Who were the main targets of this survey?

The survey targeted 111 managers in charge of DX (Digital Transformation) and AI promotion in the manufacturing industry.

What is the biggest challenge in AI utilization in the manufacturing industry?

The most cited challenge is 'insufficient amount of data for AI training' at 44.1%, followed by 'shortage of internal personnel to promote AI utilization' at 42.3%.

In AI utilization, which is considered more important: model selection or data preparation?

87.4% of the managers responded that 'organizing and structuring primary on-site information' is more important than selecting AI models or tools.

Why is structuring on-site data considered important?

The main reason given is that 'on-site judgment know-how can only be extracted from primary information' (65.6%), as high-quality data determines the accuracy and value of AI.

What is the most necessary area for investment in AI utilization in manufacturing for the future?

'Development of data collection infrastructure' is at the top with 47.7%, followed by 'improvement of data quality and cleansing' (41.4%), prioritizing investment in data infrastructure over the introduction of AI models themselves.

AI News NQ Analysis

[Manufacturing x AI Utilization Barriers] Over 40% Cite 'Insufficient Training Data' as the Biggest Challenge in AI Adoption; Nearly Half Say Investment Should Prioritize 'Data Collection Infrastructure' Over 'AI Model Implementation'

NQ Score 52/100

N1 Content Completeness 9

AI Summary (NQ-processed)

A survey by Simtops, Inc. of 111 DX and AI promotion managers in the manufacturing industry found that about 90% have started utilizing AI. The biggest challenge is 'insufficient training data' (44.1%), and 87.4% prioritize 'organizing and structuring primary on-site information' over selecting AI models. 'Development of data collection infrastructure' (47.7%) was cited as the top area for investment over the next three years, highlighting that data preparation is the key to AI success.

AI Analysis

Frequently Asked Questions

Q: Who were the main targets of this survey?: A: The survey targeted 111 managers in charge of DX (Digital Transformation) and AI promotion in the manufacturing industry.
Q: What is the biggest challenge in AI utilization in the manufacturing industry?: A: The most cited challenge is 'insufficient amount of data for AI training' at 44.1%, followed by 'shortage of internal personnel to promote AI utilization' at 42.3%.
Q: In AI utilization, which is considered more important: model selection or data preparation?: A: 87.4% of the managers responded that 'organizing and structuring primary on-site information' is more important than selecting AI models or tools.
Q: Why is structuring on-site data considered important?: A: The main reason given is that 'on-site judgment know-how can only be extracted from primary information' (65.6%), as high-quality data determines the accuracy and value of AI.
Q: What is the most necessary area for investment in AI utilization in manufacturing for the future?: A: 'Development of data collection infrastructure' is at the top with 47.7%, followed by 'improvement of data quality and cleansing' (41.4%), prioritizing investment in data infrastructure over the introduction of AI models themselves.