How Different Forms of AI Technologies Best Used for Problem-Solving?

DUBLIN – The “Artificial Intelligence in Big Data Analytics and IoT: Market for Data Capture, Information and Decision Support Services 2022 – 2027” report has been added to ResearchAndMarkets.com’s offering.

This report evaluates various AI technologies and their use relative to analytics solutions within the rapidly growing enterprise and industrial data arena. The report assesses emerging business models, leading companies, and solutions.

The report also analyzes how different forms of AI may be best used for problem-solving. The report also evaluates the market for AI in IoT networks and systems. The report provides forecasting for unit growth and revenue for both analytics and IoT from 2022 to 2027.

The Internet of Things (IoT) in consumer, enterprise, industrial, and government market segments has very unique needs in terms of infrastructure, devices, systems, and processes. One thing they all have in common is that they each produce massive amounts of data, most of which is of the unstructured variety, requiring big data technologies for management.

Artificial Intelligence (AI) algorithms enhance the ability for big data analytics and IoT platforms to provide value to each of these market segments. The author sees three different types of IoT Data: (1) Raw (untouched and unstructured) Data, (2) Meta (data about data), and (3) Transformed (valued-added data). Artificial Intelligence (AI) will be useful in support of managing each of these data types in terms of identifying, categorizing, and decision making.

AI coupled with advanced big data analytics provides the ability to make raw data meaningful and useful as information for decision-making purposes. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks.

Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service.

Select Report Findings:

Global market for AI in big data and IoT as a whole will reach $31.4B by 2027
Embedded AI in support of IoT-connected things will reach $7.2B globally by 2027
AI makes IoT data 28% more efficient and analytics 47% more effective for industry apps
Overall market for AI in big data and IoT will be led by Asia Pac followed by North America
AI in industrial machines will reach $823M globally by 2027 with collaborative robot growth at 41.9% CAGR
AI in autonomous weapon systems will reach $298M globally by 2027 with AI in military robotics growing at 41.2% CAGR
Machine learning will become a key AI technology to realize the full potential of big data and IoT, particularly in edge computing platforms
Top three segments will be: (1) Data Mining and Automation, (2) Automated Planning, Monitoring, and Scheduling, and (3) Data Storage and Customer Intelligence

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction

3.0 Overview
3.1 Artificial Intelligence and Machine Learning
3.2 AI Types
3.3 AI & ML Language
3.4 Artificial Intelligence Technology
3.5 AI and ML Technology Goal
3.6 AI Approaches
3.7 AI Tools
3.8 AI Outcomes
3.9 Neural Network and Artificial Intelligence
3.10 Deep Learning and Artificial Intelligence
3.11 Predictive Analytics and Artificial Intelligence
3.12 Internet of Things and Big Data Analytics
3.13 IoT and Artificial Intelligence
3.14 Consumer IoT, Big Data Analytics, and Artificial Intelligence
3.15 Industrial IoT, Big Data Analytics, and Machine Learning
3.16 Artificial intelligence and cognitive computing
3.17 Transhumanism or H+ and Artificial Intelligence
3.18 Rise of Analysis of Things (AoT)
3.19 Supervised vs. Unsupervised Learning
3.20 AI as New form of UI

4.0 AI Technology in Big Data and IoT
4.1 Machine Learning Everywhere
4.2 Machine Learning APIs and Big Data Development
4.3 Enterprise Benefits of Machine Learning
4.4 Machine Learning in IoT Data
4.5 Ultra Scale Analytics and Artificial Intelligence
4.6 Rise of Algorithmic Business
4.7 Cloud Hosted Machine Intelligence
4.8 Contradiction of Machine Learning
4.9 Value Chain Analysis

5.0 AI Technology Application and Use Case
5.1 Intelligence Performance Monitoring
5.2 Infrastructure Monitoring
5.3 Generating Accurate Models
5.4 Recommendation Engine
5.5 Blockchain and Crypto Technologies
5.6 Enterprise Application
5.7 Contextual Awareness
5.8 Customer Feedback
5.9 Self-Driving Car
5.10 Fraud Detection System
5.11 Personalized Medicine and Healthcare Service
5.12 Predictive Data Modelling
5.13 Smart Machines
5.14 Cybersecurity Solutions
5.15 Autonomous Agents
5.16 Intelligent Assistant
5.17 Intelligent Decision Support System
5.18 Risk Management
5.19 Data Mining and Management
5.20 Intelligent Robotics
5.21 Financial Technology
5.22 Machine Intelligence

6.0 AI Technology Impact on Vertical Market
6.1 Enterprise Productivity Gain
6.2 Digital Twinning and Physical Asset Security
6.3 IT Process Efficiency Increase
6.4 AI to Replace Human Form Work
6.5 Enterprise AI Adoption Trend
6.6 Inclusion of AI as an IT Requirement

7.0 AI Predictive Analytics in Vertical Industry
7.1 E-Commerce Services
7.2 Banking and Finance Services
7.3 Manufacturing Services
7.4 Real Estate Services
7.5 Government and Public Services

8.0 Company Analysis
8.1 Google Inc.
8.2 Twitter Inc.
8.3 Microsoft Corporation
8.4 IBM Corporation
8.5 Apple Inc.
8.6 Facebook Inc.
8.7 Amazon.com Inc.
8.8 Skype
8.9 Salesforce.com
8.10 Intel Corporation
8.11 Yahoo Inc.
8.12 AOL Inc.
8.13 Nvidia Corporation
8.14 x.ai
8.15 Tesla Inc.
8.16 Baidu Inc.
8.17 H2O.ai
8.18 SparkCognition Inc.
8.19 OpenAI
8.20 Inbenta
8.21 CISCO Systems Inc.
8.22 Infineon Technologies AG
8.23 McAfee
8.24 Happiest Minds Technologies
8.25 Tachyus
8.26 Sentrian
8.27 MAANA
8.28 Veros Systems Inc.
8.29 NEURA
8.30 Augury Systems Ltd.
8.31 glassbeam
8.32 Comfy
8.33 mnubo
8.34 C-B4
8.35 PointGrab Ltd.
8.36 Tellmeplus
8.37 moov
8.38 Sentenai Inc.
8.39 imagimob
8.40 FocusMotion
8.41 MoBagel

9.0 AI in Big Data and IoT Market Analysis and Forecasts 2022 – 2027
9.1 AI in Big Data and IoT Market 2022 – 2027
9.2 AI in Big Data and IoT Market by Solution Components 2022 – 2027
9.3 AI in Big Data and IoT Market by Management Functions
9.4 AI in Big Data and IoT Market by Technology
9.5 AI in Big Data and IoT Market by Industry Vertical
9.6 AI in Big Data and IoT Market by Solution
9.7 AI in Big Data and IoT Market by Application
9.8 AI in Big Data and IoT Market by Deployment
9.9 AI in Big Data and IoT Market by AI System
9.10 AI in Big Data and IoT Market by AI Type
9.11 AI in Big Data and IoT Market by Connectivity
9.12 AI in Big Data and IoT Market by Edge Network
9.13 AI in Big Data and IoT Market in Smart City
9.14 AI in Big Data and IoT Market by Intent-Based Networking
9.15 AI in Big Data and IoT Market by Virtualization
9.16 AI in Big Data and IoT Market by 5G
9.17 AI in Big Data and IoT Market by Blockchain Networks
9.18 AI in Big Data and IoT Market by Region

10.0 Conclusions and Recommendations
10.1 AI Predictions
10.2 Data Analytics Providers
10.3 AI and Machine Learning Companies
10.4 IoT Companies and Equipment Manufacturers
10.5 Service Providers
10.6 Enterprises