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Concierge

Responding to guest messages and pre-arrival requests

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What You Do Today

Handle messages from guests planning their trip — answering questions about the area, arranging pre-arrival requests, and building excitement for their stay.

AI That Applies

AI chatbots handle routine pre-arrival questions (parking, airport distance, nearby pharmacies) instantly, routing complex or VIP requests to you.

Technologies

How It Works

For responding to guest messages and pre-arrival requests, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Routine questions are handled automatically 24/7. You focus on the requests that need personal attention and local expertise.

What Stays

Complex requests, VIP communication, and setting the tone for a special experience still need your personal touch.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for responding to guest messages and pre-arrival requests, understand your current state.

Map your current process: Document how responding to guest messages and pre-arrival requests works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Complex requests, VIP communication, and setting the tone for a special experience still need your personal touch. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support guest messaging platforms tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long responding to guest messages and pre-arrival requests takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Customer Experience

What data do we already have that could improve how we handle responding to guest messages and pre-arrival requests?

They're setting the AI strategy for the service organization

your contact center technology lead

Who on our team has the deepest experience with responding to guest messages and pre-arrival requests, and what tools are they already using?

They manage the platforms that AI tools plug into

your quality assurance or voice of customer lead

If we brought in AI tools for responding to guest messages and pre-arrival requests, what would we measure before and after to know it actually helped?

They measure the impact of AI on customer satisfaction

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.