Artificial Intelligence in Utilities Industry Key Players - Alphabet, Amazon, Apple, Ayasdi, Baidu, Blue Yonder, Cognex, Darktrace, DataFox, Descartes Labs, E.ON, Enel, Exelon, EDF Energy, Facebook, Iberdrola, IBM, Intel

Machine learning is an artificial intelligence (AI) technology which 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.

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Many companies, like Alphabet, have already become ‘AI-first’ companies, with machine learning at their core. At the same time, many ML techniques are getting commoditized by being open sourced and pre-packaged into developer toolkits that anyone can use.
This report focuses on Artificial Intelligence in Utilities.
Scope

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.

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Reasons to buy

- 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.

Companies Mentioned:

1. Alphabet
2. Amazon
3. Apple
4. Ayasdi
5. Baidu
6. Blue Yonder
7. Cognex
8. Darktrace
9. DataFox
10. Descartes Labs
11. E.ON
12. Enel
13. Exelon
14. EDF Energy
15. Facebook
16. Iberdrola
17. IBM
18. Intel
19. iFlytek
20. Ipsoft
21. Kensho
22. Mobvoi
23. Nvidia
24. Numenta
25. National Grid
26. Palantir
27. Preferred Networks
28. Quid
29. Salesforce
30. SAP
31. Splunk
32. Sentient Technologies
33. Vicarious
34. Zephyr Health

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Some Points of TOC:

PLAYERS 3
TRENDS 4
Technology trends 4
Macro-economic trends 7
Applications of AI in Utilities 8
VALUE CHAIN 11
Ten categories of AI software 12
INDUSTRY ANALYSIS 19
The tech sector’s angle 19
The Web-Scale companies 19
Enterprise software players 20
Proprietary datasets are also important 20
AI and ML are transforming the chipset market 21
The two critical components of any successful AI engine 21
Timeline 24
IMPACT OF AI ON UTILITIES 26
Utilities case studies 26
How AI vendors sell into the utilities sector 28
Recommendations for IT vendors 28

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