The Pulse of Higher Ed

For many online and professional continuing education units, the primary barrier to adopting artificial intelligence is not access to tools, it is uncertainty about where to begin and how to proceed without disrupting daily operations. Leaders are often balancing innovation with stability, making it difficult to introduce new approaches without clear structure. 

A focused, time-bound strategy can reduce that ambiguity. A 90-day adoption framework enables leaders to move from concept to measurable impact while maintaining operational continuity and building institutional confidence. Recent analysis from EDUCAUSE highlights that artificial intelligence is already reshaping day-to-day work across higher education, reinforcing the need for leaders to guide adoption through practical, operational use cases rather than isolated experimentation (Robert, 2026). 

As a starting point, resources such as the UPCEA AI Hub provide a foundation for developing institutional awareness and fostering a culture that supports thoughtful AI implementation. 

Phase 1: Discovery (0–30 Days) 

The first phase should emphasize clarity rather than action. A common misstep in AI adoption is moving too quickly to tools without first understanding where AI can meaningfully improve performance. This stage is about identifying operational friction and establishing a baseline. 

Leaders should begin by isolating three to five persistent pain points across the unit. In online and professional continuing education environments, these often include delayed inquiry response times, inconsistent communication workflows, manual reporting processes, or slow program market analysis. The objective is not to identify every inefficiency, but to prioritize high-frequency, high-impact challenges that directly affect staff capacity or the learner experience. 

Once identified, these areas should be mapped using basic Business Process Mapping (BPM). This does not require complex tools; simple workflow diagrams are sufficient. The goal is to document the current state.  These four steps will identify the baseline workflow.   

1. Where tasks begin  
2. How information flows 
3. Where bottlenecks occur  
4. Which steps are repetitive or manual 

This baseline workflow serves two critical purposes: it clarifies where AI can be introduced without disrupting essential operations, and it establishes a benchmark for measuring future process efficiency gains. 

Equally important is leadership alignment. Leaders must define what success looks like in practical terms, whether that is reducing staff workload, improving response times, or increasing conversion rates. A clear strategic vision to share will result from the completion of Phase 1.  Without this shared understanding, subsequent efforts risk becoming fragmented or overly experimental. 

Phase 2: Pilot (30–60 Days) 

The second phase shifts from analysis to controlled experimentation. The goal during Phase 2 is not broad implementation, but targeted testing in a low-risk environment where outcomes can be evaluated. 

Select one or two use cases directly tied to the pain points identified in Phase 1. These might include automating initial responses to prospective student inquiries, generating draft marketing content, or summarizing labor market data for program planning. Priority should be given to tasks that are repetitive, time-consuming, and relatively low risk from a compliance or reputational perspective. 

At this stage, the specific tool is less important than how it is applied. Many AI platforms can support these functions, but they must be implemented with clear guardrails. Human oversight and decision-making should remain central, particularly for student-facing communications or externally published materials. This ensures quality while building trust in AI-supported workflows. 

Measurement is essential. Leaders should define a small set of metrics aligned with the original problem, such as time saved per task, reduction in response time, or improved engagement rates. Even modest improvements, for example, reducing a task from 30 minutes to 10, can yield significant cumulative benefits when scaled across a unit. 

Just as important is documenting lessons learned. What worked as expected? What required adjustment? Where did staff encounter friction? These insights not only inform whether the pilot should expand but also may lead to innovation for workflow changes that may be needed to better serve online and professional continuing education learners. 

Phase 3: Scale (60–90 Days) 

The final phase focuses on operationalizing success. When pilot efforts demonstrate measurable value, the next step is to transition from isolated experimentation to standardized practice. 

This begins by formalizing successful workflows into standard operating procedures. Documentation should clearly define when and how AI is used, where human review is required, and what quality standards must be maintained. Without this level of structure, gains achieved during the pilot phase often remain inconsistent and difficult to sustain. 

Integration into existing systems is the next priority. AI should not function as a parallel process but should be embedded into the platforms staff already use, such as CRM systems or communication tools. This reduces friction and supports broader adoption. 

With this foundation in place, expansion to adjacent functions becomes possible. For instance, if AI proves effective in managing prospective student inquiries, similar approaches can be extended to current student communications or alumni engagement. The key is disciplined scaling that will extend proven practices rather than introducing entirely new ones simultaneously. 

Leadership visibility remains critical throughout this phase. Sharing early successes such as time savings or improved response rates can reinforce adoption and build organizational momentum. It also signals that AI is not a temporary initiative, but an integrated component of how the unit operates. 

A 90-day framework does not resolve every operational challenge. Instead, it establishes a repeatable model for continuous improvement grounded in strategy and innovation. For online and professional continuing education leaders, the goal is not rapid transformation, but sustained, measurable progress that aligns with institutional priorities and builds long-term organizational capability. 

 

Reference:

Robert, J. (January 12, 2026.)  The Impact of AI on Work in Higher Education.  EDUCAUSE.  https://www.educause.edu/research/2026/the-impact-of-ai-on-work-in-higher-education  

 
Vickie Cook is a nationally recognized higher education leader specializing in enrollment strategy, online and digital learning, organizational transformation, team development, and leadership growth. She currently serves as a Senior Fellow and Strategic Advisor for UPCEA.  To learn more about UPCEA Research and Consulting, please contact [email protected].