AI Readiness Index
A practitioner-built diagnostic for distributed service organizations. Score your organization across six dimensions that determine whether an AI deployment succeeds or stalls, then see exactly where to focus first. Designed from fieldwork with hospitality operators; applicable across any distributed, people-intensive environment.
In production use, this tool is built and administered in Qualtrics (scoring logic + response collection), distributed to leadership teams via Confluence, and results are aggregated in Tableau for cross-department benchmarking and year-over-year tracking.
How to Use This Tool
Score your organization. Get a stage. Know your next move.
Rate your organization honestly on each of the six dimensions. There are no right answers, only accurate ones. The output is only as useful as the honesty you put in.
Score Six Dimensions
Each dimension is scored 1, 5. Read the level descriptions before selecting, don't anchor on the number, anchor on the reality it describes.
Calculate Your Stage
Scores are totaled (6, 30) and mapped to one of five readiness stages: Dormant, Curious, Experimenting, Scaling, or Embedded.
Act on the Recommendations
Each stage comes with three concrete next steps calibrated to where your org actually is, not where you wish it were.
Score all 6 dimensions to unlock your results.
Your AI Readiness Stage
Recommended Actions
Score Breakdown
Meridian Grand Hotels & Resorts, AI Readiness Assessment
Dimension Scores
Readiness Stage
14 / 30, Stage 2
Meridian has genuine executive enthusiasm and identifiable use cases, but the organizational infrastructure to support deployment doesn't yet exist. A broad rollout at this stage would expose the AI program to failure before it earns trust. The priority is building the foundation, not launching the product.
Key Findings
Revenue management and F&B workflows are well-documented and contain clear AI augmentation points. Dynamic pricing and kitchen staffing optimization are deployment-ready from a workflow standpoint.
Advocates exist in Revenue Management, IT, and F&B, three departments that represent Meridian's core AI use cases. Peer influence is beginning to spread horizontally. Middle management engagement is early but real.
Meridian operates 4 disconnected core systems: PMS (Opera), POS (Simphony), F&B analytics (a custom build), and HR (ADP). AI tools across any of the three planned use cases require a unified operational data layer that doesn't currently exist.
A high-profile service failure at a flagship property three years ago created lasting organizational risk-aversion. Pilots require C-suite sign-off and tend to be scoped for guaranteed success rather than learning. This culture will limit the iteration velocity AI deployment requires.
Priority Recommendations
Frontline Signal Literacy, Property Staff First
Launch a hospitality-specific AI literacy module across all 14 properties, not a generic course, but one built around Meridian's actual use cases (pricing, guest comms, maintenance). Crew-facing AI fear is the single biggest adoption barrier. Address it before any pilot launches, not after.
90-Day Data Architecture Sprint
All three planned AI use cases depend on a clean operational data layer. Recommend a focused integration sprint connecting PMS, POS, and F&B analytics before committing to any vendor deployment. The goal: one queryable data environment, even if imperfect. This is the unsexy prerequisite everything else depends on.
Governance-First Pilot Design
Before the first pilot launches, appoint an AI Program Lead with cross-departmental authority and develop a lightweight governance charter: usage guidelines, escalation paths, and a shared definition of what "responsible AI" means in a guest-facing context. Build the guardrails before the car hits the road.