The training trap: Why 80 percent of all AI training courses fall flat

Success rates for training courses are often alarmingly low: only 20 percent of training courses lead to lasting changes in behavior. ML Gruppe expert Beate Gleitsmann explains how to do better.

With the AI Act, the EU has also introduced a regulation for AI training enacted. Companies are now providing their employees with masses of training – because they have to. The idea behind the initiative is to safely promote innovation in artificial intelligence in the EU.

However, this effect often fails to materialize. Studies show that only ten to 20 percent of participants apply what they learn in training courses to their work in the long term. Even worse: employees forget up to 70 percent of the newly acquired knowledge within a month if it is not actively applied. Yet it would be so important for companies to promote innovation and remain competitive.

How can it be done better?

How knowledge becomes competence

As part of practice-oriented contract research, numerous empirical studies, workshops and hundreds of interviews were conducted in order to understand and specifically promote transformative behaviour in organizations.

The central question is always: How can training content (not only for AI training) be integrated into everyday working life in such a way that employees not only absorb new information, but also actively transfer and apply the knowledge they have acquired in their daily work?

Particular emphasis is placed on the transformative behaviour of each individual – an approach that focuses on the active role of employees in the change and further development of the organization.

Knowledge becomes competence when theoretical knowledge from training courses is transferred into practical applications. This happens through the combination of skills, experience and motivation to successfully master specific tasks or challenges.

Competence therefore does not arise solely from the presence of knowledge, but from the ability to apply this knowledge in specific situations and adapt it flexibly.

For example, an employee who has received training on the use of new software initially acquires knowledge about its functions. However, competence only develops when they put what they have learned into practice, solve problems and adapt to new circumstances.

However, people are often skeptical about innovations, especially when they change established work processes. Active change management is therefore essential in order to minimize uncertainty and promote acceptance.

Only in this way can training content become a transformative force in companies.

Successful training through the combined use of four instruments

The success of training depends on four factors that should be considered simultaneously at the start of training in a company. Two of these factors are personnel-related, while the other two are situational company factors.

The four factors for training success

Using the example of an AI training for employees let’s run through these four factors.

1. personal qualification

Personal qualifications include the skills and competencies required to use AI effectively, such as technical, methodological and social skills.

• Professional expertise means knowing how AI tools and technologies can be used in the company.
• Methodological competence describes the application of suitable methods for using AI to solve problems. Methodological competence requires practical application. Theoretical training without a direct connection to the company makes knowledge transfer difficult. Employees need to work with real data and use cases in order to develop their skills effectively.
• Social skills are required to promote collaboration and communication, especially when communicating AI results in a team. This qualification can be promoted within the company through regular training, education and further training measures.

However, successful training courses are tailored to the needs of the learners. Employees learn differently – some prefer interactive formats, others prefer structured learning modules.

AI and digitalization training should therefore take different learning styles into account, for example by combining online courses, hands-on workshops and collaborative learning groups. When employees experience for themselves how AI improves their day-to-day work, their acceptance of new technologies increases.

Training is not one-size-fits-all, AI and digitalization skills vary greatly between employees. A standardized training program for everyone leads to inefficiency. Instead, different modules with different levels of difficulty should be offered. While managers need to understand the strategic benefits of AI, IT employees need in-depth technical knowledge.

Personalized training paths significantly improve the learning experience.

2. individual motivation

Individual motivation describes the will to use AI in daily work, based on the perception of usefulness and expected benefits. It can be increased through material (monetary rewards, salary increases, bonuses) and immaterial incentives (praise, recognition, ceremonial awards).

In principle, the more desirable the incentive, the higher the motivation. Employees are immediately highly motivated if the training content is relevant to them. The content of a training course must therefore be directly linked to the employees’ work tasks and the company’s objectives. Training on generic topics without a clear link to practice leads to frustration and is quickly forgotten.

Companies should ensure that the training content is specifically tailored to the company’s digital transformation processes. For example, training on AI-supported data analysis only makes sense if employees have to work with and analyze data in their daily work.

Skepticism towards AI can have a significant impact on motivation. Without clear communication of the benefits and concrete success stories, it is difficult to convince employees of the usefulness of the training. Practical examples and interactive applications can help here.

3. provision of resources

The provision of resources includes all technical, time and personnel resources so that employees can engage with the training content. Learning is a continuous process.

Without accompanying resources such as tutorials, introductory materials or practical use cases, there is no opportunity to consolidate knowledge. Companies must ensure that employees have the necessary resources to use AI sustainably. This includes licenses, API access and information on data protection-compliant use.

Companies must ensure that their employees have access to AI tools that meet the requirements of the AI Act and the GDPR. It is also important to continuously inform employees about AI developments and opportunities.

Time is an essential resource for further training. AI training courses must be designed in such a way that they can be integrated into everyday working life. Microlearning and flexible learning times enable employees to continue their training despite a heavy workload.

And finally, there is a need for freedom: employees often have busy schedules and little time for further training. Companies should integrate training into everyday working life, for example through short, modular learning units (“microlearning”) or through AI-supported, personalized learning plans that can be used flexibly.

It also takes time to apply what you have learned. There is no room for trial and error in everyday life. This is where targeted projects help, in which employees apply the methods they have learned directly in practice. For example, companies could organize innovation workshops in which teams develop and implement their own AI applications for their department

4. corporate cultural may & should

Companies invest in training, but rarely measure its success. Clear evaluation mechanisms should be established after AI or digitalization training. In addition to traditional feedback forms, practical projects could serve as proof of performance. For example, it can be checked whether employees are actually using AI-supported tools in their work processes. The results can be used to further develop future training courses.

The social desirability of AI use is also expressed in laws and regulations, such as management principles, company agreements and communication within the company. These include expectations placed on employees and taken for granted, as well as formal policies that support or restrict the use of AI. This component is critical to ensure that employees feel empowered to use AI tools and that their use is actively encouraged by management.

Closely linked to this is the role model function of managers. They should exemplify the use of AI technologies and show how AI tools can be used sensibly in everyday working life to complete tasks more efficiently. This reduces fears of contact and promotes acceptance.

These four factors influence each other. If a person is highly qualified in AI, they will make independent efforts to reduce bureaucratic obstacles and expand their decision-making powers. If a person is aware that AI is desired in the company, their motivation to use AI increases. The AI qualification influences motivation, as knowledge and skills create security and self-confidence.

Motivation in turn leads to the person having an interest in acquiring the necessary skills to use AI. A person who uses AI to automate repetitive tasks and can therefore work faster is motivated because they are convinced that their efforts will lead to positive results such as recognition or rewards.

The isolated use of a single tool is therefore not enough. All four factors must be addressed in an integrated manner in order for the implementation of AI-oriented behaviors in the company to succeed.

Do you need effective further training?

ML Gruppe supports you throughout your employees’ entire training journey. Through emotional anchoring and rigorous measurement, we ensure the success of the training measures.