AI市場成熟:組織結構，目標，策略，資料隱私，課責，COVID-19相關AI終端用戶和供應商的企業調查

人工智慧，雖然還處於初期階段，不過，前導性的AI解決方案供應商和終端用戶企業，這幾年從概念驗證轉移到AI解決方案的實況展開。

本報告提供AI市場成熟度的相關調查，市場結構，目標與策略，資料隱私和COVID-19對市場的影響、統計等資訊。

目錄

要點

調查結果

• 成熟度簡介:結構
• 成熟度簡介:目標與策略

對市場的影響

• 資料隱私，AI責任制差距
• COVID-19(新型冠狀病毒感染疾病)

人口統計的調查

By any stretch of the imagination, it is still early days for artificial intelligence (AI). That said, pioneering AI solutions providers and end-user companies have moved from proofs-of-concept to live deployments of AI solutions over the past few years. This Omdia survey examines the maturation of the market, with views of the AI structure and strategies of end-user and vendor companies.

The survey results suggest companies that have been AI pioneers fit the classic profile of early adopters. They are willing to risk money, time, and resources with the hope that potential returns will give them competitive advantages over companies that wait for a more developed market. While some industry sectors have more mature AI ecosystems than others, in a broad sense, the AI ecosystem reflects a market that is still in the early stages.

This Omdia survey provides the results of an online survey of 365 enterprises. The survey was sent to contact databases from Omdia, AI Summit, and AI Business. Surveyed companies across different vertical industries ranged in size from small startups with less than 100 employees to global companies with 10,000+ employees. Respondents were categorized as companies that were end users of AI solutions and capabilities and vendors that market AI solutions and capabilities. The survey treated the location of the respondent as the source country. Respondents had a broad range of job functions and titles, and the companies were using or exploring AI capabilities/services.

Key Questions Addressed:

• What is the market penetration of AI technologies and solutions for enterprises?

• What is the pace of AI technology implementations and investments?

• How has the COVID-19 pandemic affected the AI plans of enterprises?

• How has data privacy and the AI accountability gap affected AI plans?

• Where does AI ownership/responsibility reside within enterprises?

• Which strategies are enterprises relying on: in-house solutions (developing internal AI expertise and IP), commercial solutions, or both?

• For what functions or business units are enterprises deploying AI (customer service, IT, operations, business intelligence, etc.)?

• Which AI use cases are enterprises implementing?

• What AI technologies are enterprises leveraging?

Who Needs This Report?

• AI technology companies
• Semiconductor and chipset vendors
• Edge computing software vendors
• Automotive companies
• Drone and robot manufacturers
• Machine vision companies
• PC and server manufacturer companies
• Investor community

Table of Contents

Key takeaways

Findings

• Maturation profile: Structure
• Maturation profile: Goals and strategy

Market impact

• Data privacy, AI accountability gap
• COVID-19

Survey demographics

List of Figures

• While AI penetration is low ... less than half have live, pilot AI projects
• Early adopters are leaning into AI
• Where does AI responsibility and decision-making reside within your company?
• What is/will be your deployment strategy for AI?
• What are the top 3 reasons your company decided to invest in in-house AI expertise?
• What are the top 3 reasons your company decided to deploy a commercial AI solution from vendors?
• For what function or business unit is your company deploying AI?
• For what function or business unit is your company's AI solution targeted?
• What specific AI use cases are you implementing?
• How important is it in your company to aim AI at the following outcomes?
• Which AI technologies is your company leveraging in its solutions?
• How do data privacy issues impact your company's momentum to leverage AI?
• How concerned is your company about AI accountability?
• Which of the following statements do you most agree with regarding the COVID-19 crisis and its impact on AI deployment?
• Near-term confidence in positive AI results despite COVID
• Confidence in AI value growing over time
• What AI capabilities are you currently using or planning to adopt in the future?
• End-user state of AI deployment: 42% live or piloting projects