Technology
There is always the “next big thing” that comes and goes, or maybe sometimes sticks around. It starts out with too many options, then those real products that deliver end up shining through as time goes on and others fade away. Some made a real difference. Others looked great in the pilot deck but didn’t survive contact with the real world.
Use this scorecard to quickly evaluate whether a proposed AI tool is a good fit for your coaching tool is a good fit for your coaching program. Each section contains program. Each section contains key considerations — rate each as Yes, No or Needs Review. key considerations — rate each as Yes, No or Needs Review.
Artificial intelligence (AI) is the latest big thing to land on our doorstep. And if your inbox is anything like mine, you’ve been bombarded with promises about how it can revolutionize coaching and development.
The truth? AI can be incredibly useful when it’s applied to the right problems, in the right way. But not every use case is worth your team’s time, budget or trust. The key is knowing exactly where AI shines, where humans still lead and how to vet whether it even belongs in your coaching loop.
Over the past few years, AI began to weave its way into coaching and learning programs in four distinct ways. Understanding each helps us evaluate not only its potential but also its limitations.
Time is the biggest constraint for most managers and representatives when it comes to practice. AI role-play tools make it possible to simulate conversations with physicians, payers or office staff without waiting for schedules to align. These simulations can range from straightforward objection-handling to complex, multi-turn clinical discussions.
Research in health professions education consistently shows that simulation improves communication skills and confidence. AI-powered versions add scalability and on-demand availability. Instead of walking into a live coaching session cold, a rep can now arrive warmed up, already having worked through relevant scenarios.
But realism matters. If simulations are too polished, they can feel artificial, failing to prepare reps for the unpredictability of real-world conversations. Used thoughtfully, AI role-play is a great warmup – not a substitute for live, human-to-human practice.
Another common application is AI-driven feedback. These systems can review call notes, presentations or other performance artifacts and generate structured guidance. The appeal is obvious: Instead of managers spending precious time on basic critiques, they can focus their energy on deeper coaching topics like mindset, strategy and clinical nuance.
Studies show AI can match human coaches on narrow, measurable objectives, such as clarity of messaging or completeness of coverage. In my experience, the best results happen when AI is positioned as a coaching assistant. Its feedback becomes the starting point for a meaningful, humanled conversation rather than the conversation itself.
If you’ve ever tried to reconcile standard operating procedures, training content and field processes across multiple regions, you know the headaches involved. AI can help by scanning documentation, identifying redundancies and highlighting inconsistencies. This is particularly valuable for onboarding programs, where small inefficiencies quickly scale into big delays.
However, in a regulated industry, AI’s suggestions are just the beginning. They must be reviewed, validated and approved by humans – preferably those who understand both the business context and compliance landscape. Skipping that step isn’t just risky; it’s unacceptable.
The final major use case is using AI to connect the dots between coaching activity, skill development and commercial results. For example, AI might reveal that reps who receive targeted coaching on clinical differentiation tend to secure better formulary access.
These insights can help focus resources where they have the greatest impact. But a word of caution: AI often identifies correlations, not causation. Without human judgment, there’s a danger of chasing patterns that don’t drive results.
In my view, the right question isn’t whether we should use AI, but “does AI make this process or outcome better than it is today?” If the answer isn’t clear, it’s worth pausing before jumping in.
AI shines when:
Tasks require scale and repetition, like practice scenarios or feedback on routine skills.
Large datasets need to be analyzed for patterns and trends.
Document-heavy processes need a first-pass review to spot errors or redundancies.
Humans excel when:
Trust, empathy and nuanced judgment are required.
Motivation and personal accountability are at stake.
Complex situations demand context and cultural understanding.
I’ve seen AI deliver real value when it removes friction and frees coaches to focus on the human side of development. I’ve also seen it fail when implemented as a trendy add-on without a clear business case.
If you’re considering adding AI to your coaching toolkit, here are some guidelines I follow:
Start with a clear problem statement. Don’t adopt AI because it’s available; adopt it because it solves a specific challenge better than your current approach.
Set clear benchmarks or goals. If onboarding a new tool, make sure your goals and outcomes are clear and reassess if these targets are being hit.
Request evidence. Whether from academic studies, industry benchmarks or supplier case studies, ask for proof that the tool works for your specific use case.
Keep governance front and center. Data privacy, regulatory compliance and human oversight should be part of the plan from day one.
Measure human impact, not just activity metrics. The real test is whether AI frees coaches to spend more time on high-value, human-led interactions.
Despite all the advances, I remain convinced that the most transformative coaching moments will always come from human interaction. AI can help reps practice more, receive quicker feedback and see clearer connections between effort and outcome. But it can’t replace the encouragement after a tough call, the shared story that reframes a challenge or the kind of trust that inspires real change.
The future of coaching in life sciences isn’t a choice between AI and humans. It’s the right blend of both – with AI handling the repeatable, scalable tasks so humans can focus on what we do best: connecting, guiding and inspiring.
Anthony Menichini is an account executive with Proficient Learning. Email him at anthony.menichini@proficientlearning.com or connect through linkedin.com/in/anthony-menichini.
