Rethinking Time to Competency in the Age of AI

In today’s rapidly evolving workforce, onboarding new employees efficiently has become a critical competitive advantage. Every extra week it takes to train an employee is a week of lost productivity.

The rise of AI is an opportunity not just to speed up learning, but to drive lasting organizational efficiency. A 2024 global survey by consulting firm Cegos found that 44 percent of employees already use AI-based learning, and 81 percent of HR leaders are integrating or planning to implement such technologies. This underscores the widespread adoption of AI in corporate learning, but it also raises a vital question: How can organizations ensure that the speed of learning does not come at the expense of depth and sustainability?

Time to competency matters

In corporate learning, time to competency is a central concept. It refers to the time it takes for employees to acquire the skills, knowledge, and confidence to perform their job effectively and independently. It is a critical metric, as shortening time to competency can boost productivity, reduce training costs, and provide a competitive edge. However, accelerated learning may prove counterproductive if the acquired knowledge is superficial or unsustainable.

AI is reshaping the learning experience

Whereas the human ability to learn has developed over millennia, AI capabilities have been developing in leaps and bounds over just a few years. When properly aligned with the best practices in how we learn, AI can transform the learning experience through three key dimensions: personalization, practice, and measurement. Consider, for instance, how field enablement for new sales employees demonstrates these dimensions in practice.

Personalized learning journeys drive engagement

First, AI-powered personalization drives higher engagement. Generative AI tools can help learners explore complex topics based on individual knowledge gaps. In the case of sales professionals, AI can analyze performance patterns and knowledge levels to create customized learning paths that focus precisely on the skills each individual needs to develop — whether product knowledge, negotiation techniques, or objection handling. This personalized approach leads to significantly greater knowledge retention and application as learning experiences become directly relevant to the role.

Accelerating skill development through simulated practice

Second, practice-based learning accelerates onboarding and skill acquisition. For example, sales professionals can use AI-powered simulators to practice customer conversations with virtual buyers who respond naturally to different pitches and approaches, while receiving instant feedback on their communication style, value proposition clarity, and response to objections. These simulated interactions build confidence before real customer engagements, drastically shortening ramp-up time.

Measuring and sustaining competency

Third, measurement ensures the knowledge truly sticks. Sustainable learning requires more than speed. It must embed deep understanding and long-term applicability. AI learning platforms track not just completion, but actual competency development through ongoing assessments. Repetitive, on-the-job training and immersive experiences can help reinforce knowledge. For instance, sales teams can utilize AI tools that analyze real customer interactions post-training, measuring how effectively new techniques are being applied and providing ongoing microlearning refreshers precisely when skills begin to fade — ensuring the investment in sales training delivers lasting performance improvement.

From skill gaps to strategic learning

Yet, even with these tools, learning and development professionals face challenges. The Cegos study reports that nearly half of HR professionals have difficulties adapting their training offerings quickly enough to meet actual needs. Employees, meanwhile, often feel their training needs are addressed too late. AI can close this gap by using learning analytics to identify skill gaps early and deliver timely interventions.

The real opportunity lies in balancing fast-track learning with long-term capability building. Companies must not only measure how fast employees reach competency, but also ensure the competency is durable. Key strategies include microlearning, adaptive content, mentorship, and integration of practical phases — all aimed at anchoring knowledge deeply while reducing unnecessary delays.

Ensuring lasting impact

To truly accelerate time to competency while ensuring deep, lasting understanding, companies need structured, scalable learning solutions. One such solution is SAP Learning Hub, which offers broad access to expert-led resources to both obtain and maintain SAP Certifications. Structured learning journeys support individualized pacing and continuous development, allowing employees to engage in professional growth without disrupting their daily responsibilities. By integrating SAP learning into everyday workflows, organizations can foster sustainable learning habits and equip employees with the skills needed for long-term performance.

In short, AI won’t replace human learning; it will enhance it, but only if we apply it with purpose. As leaders, we must measure success not just in speed, but in sustained capability.

Markus Marsch is global head of Product and Solution Learning for SAP Industries & Experiences.