Getting Ready For AI

At a recent conference for CCOs and hospitality leaders, one speaker made a bold statement: “It wouldn’t be a conference in the 2020s without a mention of AI.” And indeed, more than half of the discussions were centered around data, AI, and tech. The consensus was clear: AI is the future. But the reality for many hospitality companies is that it's hard to pivot.

For large companies, it’s like turning a ship around with a hand steer. Smaller companies have the flexibility but miss the capital. Both are valid reasons for slow adoption, but they shouldn’t be used as excuses. We’ve seen this before with the adoption of Revenue Management into hotels. A position we cannot imagine doing without today. In fact, many of the CCOs attending the conference had their start as revenue managers. But hoteliers were initially slow to adopt it—only after Marriott introduced the role, inspired by the airline industry, did other hoteliers see the value. We’ve come a long way since then, and now revenue management is one of the most tech-advanced roles in hotels, often driving the early adoption of new tech.

Just like revenue management, AI will be transformative for the industry. But getting ready for it is the new challenge.

The Importance of Clean Data: "Garbage In, Garbage Out"

Many hotels are lucky to have abundant data, which is a great starting point. However, the hospitality sector faces recurring data quality issues that can seriously hinder AI readiness. One primary challenge is dirty or inconsistent data across different systems. Hotels collect guest information at many touchpoints—website bookings, OTA reservations, front desk, restaurant POS systems, and more. Inconsistencies inevitably creep in.

A common issue is duplicate or conflicting guest profiles. A single guest might have multiple entries with slight variations, such as different name spellings, different email addresses, or a home address in one system and a work address in another. This issue is compounded when an OTA guest is transferred into a direct customer profile. Without cleaning, there’s no clear unified view of the guest, making accurate machine learning analysis or personalization nearly impossible.

Another issue is missing data. Many hotels find a significant portion of their customer data is incomplete—such as blank email addresses or unknown preferences. We all know the struggle of collecting complete guest profiles while maintaining an efficient and pleasant check-in process, where the latter often has a more direct impact on revenue through guest review scores.

Inconsistent data formats and entry errors are also common. Hospitality data often originates from manual entry by staff or guests, which leads to typos and non-standard formats. For example, a phone number might be entered with parentheses and dashes in one system but only digits in another. If not standardized, these discrepancies confuse downstream AI algorithms.

In one hotel’s data quality audit, they found frequent errors such as misspelled guest names, double "@" characters in emails, or default country codes overriding true guest data. Front-desk and reservations staff may also miscategorize entries—for example, wrongly tagging market segments, rate codes, or currency codes in the PMS. These inconsistencies lead to analysis errors, like mispricing segments in an AI-driven revenue management system if the segment codes are wrong. In short, inaccurate or non-uniform data inputs directly undermine AI accuracy.

The Impact of Poor Data Quality on AI

The impact of poor data quality on AI initiatives is significant. Machine learning models are only as good as the data fed into them. Issues like duplicates, gaps, and errors result in flawed outputs. Experts warn that advanced hotel systems, like AI-driven revenue management and automated marketing tools, heavily depend on data quality. If the data is wrong or incomplete, the AI’s decisions will be unreliable.

For example, revenue management systems (RMS) rely heavily on historical data. If there’s a change in demand or lead time strategy, a lot of manual intervention is required to force the algorithm to adjust. Hotels have even seen direct revenue loss from data issues—one study found that 8 out of 10 visitors abandon a booking site when they see the same hotel listed multiple times with inconsistent information and prices.

These examples show that messy data isn’t just a technical inconvenience—it actively blocks AI systems from delivering value. Thus, addressing data quality issues is a prerequisite for AI readiness in hospitality.

Data Validation Practices for Consistency & Reliability

So, what can we do about it? My first point of action is to implement data validation and cleansing practices at every stage of the data lifecycle. For example, when booking data flows from distribution channels (OTAs, travel agents) into a hotel’s PMS, rigorous checks can verify that all required fields are present and in the proper format. A hospitality data integration guide advises implementing “rigorous data validation checks to ensure the accuracy and completeness of booking information” before it reaches the PMS. This includes rejecting or flagging records with missing guest names, out-of-range dates or rates, and other anomalies.

Hotels should also leverage automation and AI for ongoing data quality monitoring. Modern hotel data platforms and PMS software often have built-in data integrity modules. This automated cleaning can standardize capitalization, fix common typos, and even use external reference data to verify guest identities. Critical, too, is the match & merge process. This not only unifies guest profiles but ensures that each merged profile is internally consistent—choosing the most current contact information or address. Some systems even provide data quality dashboards to highlight potential issues, such as missing emails or duplicate profiles.

Hotels committed to data reliability should also perform regular audits of their guest and booking data. Some hotels even combine this with a pre-arrival check to ensure all guest information is up to date and accurate. Training employees on proper data entry standards is also key to preventing errors at the human level.

Hospitality Best Practices & Standards for AI-Ready Data

Several best practices and standards seem clear for preparing clean data to be used in AI-driven systems, such as personalization engines, chatbots, revenue management, and guest analytics:

1. Unify and Integrate Data Sources:

Breaking down data silos is step one. Hotels should integrate their various systems (PMS, CRS, CRM, POS, loyalty programs, etc.) so that all data flows into a central repository. This "single source of truth" approach, often via a hospitality-specific CDP (Customer Data Platform) or data lake, ensures AI models have access to complete guest profiles and booking histories. Integration standards and robust APIs are used to facilitate seamless data exchange.

2. Standardize and Cleanse Data Continuously:

Hospitality leaders should treat data cleansing as an ongoing discipline, not a one-off project. They should establish data standards for key fields (e.g., consistent date formats, standardized country and currency codes, and agreed definitions for rate categories) and ensure all systems adhere to them. Before AI analysis, data should be thoroughly cleaned. The mantra here is: "Always clean up your data before analyzing it." This includes rectifying duplicates, correcting inconsistencies, and filling in missing data. The goal is to maintain "rigorous standards" of data quality at all times so AI applications can draw from accurate, uniform datasets.

3. Data Enrichment and Completeness:

Best-in-class hotels don’t stop at cleaning what they have; they enhance it. Data enrichment is a common practice where missing guest information or additional attributes are appended from reliable sources. In revenue management, enrichment might involve adding market data (such as competitor rates or event calendars) to the hotel’s own booking data. This ensures the AI pricing system has full context. Completeness is a standard: any data feed going into an AI system, whether for occupancy forecasting or guest lifetime value models, should be audited for missing values and enriched where necessary.

4. Real-Time Updates and Feedback Loops:

In AI applications like chatbots or dynamic pricing, real-time data is king. Best practices in hospitality data management include setting up real-time or near-real-time data synchronization between systems. This avoids scenarios where a chatbot is unaware of a guest’s latest reservation change, or a revenue system is working off outdated pickup numbers. By minimizing data lag, hotels ensure their AI solutions are always working with the most accurate, up-to-date information. Additionally, there’s a feedback loop: insights gained from AI (e.g., identifying a high-value guest segment) are fed back into the data platform, which might update the guest profile accordingly. This constant refinement keeps the data and AI in sync, improving over time.

These best practices are reinforced by hospitality case studies and vendor insights. For example, AccorHotels invested in AI-driven data systems and found it critical to standardize guest data across its many brands to enable personalization at scale (a fragmented data environment would have made their AI room-allocation system far less effective). On the vendor side, hotel tech firms emphasize data quality as non-negotiable for AI. A whitepaper from a hotel BI provider observed that failing to address data entry errors (like the mis-tagged codes and segments) will “result in inaccurate data being used for analysis” and thus flawed decision-making. The clear message is that AI success in hospitality is built on a foundation of clean data. Before we make any long term decisions on our tech stack or the inclusion of AI in our strategy, we should invest time and effort in getting our data in the right place.