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In the digital transformation, innovative technologies are becoming increasingly important for organisations in all sectors. The tool test in KIROI step 2 is a key process for experiencing AI solutions in practice and evaluating their potential applications in a targeted manner [1]. Decision-makers can use this procedure to comprehensively check which tools actually offer added value for their organisation. In this article, you will learn how to optimise the tool test, which industry examples illustrate it particularly well and how you can use it to support your digital project.
What characterises the tool test in KIROI step 2?
The tool test in the second step of the KIROI process makes it possible to not only analyse new AI innovations in theory, but also to test them in realistic scenarios [1]. This involves more than just functional testing. User-friendliness, integration capability and operational compatibility are assessed in a binding manner. This provides a precise picture of which systems are actually suitable as digital helpers in everyday working life.
Many companies come to us with a central challenge: they have identified numerous AI tools, but don't know which ones really fit their processes. A structured tool test answers precisely this question. It reduces the risk of wrong decisions and saves time during implementation.
In the financial sector, for example, banks are testing various AI-supported fraud detection systems. They are not only testing accuracy and speed. Integration into existing security infrastructures and adherence to compliance requirements also play a role. A thorough tool test avoids expensive incorrect implementations.
In the healthcare sector, hospitals use the tool test to evaluate diagnostic assistants. They want to ensure that the systems actually support medical decisions without jeopardising medical responsibility. The tool test answers such critical questions.
In the retail sector, companies are testing AI systems for inventory management and customer analysis. They want to know whether the tools optimise their supply chains and increase sales. A systematic tool test provides clear answers to such business questions.
Systematic preparation and implementation of the tool test
Every successful tool test begins with a thorough analysis of the specific requirements [1]. The precise definition of use cases forms the starting point. Only when the scenarios in which a tool is to be effective have been determined can the choice be targeted and efficient.
The preparation phase of the tool test
Firstly, objectives and success criteria must be clearly defined. What should the tool achieve? Which metrics show success? You answer these questions before the tool test. This creates a common benchmark for everyone involved.
In the manufacturing industry, for example, companies are testing maintenance forecasting systems. They define in advance that the system should reduce downtimes by 30 per cent. The tool test then measures whether this KPI is achieved.
In logistics, companies are testing AI systems for route optimisation. They require the tool test to demonstrate savings in transport costs. Acceptance by drivers is also measured.
In marketing, agencies test AI tools for target group analyses. They want to know whether the tool test results in better segmentation and higher conversion rates. Transparent criteria make the tool test meaningful.
BEST PRACTICE with one customer (name hidden due to NDA contract)A telecommunications company carried out a structured tool test for AI-supported customer service bots. The customer defined in advance that the tool test had to show whether the bot could solve 70 per cent of enquiries independently. Real customer interactions were simulated and systematically documented in the tool test. The result helped the company to select the best system and successfully plan the implementation.
The tool test with realistic scenarios
The tool test only works with real data and practical situations [1]. Theoretical test environments often lead to incorrect results. Users report that realistic test conditions significantly improve the quality of the tool test.
In the insurance industry, companies carry out tool tests with anonymised customer files. The tool test examines how quickly and accurately AI systems process claims. The result provides reliable information for practical use.
In legal advice, law firms are testing AI tools for file evaluation. The tool test uses real case files to check whether the system records legal issues correctly. This creates confidence in the subsequent application.
In the HR sector, companies carry out tool tests for recruiting systems. They use real applicant data to check whether the AI tools reliably identify talent. The tool test avoids bias and shows actual performance.
Stakeholder participation in the tool test
A good tool test involves various departments at an early stage [1]. Technicians, users and managers see together how the system behaves. This broad feedback helps to make better decisions.
As part of the tool test, an energy supplier can test various software solutions that optimise consumption and reduce costs [1]. The focus is on user-friendliness and interface compatibility as well as integration into existing processes. Training and employee involvement also ensure acceptance and valid feedback from the field.
In sales, teams can work directly with AI systems during tool testing. They provide feedback on usability and the time required. This perspective is often crucial for successful implementation.
Practical tips for a successful tool test
To ensure that the tool test can provide targeted impetus, it is advisable to consider the following points [1].
Multidimensional evaluation in the tool test
Check tools not only technically [1]. User-friendliness and available support also play a role. A tool test that takes all dimensions into account leads to better decisions.
In the education sector, schools and universities are testing AI tutoring systems. The tool test examines learning effectiveness, user-friendliness for pupils and teacher functions. Only this holistic assessment shows whether the system is really suitable.
In media production, studios evaluate AI tools for video editing. The tool test measures the quality of the results, speed and integration capability with existing workflows. A multidimensional view is required.
In architecture firms, professionals carry out tool tests for AI-supported design systems. They test accuracy, creativity and compatibility with CAD software. Multi-perspective evaluation provides clear results.
Systematically record feedback in the tool test
Document the results transparently and use them for targeted adjustments [1]. A tool test without structured evaluation wastes valuable knowledge. Clear documentation enables faster decisions to be made later.
In the hospitality industry, hotel chains collect guest feedback on AI concierge systems during tool tests. They document which requests the system responds to well and where problems occur. This data guides optimisation.
In the transport sector, companies carry out tool tests for AI-supported traffic forecasts. They systematically record how accurate the forecasts are and where adjustments need to be made. Structured feedback improves the system.
In retail, companies are documenting how AI personalisation systems are changing the customer experience in tool tests. They measure engagement and sales impact. Transparent recording shows the real benefits.
Time frame and resources for the tool test
A good tool test requires sufficient time and suitable resources. Tests that are carried out too briefly do not deliver reliable results. Therefore, plan the effort realistically.
In the pharmaceutical industry, tool tests for AI drug research take several weeks. Scientists need time to analyse large amounts of data. A sufficiently long tool test is essential here.
In financial analysis, banks carry out tool tests for algorithms that predict market developments. These tests require several market cycles for a valid assessment. Impatience in tool testing leads to inadequate findings.
In sport, clubs use tool tests for AI systems to analyse players. They need several seasons to collect meaningful data. An appropriate tool test timeframe is necessary for credibility.
Tool testing and digital transformation
A structured tool test is more than just a technical exercise. It accompanies your company during the transition to AI-supported working methods. The tool test creates trust and clarity during this important change.
Companies often report that a good tool test promotes internal discussions. Teams discuss which requirements are really important. These discussions lead to a better strategy for AI implementation.
A tool test also reduces resistance to new technology. If employees are actively involved in the test, they understand the benefits better. This facilitates acceptance and utilisation later on.
BEST PRACTICE with one customer (name hidden due to NDA contract)A large manufacturing company carried out a comprehensive tool test for production optimisation systems. All departments were involved, from the workshop to management. The structured tool test showed that one particular system resulted in a 25 per cent efficiency gain. The company implemented the system much faster because all stakeholders had built up trust during the tool test and understood the benefits.
Overcoming common challenges in tool testing
Not every tool test runs smoothly. Some companies struggle with data quality or a lack of expertise. However, these challenges can be solved with the right preparation.
Data quality and availability in the tool test
Many companies have difficulties providing high-quality test data. The tool test suffers if data is incomplete or unrepresentative. Good data preparation is therefore a prerequisite.
In retail, companies collect sales data for the tool test of AI recommendation systems. They must ensure that the data is up-to-date and complete. Only then will the tool test show realistic results.
In the HR sector, companies carry out tool tests with anonymised personal data. Data protection is important here, but the tool test still needs meaningful information. This balancing act must be mastered.
In medicine, clinics test AI systems with patient data under strict safety regulations. The tool test only works with real data, but must be GDPR-compliant. Good preparation resolves this conflict.
Expertise and competence in tool testing
Sometimes companies lack the technical expertise to carry out a tool test properly.
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