1. Closing the AI Talent Gap
One of the greatest challenges to achieving AI readiness is finding proficient AI talent. According to research from CDW, organizations say finding and training staff are among the biggest hurdles when implementing AI. The shortage of AI expertise limits schools’ ability to design, implement and manage AI initiatives effectively.
To address this workforce gap, districts must invest in training existing employees and create incentives to attract the top AI talent to education.
READ MORE: AI literacy starts in the classroom.
2. Improving Data Quality and Security
Successful implementation of AI depends on high-quality, well-governed data. Unfortunately, many districts 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, agencies must establish robust data governance frameworks, improve interoperability across systems and implement best practices for data management.
EXPLORE: AI is transforming business operations in K-12.
3. Adapting to AI Regulations
Evolving AI regulations and guidance present a critical challenge to AI adoption in education. Executive orders from the White House may leave districts facing shifts in regulatory frameworks that could lead to a delay or change of course on AI initiatives. Navigating these uncertainties requires schools to remain agile and adaptable as they formulate their AI strategies, ensuring compliance with emerging regulations and guidance.
