Like any new technology that becomes mainstream, generative artificial intelligence (AI) has a learning curve. Leaders are asking about where generative AI fits or doesn’t, how to use it effectively, and about the nontechnical considerations.
History teaches us that we see profound, positive change only when people, processes, skills, and culture are addressed alongside technology. Based on this learning and from the thousands of customers we talk to, our advice for those intrigued by generative AI is simple.
First, be curious. Learn what generative AI is, why it has captured people’s imaginations, and what problems it can solve. Dive deep into areas such as the security of your data when customizing models. Encourage others to learn rather than delegating them to your IT team.
Second, think big and work backwards from your customers, such as students, educators, and administrative staff. This is a standard way of thinking here at Amazon Web Services (AWS)! Really understand the opportunities in your institution—whether it’s an opportunity to increase student enrollment with the right students, create efficient processes for administrators, or enrich the student experience. Fall in love with the problem before you jump to a solution, looking for areas that reduce costs, increase resilience, or improve your services.
Finally, start now. Most initiatives take time to get traction, so start experimenting quickly. You will learn more from this than from the endless planning and waiting for the hypothetical perfect time that is typical of many technology adoptions.
Generative AI readiness assessment tool
EDUCAUSE and AWS have launched a generative AI readiness assessment tool to help higher education leaders prepare their institutions for the evaluation, adoption, and implementation of generative AI. Through a curated list of questions, this tool can help you develop a shared understanding of the strategic and tactical considerations required to ensure generative AI is implemented securely and safely, and provide visibility into an institution’s readiness for identification and adoption of generative AI solutions across three
focus areas: vision and leadership, people, and process and infrastructure.