Strategic Planning and Readiness
AI-related strategic planning is primarily motivated by institutions' need to keep up with the rapid uptake of AI tools. Nearly half of respondents (49%) agreed or strongly agreed that "we view AI as a strategic priority." The three most-selected motivators for AI-related strategic planning were the rise of student use of AI in their courses, risks of inappropriate uses of AI technologies, and concern about "falling behind" in adopting AI technologies (73%, 68%, and 59%, respectively; see figure 1). Respondents who selected "other" provided open-ended descriptions of their motivation for AI-related strategic planning. Many of these respondents mentioned students' workforce readiness. For example, one respondent wrote, "AI knowledge and use will be skills our students will need. We view this as an expansion of our digital literacy commitment to our students."

AI-related strategic planning goals and strategies are focused on supporting students' experiences. Though many institutions seem motivated by avoiding negative consequences like "falling behind," the two most-selected goals of AI-related strategic planning are focused on supporting students: preparing students for the workforce and exploring new teaching and learning methods (64% and 63%, respectively; see figure 2). In open-ended "other" responses, respondents identified goals such as improving the efficiency of institutional operations and supporting equity, inclusion, and accessibility.

Similarly, most respondents (76%) indicated that their institution's AI-related strategy is at least somewhat focused on boosting educational experiences and student services (see figure 3). Respondents also indicated that their institution's AI-related strategy is at least somewhat focused on boosting administrative processes and productivity, creating new educational models and programs, and creating new core capabilities (56%, 51%, and 44%, respectively).

The most common elements of AI-related strategy are training, training, and training. Training for faculty, staff, and students to learn new AI technology and skills were the most-selected elements of AI-related strategy (56%, 49%, and 39%, respectively; see figure 4). These data suggest that institutions are helping their community members update their skills, but there is opportunity to provide more focus on AI literacy training for students. The least-selected items include establishing senior leadership positions (7%) and budgeting for anticipated long-term costs (14%), pointing to a lack of long-term planning and infrastructure.

Over the past year, a major area of uncertainty has been the extent to which institutions can or should develop in-house AI models or work with third-party providers. To this end, we asked the 15% of respondents who indicated that they are creating technology infrastructure to run generative AI models locally why they have chosen to do so. Most responses were related to minimizing costs, shoring up data privacy and security, and investing in long-term capabilities. As one respondent described, "It will be a transformational capability." In another closed-ended survey item, just over a third of respondents (36%) agreed or strongly agreed that "we view AI as an investment rather than an added cost."