Strategic Intelligence: Tech Sentiment Polls Q1 2025

Strategic Intelligence: Tech Sentiment Polls Q1 2025

Summary

This report is based on polls designed to help us understand the current sentiment of the business community toward emerging and established technologies.

Key Highlights

  • In Q1 2025, 73% of respondents believed artificial intelligence (AI) would disrupt their industry, as the world's leading tech companies continue to adopt generative AI. However, for the fourth consecutive quarter, less than half of respondents believed AI would live up to all its promises. As more people interact with AI products, such as large language models (LLMs), they increasingly encounter the technology's limitations.
  • As a technology, cybersecurity is ubiquitous across all sectors. In Q1 2025, 67% of respondents believed cybersecurity was already disrupting their industry or would do so in the next 12 months. No industry is exempt from cyberattacks. More respondents claimed to fully understand cybersecurity this quarter (46%) in comparison to the previous quarter (37%). As cyberattacks become more sophisticated, it will be more difficult to identify and tackle cybersecurity threats.
Scope
  • Our sentiment polls ran between January and March 2025 on GlobalData's Verdict network of B2B websites, which had 10.8 million user sessions in Q1 2025. In total, 1,459 respondents participated in the survey.
  • The survey focused on seven technologies: artificial intelligence, augmented reality, cloud computing, cybersecurity, the Internet of Things, the metaverse, and robotics.
Reasons to Buy
  • This survey gives a unique insight into the views of the business community on emerging and established technologies. The responses reveal which technologies executives view as most disruptive, which they believe to be over-hyped, and how their opinion of them has changed over time.


About our tech sentiment polls
Executive summary
Level of disruption
Disruption timelines
Hype vs. substance
Technology understanding
Sentiment polling methodology
Thematic research methodology
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