Travel and Mobility · Revenue & Growth
Automate Lead Qualification in Airports with AI
We design, build, and run AI-native lead qualification for airport operators, passenger experience teams, commercial directors, and ground operations leaders. This page describes the engagement: scope, pricing, timeline, controls, and the KPIs we commit to.
Early access: we work with a small first cohort. Engagements are scoped, priced, and shipped end-to-end by our team — not referred to third parties.
In one sentence
AI-native lead qualification for airports is a phased engagement (Discovery 2 weeks → Build 8 weeks → Run continuous (4-week initial stabilization)) that ships a production workflow on top of AODB and FIDS, moves speed to lead by −77% against the airports baseline, and is operated under revenue & growth governance from day one.
Key facts
- Industry
- Airports
- Use case
- Lead Qualification
- Intent cluster
- Revenue & Growth
- Primary KPI
- speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction
- Top benchmark
- Cost per qualified meeting: $420 → $95 (−77%)
- Systems integrated
- AODB, FIDS, baggage systems
- Buyer
- airport operators, passenger experience teams, commercial directors, and ground operations leaders
- Risk lens
- security, passenger safety, airline coordination, and operational resilience
- Engagement timeline
- Discovery 2 weeks → Build 8 weeks → Run continuous (4-week initial stabilization)
- Team size
- 1 senior delivery + 1 part-time integration eng
- Discovery price
- $5k · 2-week sprint
- Build price
- $15k–$22k · 6-8 weeks
Primary outcome
separate serious buyers from noise faster
What we ship
AI qualification assistant, scoring rubric, routing rules, and CRM governance
KPIs we report on
speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction
Why Airports teams hire us for this
What separates AI-native lead qualification from "AI features added on top" is operating discipline. The pattern that works in airports is the same one that works for any high-stakes operational system: instrument the baseline, ship a thin slice to production, govern explicitly, then expand. We run every engagement against that pattern.
Recent industry benchmarks (Gartner, Salesforce Research) show airports revenue teams spend 60-70% of their week on non-selling activities. AI-native delivery targets that non-selling block first.
Industry context: Airports coordinate 30+ stakeholders per flight (airlines, ground handlers, security, retail, customs). Passenger flow metrics drive concession revenue (every minute saved at security adds ~$0.40 / pax retail spend per ACI benchmarks).
Benchmarks we hit
Reference benchmarks from production deployments of lead qualification in airports-comparable contexts. Sources noted per row. Your actuals are measured against the baseline captured in Discovery.
| Metric | Industry baseline | AI-native typical | Delta |
|---|---|---|---|
Cost per qualified meeting Includes AI infra cost, SDR time, and overhead allocation | $420 | $95 | −77% |
Lead-to-meeting cycle time Median across Salesforce-reporting B2B teams; AI-native compression validated on first thin-slice deployment | 11.4 days | 2.8 days | −75% |
Outbound reply rate Industry baseline from Gartner B2B Sales Pulse; AI-native lift from per-prospect context injection | 1.2% | 4.1% | +3.4× |
Benchmarks are reference values from comparable engagements and authoritative sector benchmarks. Your engagement's baseline is captured during Discovery and actuals are reported weekly during Run against that baseline.
How we operate the workflow
We treat the workflow as a system with five distinct layers: intake (classify and tag what comes in), context (retrieve approved sources), action (draft, route, decide), review (humans on low-confidence and high-impact cases), and learning (every reviewer action improves the next iteration). For lead qualification in airports, the layers are scoped during Discovery and built sequentially during Build.
What we build inside the workflow
The Build phase for lead qualification in airports produces six tangible artefacts: a workflow map (current and target state), a labelled test set (200-1000 cases minimum), a prompt and retrieval repository (versioned, tested, deployed), the integration layer (against AODB and adjacent systems), the reviewer queue (with SLAs and escalation paths), and the operating dashboard (KPIs, drift detection, attestation pack). All six are inspectable, all six are handed over.
Reference architecture
4-layer AI-native workflow for revenue & growth
Source intake → AI orchestration → Action → Human review & quality.See the full architecture diagram for Revenue & Growth →
AI-native vs traditional approach
How a scoped AI-native engagement compares to the traditional alternatives for lead qualification in airports.
| Dimension | Traditional (in-house build or BPO) | AI-native engagement (us) |
|---|---|---|
| Time to production | 6-12 months | 6-10 weeks (thin slice) |
| Pricing model | FTE hourly retainer or fixed staffing | Phased fixed-price (Discovery → Build → opt Run) |
| Audit / governance | Manual logs, periodic review | Versioned prompts, audit logs, reviewer queues, attestations |
| Operator throughput lift | 1.0× (baseline) | −75% |
| Cost per unit | Industry baseline | AI-native orchestration brings the same coverage to 1-2 FTE with audit-ready logs for IATA Slot Conference disputes. |
| Exit path | Multi-quarter notice + knowledge loss | Month-to-month Run, full handover plan in Build SoW |
Manual gate coordination costs 4-7 FTE per terminal; AI-native orchestration brings the same coverage to 1-2 FTE with audit-ready logs for IATA Slot Conference disputes.
Engagement scope & pricing
We run this as a fixed-scope engagement with a clear commercial envelope, not an open-ended retainer.
Revenue engagement
Three phases, billed separately. You commit one phase at a time.
Phase 1 · Discovery
$5k
2-week sprint
Phase 2 · Build
$15k–$22k
6-8 weeks
Phase 3 · Run
$2k–$3k / mo
optional, hourly bank also available
~$25k–$45k typical year 1 (60% take the run option for ~6 months)
Outbound, growth, or revenue-ops workflow, integration with your CRM, weekly operating review during Run.
Discovery is the only commitment to start. After Discovery, we scope Build with a fixed price. Run is opt-in, month-to-month, no lock-in.
The 4-phase delivery model
Phase 1 · Weeks 1–2
Discovery
We map the workflow, the systems, the decisions, and the baseline metrics. Output: a scoped statement of work.
Phase 2 · Weeks 2–4
Design
We design the operating model: data access, retrieval, prompts, review queues, controls, and the KPI dashboard.
Phase 3 · Weeks 4–8
Build
We ship a production thin slice on real data, with versioned prompts, evaluation harness, and human review.
Phase 4 · Weeks 8+
Run
We run the workflow with you weekly, expand into adjacent work, and report against baseline.
Interactive ROI calculator
Estimate your AI-native ROI for lead qualification
Reference inputs below are typical for airports teams in the revenue cluster. Adjust them to match your situation.
Projected
Current monthly cost
$24,000
AI-native monthly cost
$7,920
Annual savings
$192,960
67% cost reduction · ~468 operator-hours freed / month
Governance and risk controls
Governance fails in two predictable ways in airports: paper controls that nobody enforces at runtime, and runtime controls that nobody can document for auditors. We build for both audiences. Every guardrail is enforced in code, and every guardrail is documented in the governance map with the line of code that implements it. The map and the code are kept in sync as part of the Run cadence.
How we report ROI
The ROI calculation we refuse to fudge on lead qualification is the time-to-value curve. Most airports AI projects report ROI on cherry-picked metrics at quarter-end. We report against a baseline captured in Discovery, on a fixed metric defined before Build, with the methodology documented in the Statement of Work. Boring, defensible, repeatable.
Common pitfall & mitigation
The failure mode we see most often on AI-native lead qualification engagements in airports contexts.
Attribution loss
AI-generated touches blur the funnel; nobody knows what really worked
UTM convention + touch-level logging from day 1; weekly cohort analysis in the Run review
Build internally or work with us
The opportunity cost of building first in airports is often invisible: 6-9 months spent hiring, tooling, and converging on a reference architecture is 6-9 months of competitors shipping. The engagement model we propose front-loads the reference architecture and the senior delivery team, then transitions the operation to your team once the pattern is proven.
What to ask us before signing
- Ask for a workflow map that shows intake, retrieval, generation, review, escalation, system updates, and measurement.
- Ask for an evaluation plan using real examples from airports, not only generic test prompts.
- Ask how we will move speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction within the first 30 to 60 days.
- Ask which parts of the process remain human-owned and why.
- Ask for our exit plan: what stays with you if the engagement ends.
Recommended first project
The best first project for AI-native lead qualification in airports is a contained workflow with enough volume to matter and enough structure to evaluate. Avoid the most politically sensitive process first. Avoid a workflow with no measurable baseline. Choose a process where we can ship a production-grade thin slice, prove adoption, and then extend the same architecture to neighboring work.
A practical target is a 30-day build followed by a 60-day operating period. In the first 30 days, we map the work, connect the minimum data sources, build the assistant, and create the review process. In the next 60 days, the system handles real volume, the team measures outcomes, and we improve the workflow weekly. By day 90, leadership knows whether to expand into adjacent work.
Frequently asked questions
How do you automate lead qualification in airports with AI?+
We map the existing lead qualification workflow inside airports, identify the high-volume, high-structure tasks, and build an AI agent that handles those tasks while routing low-confidence cases to a human reviewer. The build connects to your AODB, FIDS, baggage systems, runs against a labelled test set, and ships behind a reviewer queue before it sees production traffic. We then operate it, measure speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction, and improve it weekly.
What does it cost to automate lead qualification for a airports company?+
Three phases, billed separately. Discovery sprint: $5k (2-week sprint). Build engagement: $15k–$22k (6-8 weeks). Run retainer: $2k–$3k / mo (optional, hourly bank also available). ~$25k–$45k typical year 1 (60% take the run option for ~6 months). Outbound, growth, or revenue-ops workflow, integration with your CRM, weekly operating review during Run.
What is the best AI agent for lead qualification in airports?+
There is no single "best" off-the-shelf agent for lead qualification in airports — the right architecture depends on your AODB setup, your data, and your risk profile. We typically combine a frontier LLM (Claude, GPT-4-class, or Gemini) with a retrieval layer over your approved sources, tool-use for AODB and FIDS integrations, and a reviewer queue. We benchmark candidate models against a labelled test set during Discovery and pick the one with the best accuracy/cost ratio for your workflow.
How long does it take to deploy AI lead qualification for airports?+
A thin-slice deployment in 2-week sprint after Discovery, with real airports data and real reviewers. The full Build phase runs 6-8 weeks. By day 90, speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction is instrumented, the team has a baseline, and leadership has the data needed to decide on expansion into adjacent airports workflows.
What do we own, and what do you own?+
We own the workflow design, the prompts, the retrieval architecture, the evaluation harness, and weekly improvement. Your airport operators, passenger experience teams, commercial directors, and ground operations leaders team owns data access, policy, exception approval, and final commercial decisions. At the end of the engagement, every prompt, eval, and config is handed over — no lock-in.
How do you measure revenue impact for lead qualification in airports?+
We instrument speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction from day one, paired with sector-level metrics such as queue time, baggage mishandling rate, retail revenue per passenger, and on-time turnaround. We report against baseline weekly during Run, and we publish a 90-day impact recap.
Sources we reference
The following sources inform the architecture, governance, and benchmarks we apply on airports engagements. Cited here so you can verify and dig deeper.
- ACI World Airport IT
- OECD AI Principles — OECD
- EU AI Act — European Commission
- B2B Buying Disconnect: Buying Decisions are Made Without Sellers — Forrester
- Generative AI Impact on Marketing & Sales — McKinsey
- ICAO Innovation — International Civil Aviation Organization
- ACI World Airport IT Insights — Airports Council International
- Google Search Central: helpful, reliable, people-first content
- Google Search Central: URL structure best practices
Start the engagement
Book a discovery call for Airports
Tell us about your workflow, the systems involved, and the KPI you want to move. We'll send a scoped statement of work within 5 business days.