How to Turn AI Practice into Better Field Performance
An AI role play can look impressive. A learner speaks with a lifelike customer or employee persona, encounters objections, receives a score, and tries again. But the most important question comes after the simulation ends: Will the learner handle the next real conversation differently?
That question should shape how learning leaders evaluate AI-supported practice. The technology can make realistic conversations available at scale. It can also become one more activity that learners complete without changing their behavior. The difference lies in what surrounds the simulation.
Where AI Can Improve Practice
Traditional role plays are valuable, but difficult to deliver consistently. Practice partners vary in skill. Facilitators cannot observe every exchange. Time often limits learners to one or two attempts. Some participants feel exposed in front of colleagues, while others soften the scenario for a peer. When the workshop ends, the practice usually ends with it.
AI can remove several of those barriers. Learners can practice privately, repeat a conversation, experience different reactions, and receive immediate feedback. Training teams can also identify patterns across a group and use them to focus coaching.
Early research supports the potential. A study of AI-supported medical-history practice found that the system produced medically plausible responses in more than 99 percent of 106 simulated conversations. However, agreement between the AI and human reviewers was weaker in several feedback categories [1]. A randomized study of 94 novice counselors found that simulated practice paired with structured AI feedback improved specific counseling skills. Practice without feedback did not [2].
The lesson is straightforward. Access to more practice matters, but practice alone is not enough. Learners need a clear standard, useful feedback, and another opportunity to apply it.
Build a Practice Loop
The best use of AI role play is as part of a simple practice loop that connects learning to field performance.
1. Model the skill. Define the behavior learners should demonstrate and show them what good performance looks and sounds like. For example, asking two diagnostic questions before presenting a recommendation is easier to practice and coach than a broad goal such as improving communication.
2. Practice with realism. Build the simulation around situations learners actually face. The persona, terminology, level of resistance, and business context should feel credible. Realism does not require every possible complication. It requires the right decisions and cues.
3. Reflect and repeat. Use a validated rubric to identify one or two improvement priorities. Then require another attempt so the learner can apply the feedback immediately. A score without reflection and repetition is unlikely to change behavior.
4. Coach in the field. Give managers the same behavioral standard and help them reinforce it during ride-alongs, call reviews, one-to-one coaching, and preparation for upcoming conversations. Simulation scores are useful, but the real measure is whether the behavior appears more consistently at work.
Keep People in the Process
AI can expand practice and improve consistency, but it cannot replace an experienced coach. A scoring model may reward the presence of a phrase without recognizing whether it fit the situation. It may also deliver feedback that sounds confident even when it is incomplete or wrong.
Managers and facilitators provide judgment, context, and accountability. They help learners interpret subtle reactions, decide when to adapt the model, and connect practice to current business priorities. In life sciences, human oversight is especially important when scenarios involve approved product language, medical information, customer data, adverse-event reporting, or other regulated content.
Research with managers reflects this balance. Managers valued adaptive, low-risk simulations, but also wanted transparent feedback and control over AI-generated personas [3]. The technology should support coaching, not remove coaching from the system.
Start with One Conversation
A focused pilot is more useful than a broad rollout. Choose one important conversation that occurs often enough to justify repeated practice. A product-launch objection, a coaching discussion after a field observation, or a manager conversation about performance can provide a practical starting point.
Define the target behavior and scoring rubric before selecting features. Test the scenario with strong performers, managers, and the functions responsible for content and risk. Confirm what the system records, who can access transcripts and scores, how long information is retained, and whether any data may be used to train the model. The NIST AI Risk Management Framework provides a useful structure for addressing those questions [4].
During the pilot, look beyond satisfaction and completion. Are learners repeating the simulation? Does the feedback identify meaningful differences in performance? Do managers observe the targeted behavior afterward? Those measures tell you whether the practice is transferring to the job.
AI role play can give learners more opportunities to prepare for important conversations. Its value, however, depends on the learning system around it. Define the behavior, build a credible scenario, validate the feedback, require another attempt, and equip managers to reinforce the skill. That is how AI practice becomes better field performance.
Continue the Conversation
Romar Learning Solutions will explore this topic during the Romar Training Leaders Round Table on Friday, September 25, 2026, at 1:00 p.m. ET. AI-Powered Role-Plays: What’s Working, What’s Not, and What’s Next will be a candid peer discussion for learning leaders who are using, evaluating, or trying to understand this technology.
References
1. Holderried, Fabian, et al. 2024. “A Language Model-Powered Simulated Patient with Automated Feedback
for History Taking: Prospective Study.” JMIR Medical Education 10: e59213. https://doi.org/10.2196/59213
2. Louie, Ryan, et al. 2025. “Can LLM-Simulated Practice and Feedback Upskill Human Counselors? A
Randomized Study with 90+ Novice Counselors.” https://doi.org/10.1145/3772318.3791821
3. Wilhelm, Lena T., et al. 2025. “How Managers Perceive AI-Assisted Conversational Training for Workplace
Communication.” https://doi.org/10.1145/3719160.3736639
4. National Institute of Standards and Technology. 2024. Artificial Intelligence Risk Management Framework
Generative Artificial Intelligence Profile. NIST AI 600-1. https://doi.org/10.6028/NIST.AI.600-1
