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【分析レポート:アプリケーション】企業市場の人工知能(AI):機械学習(マシンラーニング)

AI in the Enterprise: Machine Learning

2Q 2017 | Application Analysis Report | AN-2656 | 26 pages | 5 tables | 1 chart | PDF |

 

出版社 出版年月価格 ページ数図表数
ABI Research
ABIリサーチ
2017年5月お問い合わせください 26 6

サマリー

マイクロプロセッサの進歩、安価なストレージとクラウドサービス、新世代の機械学習(ML)アルゴリズム、そして特に大規模のデータによって、人工知能(AI)と機械学習は近年爆発的に成長している。物理的なサイズとコストによる制限がなくなったデジタル写真のように、収益、規模、リソース数にかかわらず、すべての企業で、機械学習が利用可能になった。米国調査会社の調査レポート「【分析レポート:アプリケーション】企業市場の人工知能(AI):機械学習(マシンラーニング)」は、販売、人事、マーケティング、法務、財務、IT、ビジネス開発など、すべての産業界や組織にわたって一般的な企業ビジネスソリューションを支援する機械学習技術とそのアプリケーションについて検証している。

Microprocessor advancements, the availability of inexpensive storage and cloud services, a new generation of machine learning (ML) algorithms, and particularly data at scale, are the drivers for artificial intelligence (AI) and ML’s recent explosive growth. Much like the introduction of digital photography, which was no longer limited by the size and cost of physical film, ML is now available for all businesses, regardless of the revenues, size of the organization, and number of resources.

This report examines ML technologies and their applications used in support of common enterprise business solutions across all industries and all organizations including sales, human resources, marketing, legal, finance, IT, business development, and more.

 

ABIリサーチの調査レポートの詳細については、サンプルをご請求ください。

(株式会社データリソース 03-3582-2531、office@dri.co.jp)

 



目次

  • 1. THE AGE OF THE INTELLIGENT ENTERPRISE
    • 1.1. Executive Summary
    • 1.2. Key Findings and Strategic Recommendations
    • 1.3. Building the AI Business Case for Enterprise Adoption
    • 1.4. AI Jobs for Data Scientists and Future Workers
    • 1.5. Key Market Forecast
  • 2. AI IN THE ENTERPRISE MARKET ASSESSMENT
    • 2.1. Enterprise AI Market Dynamics
    • 2.2. Benefits of Artificial Intelligence
    • 2.3. Quantifying the Artificial Intelligence in the Enterprise Opportunity
  • 3. SELECTING AN ARTIFICIAL INTELLIGENCE TYPE
    • 3.1. Machine Learning
    • 3.2. Deep Learning
  • 4. ECOSYSTEM VENDOR HIGHLIGHTS FOR ARTIFICIAL INTELLIGENCE IN THE ENTERPRISE
    • 4.1. AI Acquisitions, Mergers, and Acqui-Hires
    • 4.2. Vendor Profiles

 

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プレスリリース

[サマリー訳]

マイクロプロセッサの進歩、安価なストレージとクラウドサービス、新世代の機械学習(ML)アルゴリズム、そして特に大規模のデータによって、人工知能(AI)と機械学習は近年爆発的に成長している。物理的なサイズとコストによる制限がなくなったデジタル写真のように、収益、規模、リソース数にかかわらず、すべての企業で、機械学習が利用可能になった。米国調査会社の調査レポート「【分析レポート:アプリケーション】企業市場の人工知能(AI):機械学習(マシンラーニング)」は、販売、人事、マーケティング、法務、財務、IT、ビジネス開発など、すべての産業界や組織にわたって一般的な企業ビジネスソリューションを支援する機械学習技術とそのアプリケーションについて検証している。

 


 

[プレスリリース原文]

ABI Research Forecasts Almost One Million Businesses Worldwide Will Adopt AI Technologies by 2022

Artificial Intelligence Accelerates in Enterprises, But Is Not the Ideal Solution for All

 

Oyster Bay, New York - 20 Jun 2017

ABI Research predicts the number of businesses adopting artificial intelligence (AI) technologies worldwide will grow considerably, up from 7,000 this year to nearly 900,000 in 2022, a CAGR of 162%. AI is no longer limited to science fiction and movies, with significant strides being made in cloud processing, storage capacity, and machine learning algorithms to enable computer systems to surpass humans in winning strategy games and television shows. Increasingly, businesses are applying these technological advancements to deliver automation and innovation that equal or exceed human capabilities.

“Even though nearly one million businesses will adopt AI by 2022, it will not be a great fit for every company,” says Jeff Orr, Research Director at ABI Research. “Many businesses will have to adapt their corporate governance policies to deal with the lack of a guaranteed outcome when implementing machine learning. While most enterprises start using machine learning to analyze their existing business for insights, the technologies have far-reaching application in specific industries, ranging from reduction of false positives in fraud detection to powering conversational interfaces for chatbots and virtual assistants.”

While some of the world’s largest and innovative enterprises, such as Amazon, American Express, Citrix, Coca Cola, Facebook, Google, Netflix, PayPal, and Uber, already deploy projects powered by machine learning, ABI Research finds that not all will benefit. Organizations that are comfortable with uncertainty in outcomes and measuring changes in key performance indicators (KPIs) will find the most to gain from enacting machine learning projects. On the other hand, companies that focus only on ROI timetables will find emerging technologies, including machine learning, cybersecurity, and IoT, to be frustrating to implement and difficult to measure.

Several SaaS solutions are available for machine learning and businesses looking to experiment will have many vendors to choose from. Best practices include starting off with a pilot project and requesting case studies about enterprises that have already gone through their first operational deployment. “It is the companies that choose to ignore AI entirely that will quickly find themselves at a competitive disadvantage,” concludes Orr.

These findings are from ABI Research’s AI in the Enterprise: Machine Learning report.

 

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