Machine learning is an artificial intelligence (AI) technology that allows machines to learn by using algorithms to interpret data from connected ‘things’ to predict outcomes and learn from successes and failures.
There are many other AI technologies - from image recognition to natural language processing, gesture control, context awareness, and predictive APIs - but machine learning is where most of the investment community’s funding has flowed in recent years. It is also the technology most likely to allow machines to ultimately surpass the intelligence levels of humans.
Many companies, such as Alphabet, have already become ‘AI-first’ companies, with machine learning at their core. At the same time, many ML techniques are being commoditized by being open sourced and pre-packaged into developer toolkits that anyone can use.
This report focuses on Artificial Intelligence in Tourism.
This report is part of our ecosystem of thematic investment research reports, supported by our “thematic engine”. About our Thematic Research Ecosystem -
- GlobalData has developed a unique thematic methodology for valuing technology, media and telecom companies based on their relative strength in the big investment themes that are impacting their industry. Whilst most investment research is underpinned by backwards looking company valuation models, GlobalData’s thematic methodology identifies which companies are best placed to succeed in a future filled with multiple disruptive threats. To do this, GlobalData tracks the performance of the top 600 technology, media and telecom stocks against the 50 most important themes driving their earnings, generating 30,000 thematic scores. The algorithms in GlobalData’s “thematic engine” help to clearly identify the winners and losers within the TMT sector. Our 600 TMT stocks are categorised into 18 sectors. Each sector scorecard has a thematic screen, a risk screen and a valuation screen. Our thematic research ecosystem has a three-tiered reporting structure: single theme, multi-theme and sector scorecard. This report is a Multi-Theme report, covering all stocks, all sectors and all themes, giving readers a strong sense of how everything fits together and how conflicting themes might interact with one another.
- Our thematic investment research product, supported by our thematic engine, is aimed at senior (C-Suite) executives in the corporate world as well as institutional investors.
- Corporations: Helps CEOs in all industries understand the disruptive threats to their competitive landscape
- Investors: Helps fund managers focus their time on the most interesting investment opportunities in global TMT.
- Our unique differentiator, compared to all our rival thematic research houses, is that our thematic engine has a proven track record of predicting winners and losers.
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Applications of AI in Tourism
Ten categories of AI software
The tech sector’s angle
The Web-Scale companies
Enterprise software players
Proprietary datasets are also important
AI and ML are transforming the chipset market
The two critical components of any successful AI engine
IMPACT OF AI ON TOURISM
Tourism case studies
Recommendations for IT vendors
Recommendations for tourism companies
Listed tech companies
Privately held tech companies
History of machine learning
How does deep learning work?
APPENDIX: OUR “THEMATIC” RESEARCH METHODOLOGY
New business models such as shared economy have altered the way customers see the tourism industry, and this puts pressure on the tourism players to invest in various IT solutions in order to maintain customer loyalty.
This report focuses on ‘Artificial Intelligence in Tourism’, and it presents different case studies of how big tourism players have started using AI solutions to attract and retain customers. The report offers insight into:
- The specific manifestations of AI that have the most relevance for tourism industry.
- The various applications of AI in tourism.
- How tourism companies are already instilling AI-based solutions.
- The factors tourism companies need to consider when introducing AI solutions and choosing their AI vendors.
The material was prepared in September, 2018.