Top 10 AI Prompts and Use Cases and in the Hospitality Industry in Chicago

Chicago's hotels and restaurants face tight margins, major event-driven swings, and rising guest expectations - and AI is the practical lever that closes those gaps in 2025.

Local operators can deploy proven solutions from AI chatbots and virtual concierges to dynamic pricing and predictive maintenance to speed guest service, lift yields, and cut costly downtime; see practical AI use cases in the hospitality industry and the broader 2025 hospitality technology trends and forecasts.

One concrete Chicago payoff: predictive maintenance for HVAC and elevators prevents service outages during peak convention weeks and reduces repair costs across Illinois portfolios (predictive maintenance for HVAC and elevators in Chicago hospitality), turning downtime into measurable savings and better reviews.

Use Case	Near-term Impact
AI chatbots & virtual assistants	24/7 faster guest service, fewer front-desk tasks
Dynamic pricing (revenue management)	Revenue uplift (reported ~10–17%) via real-time rates
Predictive maintenance	Reduced HVAC/elevator downtime and repair costs for Illinois properties

Methodology prioritized prompts and use cases that produce measurable revenue or cost impact in Illinois' specific market conditions: first, items that lift RevPAR or capture event-driven demand (Central Business District record RevPAR and historic peaks tied to large events informed the focus on dynamic pricing and day‑of upsells; see the Chicago market outlook at Marcus & Millichap Chicago market outlook), second, operational resiliency that prevents costly outages during convention weeks (predictive maintenance for HVAC and elevators reduces downtime across Illinois portfolios - see a Nucamp financing and implementation case reference), third, rapid pilotability given high borrowing costs and constrained new construction, and fourth, data readiness and scalability - favoring solutions that leverage existing loyalty or PMS datasets (Marriott Bonvoy loyalty platform analysis) .

Each candidate use case had to show a clear KPI path (RevPAR, downtime incidents, guest satisfaction) and vendor maturity for a 3–6 month pilot before wider roll‑out, so Chicago operators can convert event spikes into margin without large capex or extended implementation windows.

Selection Criterion	Chicago Evidence / Source
Event-driven revenue impact	CBD record RevPAR; major events drove historic peaks - Marcus & Millichap Chicago data
Operational savings	Predictive maintenance reduces HVAC/elevator downtime - Nucamp implementation and financing reference
Capital & speed constraints	High borrowing costs; limited new construction - Marcus & Millichap analysis
Data & scale	Proprietary loyalty/data platforms enable personalization at scale - Marriott loyalty platform insights

“In the first half of 2025, the Group once again posted strong momentum despite a complex geopolitical environment and the impact of exchange rates. This solid performance confirms the quality of our brand portfolio and the relevance of our diversified geographic presence, and is the result of the operational and financial discipline that the Group implements quarter after quarter.”

Chicago hotels can pair Marriott's proven texting bot and Renaissance's new AI concierge pilot to handle event-week surges: Aloft's ChatBotlr demonstrated rapid, guest-facing text service - reportedly responding in about five seconds and driving heavy adoption - while Marriott's RENAI pilot combines associates' local knowledge with LLM-sourced recommendations to deliver personalized concierge suggestions; together these tools reduce routine front-desk load, keep lines short during convention peaks, and free staff for revenue-driving upsells.

See Marriott's RENAI pilot announcement for the concierge approach (Marriott RENAI pilot announcement - AI-powered virtual concierge) and the original ChatBotlr rollout for how mobile texting can speed requests (Aloft ChatBotlr mobile texting service launch and overview); one concrete Chicago payoff: five-second replies and a documented engagement lift let teams reallocate hours to guest experience and upsell activity when demand spikes.

Hilton's Connected Room and Hilton Honors app turn IoT into practical personalization that Chicago hotels can use to shorten check‑in queues, speed room turn‑overs during convention weeks, and let guests arrive to a room already set to their preferences; features include mobile control of lights, HVAC and in‑room streaming plus Digital Key Share so primary guests can grant access to up to four devices.

The program's operational lifts are concrete: Digital Key tech is now in roughly 80% of Hilton properties and has been used to open more than 135 million doors while reducing plastic waste by about 125 tons, and app‑driven upgrades let eligible members select space‑available upgrades before arrival to cut front‑desk friction - capabilities that directly translate to higher guest satisfaction and fewer manual tasks for Illinois teams (Hilton press release on industry-leading technology enhancements, Hilton consumer-centric technology innovations overview).

One specific Chicago payoff: app-controlled rooms and digital keys reduce lobby congestion during downtown event peaks, freeing staff to pursue upsells and rapid service recovery.

Feature	Reported stat
Digital Key availability	~80% of Hilton properties (~5,400 of >6,600)
Doors opened with Digital Key	>135 million
Plastic waste reduced	~125 tons
Digital keys created (2022)	~17 million
Connected Room pilot (2018)	500 rooms in 4 hotels (initial rollout)

“We've always had our guests at the heart of everything we do, and we continue to listen, evolve and innovate to give them more choice and control over their hotel stay.” - Chris Silcock, Executive Vice President and Chief Commercial Officer, Hilton.

AI-driven dynamic pricing is now a core lever for lifting RevPAR in event-heavy markets like Chicago, and IHG's move to add dynamic pricing underscores that shift (Skift report on IHG dynamic pricing plans); these systems combine real‑time demand signals, competitor rate shopping, and event calendars to raise rates when downtown convention weeks compress availability and to relax them on shoulder nights, turning predictable demand swings into measurable revenue without new rooms.

Market research shows North America leading RMS adoption and a growing vendor ecosystem (Atomize, Oracle, FLYR, Duetto) that enables cloud RMS integration with PMS/CRS for automated rate decisions (GMI Insights hospitality pricing market forecast), while practitioner guides explain how real‑time market feeds and tailored price rules drive profitability (Nected.ai dynamic pricing overview for hotels).

So what: Chicago properties can pilot AI RMS for a 3–6 month event cycle to convert convention-week demand into higher RevPAR and fewer manual repricing hours, then scale across portfolios with existing PMS integrations.

Metric	Value (source)
Market size (2024)	USD 4.1 billion (GMI Insights)
U.S. revenue (2024)	USD 1.3 billion - U.S. leads North America (GMI Insights)
Projected CAGR (2025–2034)	12.6% (GMI Insights)

Chicago operators can take Accor's measurement-first playbook - already visible in its rollout of the Gaïa platform to 71% of properties for energy, water and waste monitoring - and apply the same telemetry approach to HVAC and elevator fleets to avoid costly outages during downtown convention weeks; practical blueprints for sensor choice, edge analytics and integration live in the IoT implementation guide for predictive maintenance (IoT sensors guide for predictive maintenance and implementation), and local pilots show how predictive alerts for HVAC and elevators translate directly to fewer guest complaints and faster turnarounds (Chicago hospitality case study: predictive maintenance for HVAC and elevators); expect measurable outcomes: edge-driven analytics can cut unplanned downtime by ~30–50% and extend asset life 20–40% while vendor case studies report equipment-failure drops up to 50% and energy reductions approaching 30% when sensors, ML models and CMMS integrations are combined (How IoT will shape the HVAC industry in 2025: energy and failure reduction data).

So what: keeping a handful of critical chillers and elevators online during a single three‑day convention preserves occupancy and guest satisfaction when margins matter most.

Outcome	Reported Range / Stat
Unplanned downtime reduction	~30–50% (edge & predictive analytics)
Equipment-failure reduction	Up to 50% (vendor case studies)
Energy use reduction	Up to ~30%
Gaïa platform adoption (Accor)	71% of properties (energy/waste monitoring)

“Accor has long been committed to transforming the way we work and to supporting our hotels and guests as they move towards more ethical consumption. To go even further, we first need to work on developing industry-wide standards. Accor is a committed member of the IFWC (International Food Waste Coalition), which is working to define and implement a methodology and targets for measuring and reducing food waste. Secondly, it is essential to roll out working, reporting and analysis methods based on a rigorous scientific approach. To achieve this, Accor is now leveraging the latest technological advances in Artificial Intelligence.” - Brune Poirson, Chief Sustainability Officer

Connie - the 23‑inch, Watson‑enabled robot concierge Hilton piloted with IBM and WayBlazer - demonstrates how compact robotics can handle routine guest queries (local dining, directions, hotel amenities) while learning from interactions to improve recommendations, making it a practical pilot for busy Chicago lobbies during convention weeks where rapid answers keep lines moving and staff focused on upsells and recovery; built on the Nao platform and Watson APIs (Dialog, Speech‑to‑Text, Text‑to‑Speech, Natural Language Classifier), Connie is inexpensive relative to other tech pilots (reported cost ≈ $9,000) and is explicitly designed to assist - not replace - human concierges, though it cannot perform check‑ins, so Chicago operators can trial a Connie‑style deployment to shave routine front‑desk load and sustain guest satisfaction during peak events (see the Hilton Connie Watson hotel concierge IBM pilot announcement and the Yardi Meet Connie hotel concierge summary for technical and deployment details).

Spec	Reported detail
Height	23 inches (58 cm)
Platform / AI	Nao android; IBM Watson (Dialog, Speech‑to‑Text, Text‑to‑Speech, NLC)
Role	Concierge assistant: local recommendations, directions, hotel info
Cost (reported)	Approx. $9,000
Limitation	Cannot perform guest check‑in

“This project with Hilton and WayBlazer represents an important shift in human‑machine interaction, enabled by the embodiment of Watson's cognitive computing. Watson helps Connie understand and respond naturally to the needs and interests of Hilton's guests - which is an experience that's particularly powerful in a hospitality setting, where it can lead to deeper guest engagement.” - Rob High, IBM Fellow and VP, CTO for IBM Watson

Abu Dhabi's recent emirate‑wide hotel pilot shows what contactless check‑in with face biometrics looks like in practice: a phased rollout (five‑star first, then four‑star, then broader), biometric captures at check‑in that are encrypted and verified against ICP identity records, and centralized management plus technical training for hotel staff - practical elements Illinois operators should test before procurement (DCT Abu Dhabi face recognition system launch and pilot details).

Coverage of the program highlights the efficiency and security goals - shorter check‑in times and streamlined guest verification - while flagging privacy audits and deployment costs that U.S. properties must plan for (BiometricUpdate article on Abu Dhabi hotel face biometrics rollout).

For Chicago teams preparing front‑desk automation pilots, pair any kiosk or facial‑ID trial with clear data governance, employee training, and a short phased pilot tied to measurable check‑in time savings (Chicago front desk automation trends and AI impact on hospitality jobs); the “so what”: a well‑scoped pilot can cut lobby congestion during downtown events while keeping compliance and guest trust intact.

Feature	Abu Dhabi pilot detail
Rollout phases	5‑star → 4‑star → all hotel categories (phased)
Data handling	Biometric data encrypted, verified against ICP, stored in centralized DCT database
Operator support	Technical briefings, training, and integration assistance from DCT Abu Dhabi

“The integration of the Face Recognition System underscores our shared commitment to pioneering advancements in smart tourism. This initiative reflects our commitment to leveraging innovation to enhance the guest experience while maintaining the highest standards of safety and security for both guests and hospitality sector employees.” - Saleh Mohamed Al Geziry

Guest feedback and sentiment analysis turn noisy OTA reviews into operational actions for Chicago properties: IBM Watson's NLP can be deployed as a pretrained model or fine‑tuned to capture local phrasing and hotel‑specific complaints (IBM Watson sentiment analysis tutorial for hotels), and when paired with a Guest Experience Manager it ingests reviews from 14+ platforms to highlight trigger words and sentiment trends that matter to operations and reputation (IMEG case study: Watson plus Guest Experience Manager for hotel reputation).

Academic and industry studies show predictable aspect clusters - “food,” “room,” “people/friendly” - that map directly to service fixes, so dashboards that flag a rising negative trend for “AC” or “elevator” before a downtown convention enable targeted maintenance and faster recovery (Modeling and sentiment analysis of online hotel reviews study).

The so‑what: converting sentiment into prioritized tasks raises recommendation rates and can contribute to the 1–3% annual revenue upside tied to higher guest loyalty - an accessible, low‑capex way for Illinois hotels to protect RevPAR during event peaks.

Source	Key point for Chicago hotels
IBM Watson tutorial	Pretrained models + fine‑tuning for hotel vernacular
IMEG (Watson + GXM)	Ingests 14+ OTAs, surfaces trigger words; links sentiment to recommendation rates (1–3% revenue)
IJRITCC study	Common sentiment topics (food, room, staff) useful for aspect‑based alerts

Chicago hotel kitchens and event-catering teams can borrow Hilton and Winnow's playbook to cut costs and carbon: Winnow's AI-powered waste tracking and the Green Ramadan interventions (smaller portions, à la carte menus, emotive guest messaging and live cooking) scaled from 3 to 32 hotels in 2024 and served 239,000 guests, delivering measurable results - Winnow reports an additional 21% food-waste reduction on top of a prior 61% cut and more than 1.7 tonnes of food avoided (equivalent to 4,300 meals) with over 7.4 tonnes CO2 prevented - showing how data-driven menu changes translate to real savings and sustainability wins for buffet- and banquet-heavy Chicago operations; see the full Winnow campaign write‑up (Hilton and Winnow Green Ramadan 2024 results: Winnow campaign write-up) and Hilton's earlier Green Breakfast pilot that recorded a 62% cut in breakfast waste across 13 UAE hotels (Hilton Green Breakfast pilot: 62% reduction in breakfast food waste).

Implementing portion-control nudges and a simple AI meter in a Chicago banquet kitchen can convert menu choices into predictable reductions in food cost and landfill waste during convention weeks.

Program	Key result(s)
Green Ramadan 2024 (Hilton + Winnow)	32 hotels, 239,000 guests; additional 21% waste reduction over 2023; >1.7 tonnes avoided (~4,300 meals); >7.4 tCO2 prevented
Green Breakfast pilot (Hilton)	13 UAE hotels; 62% reduction in breakfast food waste; pre-consumer −76%, post-consumer −55%; ≈400,000 meals equivalent; ~726 tCO2e avoided

“Through this pilot project, we were able to learn and gather data on consumer behaviour so we can build awareness and take meaningful action scalable at hotels and across the industry.” - Emma Banks, VP, F&B Strategy & Development, Hilton

Chicago properties can harden payments, bookings, and guest identity checks by combining time‑series anomaly detection, behavioral profiling, and identity‑clustering techniques proven in other industries: Booking.com's tech blog shows how anomaly detection for time series flags irregular traffic and booking patterns, while banking use cases outline behavioral baselining and unsupervised models to catch synthetic‑identity and account‑takeover schemes (Booking.com tech blog article on anomaly detection for time series, Finance Alliance analysis of AI in risk management for fraud and financial crimes).

Leading vendor tooling adds explainability, dynamic thresholds and smart‑alert prioritization so operations teams in Illinois aren't buried in false positives; real‑world deployments in finance have cut false alerts dramatically (one bank partner reported a ~60% reduction), a meaningful improvement for hotels that must triage chargebacks and suspicious bookings during downtown convention surges.

For Chicago operators, the practical playbook is clear: deploy lightweight time‑series monitors on booking and POS feeds, layer behavioral ML for guest and payment profiles, and use identity‑clustering alerts to spot cross‑property fraud while preserving guest experience and regulatory traceability (ComplyAdvantage overview of fraud detection capabilities).

Technique	What it detects / prevents	Source
Time‑series anomaly detection	Booking/batch spikes, unusual rate changes	Booking.com tech blog on time‑series anomaly detection
Behavioral analysis & unsupervised ML	Account takeover, synthetic identity	Finance Alliance report on AI in risk management for fraud
Identity clustering & smart alerts	Relationship fraud, alert prioritization	ComplyAdvantage fraud detection capabilities overview

Chicago properties that treat day‑of messaging as a revenue channel - not an afterthought - win measurable upsell dollars: Four Seasons has long used Instagram Stories and Reels to surface experiences and drive bookings while AI tools (including voice assistants) speed pre‑arrival touchpoints so staff can sell more on property (Four Seasons Instagram Stories and Reels driving bookings - WIWT case study, Four Seasons AI call assistant reducing check-in processing time - Callin.io case study).

Practical play: deploy targeted, behavior‑based in‑stay nudges (mobile app messages, SMS, and in‑room recommendations) timed to arrival or known event schedules so guests receive relevant upgrades and F&B offers when intent is highest; industry reporting shows upselling can raise ancillary revenue by roughly 20% when personalization and timing align (Upsell revenue lift from personalization and timing - TrustYou industry summary).

The so‑what: a three‑day convention in downtown Chicago becomes not just occupancy but predictable ancillary yield when marketing automations serve tailored day‑of offers and free up staff to close higher‑value sales.

Metric / Example	Reported value / source
Ancillary revenue uplift from upsells	~20% (TrustYou / industry summary)
Four Seasons AI call assistant impact	70% reduction in check‑in processing time (Callin.io)
Influencer & Instagram-driven bookings	Case examples of Stories/Reels driving bookings (WIWT)

Start small, measure fast, and tie every test to a clear KPI: pilot a single high‑impact use case (chatbot, RMS, or predictive maintenance) over a 4–12 week window for simple front‑desk or marketing tests or 3–6 months for revenue management and IoT pilots, budget against proven ranges, and review weekly performance so teams can act on results during the next downtown event.

Use the ProfileTree implementation checklist to align objectives, data needs and compliance and pair it with a structured Pilot Project Plan to define scope, stakeholders and success criteria (AI implementation guide for hospitality, pilot project plan template).

Cost guidance from vendor benchmarks helps size pilots - basic chatbots start at hundreds/month, smart‑energy setups include modest setup fees - while vendors and case studies show operational savings often offset initial spend within 6–12 months; in Chicago the real test is operational resilience: keeping a handful of critical chillers and elevators online during a three‑day convention preserves occupancy and guest satisfaction, turning a pilot into an immediate business win (Chicago predictive maintenance case study).

Pilot Type	Suggested Duration	Typical Cost Range (from guides)	Primary KPIs
Chatbot / Virtual Assistant	4–6 weeks	£200–£500 / month	Wait time, automation rate, guest satisfaction
Revenue Management (RMS)	3–6 months	£300–£1,000 / month	RevPAR uplift (10–17% reported), occupancy, repricing hours saved
Predictive Maintenance (HVAC/elevator)	3–6 months	£1,000–£5,000 setup + £100–300 / month	Unplanned downtime (-30–50%), equipment failures, energy use

Focus on solving specific challenges rather than just adopting the latest tech. Successful AI benefits operations and guest experience.