Implementation of Artificial Intelligence (AI)

The use of artificial intelligence (AI) in work processes has become commonplace,

often embedded in standard office software or even offering standalone, purpose-built applications.

  • 1. Task definition and scope

    The AI development process starts with a clearly defined task and project scope. Working with stakeholders and end-users, we understand their requirements and expectations.

  • 2. Data collection and preparation

    Quality data is essential for the success of AI. We identify relevant data sources and collect data to train and validate AI models. Data undergoes pre-processing, cleaning and transformation to ensure accuracy and usability.

  • 3. Model selection and development

    On the basis of the baseline and the available data, we select the appropriate AI models, such as guided learning, unsupervised learning or reinforced learning. Our specialists will develop, test and then fine-tune the necessary models.

  • 4. Training and validation

    In this phase, AI models are trained with the data prepared. We use different performance metrics to assess the accuracy and efficiency of the models. If necessary, we adapt the models and repeat the training process.

  • 5. Testing and deployment

    Once the models meet the desired performance characteristics, we test them thoroughly in real scenarios. We make sure that the AI system works as intended and does not produce biased or biased results. After successful testing, the model is ready to be integrated into daily use.

  • 6. Integration and monitoring

    AI models are integrated into existing systems or applications, making them capable of generating real-time insights and predictions. We will create a robust monitoring system to continuously assess AI performance and identify potential problems.

  • 7. Feedback and improvement

    Continuous feedback from users and stakeholders is essential to improve AI. We collect user feedback and monitor AI performance in real-world situations. This feedback loop allows us to make the necessary adjustments and improvements.

  • 8. Training and skills development

    To support users and ensure successful AI development, we provide training and contribute to user skills development.

Some examples of how artificial intelligence can be used

The use of artificial intelligence or AI is mainly helpful in increasing work efficiency, personalising the customer experience, speeding up the various steps of customer interaction and supporting business decisions with rapidly available information on a daily basis.

For example, AI-based systems can automate processes, AI algorithms can be used to understand and predict customer preferences (enabling personalised service and targeted marketing campaigns), AI can be used to drive business analytics based on big data, enabling informed decisions at all levels.

While AI gives us many new possibilities, we firmly believe that the true potential of AI lies in the collaboration between computer and human - AI systems complement the skills of workers, enhancing their ability to perform everyday tasks.

Cooperation

Our AI development process typically involves a number of steps from task definition to deployment. We do the process in stages, and the scope of each successive step can be better assessed by the execution of the previous one. As a rule, we agree with the client on the form of cooperation through a framework agreement, where the work and the scope of work are agreed upon on a rolling basis over time, according to the deliverables of the previous step.