, send to a 1-hour review queue.
• Channel-aware urgency: Weight “phone” and “direct referral” +10 urgency; deprioritize after-hours web form by -5 but keep SLA sane.
• VIP overrides: Maintain a target-account list that always escalates to your best rep regardless of score.
• Shadow routing: First week, route normally but “shadow” the AI decision in a field. Compare outcomes before flipping to live.
• Meaningful response SLA: Track first substantive reply (answers their ask or proposes times), not just an automated “got it.”

Step-by-step

1. Baseline rules: Define Fit by role match, company_size bands, and budget bands. Define Urgency with modest boosts: keywords (+30), timeline ≤ 30 days (+20).
2. Scoring: Fit (0–100), Urgency (0–100). Start with Priority = 0.6*Fit + 0.4*Urgency.
3. Extraction prompt: Have the AI normalize free text, return scores, a confidence_score, a route, and a one-line opener the rep can send.
4. Routing map: 80+ Enterprise (15-min SLA), 60–79 SDR (15–30 min), 40–59 Channel Specialist (24 hrs), <40 Nurture/Request Info. Confidence <75 or within ±5 of a boundary = review queue.
5. CRM automation: On create: set owner by route, apply SLA timers, post a push alert, prefill the opener into the task so reps can send in one click.
6. Human loop: Weekly 30-minute review: sample 20 routed + 20 reviewed leads; record false positives/negatives and adjust weights.
7. Shadow → live: Run shadow for 7 days on 10% traffic. If KPIs improve, go live for 50% and re-check in week two.

Copy-paste AI prompt

“You are a lead triage assistant. Input fields: name, message, company_size, industry, role, budget, timeline, contact_channel. Return strict JSON only with: {fit_score:0-100, urgency_score:0-100, confidence_score:0-100, recommended_route: one of [‘Enterprise’,’SDR’,’Channel Specialist’,’Nurture’,’Request Info’], reason_short: string (max 18 words), follow_up_text: string (one-line opener proposing next step)}. Scoring: company_size bands (1-10:0, 11-49:+10, 50-199:+30, 200+:+50), budget bands (<10k:0, 10-24k:+10, 25-99k:+30, 100k+:+50), role match to buyer persona +10. Urgency: keywords [‘today’,’ASAP’,’this week’,’this month’] +30; explicit timeline ≤30 days +20; contact_channel=’phone’ or ‘referral’ +10. If timeline or budget missing, set recommended_route=’Request Info’ and write a polite follow_up_text asking for both. Keep outputs consistent and deterministic.”

What to expect from the prompt

• Consistent JSON the CRM can parse without manual cleanup.
• Short rationale so reps understand the decision at a glance.
• Send-ready opener to increase first meaningful response rate.

Metrics that prove it’s working

• Speed to first meaningful response: target ≤ 30 minutes for top tiers.
• SQL rate from routed leads: monitor lift vs. baseline (aim for +10–25% within 30–60 days).
• Handover quality (rep-rated 1–5): aim for ≥ 4.0 on top tiers.
• CSAT on first touch: lightweight survey or reply sentiment; aim for ≥ 4/5.
• Misroute rate: percentage of leads rerouted within 24 hours; keep < 8% and trending down.
• SLA breach rate: especially for Enterprise and SDR queues; keep < 5%.

Common mistakes and fixes

• Overweighting urgency: Cap urgency boosts and use the confidence gate. Review weekly.
• Ignoring channel and timing: Add channel-aware modifiers and office-hours rules; maintain fair SLAs.
• Dirty inputs: Normalize currency and company_size; enrich missing fields automatically.
• No shadow period: Always run shadow routing for a week to capture baseline deltas.
• One-way automation: Add a “wrong route” button for reps; capture the correct route and reason to retrain.

One-week action plan

1. Day 1: Define Fit/Urgency rules, thresholds, and VIP override list. Create review queue in CRM.
2. Day 2: Implement the prompt and webhook. Add fields for scores, route, confidence, reason, opener.
3. Day 3: Run on 1,000 historical leads. Compare to past outcomes; tune weights and boundaries.
4. Day 4: Set SLAs, push alerts, and prefilled opener tasks. Build the shadow-routing workflow (10% traffic).
5. Day 5: Launch shadow. Start dashboard for response time, SQL rate, handover quality, CSAT, misroute rate.
6. Day 6: Rep feedback session; adjust opener tone and boundary rules. Confirm VIP overrides work.
7. Day 7: Go/no-go: if speed-to-meaningful-response improves ≥ 20% and misroutes ≤ 10%, expand to 50% traffic.

Closing: Build the two-score model, gate with confidence, measure meaningful response, and shadow before scaling. Your move.

[Rick Retirement Planner]

Short take: Your two-score model + shadowing is the right foundation. One simple concept that will make or break the customer experience is the confidence gate — use it to decide when AI routes autonomously and when a human should check first.

Confidence gate — plain English: Think of the confidence score like the AI saying, “I’m X% sure I understood this lead correctly.” If the AI is highly sure, let it act. If it’s unsure, route the lead to a human reviewer. That keeps fast wins truly fast and prevents awkward misroutes that annoy prospects and waste reps’ time.

What you’ll need

• Lead data: role, company_size, industry, budget_range, timeline, contact_channel, raw_message.
• An AI extractor that returns fit_score, urgency_score and a confidence_score (0–100).
• CRM rules to assign owner, set SLAs, and create a short human-review queue.
• Dashboard for speed to first meaningful response, misroute rate, and rep feedback.

How to implement (step-by-step)

1. Decide thresholds: pick a confidence threshold (start 75) and a buffer from routing boundaries (start ±5 points).
2. Map routing logic: if confidence ≥ threshold and priority is clearly in one band (outside buffer) → auto-route + SLA; else → human-review queue (1 hour).
3. Prefill the rep task: include the AI’s short reason and a one-line opener so reps can respond quickly if they approve the route.
4. Shadow for 7 days on a slice of traffic: store the AI decision in a field while humans follow existing routing; compare outcomes before flipping live.
5. Run weekly reviews: sample routed vs. reviewed leads, capture misroutes and adjust confidence threshold or scoring weights accordingly.

What to expect

• Short-term: fewer obvious misroutes, slightly more human review load during tuning.
• 2–4 weeks: measurable drop in misroutes and faster meaningful responses on top tiers as you tune thresholds.
• Ongoing: use rep feedback and misroute labels to lower review volume while keeping CSAT high.

Quick tuning tips

• Start conservative on confidence, then lower it slowly as misroute rate drops.
• Always auto-escalate known VIP accounts regardless of score.
• Track “first meaningful response” not just first touch — that’s the customer-experience signal that matters.

[Author]

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