What Do Companies Prioritize in AI Adoption? MiDATA Survey Reveals Decision-Making Criteria
A survey of 200 business leaders by MiDATA found that 'Information Security/Governance' is the top priority for AI adoption, while over 50% of companies remain cautious with no immediate plans for implementation.
📋 Article Processing Timeline
- 📰 Published: April 25, 2026 at 01:00
- 🔍 Collected: April 24, 2026 at 16:31
- 🤖 AI Analyzed: April 24, 2026 at 22:02 (5h 30m after Collected)
## Press Release Information
Title: What Do Companies Prioritize in AI Adoption? Decision-Making Criteria Revealed in Latest Survey (Conducted by MiDATA, Leading through AI x Data)
Company: MiDATA
Main Text:
As the debate surrounding the adoption of AI and data utilization intensifies, the issues companies must consider are increasing. While options for tools and services expand, decision-making factors have become multifaceted, including cost-effectiveness, operational load, internal systems, and alignment with existing business processes. Consequently, corporate decision-making has become more cautious and realistic, rather than being driven solely by expectations or hype.
In this context, it is crucial to understand what criteria companies use when deciding to introduce or expand AI and data utilization. Furthermore, grasping how they envision the implementation process and what kind of promotion structure they consider desirable is essential for understanding the reality of corporate decision-making.
To address this, MiDATA (https://midata.co.jp/) conducted a 'Survey on Key Points Prioritized in Decision-Making for the Introduction/Expansion of AI and Data Utilization,' targeting 200 full-time employees, executives, and officers aged 30-59. This survey analyzes various aspects, including viewpoints prioritized when newly introducing or expanding AI/data, the envisioned implementation approach, and the desirable promotion structure.
### Survey 1: Viewpoints Prioritized in Decision-Making for New Introduction or Expansion of AI/Data Utilization
The most common response was 'Information Security/Governance Compliance (access management, auditing, risk management, etc.)' at 27.5% (55 people). While AI and data utilization offer high convenience, they also carry risks such as information leakage and unauthorized use. This result shows that companies prioritize verifying whether a safe operational system is in place above all else.
This was followed by 'AI Performance/Quality (accuracy, stability, explainability, etc.)' at 25.5% (51 people) and 'Business Suitability (usability on-site, ease of integration into business processes)' at 24.5% (49 people). There is a strong tendency to emphasize 'practicality'—whether the tool can be used in actual business and if the expected results can be obtained stably.
Furthermore, 'Economics (initial/operational costs, KPIs, ROI outlook)' accounted for a significant 20.5% (41 people), indicating that the balance between implementation effects and costs is an important decision-making factor.
Other concerns included 'Data/Platform Maturity (data quality, maintenance costs, access rights, ease of integration)' at 15.0% (30 people) and 'Legal/Regulatory Compliance (personal information, copyright, industry regulations)' at 14.5% (29 people).
On the other hand, 'Operational/Adoption Design (education, rules, workload, ease of ongoing operation)' was at 13.5% (27 people) and 'Legacy System Integration/Scalability (scale, future requirements, response to additional functions)' was at 10.0% (20 people). This indicates a trend of prioritizing immediate safety and practicality over post-adoption operation or future scalability.
Notably, 'Reliability of Vendors/Support Companies (track record, support scope, continuity, failure response, etc.)' was the lowest at 4.0% (8 people) compared to other items.
### Survey 2: Preferred Introduction Approach
The most common response was 'No plans for introduction' at 50.5% (101 people), accounting for half of the respondents. While the necessity of AI and data utilization is widely recognized, many companies remain cautious about actual implementation. Challenges such as assessing investment-to-effect ratios and organizing internal structures may be acting as hurdles to the decision to introduce AI.
Next was 'Policy not yet decided/Unknown' at 21.5% (43 people), showing that about 20% are in the consideration phase. This group is likely exploring suitable introduction methods while gathering information and making internal adjustments, with decisions potentially progressing as market trends and successful cases accumulate.
Title: What Do Companies Prioritize in AI Adoption? Decision-Making Criteria Revealed in Latest Survey (Conducted by MiDATA, Leading through AI x Data)
Company: MiDATA
Main Text:
As the debate surrounding the adoption of AI and data utilization intensifies, the issues companies must consider are increasing. While options for tools and services expand, decision-making factors have become multifaceted, including cost-effectiveness, operational load, internal systems, and alignment with existing business processes. Consequently, corporate decision-making has become more cautious and realistic, rather than being driven solely by expectations or hype.
In this context, it is crucial to understand what criteria companies use when deciding to introduce or expand AI and data utilization. Furthermore, grasping how they envision the implementation process and what kind of promotion structure they consider desirable is essential for understanding the reality of corporate decision-making.
To address this, MiDATA (https://midata.co.jp/) conducted a 'Survey on Key Points Prioritized in Decision-Making for the Introduction/Expansion of AI and Data Utilization,' targeting 200 full-time employees, executives, and officers aged 30-59. This survey analyzes various aspects, including viewpoints prioritized when newly introducing or expanding AI/data, the envisioned implementation approach, and the desirable promotion structure.
### Survey 1: Viewpoints Prioritized in Decision-Making for New Introduction or Expansion of AI/Data Utilization
The most common response was 'Information Security/Governance Compliance (access management, auditing, risk management, etc.)' at 27.5% (55 people). While AI and data utilization offer high convenience, they also carry risks such as information leakage and unauthorized use. This result shows that companies prioritize verifying whether a safe operational system is in place above all else.
This was followed by 'AI Performance/Quality (accuracy, stability, explainability, etc.)' at 25.5% (51 people) and 'Business Suitability (usability on-site, ease of integration into business processes)' at 24.5% (49 people). There is a strong tendency to emphasize 'practicality'—whether the tool can be used in actual business and if the expected results can be obtained stably.
Furthermore, 'Economics (initial/operational costs, KPIs, ROI outlook)' accounted for a significant 20.5% (41 people), indicating that the balance between implementation effects and costs is an important decision-making factor.
Other concerns included 'Data/Platform Maturity (data quality, maintenance costs, access rights, ease of integration)' at 15.0% (30 people) and 'Legal/Regulatory Compliance (personal information, copyright, industry regulations)' at 14.5% (29 people).
On the other hand, 'Operational/Adoption Design (education, rules, workload, ease of ongoing operation)' was at 13.5% (27 people) and 'Legacy System Integration/Scalability (scale, future requirements, response to additional functions)' was at 10.0% (20 people). This indicates a trend of prioritizing immediate safety and practicality over post-adoption operation or future scalability.
Notably, 'Reliability of Vendors/Support Companies (track record, support scope, continuity, failure response, etc.)' was the lowest at 4.0% (8 people) compared to other items.
### Survey 2: Preferred Introduction Approach
The most common response was 'No plans for introduction' at 50.5% (101 people), accounting for half of the respondents. While the necessity of AI and data utilization is widely recognized, many companies remain cautious about actual implementation. Challenges such as assessing investment-to-effect ratios and organizing internal structures may be acting as hurdles to the decision to introduce AI.
Next was 'Policy not yet decided/Unknown' at 21.5% (43 people), showing that about 20% are in the consideration phase. This group is likely exploring suitable introduction methods while gathering information and making internal adjustments, with decisions potentially progressing as market trends and successful cases accumulate.