SHI Survey Shows Only 13% of Research Institutions Are Prepared for AI
Most institutions have an AI strategy but lack the infrastructure, training, and data policies needed for implementation
AI’s applications span a wide range in academia, with two use cases leading the way: chatbots (36.8%) and research tools (35.3%). The top five use cases are rounded out by automation of administrative tasks, predictive analytics, and student services (all 29.4%).
“Top research universities are at the forefront of innovation and have led to life-altering inventions like insulin, Wi-Fi technology, and the pacemaker, so getting AI implementation right for these schools is critical,” said
According to the survey results, more than 80% of respondents didn’t feel policies had been fully implemented, but 70% were either already using AI or plan to within the next 12 months.
Other key findings from research institutions include:
- 48.6% either have basic training in place or plan to provide training for their staff
- Institutions need the most assistance developing an AI funding strategy (47.1%) and identifying available grants (44.1%)
- Top barriers to implementation include too many competing priorities (36.8%), inadequate funding (36.8%), and lack of AI strategy or ownership (30.9%)
- Only 16% say their infrastructure is ready for extensive AI integration for students
- 29.4% have only somewhat or partially implemented data security and privacy measures into their AI systems
"The best thing institutions can do is hop on board the speeding AI train and figure out how to use it to best reach their destination or goal," said
Read the full report for complete findings and learn how SHI and
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