The two operational priorities that define AI success

To translate AI strategy into reality, leaders must execute two immediate operational priorities.

Establish Custom Evals

An AI eval (evaluation) is a structured process to systematically test and measure an AI system's quality, performance and reliability, assessing if it meets goals like accuracy, safety and usefulness.

Generic evals (and benchmarks) are insufficient. Every travel company must create evals that measure AI performance against exceptional human employees on specific, high-value tasks. This could be complex itinerary planning, real-time disruption management or crafting personalized marketing campaigns. You cannot manage what you do not measure, and custom evals are the only way to truly understand an AI's readiness for deployment.

Some examples:

• Can this agent rebook a cancelled flight with 99% accuracy regarding airline interline agreements?
• Does this agent correctly apply EU261 compensation rules across 100 disruption scenarios with 98% accuracy?
• Does the recommendation engine surface properties matching stated guest preferences (e.g., pet-friendly, pool, accessibility) with 95% accuracy?
• Does the revenue management AI match or exceed a senior analyst's pricing decisions across 500 historical demand scenarios?
• Does the duty of care agent locate and contact all travelers in an affected region within the contractual SLA?
• Does the AI correctly identify when to escalate emotionally charged interactions to human agents 98% of the time?

Prioritize Clean Data & Governance

Data governance has shifted from back-office compliance function to strategic imperative. The problem is foundational: AI systems are only as reliable as the data that feeds them, and probabilistic, agentic systems that act autonomously amplify the consequences of bad data. Inaccurate inventory, stale pricing or inconsistent customer records become not just operational annoyances but sources of cascading errors when agents are booking, rebooking and managing disruptions.

The companies closing the gap between AI experimentation and AI transformation are those treating data governance not as an afterthought but as a precondition, investing in data lineage, quality monitoring and clear ownership before scaling autonomous systems. In the agentic era, governance isn't bureaucratic overhead; it's the trust layer that determines whether your AI can act with confidence or is flying blind.

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