Market Research Logo

Artificial Intelligence in Big Data, Commercial Apps, Mobility and Search

Artificial Intelligence (AI) facilitates the efficient and effective supply of information to enterprises for optimized business decision-making. Major IT and software vendor companies are investing billions to generate revenue from AI based commercial solutions in various areas including robotics, machine translators, chat bots, voice recognizers, business intelligence systems, mobility control systems, intelligent search, and more.

The AI based solution market is valued at US$ 900 million globally by year end 2013 and is expected to grow exponentially over the next five years. Some of the biggest opportunity areas are commercial applications, search in the Big Data environment, and mobility control for generation of actionable business intelligence. The entire mobile/wireless ecosystem is well-positioned for AI via the growing adoption and expanded usage of consumer and enterprise electronics devices including smartphone, tablet, portable devices and wearable technologies.

Artificial Intelligence in Big Data, Commercial Apps, Mobility and Search evaluates the market for AI solutions within commercial applications, business intelligence, search analytics in mobility environment, and more. This report analyzes the potential for enterprises to improve performance through AI, development of AI solution in cloud environment, and AI for Big Data control. The report also includes vendor analysis and market predictions.

Target Audience

  • AI companies
  • Big Data companies
  • Mobile network operators
  • Wireless device manufacturers
  • Wireless application developers
  • Analytics and data reporting companies
  • Cloud infrastructure and service providers
Companies in Report:

APPLE, GOOGLE, MICROSOFT, IBM, HP, INTEL, SAP, FACEBOOK, ORACLE, I2 TECHNOLOGIES, SAS SYSTEM, DELL, AMAZON, HTC, LG, SONY, NOKIA, LENOVO, SAMSUNG, OPTIMIZELY, NETFLEX, MOTOROLLA, TOSHIBA, DATLOGIC, HONEYWELL, TERADATA, TABLEU, QLIKTECH, TETAPP, FUZITSU, CLOUDERA, 10GEN, SPLUNK, ACTIAN, CISCO, ACCENTURE, HITACHI, VMWARE

General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.


1.0 Executive Summary
2.0 Introduction
3.0 Artificial Intelligence
3.1.1 Definitions
3.1.2 Philosophy
3.1.3 The Birth Of AI
3.1.4 AI Timeline
3.1.5 AI Research
3.1.6 Recent AI Research And Implementation
3.1.7 Looking For AI
3.1.8 Fundamental Concern Of AI
3.1.9 AI Tools
4.0 Basic Elements Of AI
4.1 Strong Artificial Intelligence
4.2 Weak Artificial Intelligence
4.3 AI And Nature
4.4 Knowledge Representation
4.5 AI Languages And Tools
4.6 Principal AI Application Areas
4.7 Comparison Between AI And General Programmer
4.8 Three Laws Of Robotics
4.9 Turing Test
5.0 AI Future Prediction
5.1 AI Dominant Global Market And Dominant Global Players
5.2 Where Is AI?
5.3 AI Based Applications
5.4 AI Ultimate Future And Prediction
5.5 AI Market
5.6 The Business Case For Artificial Intelligence
6.0 Big Data Case For Artificial Intelligence
6.1 What Is Big Data?
6.2 Big Data Means Five `v’
6.3 The Application Of Artificial Intelligence
6.4 Big Data Forecasts And Market Estimates
6.5 Big Data Market Highlights And Trends
7.0 Artificial Intelligence In Mobile Devices
7.1 AI On Your Smartphone Is Helpful
7.2 AI Mobility
7.3 Mobile Data As The Engine For AI
8.0 Vendor Analysis
8.1 Ibm
8.2 Optimizely
8.3 Amazon
8.4 Netflix
8.5 Apple
9.0 Apps & Faq
10.0 Mobility, Search & Big Data
11.0 Conclusions And Recommendations
List of Graphs
Figure 1: General Areas of Artificial Intelligence
Figure 2: Artificial Intelligence Breakthroughs 1950-2013
Figure 3: Artificial Intelligence Timeline1941-1991
Figure 4: The Basic Picture Underlying Russell’s Account of Intelligence/Rationality
Figure 5: Fundamental parts of AI
Figure 6: Neural Network
Figure 7: Elements of AI
Figure 8: AI Tools
Figure 9: Robot Laws
Figure 10: Turing Test system of AI Justify
Figure 11: AI Vendor Revenue 2012
Figure 12: AI Based Global Dominant Players
Figure 13: Where is AI?
Figure 14: AI Application Areas
Figure 15: AI Implementations Region
Figure 16: Big Data Market Overview 2013-2020
Figure 17: Big Data Revenue by Segments 2012-2013
Figure 18: Big Data Revenue by Type 2012-2013
Figure 19: Hadoop Based Company Revenues 2012-2013
Figure 20: Big Data Drivers
Figure 21: Big Data and Sample Application
Figure 22: Big Data and IT Spending
Figure 23: AI and Operating System Market Size
Figure 24: Mobile and AI sector Investment 2010-2013
Figure 25: Mobile Based AI Field Revenue CAGR 25%
Figure 26: Recent stored Data-Petabytes
Figure 27: AI Global Market Trends 2012-2220
Figure 28: BI market will Grow Smoothly
Figure 29: Big Data Search India-USA-Singapore-AUS-S. Korea
Figure 30: Big Data, AI, BI Search Comparison
Figure 31: AI Based New Sectors and Opportunity Smart Device Market Share%
Figure 32: AI Based JOB sector-Java, C+HTML,PHP
Figure 33: Top Auto Data Capture Suppliers Revenues 2013
List of Tables
Table 1: Significant AI Researcher
Table 2: Four Possible Goals for AI
Table 3: AI’s Domain of Study
Table 4: Comparison of AI with Conventional Programming.
Table 5: AI Implemented Fields
Table 6: 2012 Worldwide Big Data Revenue
Table 7: Most Transformational Technologies 2013-2020
Table 8: Sector Sales Analysis
Table 9: Top Business Priorities in AI

Download our eBook: How to Succeed Using Market Research

Learn how to effectively navigate the market research process to help guide your organization on the journey to success.

Download eBook

Share this report