ARKONIS

Whitepaper · Hospitality AI readiness

The AI-ready hotel starts with trusted data.

A practical operating model for hotel owners and general managers who want AI to improve revenue, cost, and guest experience without losing control of their data.

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Executive summary

Hotels need a decision foundation before they need more AI pilots.

Most hotel AI projects struggle because they start above the operating layer. The model is visible, but the data below it is fragmented, disputed, or owned by no one.

The practical answer is a sequence: trusted data first, owned decision systems second, controlled AI workflows third. This whitepaper gives hotel leaders a short diagnostic and a 90-day starting plan.

01Trusted data

Definitions, reconciliation, quality, access, and ownership.

02Decision systems

Named owners, decision cadence, metrics, and action paths.

03AI workflows

Forecasts, assistants, alerts, and automations with controls.

Inside the paper

What the paper covers

Four linked arguments that move from why hotel AI stalls to what leadership can do in the first 90 days.

01 The AI value gap

Why most AI failures are operating-model failures, not model failures.

02 Hotel data fragmentation

How split PMS, channel, guest, and reporting data blocks revenue decisions.

03 Proven travel examples

What leaders can learn from Hilton, Booking.com, Marriott, and experimentation-led teams.

04 The 90-day path

A practical AI readiness assessment that leadership can use immediately.

Source base

The paper uses research and examples from consulting firms, hospitality technology reports, peer-reviewed business cases, and product case studies.