Many AI agents make a strong first impression. But it’s only when you ask critical questions that you can see how capable they really are. This article explains how to identify effective AI agents, which factors are often underestimated, and which criteria are crucial when making a selection.
AI must work within the workflow
“If you digitize a bad process, you end up with a bad digital process.” This oft-cited principle applies particularly to AI. According to a McKinsey study, over 70% of digitization projects fail not because of the technology, but due to flawed processes and a lack of integration.1
A powerful AI agent demonstrates its value when integrated with existing processes. Rather than simply taking over individual tasks, it improves entire workflows.
Therefore, please keep the following points in mind:
- Seamless integration with existing tools (e.g., CRM, ERP, support systems)
- User-friendliness, so that teams actually use the AI
- Automation of end-to-end processes, not just individual tasks
In its guidelines on the use of AI, the European Commission also emphasizes that “trustworthy AI must be embedded in existing organizational processes in order to create real added value.” 2
Data quality determines the value
An AI agent is only as good as the data it accesses. This fact is often underestimated. Gartner predicts that by 2027, around 60% of all AI projects could fail due to poor data quality. 3
Important questions you should ask providers:
- Where does the data come from?
- How up-to-date and comprehensive are they?
- How are they maintained and validated?
Are these proprietary datasets that have been built up over the years, or are they from generic sources?
It’s especially worth taking a closer look at purchased data. According to the OECD, transparency and traceability of data sources are key prerequisites for trustworthy AI. 4
Another often-overlooked aspect is contextualization. Good AI agents understand the context of data within a given use case.
Openness is often underestimated
Many companies fall into the trap of closed systems. In the short term, they seem convenient. In the long term, they create dependencies.
Open systems offer clear advantages:
- Data portability: You retain control over your data
- Flexibility: Integration with other tools and platforms
- Scalability: Adapting to growing demands
The General Data Protection Regulation (GDPR) explicitly strengthens the right to data portability.5 This is an aspect that is becoming increasingly important when selecting AI tools.
Closed systems, on the other hand, can stifle innovation and make it difficult to switch to better solutions. This poses a significant risk, especially in the dynamic AI market.
Other important decision-making criteria
In addition to these three key points, there are other factors that are often overlooked:
1. Transparency and explainability
Can the provider provide a clear explanation of how the AI arrives at its results? According to the EU AI Act, explainability of decisions is increasingly becoming a regulatory requirement.6
2. Security and Privacy
Data protection plays a key role, especially in Europe. Check:
- Where is the data stored?
- Are they used for training purposes?
- Do the processes comply with GDPR requirements?
3. Maintainability and Support
AI is not a "set-and-forget" system. Regular updates, monitoring, and support are crucial for long-term success.
4. Cost-benefit ratio
It’s not the number of features that counts, but the actual impact. A Harvard Business Review analysis shows that successful AI projects are characterized above all by clearly measurable business value.7
Bottom line: Practical value rather than a showcase of features
Ultimately, you don’t need an AI agent with the longest list of features. What matters is whether it works in your day-to-day workflow, is based on high-quality data, and offers you long-term flexibility.
The best AI is the one that reliably solves problems.
Sources:
1 - McKinsey – Unlocking Success in Digital Transformations
2 - European Commission – Ethics Guidelines for Trustworthy AI
3 - Gartner – Lack of AI-ready data jeopardizes AI projects
4 - OECD – Principles on Artificial Intelligence
5 - European Union – General Data Protection Regulation (GDPR), in particular the right to data portability (Art. 20)
6 - EU AI Act - Overview and Regulatory Requirements
7 - Harvard Business Review – Prioritizing AI Investments That Create Real Value
