Getting started in practice: Discovering and testing tools
An essential aspect of the second step is to become familiar with and practically apply the diverse range of tools and technologies. This is not just about evaluating individual tools, but also about gaining an understanding of how they work and their specific fields of application. In practice, targeted experimentation proves to be the key to success. Those who test various solutions quickly recognise which technologies help to optimise processes or unlock new potential.
Industrial application examples
In the field of logistics, the use of AI-powered tools enables, for example, more precise demand forecasting and optimised route planning. Companies that have tried these technologies report measurable efficiency gains and improved resource utilisation. Likewise, AI tools in marketing offer the possibility to automatically generate personalised campaigns, thereby refining customer engagement – a great advantage in competitive markets.
KIROI BEST PRACTICE at company XYZ (name changed due to NDA contract) In industry, production planning has been significantly improved through the targeted use of AI-based analytical tools. Following an extensive testing phase, a tool was identified that independently creates forecasts based on historical data, thereby optimising inventory management. This allowed warehouse costs to be considerably reduced while simultaneously increasing delivery capability.
AI tools are also opening up new possibilities in customer service. Automated chatbots that respond to natural language, for example, can answer queries more quickly and thus increase customer satisfaction. However, the continuous adaptation and improvement of these systems requires prior trial and error and learning to develop tailor-made solutions.
KIROI BEST PRACTICE at ABC (name changed due to NDA contract) Here, customer feedback was analysed using AI-driven text analysis tools to identify weaknesses in the service at an early stage. Through regular testing of various tools, a system was implemented that automatically extracts relevant topics and derives recommendations for action for the team.
Methodical approach: Testing with concrete examples
Trying out different tools should be done systematically: first, individual needs are identified, then tools with different focuses are selected and tested in pilot projects. Variations in usability, integration into existing systems, and possibilities for adaptation to the respective industry can be crucial here. Gathering feedback from the user teams is essential.
KIROI BEST PRACTICE at DEF (name changed due to NDA contract) In a production environment, an AI tool was deployed that detects malfunctions early by automatically analysing machine data. Following intensive testing, it was found that the intuitive visualisation of data could be used with just a few steps by employees without deep technical knowledge. This result led to rapid implementation across the entire plant.
Furthermore, experimenting with different AI tools allows for a better assessment of which investments will truly yield high returns and which are more likely to be mere gimmicks. Particularly for companies just beginning to implement technology, hands-on experience offers many learning opportunities and promotes the adoption of new solutions.
Harnessing and understanding technological diversity
The range of available AI tools is broad: language models assist with text creation and brainstorming, image generators create visual content, and data analysis algorithms help to recognise patterns in large amounts of information. It is important to carefully examine how these tools can be integrated into existing workflows and what requirements they must meet.
One example is the use of AI-generated quizzes and interactive learning games, which can significantly improve the efficiency of knowledge transfer, for instance in the education sector. Likewise, compelling audio files or podcasts can be created with speech synthesis tools, opening up new communication channels.
My analysis
The targeted application and experimentation with AI tools are essential for achieving sustainable success within systematic approaches. Only those who actively test the diverse possibilities can identify and effectively use the appropriate tools. Companies benefit when they take the time to try out different technologies and thus continuously refine their individual toolbox.
Further links from the text above:
[1] Step 2 of the KIROI Strategy: Explore practical examples of tools
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