AI Training Guide for L&D: Introduction

AI Training Guide for L&D: Introduction

As of 2026, organizing AI training processes is undoubtedly the most critical task within existing L&D responsibilities. Our guide provides you with real-world applications to support you through this challenging journey.

This guide is expected to be published in four sections. These sections will cover the entire journey from the initial stages to end-of-process evaluation and future planning.

The first thing to understand about AI is the reality that needs do not emerge as easily as one might think. Teams and companies may not find the desired usage patterns, or even the right solution, on the first try. Two prominent methods can be used to resolve this confusion.

In the first method, a pilot mixed group is established within the company. This group undergoes the training process, helping to identify how training should be structured for specific teams; future plans are then shaped based on this group's experience.

Two key aspects are important here: First, the individuals selected for this group must have end-to-end command of their business processes within their domain and be accustomed to basic technology usage. Second, the instructor implementing this training process must have strong business analysis capabilities, preferably a background in computer engineering, and prior experience in managing consulting processes.

Fundamentally, the goal is to transfer the instructor's expertise and experience as quickly as possible, allowing participants to combine this knowledge with their own business processes to create meaningful insights. An ideal training process, like in any other field, consists of the optimized alignment of the right instructor, the right curriculum, and the right audience.

In the second method, an experienced consultant applies a gap analysis (gap analysis) to the organization. The outputs are evaluated with the management team, structures requiring transformation are clearly identified, suitable technologies are determined, and business processes are redesigned. These processes are then implemented team by team through training and consulting activities.

The outputs are reported with a focus on efficiency; the human hours and cost items saved are determined. Future plans are defined by optimizing the roadmap of the current AI ecosystem. These steps are recorded in the annual plan.

The most important consideration here is, first, that the consultant has a strong track record in digital transformation, and second, that managers have established the necessary foundation for this transformation process. Managers should have a clear understanding of the importance of data and how AI processes handle it, enabling them to recognize what they are permitting and what they are requesting.

In summary, the fundamental motivation in AI training processes is for participants to draw conclusions and insights related to their own business processes from the stream of knowledge provided by the consultant or instructor.

Training and consulting processes that do not develop around this motivation turn into inefficient endeavors characterized by impressive rhetoric but unmeasurable results, and a high post-process dependency on the instructor or consultant.

See you in the next section.