Our comments:
Trainer
Significant Impact of AI on the Job
40%
level of automation
0%
100%
This section reviews the 3 main tasks associated with the job studied and assesses the potential level of automation induced by AI ('AI Automation Impact').
The modeling uses 8 criteria detailed on the 'Methodology' page.
The modeling uses 8 criteria detailed on the 'Methodology' page.
| Tasks | AI Automation Impact |
|---|---|
| Design and facilitate training sessions | Significant |
| Assess training needs and adapt content accordingly | Moderate |
| Ensure post-training follow-up and evaluate participants' achievements | Significant |
Through our research, we have identified two pivotal categories of skills that will be impacted by AI-driven automation :
- 'At-risk skills,' which are likely to become obsolete due to their susceptibility to automation
- 'Future-proof skills', which are projected to retain their value and resist automation, thereby ensuring their relevance in the forthcoming job market.
| At-risk Skills | |
|---|---|
| DesignTraining Sessions | The advent of AI-based tools could potentially automate training session design to a certain extent. AI can generate tailored content, personalize learning resources based on each learner's needs, and even create interactive scenarios. This will reshape the competency from a manually intensive task to a more supervisory or strategic role, |
| Conducting Training Sessions | Online learning platforms, MOOCs, chat agents, and other technological tools may reduce the need for human facilitators to convey information. The movement toward virtual training environments could change the focus of this competency, emphasizing the ability to design and manage online learning experiences rather than traditional classroom facilitation. |
| Future-proof Skills | |
|---|---|
| Assessing Training Needs | The human added value in the training realm increasingly lies in analysis, assessment, and adaptation rather than in direct creation and facilitation. While AI can provide data, the interpretation of this data and the understanding of contextual nuances require human intervention. |
| Ensuring Post-training Follow-up and Evaluating Achievements | AI can collect data on learner performance, but understanding why some concepts are well assimilated while others are not, and adapting the training accordingly, requires human expertise. Moreover, validating skills in real-world scenarios is often better assessed by human experts. The post-training follow-up and evaluation of acquired skills entail not only collecting and analyzing data but also engaging with learners to ensure that the training objectives have been met and to identify areas for improvement. |
How does AI impact this job type ?
Get the full analysis
The role of a trainer can benefit from the integration of AI to enhance the effectiveness of training sessions and personalize the learning experience. For example, AI can dynamically tailor training content based on specific learner needs or provide in-depth analytics on areas for improvement. Furthermore, AI-based post-training tracking tools can offer real-time feedback, enabling a faster assessment of learning outcomes.
However, engagement, real-time adaptability, and emotional connection during the design and delivery of training are distinctly human elements that AI cannot replace. Trainers play a critical role in creating a supportive and interactive learning environment.