AIROI - Artificial Intelligence Return on Invest
The AI strategy for decision-makers and managers

Business excellence for decision-makers & managers by and with Sanjay Sauldie

AIROI - Artificial Intelligence Return on Invest: The AI strategy for decision-makers and managers

Start » Test AI tools now: How decision-makers find the best system

19 June 2026

Test AI tools now: How decision-makers find the best system

Digital Leadership Automation Digital transformation Artificial intelligence Artificial Intelligence Special Topics

Why do so many leaders fail to find the right digital tool for their company, even though the market seemingly offers endless possibilities?

This question concerns numerous executives in companies of various sizes, because the selection of intelligent systems grows daily and the promises of the providers often sound enticingly similar. Who now Testing AI tools The decision-maker faces a complex challenge that goes far beyond technical specifications. The real art lies in distinguishing between marketing promises and actual added value. Decision-makers must not only know their own requirements precisely, but also understand the dynamics of the market. This article will guide you through the entire evaluation process and provide insights for informed decision-making.

Understanding the starting point: Why systematic action is crucial

Before delving into technical details, you should first analyze the basic situation in your company and understand which processes could actually benefit from intelligent automation. Many executives start their search with a vague idea of what they want to achieve, and end up frustrated because the chosen system does not meet their expectations. The reason is often a flawed needs analysis that should have been carried out at the very beginning of the selection process.

For example, a medium-sized manufacturing company was looking for a solution to optimize its quality control. The management team tested various image recognition systems without first defining which types of errors should be prioritized. A retail company, on the other hand, invested significant resources in a forecasting system that was not compatible with the existing warehouse management system. A logistics service provider experienced a similar situation when the chosen route optimization tool was unable to meet the specific requirements of its fleet of vehicles.

Best practice with a AIROI customer

An internationally operating machine engineering company faced the challenge of accelerating its bid creation process while simultaneously reducing the error rate. Previous attempts to find a suitable system had failed multiple times because the technical requirements were not clearly formulated. As part of the transruptions coaching support, we first developed a detailed process map that identified all relevant interfaces. It became clear that the real problem lay not in the technology but in unclear internal responsibilities and redundant work steps. After a three-month analysis phase, a pilot project with a clearly defined scope was launched. The responsible parties tested three different providers in parallel under realistic conditions. In the end, the decision was made to choose a system that was not originally on the shortlist but best met the specific requirements.

Testing AI tools now: The structured evaluation process

A systematic evaluation process begins with the definition of measurable success criteria that later serve as the basis for evaluation. These criteria should encompass both quantitative and qualitative aspects and be supported by all relevant stakeholders. The involvement of various departments is essential because different perspectives can uncover blind spots.

In the financial sector, decision-makers often report the challenge of reconciling compliance requirements with the speed of innovation. An insurance company therefore developed a multistage testing process that integrated regulatory audits at an early stage. A banking institution, in turn, relied on so-called sandbox environments in which new systems could be tested in isolation. A financial services provider from the advisory segment used external expertise to ensure the objectivity of the evaluation.

Define technical requirements precisely

The technical specification forms the foundation of any successful implementation and should therefore be prepared with utmost care. This involves not only obvious factors such as computing power or storage capacity, but also more subtle aspects such as scalability, maintenance effort, and the future viability of the chosen architecture.

For example, a healthcare provider had to ensure that the chosen system met strict data protection requirements while also being able to communicate with various medical devices. A pharmaceutical company, on the other hand, needed a solution that could be used both in research and in production. A hospital network, in turn, was looking for a system that could connect different locations without restricting the autonomy of individual facilities [1].

This is how decision-makers find the best system: taking organizational factors into account

In addition to the technical aspects, organizational factors play at least equally important role in system selection. The best technology is of little use if employees do not accept it or lack the necessary skills to use it. Therefore, it is necessary to assess what change management measures will be required already during the evaluation phase.

For example, a trading company realized that the planned automation of customer support would require significant training measures. A energy provider, however, found that the existing IT department did not have the necessary expertise and needed external support. A media company, in turn, had to first overcome internal resistance before the actual implementation could begin.

Properly set up pilot projects and test AI tools

Pilot projects offer the opportunity to test a system under realistic conditions without exposing the entire company to risk. However, the success of a pilot project depends significantly on how well it was prepared and what framework conditions were defined. A too narrow-scoped pilot may not yield sufficient insights, while a too broad-scoped pilot quickly exceeds the available resources.

For example, a automotive supplier started a pilot in a single production line and gradually expanded it to other areas. A telecommunications provider, on the other hand, tested different systems in parallel in different regions to generate comparative data. A chemical company, meanwhile, used a simulated dataset to evaluate the system first without any production risk [2].

Best practice with a AIROI customer

A service company in the field of personnel recruitment sought support in selecting a system for automated preliminary qualification of applications. The management had already seen several demos from various providers and was impressed by the presentations. However, as part of the transruptions coaching process, we initially developed a test protocol that depicted the real requirements and did not merely demonstrate the strengths of the systems. It turned out that none of the presented systems fully met the specific industry requirements. We accompanied the company in negotiations with the providers to agree on individual adjustments. A six-week pilot run with real application data ultimately provided the necessary insights for a well-informed decision. The chosen system is now successfully supporting the recruitment process and significantly relieving the specialist departments.

Providers give it a critical rating: What you should pay attention to

The evaluation of providers goes far beyond comparing feature lists and pricing models. Decision-makers should also consider the provider’s stability, industry experience, and the quality of support. A cheap system can quickly prove expensive if the provider does not provide the necessary support for implementation.

For example, a construction company experienced that the chosen provider was taken over by a competitor shortly after the contract was signed and stopped product development. A textile company, on the other hand, benefited from working closely with a specialized provider who understood the industry-specific challenges. A food manufacturer, in turn, chose a larger provider deliberately because long-term stability was more important than individual functional advantages [3].

Use references and testimonials

References from existing customers can provide valuable insights into the actual performance of a system and should therefore be actively sought. It is important not only to contact the references suggested by the vendor, but also to independently research reviews. Industry associations and specialist forums often provide authentic assessments from users.

One real estate company, for example, organized a visit to a comparable company that was already using the preferred system. A transportation company, on the other hand, used a network of industry contacts to gather first-hand experience. A craft business, in turn, found direct exchange with other users through an online forum, providing valuable practical tips.

Calculate costs realistically and quantify added value

The total cost of an implementation encompasses far more than the obvious license fees and should therefore be calculated comprehensively. Training costs, integration effort, ongoing maintenance, and potential productivity losses during the deployment phase must also be taken into account. Only in this way can a realistic business case be created that withstands critical scrutiny.

For example, a furniture manufacturer underestimated the effort involved in data migration and had to subsequently adjust its budget. A sports goods retailer, however, planned a buffer for unforeseen challenges from the outset. A publishing house, in turn, developed a phased implementation plan that spread the costs over a longer period, thus minimizing the financial risk.

My AIROI Analysis

Selecting the right intelligent system is like solving a complex puzzle where technical, organizational, and strategic factors must all be taken into account. Anyone who wants to test AI tools should be aware that success does not depend solely on the technology; it is significantly determined by the quality of the selection process and the accompanying change management measures. In my many years of assisting companies with digitalization projects, I have repeatedly observed that the most successful implementations were those in which sufficient time was invested in preparation.

The transruptions coaching accompaniment has proven to be a valuable support in this process, as it helps uncover blind spots and structure decision-making processes. Decision-makers often report that they have recognized aspects through the external perspective that would have been overlooked internally. At the same time, experience shows that rushing into things almost always leads to additional costs and frustration, while a methodical approach delivers better results in the long run.

Decision-makers do not find the best system by chance, but through systematic evaluation and critical reflection on their own requirements. The investment in a structured selection process is proven to pay off many times over, because it avoids misjudgments and increases acceptance within the company. The key lies in understanding technology as a tool that supports people, and not as an end in itself.