The global AI software market is projected to reach $136.6 billion this year. Only 16 percent of travel firms currently use AI for daily operations, illustrating a significant adoption gap. This article details where AI is delivering measurable gains in revenue, efficiency, and cost reduction for the resort sector.
Key Takeaways
- 16% of travel-related firms (transportation, accommodation, and food services) reported using AI/ML in 2024 (latest available in the survey) for business operations
- 29% of surveyed hotels reported using AI/ML for pricing and revenue optimization in 2023 (AI-enabled pricing adoption)
- 15% of hotel website traffic is estimated to originate from mobile in 2024 according to industry analytics benchmarks (channel share relevant for AI personalization)
- 22% of businesses worldwide used big data or AI to analyze customer behavior in 2023
- 44% of organizations reported using AI for forecasting demand and improving operations in 2024
- 78% of data professionals report a need for improved AI governance practices in 2024 (governance need metric)
- $136.6 billion projected 2024 global AI software market size according to IDC
- $379.6 billion projected global AI market size by 2024 according to IDC (AI systems including software and services, segment definition varies by IDC release)
- £2.4 billion UK public cloud spend in 2024 projected by IDC
- 30% reduction in call deflection reported for AI virtual agent deployments summarized in the ServiceNow State of AI report (call deflection metric)
- 18% average improvement in revenue per available room (RevPAR) for hotels using advanced demand forecasting (AI/ML-enabled) in a 2020 case study compilation
- 35% reduction in hotel housekeeping time possible via AI-enabled scheduling and task optimization (reported as typical range in a leading hospitality operations analytics study)
- 19% reduction in energy costs is reported in buildings where AI-enabled building management systems are used (industry meta-analysis; 2021).
AI adoption is rising fast in travel and hospitality, driving better forecasting, operations, and customer experiences.
Related reading
01 · Category
User Adoption6 stats
01
16% of travel-related firms (transportation, accommodation, and food services) reported using AI/ML in 2024 (latest available in the survey) for business operations
02
29% of surveyed hotels reported using AI/ML for pricing and revenue optimization in 2023 (AI-enabled pricing adoption)
03
15% of hotel website traffic is estimated to originate from mobile in 2024 according to industry analytics benchmarks (channel share relevant for AI personalization)
04
54% of surveyed business leaders report that their organization uses or plans to use generative AI within 12 months (2023 survey).
05
56% of hotel marketers use some form of personalization to improve performance (2023 survey).
06
26% of US adults report using chatbots at least occasionally (2023 survey).
Interpretation
User Adoption Interpretation
For the user adoption angle, AI is moving from pilots to real usage but unevenly, with only 16% of travel firms using AI/ML in 2024 and 29% of hotels already applying it to pricing, while broader intent is rising as 54% of business leaders plan to use generative AI within 12 months and 26% of US adults already use chatbots.
02 · Category
Industry Trends3 stats
01
22% of businesses worldwide used big data or AI to analyze customer behavior in 2023
02
44% of organizations reported using AI for forecasting demand and improving operations in 2024
03
78% of data professionals report a need for improved AI governance practices in 2024 (governance need metric)
Interpretation
Industry Trends Interpretation
For the resort industry, the strong push toward practical AI use is clear as 44% of organizations already apply it to forecast demand and improve operations in 2024, while 78% of data professionals call for better AI governance, signaling that industry momentum is moving from experimentation to responsible scaling.
03 · Category
Market Size5 stats
01
$136.6 billion projected 2024 global AI software market size according to IDC
02
$379.6 billion projected global AI market size by 2024 according to IDC (AI systems including software and services, segment definition varies by IDC release)
03
£2.4 billion UK public cloud spend in 2024 projected by IDC
04
3.1 million Americans worked in accommodation and food services in 2023 according to BLS (employment base where AI automation can apply)
05
2.7 million Americans worked in food services and drinking places in 2023 according to BLS (automation impact context for service operations)
Interpretation
Market Size Interpretation
For the market size angle, IDC projects the global AI software market could reach $136.6 billion in 2024 and the broader AI market including systems and services could total $379.6 billion, while the UK alone is set to spend £2.4 billion on public cloud, signaling strong expansion capacity for AI-enabled resort operations that can serve large workforces like the 3.1 million Americans in accommodation and food services in 2023.
More related reading
04 · Category
Performance Metrics6 stats
01
30% reduction in call deflection reported for AI virtual agent deployments summarized in the ServiceNow State of AI report (call deflection metric)
02
18% average improvement in revenue per available room (RevPAR) for hotels using advanced demand forecasting (AI/ML-enabled) in a 2020 case study compilation
03
35% reduction in hotel housekeeping time possible via AI-enabled scheduling and task optimization (reported as typical range in a leading hospitality operations analytics study)
04
24/7 availability of AI chatbots reduces wait time; average chatbot response time reported as under 1 second in a 2021 vendor benchmark
05
2.3x increase in speed-to-insight when analytics teams adopt automated machine learning pipelines (2023).
06
12% of hospitality organizations report using ML for demand forecasting (2023 survey).
Interpretation
Performance Metrics Interpretation
Across performance metrics, hospitality operators are seeing AI move from promise to measurable gains, with results such as a 30% call deflection lift from virtual agents and a 2.3x faster speed-to-insight for analytics teams.
05 · Category
Cost Analysis1 stats
01
19% reduction in energy costs is reported in buildings where AI-enabled building management systems are used (industry meta-analysis; 2021).
Interpretation
Cost Analysis Interpretation
The industry meta-analysis found that AI enabled building management systems can reduce energy costs by 19%, underscoring a clear cost analysis win for resorts seeking lower operating expenses.
report visual · Key figures
AI adoption vs. readiness signals in hospitality
Adoption is rising, while governance and operational use cases show strong pull—especially in forecasting, customer behavior analysis, and chatbot uptake.
16%
16% of travel-related firms (transportation, accommodation, and food services) reported using AI/ML in 2024 (latest avai
29%
29% of surveyed hotels reported using AI/ML for pricing and revenue optimization in 2023 (AI-enabled pricing adoption)
22%
22% of businesses worldwide used big data or AI to analyze customer behavior in 2023
78%
78% of data professionals report a need for improved AI governance practices in 2024 (governance need metric)
26%
26% of US adults report using chatbots at least occasionally (2023 survey).
source-verifiedoecd.org · phocuswright.com · unctad.org · gartner.com · pewresearch.org2024
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
James Okoro. (2026, February 13). AI In The Resort Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-resort-industry-statistics
MLA
James Okoro. "AI In The Resort Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-resort-industry-statistics.
Chicago
James Okoro. 2026. "AI In The Resort Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-resort-industry-statistics.
Sources & references
21 datasets cited across this report · attribution is report-level
+6 additional datasets cited (not shown individually)