2. Improving Data Quality and Security
Successful implementation of AI depends on high-quality, well-governed data. Unfortunately, many higher ed institutions have challenges with data quality and security. When AI projects are undertaken without sufficient preparation, poor data governance can lead to inaccurate results.
To guarantee data readiness, institutions must establish robust data governance frameworks, improve interoperability across systems and implement best practices for data management.
READ MORE: Strong data governance is vital for successful AI implementations.
3. Adapting to AI Regulations
Evolving AI regulations and guidance present a critical challenge to AI adoption in higher education. Executive orders from the White House may leave colleges and universities facing shifts in regulatory frameworks that could lead to a delay or change of course on AI initiatives. Navigating these uncertainties requires organizations to remain agile and adaptable as they formulate their AI strategies, ensuring compliance with emerging regulations and guidance.
