stripeJun 23, 2026

Four travel and hospitality trends from HITEC 2026 (opens in new tab)

Hospitality’s AI opportunity is growing, but most operators lack the data, infrastructure, and operational systems needed to turn investment into measurable returns. AI is reshaping how travelers discover and book hotels, while fragmented data and outdated payment systems create lost revenue and guest frustration. The strongest strategy is to connect accurate data, intelligent workflows, and seamless payments so technology improves the experience without becoming visible to guests. ## AI Is Changing the Direct-Booking Battle - Hotels historically relied on SEO to compete with OTAs such as Expedia and Booking.com. - AI-generated search answers are reducing traditional website traffic: - 65% of Google searches with AI Overviews end without a click. - The figure rises to 78% on mobile. - Traditional search traffic is declining by about 25%. - AI systems prioritize accurate, structured, machine-readable information rather than keyword density and backlinks. - More than 90% of accommodation websites are reportedly undetected by AI models. - Hotels should audit whether AI tools can correctly describe: - Room categories - Amenities - Policies and cancellation terms - Local context - Real-time availability - Winning direct bookings will require both AI discoverability and a modern checkout experience supporting local currencies, payment methods, and fraud protection. ## Hospitality AI Is Held Back by Fragmented Data - Only about 25% of hospitality businesses are actively scaling AI, and fewer than 10% are considered “AI future-built.” - Property management, CRM, loyalty, food and beverage, and payment systems often operate in silos. - Incomplete data weakens: - Personalization - Guest profiles - Financial reconciliation - Operational decision-making - The main challenge is not building AI features but operationalizing them reliably in real workflows. - Successful examples connect live data to timely actions: - Delta’s AI concierge uses customer and operational data to provide context-aware support. - Wynn’s revenue managers receive predictive alerts and recommended actions. - For most operators, better data connectivity matters more than using a more advanced AI model. ## Payment Friction Directly Affects Revenue - Payments are increasingly viewed as a competitive capability rather than a back-office commodity. - Survey findings cited in the article include: - 90% of executives consider payments important to growth. - 37% say limited payment options most harm the guest experience. - 58% report that fraud tools block legitimate transactions. - 74% say fragmented systems create excessive reconciliation work. - Guests may abandon a hotel when their preferred payment method is unavailable, shifting the booking to an OTA that supports it. - Modern payment infrastructure allows smaller operators to offer international payment methods and currencies without building large in-house teams. ## Invisible Technology Creates the Best Guest Experience - Guests have little tolerance for technology failures and may simply avoid returning rather than complain. - Effective hospitality technology should anticipate needs without drawing attention to itself. - The desired experience includes details such as: - A room set to the guest’s preferred temperature - Familiar television channels - Preferred pillow firmness - Hospitality is moving from remembering information guests explicitly provided to predicting preferences based on connected guest data. Operators should prioritize clean, connected data, AI systems tied to real operational actions, and flexible payment infrastructure. The goal is not to add AI for its own sake, but to make booking and stays more seamless while quietly improving revenue, efficiency, and guest loyalty.

data-integrationaillmai-agent+4

lineMay 28, 2026

From Tokyo to Fukuoka, Finding Answers in the Field: Our CS InquiryChat Implementation Journey (opens in new tab)

MessagingHub replaced Demaecan’s third-party customer-service chat with its own InquiryChat platform. The transition eliminated annual licensing costs, improved operational flexibility, and reduced repeat customer inquiries by about 20%. The project’s success depended not only on rebuilding features, but also on observing call-center work directly, aligning distributed stakeholders, and balancing agent convenience with user privacy. ## Why Demaecan Chose an In-House Platform - Demaecan’s existing chat provider was being discontinued, prompting a choice between upgrading to another vendor solution or adopting InquiryChat. - InquiryChat was selected because it offered: - Zero licensing costs - Flexible customization for Demaecan’s processes - Direct internal technical and operational support - Secure access to customer information - Real-time analytics and reporting - The migration had to preserve service continuity while changing agents’ workflows and tools. ## Problems with the Existing Service - Agents could preview messages before users sent them, creating a transparency and privacy concern. - Chat sessions were not preserved when users left, forcing customers to repeat their inquiries. - Vendor customization and integration options were limited. - Some existing features appeared useful on paper but were not actually used in call-center operations. - Because agents were accustomed to the existing system, they needed evidence that the replacement would improve their daily work—not merely solve problems identified by headquarters. ## Understanding Real Call-Center Needs - Requirements initially arrived indirectly through multiple departments, making their operational context difficult to understand. - The team reorganized requirements into: - Standard platform features - Features requiring customization or review - Features requiring new development - PM Kim Seri visited two call centers in Fukuoka, observed peak-hour operations, and interviewed agents and managers. - Field research revealed: - A manager-support chat feature was unnecessary because agents used hand signals instead. - Audio alerts were ineffective because office environments kept sound muted. - Integration with the existing CS system was essential because agents had to summarize and record every interaction after a chat. - These observations helped the team remove unnecessary features while raising the priority of workflows that were genuinely critical. ## Managing a Distributed Migration - The project involved teams in Korea and Japan, two Fukuoka call centers, external operators, and multiple internal departments. - To create a shared operating model, the PM: - Built a Jira dashboard to visualize progress and support data-driven decisions. - Created a master specification as a single source of truth. - Led internal product and development QA to ensure the original intent was implemented correctly. - Produced detailed operational guides for launch and adoption. - These processes reduced ambiguity across the project and supported a smoother transition from development to live operations. ## Focus Group Testing and Interviews - A focus group test (FGT) brought all stakeholders together to act as customers and CS agents. - Participants followed complete scenarios, from opening an inquiry through resolution. - The FGT exposed user-experience issues before formal QA, including: - Unclear role and status indicators - Android push-notification instability - Keyboard and input-field overlap - Missing timestamps on links - Push-title wording issues - Chat logs not consistently reaching the CS system - Several issues were fixed immediately, while clearer role and status displays were scheduled for further improvement. - Follow-up focus group interviews found the system generally stable and easy to operate, though some participants initially needed time to understand operator and manager roles. ## Balancing Agent Convenience with Privacy - Agents strongly valued the old “message being typed” preview because it helped them anticipate replies and reduce average handling time (AHT). - From a platform perspective, previewing unsent or deleted text was a serious information-ownership and privacy risk. - Rather than simply removing the feature, the team introduced a typing-status indicator. - This preserved agents’ awareness of the customer’s response rhythm without exposing the customer’s actual unfinished text. The project demonstrates that successful system internalization requires more than feature parity. Direct observation, structured stakeholder coordination, realistic user testing, and privacy-conscious design enabled Demaecan to reduce costs while improving both operational efficiency and customer experience.

data-integrationandroiduser-researchjira+3

datadogJun 23, 2025

How we built reliable log delivery to thousands of unpredictable endpoints (opens in new tab)

Datadog’s Log Forwarding system resembles a package delivery network: it must move large volumes of data efficiently and reliably to many unpredictable destinations. Kafka provides ordered transport, but its FIFO behavior creates difficult tradeoffs when endpoints are slow or unavailable. The central challenge is preserving delivery guarantees without losing logs, creating duplicates, blocking unrelated destinations, or overwhelming customer infrastructure. ## What Log Forwarding Does - Datadog forwards processed, enriched logs as schemaless JSON records. - Destinations can include: - Elasticsearch - Splunk - Generic HTTP endpoints accepting JSON `POST` requests - The system must support thousands of tenants and external endpoints with widely varying reliability and performance. ## Kafka as the Distribution Network - Logs move through Datadog on Kafka topics, analogous to packages traveling on conveyor belts. - Each Kafka partition provides strict FIFO ordering: - Records are read in the order they were written. - Kafka offsets must be committed in that same order. - Logs for different destinations are spread across multiple partitions, so records for a single destination may need to be regrouped during delivery. - Assigning a dedicated Kafka partition to every destination would be simple conceptually but infeasible at scale. ## Reliability Challenges - External endpoints may be: - Temporarily unavailable - Slow or unstable - Unreachable for hours or days - The system must avoid: - Losing customer logs - Sending duplicate logs - Delaying all destinations because one endpoint is unhealthy - Excessive resource usage - Overwhelming or effectively DDoSing a customer endpoint - Sending one HTTP request per log would be inefficient, so logs should be buffered and delivered in batches, much like packages going to the same address. ## Kafka Ordering and Blocked Progress - Waiting for each forwarding request to succeed before reading more Kafka data protects against data loss but can halt progress. - Continuing to read and acknowledge Kafka records before successful delivery risks losing logs. - Because offsets must be committed in order, one unavailable destination can block later records in the same partition—even if those records belong to healthy destinations. - This makes coordination between Kafka consumption, retries, batching, and concurrent delivery especially complex in a multi-tenant system. ## Lessons from Log Archives - Datadog had prior experience with similar delivery problems in its Log Archives feature. - Archiving was easier because: - Cloud object storage endpoints are generally more reliable. - Archiving has lower latency requirements. - Those lessons helped the team anticipate reliability and ordering pitfalls in Log Forwarding. ## Dedicated Kafka Topics per Destination - A possible solution would be to assign one or more Kafka partitions to each destination. - This would isolate destinations so that one slow endpoint could not block others. - However, the approach would require an impractically large number of Kafka topics or partitions as the number of customers and destinations grows.

data-integrationdatabase-designdatadogkafka+4