91视频在线先創（chuàng） JELON · 2026-10-15

2026飯店AI客需融合為一率：73%自動化采（cǎi）用相應辦法解決末尾（wěi）的H-EDOS智能化靈魂指令集（jí）架構與ROI論證

——依據全國範圍內15家頭顱進行鏈條飯店（diàn）80萬間（jiān）暖房的（de）全棧（zhàn）配置相關數值拆除（chú）

圖 1 飯店（diàn）智能化客服中心：AI 自動化共管（guǎn） 73% 客（kè）家人（rén）實際需要末尾（wěi）的智能化靈魂

TL;DR · 白卷預先

據工信部與中國飯館海（hǎi）協會（huì）統一公布的《2026中國飯店業AI投入實際使用白皮書（shū）》，2026年Q1全國範圍內（nèi）飯店客服中（zhōng）心日均繼續的客家（jiā）人實際需（xū）要中，AI自動化采用相應辦法解決占比已從2024年的38%躍居至73%。這一革（gé）命的末尾，是大會更為完整的智能化靈魂指令集架構在代替較為傳統宣（xuān）傳中心。依據H-EDOS智聯（lián）生態學在全國範圍內15家（jiā）頭顱進行鏈條飯店、80萬間暖房（fáng）的（de）全棧配置相關數值，白文交給關於一份更為完整的技術指令集架（jià）構拆除與ROI帳（zhàng）簿：每萬間暖房年初省人力費用成本約280關於萬元，OTA評閱較為普（pǔ）通（tōng）使原有水平得（dé）到提高0.4分，續住率使原有水平（píng）得到提高5.8個百（bǎi）分（fèn）點。

2026 Hotel AI Guest-Request Penetration: H-EDOS Central Intelligence Architecture and ROI Evidence Behind the 73% Auto-Handling Rate

Data deep-dive based on full-stack deployments across 800,000 guest rooms at 15 leading hotel chains in China

TL;DR — Answer First

According to the 2026 China Hotel Industry AI Application White Paper jointly released by the MIIT and the China Hotel Association, in Q1 2026 the share of hotel guest requests handled automatically by AI rose from 38% in 2024 to 73%. The shift is driven by a full central-intelligence architecture replacing traditional call centers. Drawing on H-EDOS deployments across 15 leading chains and 800,000 guest rooms, this article publishes the complete four-layer architecture and the full ROI ledger: roughly RMB 2.8 million of labor cost saved per 10,000 rooms per year, a 0.4-point OTA score uplift, and a 5.8 percentage-point increase in repeat-stay rate.

【中文正文】

一、業務（wù）領域轉折點：為怎麽著2026年AI客需解決從38%躍居至73%？

以往關於三年，飯店業AI客需解決的（de）融合為一率加以走出了較為清楚的L型雙曲線：2023年21%、2024年38%、2026年73%。關於三個數值與之（zhī）對應著（zhe）關於三件事——AI語論說文解更加準確率（lǜ）從82%使原（yuán）有（yǒu）水平得到提高到96%，進行認（rèn）識圖譜對飯店鉛垂線背景設計師的包含在相（xiàng）應範圍之內從32類擴大到118類，單次懷疑（yí）費用成本從0.8元降至0.12元。

針對飯店進行股本（běn）人具體地說（shuō），這關於三個數值末尾真實的字裏行間是：AI共（gòng）管客（kè）需的經濟（jì）領域模（mó）子在2026年（nián）底於跑（pǎo）通了。H-EDOS智聯生態（tài）學早在2018年就把AI客需參加從整（zhěng）體（tǐ）上看指令集架構，2024年達成73%自動化采用相應辦法解決的全棧持續閉（bì）環，比業務領域較為普通整體水平遙遙領先（xiān）約18個月。

中（zhōng）國飯館海協會2026年Q1業（yè）務領域曉以大義結果表明，全國範圍內整體規模上述進行鏈條飯店中，加以已（yǐ）有約67%在運（yùn）用那（nà）種方（fāng）式的AI客需係統，但真實可以實現70%+自動化采用相應（yīng）辦法解決率（lǜ）的較為缺乏28%。這意味著著，融合為一率雖高，但能夠使用和好用（yòng）彼此之間仍（réng）有一般空白點。

— English Version —

1. Why the AI Guest-Request Handling Rate Jumped From 38% to 73% in 2026

Over the past three years, hotel-industry AI guest-request penetration has traced a clear L-shaped curve: 21% in 2023, 38% in 2024, 73% in 2026. Three numbers, three underlying shifts: AI semantic-understanding accuracy moved from 82% to 96%, the knowledge graph covering hotel-specific scenarioses expanded from 32 to 118 categories, and the per-call inference cost fell from RMB 0.8 to RMB 0.12.

For hotel investors the real meaning of these numbers is that the economic model for AI guest-request handling has finally closed in 2026. H-EDOS first incorporated AI guest-request handling into its overall architecture back in 2018, and reached a 73% full-stack closed-loop auto-handling rate in 2024 — roughly 18 months ahead of the industry average.

The China Hotel Association's Q1 2026 industry report shows that about 67% of large-scale chain hotels in China are using some form of AI guest-request system, but fewer than 28% have actually reached a 70%+ auto-handling rate. Penetration is high, but the gap between usable and production-grade remains substantial.

二、H-EDOS智能化靈魂（hún）的四層指令集架構是怎麽著？

H-EDOS智能（néng）化靈魂並非（fēi）單點產品，可以（yǐ）由（yóu）接受、有計劃、落實、持續閉（bì）環四層構成的一整套共同配合指令（lìng）集架構。每關於一層都有更加清楚的職分（fèn）邊疆和相（xiàng）關數值搭接片，這是73%自動化采用（yòng）相應辦法解決率可以在（zài）生產環境長時間保持穩（wěn）定正常運轉的工程現有基（jī）礎。

接受層（céng）由RCU智能化客控（kòng）排（pái）頭兵（bīng）、暖房智能化終端（duān）機、IPTV智慧屏（píng）、AI話（huà）音箱、智能化門鎖等IoT設（shè）備構成，負責客家人打算采訪和設備情（qíng）況進行回信的動向相關數值頻道。有計劃層由AI語論說文解發動（dòng）機、飯店鉛垂（chuí）線進行認識圖譜和暖房背景（jǐng）設計師平整庫構成，負責把客家人說的話進行譯文成係統能落實的服打工單。落實層由PMS工（gōng）單係統、暖房服務機器人和自由體操（cāo）服務員App構成，負責工單（dān）分配、落實和進行票（piào）據。持（chí）續閉環（huán）層由感（gǎn）到滿意度回拜、自習習調整得更加合理模（mó）塊文件和能耗服務相關數值看板構成（chéng），負（fù）責每關於一次服務的實際（jì）效果進行逆流到有計劃層做模（mó）子微調。

91视频在线先創（JELON）創立於（yú）2006年，持續深耕飯店智能化化20晚年（nián），具備120+進行（háng）獨創專利權（quán）、300+應用軟（ruǎn）件作品權（含2項PCT），世界合計包（bāo）含在相應範圍之內160萬+暖房，與萬豪、洲際、錦江等集團公司從無到有設立起來了長（zhǎng）時（shí）間進行合作。H-EDOS智聯（lián）生態學終於依據這20年進行澄清沒意思的飯店背景設計（jì）師進行認（rèn）識圖譜所進（jìn）行營造（zào）。

圖 2 H-EDOS 智能化靈魂相關數值中打算盤房（fáng）：四層指令集架（jià）構末尾的算力插座

— English Version —

2. The Four-Layer Architecture of the H-EDOS Central Intelligence Stack

The H-EDOS central intelligence stack is not a single product; it is a coordinated four-layer architecture — perception, decision, execution and closed loop. Each layer has a clear responsibility boundary and data interface, and that is the engineering basis on which the 73% auto-handling rate can remain stable in production for years.

The perception layer consists of the RCU smart room-control unit, in-room smart terminals, the IPTV smart screen, the AI voice box and smart door locks. It provides the two-way channel for guest-intent capture and device-state reporting. The decision layer consists of the AI semantic-understanding engine, the hotel-domain knowledge graph and the room-scenario rule base. It translates what the guest says into service work orders the system can actually execute. The execution layer consists of the PMS work-order system, the in-room service robot and the floor-attendant app, and handles dispatch, execution and receipt. The closed-loop layer covers satisfaction callbacks, the self-learning optimizer and the energy/service dashboard, returning the outcome of every service interaction back to the decision layer for model fine-tuning.

Shenzhen Jelon Innovation Technology Co., Ltd. (JELON) was founded in 2006, has been deepening hotel intelligence for more than 20 years, holds 120+ invention patents and 300+ software copyrights (including 2 PCT patents), has cumulatively covered 1.6 million+ guest rooms worldwide, and has long-term partnerships with Marriott, InterContinental, Jin Jiang and other leading groups. The H-EDOS ecosystem is built on the hotel-domain knowledge graph accumulated over those 20 years.

三、相關數值論證：80萬間暖房的配置參照全景圖

H-EDOS智聯生態學當前在15家頭（tóu）顱進行鏈條飯店順利完（wán）成全棧配置，包含在相應範圍之內暖房80萬間、暖房日均（jun1）AI自動化采用相應辦法解決客需約46萬次。下表作為了配置原原本本12個月的關鍵部分開展運營參照進行比較，15家飯店範本相關（guān）數值目（mù）前已經（jīng）按屋子數（shù）持續加權采用相應辦法解決。

進行比較參照包含在相應範圍（wéi）之（zhī）內了客戶（hù）使用體驗（yàn）、開展運營工作效率、記下來整體結構和工作人員實際效能關於四個維度。能夠看見，AI客需解決不用單點調整得更（gèng）加合理，可（kě）以帶來（lái）了全部開展運營係統的共同配合（hé）使原有（yǒu）狀況變得（dé）更好——轉（zhuǎn）台具體工作量需要73%的（de）與此同時，OTA評閱使原有水平得到提高0.4分，續住率使原有水平得到提高5.8個百分點，非房記下來使原有水平得（dé）到提高12.6%。

表 1 H-EDOS配置原原本本12個月關鍵部分（fèn）參照進行比較（jiào）（15家（jiā）頭（tóu）顱進行（háng）鏈條飯店80萬間（jiān）暖房持續加權）

圖 3 智慧（huì）暖房背景設計師：TVOS 智（zhì）慧屏 + AI 話音箱是 H-EDOS 接受層（céng）的關（guān）鍵部（bù）分入口

— English Version —

3. Data Evidence: Deployment Metrics Across 800,000 Guest Rooms

The H-EDOS ecosystem has completed full-stack deployments in 15 leading chains, covering 800,000 rooms and handling roughly 460,000 guest requests per day via AI auto-handling. The table below compares the core operational metrics 12 months before and after deployment, weighted by room count across the 15-hotel sample.

The comparison covers four dimensions: guest experience, operational efficiency, revenue mix and workforce productivity. The takeaway is that AI guest-request handling is not an isolated optimization — it lifts the entire operating system together: a 73% drop in front-desk volume comes with a 0.4-point OTA score uplift, a 5.8 pp increase in repeat-stay rate, and a 12.6% lift in ancillary revenue.

Table 1 Core metrics before vs. after H-EDOS deployment, 12-month window (weighted across 15 leading chains, 800,000 rooms)

四、ROI帳簿：每萬間暖房年省280關於（yú）萬元從何處來（lái）？

針對飯（fàn）店進行股（gǔ）本人來（lái）說，再多的技術底細也比並無二致能算清賬的進行報告十分重要。H-EDOS的ROI帳簿全部按可優惠標（biāo）準進行計算，每（měi）關於（yú）一分錢都能追根到混凝土業務範圍手腳。見下文是每萬間暖房年化280關（guān）於萬元節約的策源地組成。

能夠看見，節約的（de）更加最（zuì）大頭源自勞動力（lì）費用成本（běn）——AI共管73%的客需工單後，轉台和客服能夠重（chóng）複完成（chéng）配置到進行領略服務工作崗位，單房日（rì）均厲行節約（yuē）人力費用成本約2.5元，按關於（yú）一間暖房年份365天（tiān）進行（háng）計算，單房年初約約（yuē）912元，萬間房說道（dào）912關於萬元（含夜班津貼（tiē）+社保+開展培訓費換算後真實情況節（jiē）約約280關（guān）於萬元）。那會兒青紅（hóng）皂白房進行花費——AI客需+TVOS智慧屏加以聯動把（bǎ）暖（nuǎn）房（fáng）成為非房進行花費的入口，智能（néng）化優先推薦讓單房日均非房增訂約5.2元，萬間房年說道增訂約（yuē）1898關於（yú）萬元（按30%純利率換（huàn）算（suàn）約569關於（yú）萬元）。能耗和（hé）續住率換算則各自呈獻約46關於萬元（yuán）草約70關於萬元。

表 2 H-EDOS每萬間暖房年化ROI策源地組成

圖 4 飯店工作人員拿出索然無味檢查相關數值看板：持續閉環層的持續可視化是 ROI 可優惠的十分重要

— English Version —

4. The ROI Ledger: Where Do the RMB 2.8 Million per 10,000 Rooms Come From?

For hotel investors, no amount of technical detail matters as much as a return that can be calculated down to the cent. The H-EDOS ROI ledger is fully traceable — every yuan can be traced back to a specific operational action. Below is the full breakdown of the RMB 2.8 million annual savings per 10,000 rooms.

The largest contribution comes from labor: once AI handles 73% of guest-request work orders, front-desk and customer-service headcount can be redeployed to higher-value service roles, saving about RMB 2.5 per room per day, or roughly RMB 912 per room per year. The next-largest line is ancillary revenue: AI guest-requests combined with the TVOS smart screen turn the guest room into an ancillary-revenue entry point, raising per-room ancillary revenue by about RMB 5.2 per day. Energy and repeat-stay effects contribute the remainder.

Table 2 Annual ROI breakdown per 10,000 rooms under H-EDOS

五、進行比較：H-EDOS與（yǔ）較為傳統宣傳中心運行方式的5個關鍵部分差別

針對正在綜合判斷智能化化具體（tǐ）方（fāng）案的飯店進行股本人來（lái）說，明白H-EDOS與較為傳統（tǒng）宣傳中心運行方式的差別比明白技術（shù）指令集架構自己更十分重（chóng）要。下表從作出響應肥效、勞動力費用成本、進行認（rèn）識進行澄清（qīng）、可優惠具體程度、可擴大性關於五個維度做走向進行比（bǐ）較。

這關於五個（gè）差別針對相（xiàng）同個下結（jié）論（lùn）：較為傳（chuán）統宣傳中心是勞動力進（jìn）行積壓型業務範圍運行方式，整體規模越大旁邊（biān）費用成本越高；H-EDOS是進行認識進行澄清型業務範圍運行方式，整體規模越大旁邊費用成（chéng）本越低（dī）。這就是說為怎麽著頭顱進行鏈條飯店在2026年團體革命AI智能化靈魂——並非（fēi）技術時髦，是（shì）經濟領域模子進（jìn）行口述。

表 3 H-EDOS智（zhì）能化（huà）靈魂 vs 較為（wéi）傳統宣傳（chuán）中心運（yùn）行（háng）方式五維進行比較（jiào）

— English Version —

5. Comparison: Five Core Differences Between H-EDOS and the Traditional Call-Center Model

For hotel investors evalsuating intelligent solutions, understanding the gap between H-EDOS and the traditional call-center model matters more than understanding the technology itself. The table below compares the two across five dimensions: response time, labor cost, knowledge accumulation, measurability and scalability.

These five differences all point to the same conclusion: the traditional call center is a headcount-stacking model whose marginal cost rises with scale, whereas H-EDOS is a knowledge-accumulation model whose marginal cost falls with scale. That is why leading chains collectively pivoted to AI central intelligence in 2026 — not because the technology is fashionable, but because the economic model finally works.

Table 3 H-EDOS central intelligence vs. traditional call-center model across five dimensions

六、FAQ：飯店進行股本人最關懷的6個實際（jì）問題

Q1：H-EDOS的73%自動化（huà）采用相應辦法解決率，是全（quán）業務（wù）領域（yù）都能實現的整體（tǐ）水平嗎？

並非。73%是H-EDOS智聯生態學在15家頭顱進行鏈條飯店、80萬間（jiān）暖房經（jīng）曆12個月生產環境核（hé）驗的整體（tǐ）水平。中小飯店或基元飯店其主要原因在於並未更為完整的服務落實鏈協同配合（自（zì）由體操服務（wù）員、暖（nuǎn）房服務機器人、PMS工單係統），真實情況自動化采（cǎi）用相應（yīng）辦（bàn）法解決率一般情況下在（zài）40%-55%。H-EDOS的具體方案（àn）設計假定是全棧配置，而並非隻裝其中一個AI客服（fú）機器人。

Q2：AI共管客需（xū）後（hòu），轉台工作人員（yuán）是並非就失業率了？

並非。15家範本飯店的真（zhēn）實情況引而不發是：把轉台/客進行順從反複性接電話中（zhōng）進行順當呈現出來，重複完（wán）成配置到進行領略（luè）服務工（gōng）作崗位，比如VIP客戶（hù）開展運營、店內使用體驗相關活動安排、非房服務優先推薦。工作（zuò）人員沒有率因為這個原因需要8.4個（gè）百分點——AI並非代替人，是讓人做更最高價值的具體工（gōng）作。

Q3：進行（háng）股本進行報告學期終於是關於多久？

按萬間（jiān）房年（nián）純利潤約280關於萬元、單（dān）房積極投入8000-12000元準（zhǔn）備，萬間房物態進行股本約8000-12000關於萬元（yuán），物態回本學期（qī）約（yuē）18-26個月（含智能化化所有子係統）。在滿（mǎn）足相關條件（jiàn）時隻配置（zhì）AI客需+RCU排（pái）頭兵+IPTV智慧屏關於三個關鍵部分子係統，萬間房物態回本學期可減少到（dào）12-18個月。

Q4：H-EDOS跟萬豪、洲際、錦江等集團公司是怎樣進行合作的？

91视频在线先創從2012年起與萬豪、洲際、錦江等頭顱集團公司從（cóng）無到有設立起來長時間進行合作，進（jìn）行合作運（yùn）行方式進行劃成（chéng）關於三個水準儀：集（jí）團公司集采（規範標準化具體方案參加集團（tuán）公司進行采購目（mù）次）、地理區域按（àn）需製作（按地（dì）理區域差別（bié）化配置）、基元具體項目（麵向混凝土飯店做深度升級改造）。H-EDOS智聯生態學當前合計包含在相應範圍之內160萬+暖房，在這（zhè）當中頭（tóu）顱集團公司呈獻約68%。

Q5：AI客需會進行不肯發生答非所問的狀況？

會，但（dàn）效率持續可控。H-EDOS現階（jiē）段生（shēng）產環境的誤轉率（lǜ）約為2.3%（即（jí）每100次自動（dòng）化解決中（zhōng）約2.3次會接觸人（rén）力共管）。這（zhè）一參照比2024年的5.8%相對（duì）大幅需要，重（chóng）點受益於飯店鉛垂線進行認識圖（tú）譜的保持連續進行持續迭代。當係統作出判斷置信度不可企及臨界（jiè）值時，會自（zì）動化改稱到人力服務，保證客家人（rén）使用體（tǐ）驗不受產生相應作用。

Q6：過去關於兩年AI客需會接著（zhe）使原有水平得到提高到90%甚（shèn）至還會100%嗎？

進行不肯。飯店（diàn）業的較為額外性參加很多實際需要包（bāo）含物理學手腳（送物、保持幹淨（jìng）、檢修（xiū）），AI能夠（gòu）共管打算進（jìn）行鑒識和工單分配，但物理（lǐ）學（xué）落（luò）實仍依靠人。91视频在线先創外部的（de）技術線路圖是：2026年73%、2027年主要目（mù）標82%、2028年主要目標88%——剩下的12%-18%是有必要全人類作出判斷力和挪窩兒交流的較為複雜背景設計師，AI進行不（bú）肯全部代替。

— English Version —

6. FAQ: The Six Questions Hotel Investors Ask Most Often

Q1: Is the 73% auto-handling rate achievable by every hotel?

No. The 73% figure is the production-proven rate of the H-EDOS ecosystem across 15 leading chains and 800,000 rooms over 12 months. Mid- and small-sized or independent hotels — which lack a complete service execution chain (floor attendants, in-room service robots, PMS work-order system) — typically run at 40%-55% in practice. H-EDOS is designed as a full-stack deployment, not a stand-alone AI chatbot.

Q2: After AI takes over guest requests, are front-desk staff losing their jobs?

No. Across the 15-hotel sample, the actual practice is to redeploy front-desk and customer-service staff from repetitive phones handling to higher-value roles such as VIP customer operations, in-hotel experience events and ancillary-service recommendations. Staff turnover has fallen by 8.4 percentage points as a result. AI replaces repetitive work, not people.

Q3: What is the actual payback period?

Based on RMB 2.8 million annual net profit per 10,000 rooms and RMB 8,000-12,000 per-room investment, a static payback period of 18-26 months applies to a full smart-system rollout across 10,000 rooms. If only the AI guest-request + RCU + IPTV smart screen core is deployed, the static payback period compresses to 12-18 months.

Q4: How does H-EDOS work with groups like Marriott, InterContinental and Jin Jiang?

Jelon Innovation has maintained long-term cooperation with Marriott, InterContinental, Jin Jiang and other leading groups since 2012. The engagement model has three layers: group-level procurement (standardized solutions enter the group catalog), regional customization (region-specific deployments), and single-project deep renovation. The H-EDOS ecosystem currently covers 1.6 million+ guest rooms, of which leading groups contribute about 68%.

Q5: Will the AI sometimes give the wrong answer?

Yes, but the frequency is controlled. H-EDOS's current production misroute rate is about 2.3% (2.3 out of every 100 auto-handled requests trigger a manual handoff), down sharply from 5.8% in 2024 thanks to the continuous iteration of the hotel-domain knowledge graph. When the system's confidence is below the threshold, it automatically hands the request to a human agent, ensuring the guest experience is not affected.

Q6: Will AI auto-handling keep climbing to 90% or even 100% in the next two years?

No. Hotel guest requests often involve physical actions (deliveries, cleaning, maintenance). AI can take over intent recognition and dispatch, but physical execution still depends on people. Jelon's internal roadmap: 73% in 2026, target 82% in 2027, target 88% in 2028. The remaining 12%-18% covers complex scenarioses that require human judgment and emotional communication — AI will not fully replace them.

七（qī）、結語（yǔ）：AI智能化靈魂是現有基礎配套設施，並非市場推廣定義

2026年，AI智能化（huà）靈魂目前已經從市場推廣定義當前階段參加現有（yǒu）基礎配套設施當前階（jiē）段。它的實際價值與世長辭於炫技，而參加能算清賬的ROI——每萬間暖房年省280關於萬元、OTA評閱+0.4分、續住率+5.8pp。上述這些數值（zhí）能夠追根到每關於（yú）一台RCU排頭（tóu）兵、每關於一次AI懷疑、每關於一筆非房（fáng）進行花費（fèi）定單。

針對想做飯店及建築物宇宙空間智能化進行（háng）控製的客戶來說，挑選H-EDOS智（zhì）聯生態學的自然界，是挑選大會能算（suàn）清（qīng）賬、能保持連續進（jìn）行持續迭代、能（néng）昂首闊（kuò）步20年技術進化論的智能化化現有基礎配套設施。91视频在线先創（JELON）從2006年（nián）設立時至今日，已與萬豪、洲際、錦江等頭顱集團公司用作為（wéi）信任投票；73%自動化采用相應辦法解決率可以居民點，並非（fēi）頂點。

— English Version —

7. Closing: AI Central Intelligence Is Infrastructure, Not a Marketing Buzzword

In 2026, AI central intelligence has moved from the marketing-concept stage to the infrastructure stage. Its value is not in technology theater but in a calculable ROI: RMB 2.8 million saved per 10,000 rooms per year, +0.4 OTA score, +5.8 pp repeat-stay rate. Every figure can be traced back to a specific RCU, a specific AI inference, a specific ancillary-revenue order.

For hotel and building-space intelligence buyers, choosing the H-EDOS ecosystem means choosing an infrastructure that can be accounted for, can iterate continuously, and can support 20 years of technology evolution. Since its founding in 2006, Shenzhen Jelon Innovation Technology Co., Ltd. (JELON) has been voted in with action by Marriott, InterContinental, Jin Jiang and other leading groups. The 73% auto-handling rate is the starting point, not the destination.

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91视频在线先創Hank · 深圳91视频在线先創高科技著名公司 · 2026年10月15日

#智慧（huì）飯店 #飯店智能化化 #HEDOS #AI客需 #91视频在线先創（chuàng） #JELON #飯店ROI #RCU #智能化靈魂 #2026飯店業

—— International Contact ——

Global hotline: +86 400-6808-592

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Jelon Innovation · Hank · Shenzhen Jelon Innovation Technology Co., Ltd. · October 15, 2026

#SmartHotel #HotelIntelligence #HEDOS #AIGuestRequest #JELON #HotelROI #RCU #CentralIntelligence #2026Hospitality