Enrollment leaders don’t need another mandate to “use AI.” They need relief.
Most teams are already stretched thin by demands to manage inquiry volume, follow-up expectations, data hygiene, and prospect responsiveness. They lack the capacity to take on yet another complex initiative, especially one that feels abstract or disruptive. AI mandates promise transformation when what teams really need is time back.
The good news? You don’t need an AI strategy to get started. You just need one clear use case, a small pilot, and reliable guardrails. The process outlined below offers a practical approach that requires no workflow overhauls, team reorganization, or steep learning curves.
Step 1: Pick One Workflow Problem
Start by grounding the conversation in real work. Ask your team a simple question: Where does time quietly disappear every week? Record the answers.
Then, choose one workflow that meets three criteria:
- It happens daily or weekly.
- It consistently drains staff time or attention.
- You can define what “better” looks like (faster, clearer, more consistent).
The workflow you choose shouldn’t be extravagant or complex. Identify one self-contained workflow to build confidence, reduce risk, and prevent AI from feeling like just another system to manage.
For many enrollment teams, this exercise surfaces the same pressure points:
- Drafting responses to common inquiries
- Prioritizing follow-ups across a crowded queue
- Summarizing notes or spotting patterns across interactions
Smarter enrollment operations already hinge on sending the right message at the right moment. The benefit of this approach is that it starts with the operational bottleneck, not the technology.
Step 2: Define the Current State in Plain Language
Before introducing AI, clarify how the work happens today. This doesn’t require a process map yet—just a shared understanding.
Document three things:
- What triggers the workflow (for example, a new inquiry or application update)
- What staff do manually right now
- What slows them down (system switching, repeated questions, searching for information)
Choose one or two simple baselines you can estimate without new reporting:
- Average response time
- Weekly hours spent on the task
- Size of the inquiry or follow-up backlog
This creates a reference point without adding measurement overhead.
Step 3: Decide What AI Will and Won’t Do
Clarity builds trust. A useful rule of thumb: AI should operate behind the scenes, while people maintain responsibility for judgment, tone, and relationships.
For your pilot, assign AI a narrow, well-defined role.. It might:
- Draft a first-pass response for staff to review
- Summarize interaction notes or surface common themes
- Suggest prioritization based on your defined criteria
Be equally explicit about boundaries. AI will not:
- Make admissions or enrollment decisions
- Override your institution’s tone or policies
- Use data that staff can’t see or verify
This kind of transparency directly addresses common concerns about workload, bias, and data privacy that continue to shape AI adoption in higher education.
Step 4: Set Guardrails Before Testing Anything
Guardrails aren’t bureaucracy; they’re what make experimentation possible.
Before launching a pilot, align on a short checklist:
- Data: What information can and cannot be used
- Review: Who approves outputs before they’re used
- Transparency: When staff should disclose AI assistance
- Equity: Which decisions remain fully human by design
- Storage: Where outputs live, and where they don’t
Keep guidelines to one page and share them with everyone involved. When staff know the boundaries, they’re far more willing to engage.
Step 5: Run a Two-Week Pilot With One Clear Win
Keep your pilot focused and manageable, limited to:
- One team or program
- One workflow
- One success metric
Your goal isn’t to prove AI’s potential, but rather to test whether a specific use case reduces cognitive or operational load.
Meaningful wins might include:
- Less time spent drafting responses
- Faster movement through inquiry backlogs
- Fewer internal handoffs or clarification loops
- More consistent information shared with learners
If, at this point, the pilot creates extra steps or uncertainty, that’s not failure—it’s valuable feedback.
Step 6: Collect Staff Feedback
Adoption lives or dies with staff experience.
At the end of each week, ask four questions:
- What got easier?
- What got harder or more frustrating?
- What would make this usable next week?
- What steps or tasks became redundant?
If, after the trial ends, the tool continues to create extra review work or confusion, it’s not reducing load yet, no matter how promising it looks on paper. At that point, you’ve reached a decision moment.
Step 7: Decide Whether to Scale, Revise, or Stop
At the end of the pilot, make a clear call:
- Scale if staff time dropped and confidence increased
- Revise if the value is real, but friction remains
- Stop if the effort adds work or introduces risk you can’t mitigate
Incremental progress beats big-bang transformation, but don’t be afraid to stop if the results aren’t adding up.
Step 8: Repeat
When you’re ready to expand, choose an adjacent workflow that uses similar data or serves the same team. This is how AI becomes a modular layer that complements existing systems, rather than another tool staff must manage.
A Practical Starting Line
You don’t need to overhaul enrollment operations to use AI responsibly. Start with one workflow, allow people to maintain control, and measure whether workloads actually get lighter. When AI reduces rather than adds load, adoption follows naturally.
