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Author: Martin Lehtio
Title: Chief Operating Officer
Link: /life-at-iqor/leadership/martin-lehtio
AI Training: Building Confidence Before Day One
Knowing something isn't the same as being able to do it. Here's how AI-powered simulation closes the gap between training and readiness.
“The best learning technology gives employees more than training—it gives them experience. We want our employees to get as many ‘at bats’ as possible, practicing a variety of customer interactions rather than repeating the same scenarios, so they’re more confident and prepared when engaging with real customers.”
— Martin Lehtio, Chief Operating Officer, iQor
Training has always had a gap: knowing something is not the same as being able to do it. An employee can finish every module and pass every test, yet still feel unready for their first real customer. In customer service, that gap shows up as lower confidence, more escalations, and customers who notice the difference.
The gap is widening. As agentic AI resolves more of the simple, repeatable contacts, the conversations that reach people are the harder ones: the exceptions, the escalations, the emotional moments. Readiness has never mattered more.
AI can help close the gap by shifting the goal from completing content to demonstrating readiness.
Practice is becoming the center of learning
The World Economic Forum expects nearly 40% of job skills to change by 2030, and 77% of employers plan to upskill their workforce in response to AI. With that much change, one-time training and static knowledge checks can't keep pace.
Simulation offers a practical way forward. AI role-play puts learners in front of different customers, emotions, and challenges. It gives them a safe place to try, stumble, get feedback, and improve. Scenarios can now be built from real customer interactions instead of generic scripts, so practice reflects what employees will actually face.
One major U.S. wireless carrier shows what this looks like at scale. To improve readiness across a large, complex operation, the program introduced realistic AI-powered role-play scenarios that employees could practice at their own pace, focusing on the skills and interactions where they needed the most support. The practice doesn’t stop after training. Once in production, employees can return to targeted simulations based on performance and NPS insights to keep building their skills. Today, the program includes more than 900 scenarios and has delivered over 600,000 simulations, creating a scalable way to turn real customer needs into continuous practice.
That speed changes the trainer's role, too. Trainers spend less time repeating the same lesson and more time on what needs a human touch: judgment, empathy, tough conversations, and confidence.
Personalization should be specific, not isolating
Personalized learning is sometimes pictured as employees trained alone by an algorithm. That misses the point. The real value is relevance. AI identifies where someone needs help and suggests the right practice, while trainers and peers still guide and support.
Someone who struggles with discovery questions shouldn't have to redo the whole course. Someone who understands a policy but can't explain it needs different help than someone who can't find the answer at all. When feedback is that specific, practice feels useful rather than punitive.
Gallup's research points the same way. Employees use AI more meaningfully when they understand how it applies to their role and how their manager expects them to use it.
Readiness is a human outcome
Completion rates and test scores still matter. But readiness also means confidence, less overwhelm, and knowing when to ask for help. The goal isn't to finish training faster. It's to become skilled faster.
Across multiple iQor programs, AI-simulated training has cut time to proficiency by roughly 50%.
For a leading home services provider, simulation was part of a broader AI-supported model connecting recruiting, training, analytics, and coaching—helping employees build capability while the operation scaled.
“We trust them to incubate new ideas and scale what works across our enterprise.”
— Leading home services provider
Learning should follow the work
The deeper shift is from training as an event to readiness as an operating loop. Customer interactions reveal new needs. Those needs shape simulations and coaching. Practice builds capability, and performance generates new evidence. Then the loop repeats.
AI makes that loop faster and more individual. People make it meaningful. Together, they produce a workforce that meets customers with confidence on Day One and keeps learning after it.
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