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

Billings already anchors Montana's visitor economy - hosting about 2.6 million visitors who spend roughly $621 million locally and accounting for 20.8% of statewide travel while Montana saw 12.5 million visitors and $5.82 billion in spending - so local hotels and restaurants must scale service as seven new hotels push Billings' room inventory past 5,100 guest rooms; practical AI skills can help do that without large hires.

Training that teaches how to use AI tools, write effective prompts, and apply AI across business functions can make revenue management and back‑office tasks more efficient, connect guest needs to nearby attractions like Yellowstone, and is the focus of Nucamp's AI Essentials for Work bootcamp for hands‑on prompt training.

Program	Length	Early Bird Cost	Details
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work syllabus and curriculum | Register for AI Essentials for Work bootcamp

Selection prioritized measurable, fast wins for Billings properties - small and mid‑size hotels that must scale service as inventory grows - using a pragmatic 5‑step approach drawn from MobiDev's playbook and industry experts: identify business goals (examples: raise revenue 5%, NPS > 40, cut payroll 10%), map operational friction (check‑in queues, housekeeping delays, static pricing), audit digital readiness (PMS/CRM/APIs), match problems to proven AI patterns (chatbots, dynamic pricing, predictive housekeeping, energy optimization), then run a single‑property pilot and measure quarterly KPIs.

This method leans on property‑level PMS data as the most actionable training signal and favors narrow, testable use cases that can iterate in weeks rather than months - so a Billings inn can validate an AI housekeeping or pricing prompt on one month's occupancy before scaling across the market.

For detailed steps see MobiDev's 5‑Step AI in Hospitality Roadmap and the HospitalityNet Expert Panel on PMS‑Driven ML Priorities.

Step	Action
1	Identify business priorities (revenue, NPS, payroll)
2	Map operational pain points
3	Evaluate data & digital readiness (PMS/CRM/APIs)
4	Match problems to AI use cases
5	Pilot small, measure KPIs, iterate

An AI agent or virtual concierge can turn busy front desks in Billings into revenue engines and local-experience guides: Canary's hospitality AI automates roughly 80–82% of guest messages, supports 100+ languages, and powers webchat, voice and messaging so staff can spend fewer minutes on check‑in and more time recommending Yellowstone outings or local dining; Canary reports a 40% decrease in front‑desk work and up to a 200% boost in upsell revenue when properties use its tools.

For small and mid‑size Billings hotels facing seasonal surges, Canary's suite - from Canary AI guest messaging for hotels to the new Canary AI Voice for hotels - captures missed calls and automates routine requests, letting one staffer oversee multiple properties without losing service quality; see practical deployment tips in Canary's Canary digital concierge guide.

Metric	Canary claim
Guest messages auto‑handled	~82% automatically
Front‑desk work reduction	40% decrease
Upsell revenue uplift	200% boost

“A new era of guest communication is unfolding, presenting hotels with an unprecedented opportunity to redefine hospitality,” said SJ Sawhney, Co‑founder and President at Canary Technologies.

For Billings hotels and restaurants looking to scale guest service without big hires, Sendbird's low‑code AI chatbots let staff publish a ChatGPT‑powered web widget in minutes, train it on first‑party content (a bot can learn up to 100 subpages), and run omnichannel messaging across mobile, web, SMS and email so routine questions about check‑in, parking or Yellowstone directions are handled 24/7 while complex cases route to humans; the onboarding and dashboard workflow make design and testing accessible to non‑developers and support OpenAI integration for richer responses, with RAG/vector retrieval used to reduce hallucinations.

Practical deployment tips and step‑by‑step setup live in Sendbird's implementation guide, and their hospitality playbook catalogs real use cases and best practices for travel operators.

For Billings properties starting small, this means faster response times, more direct bookings via proactive messaging, and a single hub to manage escalation and analytics.

Read Sendbird's beginner setup and its 2025 hospitality use cases to plan a pilot for one property before scaling.

Metric	Value (Sendbird)
Monthly active users	300M+
End users reached	6 billion+
Uptime	99.9%+
Benchmarked accuracy win rate	90%+ vs peers

“I'm confident if we did this in house, it would have been at least six to nine months without Sendbird.” - Jelena Vukadinovic, Hostelworld

Dynamic pricing turns local market signals - seasonality tied to Yellowstone visits, weekend demand spikes, and the extra supply from seven new Billings hotels - into nightly rates that protect a property's floor price and lift RevPAR; hosts can use Airbnb's built‑in Smart Pricing as a no‑cost baseline (set minimum and maximum limits and override specific dates) while adopting third‑party engines for finer control and multi‑listing automation.

Start by calculating a true daily floor (one example calculation in the research gives a $63/night baseline), then layer rules for lead time, local events and “orphan nights”; Smart Pricing automates many updates but can undervalue unique or premium rooms, so manual overrides around local events are essential.

For small inns or multi‑unit managers in Billings, tools like PriceLabs, Wheelhouse or Guesty's optimizer add competitor tracking, PMS integrations and per‑night rules that have helped some hosts boost revenue (reports show up to a 40% uplift for hosts using advanced dynamic tools).

For a quick pilot, run Smart Pricing for one property, compare to a PriceLabs or Wheelhouse trial, and measure occupancy and ADR over 90 days. See the Airbnb Smart Pricing help center and a practical 10‑step pricing guide for implementation details.

Tool	Key point
Airbnb Smart Pricing help center	Free, sets nightly rates within host‑defined min/max; good baseline but can underprice unique listings
Dynamic pricing platforms comparison: PriceLabs, Wheelhouse, and Beyond Pricing	Paid platforms with competitor tracking, PMS integrations, and granular rules for multi‑listing revenue management

Predictive housekeeping and AI staff scheduling turn noisy shift boards into a clear, demand‑driven plan: models ingest occupancy, check‑out times and guest preferences to prioritize cleanings, draft compliant shifts, and cut overtime so small Billings inns can handle seasonal Yellowstone surges and the city's growing room supply without hiring extra staff.

MobiDev's playbook highlights scheduling as a fast AI win - optimizing staff levels, reducing payroll waste, and embedding KPIs so pilots show value in weeks - while a AIRMEEZ case study reports predicted cleaning priorities that left

“rooms ready when guests needed them,”

directly reducing check‑in delays and negative reviews.

Pairing these workflows with housekeeping rules from Lingio's operations guide (IoT sensors, room status feeds, and predictive maintenance signals) creates a closed loop: fewer emergency cleanings, better shift satisfaction, and measurable lifts in operational efficiency and CSAT. Start with a single‑property pilot, track task‑automation and hours saved, then scale across Billings properties to protect RevPAR during peak weekends.

See implementation details in MobiDev's AI in Hospitality roadmap, the AIRMEEZ predictive‑housekeeping case study, and Lingio's housekeeping & maintenance use cases.

Metric	What to measure
Operational efficiency	Task‑automation rate; hours saved per week
Labor cost	Overtime hours; payroll reduction
Guest experience	Rooms ready at check‑in; CSAT / NPS change

Guest sentiment analysis turns mountains of TripAdvisor and on‑site reviews into an operational roadmap for Billings hotels: use NLP to split reviews into sentences, map phrases to amenities (food, parking, cleanliness, quietness) and score each sentence so managers see which amenities drive negative reviews before a Yellowstone weekend surge; practical pipelines - outlined in AltexSoft's guest sentiment analysis roadmap - recommend starting with a modest annotated sample (≈1,000 reviews for ~70% accuracy) and scaling toward 15,000+ to reach ~90% accuracy, while using hotel‑specific corpora like the TripAdvisor collection to train aspect models and reduce hallucination in summaries.

Running an amenity‑level sentiment report will highlight the one fix that most often prevents one‑star stays (for many properties that's breakfast or parking), letting small Billings inns target that fix quickly and protect RevPAR during peak weekends.

For implementation details and datasets see AltexSoft's guide and the TripAdvisor reviews dataset on Kaggle.

Dataset	Approx. samples
TripAdvisor (Kaggle)	6,444
TripAdvisor (pre‑processed, cited)	~20,000
Stanford Sentiment Treebank	~12,000 sentences
Sentiment140 (Twitter)	1.6 million
Restaurant Review Dataset	52,077
OpinRank (TripAdvisor + Edmunds)	~300,000

“The more data you have the more complex models you can use.” - Alexander Konduforov, Data Science Competence Leader at AltexSoft

Predictive maintenance layered with AI energy management turns HVAC and plant rooms from cost centers into savings engines for Billings hotels: Recogizer's self‑learning energyControl trims HVAC to “only as much energy as actually needed every minute,” claiming average building savings of about 28% and a cited hotel case that consumed 30% less energy (Recogizer hotel energy efficiency with AI); for properties willing to combine controls, monitoring and plant upgrades, Spacewell's DoubleTree project shows how a full BMS, CHP and targeted controls can cut site energy use dramatically - reported as 65% savings in a 170‑room example - while enabling predictive maintenance and real‑time load shifting (Spacewell hotel energy management case study).

Complementary predictive‑maintenance pilots - Dalos' IoT sensor program for a luxury chain - reduced emergency repair costs by 30% and improved equipment uptime by 20%, protecting guest experience during peak Yellowstone weekends (Dalos predictive maintenance for luxury hotels case study).

So what: targeting AI‑driven HVAC optimization plus simple sensor‑based monitoring can deliver double‑digit energy cuts or tens‑of‑percent maintenance savings that defend margins and free budget for guest‑facing investments; start with a BMS/audit, add sensors, then pilot predictive controls on a single property.

Metric	Reported result	Source
Average energy savings (AI HVAC)	~28%	Recogizer hotel energy efficiency with AI
Energy reduction (Recogizer case)	30% less energy	Recogizer hotel energy efficiency with AI
Project energy savings (BMS + CHP)	65%	Spacewell hotel energy management case study
Maintenance cost reduction (predictive maintenance)	30% lower costs	Dalos predictive maintenance for luxury hotels case study
Equipment uptime improvement	20% improvement	Dalos predictive maintenance for luxury hotels case study

Fraud detection and booking security are essential for Billings properties that see seasonal spikes tied to Yellowstone and a growing share of last‑minute reservations: Amadeus Fraud Alert performs real‑time screening

before the point of authorization and ticket issuance

, integrates directly with the PNR, and leverages ACI Worldwide to flag risky transactions so a Red result automatically halts ticketing while Green clears it - helping small hotels avoid costly chargebacks and fraudulent bookings at the moment of purchase.

Combine that airline‑industry capability with broader travel‑fraud patterns - stolen‑card use, chargebacks, fake OTAs and account takeover - that rose sharply in recent years (AltexSoft cites a 52.2% increase in suspected digital fraud between 2019–2021) to build a practical stack: require CVV/AVS checks, enable 3‑D Secure where available, log device/IP signals and use partner blacklists.

For Billings operators, the

so what

is concrete: automated, PNR‑aware screening stops many bad payments before rooms are sold, protecting limited staff from hours of manual dispute work and preserving margin.

Read the Amadeus product overview and the AltexSoft travel‑fraud primer for practical next steps.

Feature	What it does
Real‑time fraud screening	Screens transactions before authorization and ticket issuance (Amadeus Fraud Alert real-time screening product page)
PNR integration	Uses booking details, traveler history and device/IP to improve detection
Risk outputs	Green = auto‑ticket, Amber = manual review, Red = reject/halt ticketing
Checks & signals	72‑hour/last‑minute rules, international card checks, device IDs and partner blacklists
Industry context	Travel fraud types and rising trends explained (AltexSoft travel-fraud protection primer)

Winnow's AI-driven kitchen tools give Billings restaurants and small hotels a practical way to stop dollars walking out the back door: their Throw & Go workflow and analytics are proven to halve food waste at scale, record each discarded item in about 3 seconds, and surface the single menu item or station that's costing the most - so a one‑property pilot can identify fixes (portioning, prep schedules, buffet adjustments) that commonly translate to 3–8% food‑cost savings and pay back inside 12 months.

Used in 3,000+ kitchens and documented across hospitality case studies, Winnow's combination of quick capture, photo + AI recognition and daily reports lets local chefs replace guesswork with targeted changes before peak Yellowstone weekends, then reinvest the savings into guest experience or staffing rather than comping losses; start small, track waste by dish, and scale the playbook across Billings properties.

See Winnow's product overview and real hotel case studies to plan a pilot.

Metric	Reported value
Typical food waste reduction	~50% (proven at enterprise scale)
Food cost savings	3–8%
Kitchens using Winnow	3,000+
Meals saved / year	60 million
CO2e prevented / year	106,000 tonnes
ROI timeline	Typical payback < 12 months (~95% of cases)

“Our target was to halve food waste by 2024, and we actually reduced it by 64%. We're the first corporate dining food service provider in the US to have achieved this.” - Paul Fairhead, CEO of Guckenheimer

Voice assistants and touchless in‑room controls are a practical, low‑lift way for Billings properties to triage routine requests and deliver local tips - Aloft's “Project Jetson” proved an in‑room iPad running Siri can set lighting scenes, adjust temperature, start music and answer “what's nearby?” so guests get Yellowstone directions or dinner suggestions without tying up the front desk; see the Architectural Digest write‑up on Aloft's voice rooms for setup details (Aloft Project Jetson voice-activated hotel rooms - Architectural Digest).

Industry data shows nearly half of travelers consider in‑room tech when choosing a hotel and voice systems already handle a large share of routine service requests, making them a clear tool for small Billings hotels to improve hygiene, shorten queues, and redeploy staff toward higher‑value concierge work - especially during Yellowstone weekends (Hotels voice assistants market spotlight - SpeechTech).

Design the pilot to include opt‑out/privacy controls and a daily reset policy so guest data is protected while the property captures immediate labor savings and faster guest answers.

“Voice technology is becoming a critical component of the future guest experience, and with home assistants a normal part of life for many consumers, now is the time for hoteliers to leverage that convenience to drive satisfaction and revenue for their properties.” - Robert Stevenson, CEO of Intelity

AI-driven marketing automation ties first‑party guest data to timely, local offers so Billings hotels and restaurants can sell more without extra headcount: capture emails at booking, on Wi‑Fi logins and front‑desk check‑ins, then use segmentation and triggered flows - welcome, pre‑arrival upsells, abandoned booking reminders - to reach nearby drive‑markets (Yellowstone weekend travelers and Montana staycationers) with personalized offers; local teams can follow the Billings‑specific playbook in Email Marketing in Billings, MT and the hotel best practices in the Hotel Email Marketing Best Practices (Revinate).

The practical payoff is concrete: industry briefs show strong mobile engagement (mobile opens >60%), high ROI (reported averages near $36 per $1 spent), and real examples where automated pre‑arrival upsells produced same‑day revenue (a spa secured six bookings by noon).

Start with a one‑property drip and a local targeting segment (guests within a 300‑mile drive, past‑stay VIPs), measure conversions over 90 days, then scale the winning automations across Billings properties.

Metric	Value	Source
Average email ROI	$36 per $1 spent	MediaBoom / industry briefs
Mobile open rate	>60%	Hotel email best practices
Pre‑arrival upsell example	6 spa bookings by noon	Revinate vignette

Start small: pick one narrow pilot (dynamic pricing, a chatbot‑driven virtual concierge, predictive housekeeping, or an energy pilot), set clear KPIs, and measure over a defined window before rolling tools across Billings' growing room supply - MobiDev's 5‑step roadmap is a practical playbook for that staged approach (MobiDev's 5‑Step AI Roadmap for Hospitality Integration).

Aim for concrete, industry‑backed wins: dynamic pricing pilots often show a 10–15% RevPAR lift, energy controls can cut consumption near 28–30%, and kitchen AI typically trims food costs by 3–8% - changes that many mid‑size hotels recoup inside a year.

Pair pilots with staff training so teams know how to tune prompts and tools - see Nucamp AI Essentials for Work syllabus: Practical Prompting & Workflow Training for practical, non‑technical prompt and workflow training - then scale the highest‑ROI plays across properties to protect margins and improve guest experience during Yellowstone weekends and peak local demand.

Pilot	Quick Target	Representative Result (source)
Dynamic pricing	Improve RevPAR	+10–15% RevPAR (Worldie / McKinsey)
Energy & HVAC controls	Reduce utility spend	~28–30% energy savings (Recogizer)
Kitchen waste reduction	Lower food cost	3–8% food‑cost savings; ~50% waste cut (Winnow)

“The more data you have the more complex models you can use.” - Alexander Konduforov, Data Science Competence Leader at AltexSoft