Background and initial situation

An international technology company with over 1,000 employees wanted to improve the efficiency and effectiveness of its internal training and development programs. Previously, the challenge was that the training department was not always able to respond to individual learning needs in a timely manner and create a personalized learning environment. The standardization of training measures often led to insufficient involvement and lower employee satisfaction.

Aims of the project

The main goal of the project was to personalize and optimize the training and development experience for employees. This was to be achieved by automating training workflows and providing real-time support. Specific objectives include:

  • Improve personalization of training content.
  • Increase employee satisfaction and engagement.
  • Automate the answering of frequently asked training questions.
  • Reduce time spent by training staff on administrative tasks.

3. Solution implementation

To achieve these goals, the AI was integrated into the company’s existing learning and development systems. The implementation process followed a structured plan:

a) Needs analysis and planning:
  • Identification of training needs: Analyze frequently asked questions and common learning needs through feedback and employee surveys.
  • Determination of integration points: The IT department identified the relevant systems, including the learning management system (LMS) (e.g. Moodle) and the HR management platform (e.g. SAP SuccessFactors).
b) Development and integration:
  • Creation of conversation workflows: Development of specific workflows to answer frequently asked training questions and provide personalized learning paths.
  • Technical integration: The AI was seamlessly integrated into the LMS and the HR platform.
c) Training and knowledge database:
  • Data import and structuring: Historical training data and frequently asked questions were used to expand the AI knowledge base.
  • Regular updates: Planning regular updates to the knowledge base to ensure the relevance and accuracy of the information provided.

Application example and process scenario

Scenario: Individual learning support for an employee

  • Step 1: An employee interacts with the embedded chat system on the LMS to get assistance with a training task.
    – Employee: “Hi AI, I need help understanding the latest module on data security. Do you have any learning materials or tips?”
  • Step 2: The AI processes the request and retrieves relevant learning materials and tips from the LMS.
    – AI: “Hello, I can help you. The data security module contains various resources. Would you like some additional reading recommendations or an overview of the important points of the module?”
  • Step 3: The employee states exactly what they need.
    – Employee: “Please provide an overview of the important points.”
  • Step 4: The AI gives an overview of the most important points of the module on data security with references to specific learning materials.
    – AI: “No problem. Here are the key points of the data security module:
    1. basic principles of data security.
    2. technical measures for data security.
    3. legal requirements and compliance.
    You can read the details on each of these points in the resources provided. Would you like more details or additional learning materials?”
  • Step 5: The employee decides on additional learning materials.
    – Employee: “Yes, please send me additional learning materials.”
  • Step 6: The AI provides additional learning materials and saves the request for future recommendations.
    – AI: “Sure, here are some more in-depth articles and videos on the key points. These should help you understand the module better.”

Results and benefits

Following the implementation of AI, there were clear improvements in the training and development processes:

  • Personalized learning experience: Employees received customized training content and support tailored to their individual learning needs.
  • Increased employee engagement: Improved personalization and accessibility of learning materials led to higher engagement rates and satisfaction.
  • Efficient use of resources: Training staff were able to focus on more strategic tasks as repetitive requests were efficiently automated.
  • Faster problem resolution: The average turnaround time for training questions was reduced by 60% and employees were able to access the information they needed more quickly.

Conclusion

The integration of AI into the company’s training and development processes led to a significant increase in efficiency and satisfaction for both employees and the training department. By automating repetitive tasks and providing personalized learning paths, valuable time was freed up and the quality of the training experience was significantly improved. Future enhancements could include the implementation of additional languages to support a global team and the integration of advanced analytics tools to further optimize training programs.