AI is no longer just hype. Modern AI agents are revolutionizing our working world by taking on tasks independently, making decisions and speeding up processes. But this new freedom also brings new risks. How can companies use AI agents responsibly without losing control and trust?
How AI agents are revolutionizing the world of work
AI agents offer enormous potential to support human work and improve business results. They can be integrated into existing work processes to speed up tasks and increase employee performance – for example, through systems such as IBM‘s SWE-1.0, which efficiently supports developers in finding and fixing software errors.
In addition, AI agents enable the automation of time-consuming routine tasks. IBM’s “AskHR” via watsonx Orchestrate, for example, now automates 94% of HR queries – relieving the HR department and freeing up time for strategic issues.
Another benefit is increased efficiency: AI agents work around the clock, handle multiple tasks simultaneously and accelerate business processes. Autonomous agents for sales and service – for example through the integration of Salesforce components – help our customers to increase productivity and security at the same time. BI2run provides support with strategic consulting and implementation.
Last but not least, AI agents also improve decision-making by linking information from various sources and providing well-founded, personalized answers. In collaboration with an international life sciences company, IBM accelerated the creation of comprehensible, fact-based technical documentation.
Potential risks – but also clear solutions
As much potential as there is in AI agents, it is also important to use them responsibly. This is not about stirring up fear – but about conscious control. Companies that use AI proactively and prudently will benefit twice over in the long term: through innovation and through trust.
Typical risks in dealing with AI agents:
- Building trust: AI agents are increasingly working autonomously – this can create uncertainty. Clear transparency about decisions and their basis is therefore crucial.
- Data security: Autonomous systems often access sensitive information. Governance tools such as watsonx.governance help to ensure data protection and compliance throughout.
- Error prevention: Like humans, AI can draw the wrong conclusions. This makes structured test procedures and human control mechanisms (“human-in-the-loop”) all the more important.
- Explainability: AI decisions should remain comprehensible. Tools for model transparency and training documentation are essential for this.
New challenges for companies
The implementation and use of agent-based AI systems entails a number of specific challenges that are particularly pronounced due to their complexity and openness.
1. Evaluation
A key challenge lies in evaluating the performance and accuracy of AI agents. Due to the high system complexity and the open structure of these systems, it is difficult to objectively measure how well an agent works. This problem has increased significantly compared to conventional systems.
2. Mitigation and maintenance
Another problem is reliably detecting faults or maintenance requirements in the system. The complexity and openness of agent-based systems makes it difficult to identify sources of errors and initiate targeted measures to rectify them. This challenge has also increased in comparison to traditional system architectures.
3. Reproducibility
The challenge of reliably reproducing the behavior of an agent or its results is new. This is often not possible because the tools or resources required for execution have been changed or are no longer available. The openness of such systems plays a key role here.
4. Responsibility (accountability)
It is particularly difficult to clearly assign responsibility for certain actions of an agent-based AI system. This problem is exacerbated by the complexity and openness of the systems, especially as components from different providers are often used.
5. Regulatory conformity (compliance)
Finally, compliance with legal regulations is a significant hurdle. As agent-based AI systems are very complex and are often not structured transparently enough, the necessary information basis to ensure full compliance is often lacking. This challenge is also exacerbated by the openness of the systems.
Social impact of AI:
The growing use of AI systems is not only accompanied by technological advances, but also by far-reaching social changes. For example, the perceived performance level of AI compared to humans can affect the self-esteem of workers and undermine their dignity. The ability to make independent decisions – human freedom of action – could also be restricted by the increasing autonomy of AI systems.
In addition, the widespread use of AI has a high potential for job losses, as many complex tasks could be automated. Finally, the environment is also affected: The complexity and redundancy of AI processes can lead to unnecessary resource consumption and a higher environmental impact.
These developments show: AI offers enormous potential – at the same time, its social impact must be considered and shaped responsibly.
Trustworthy AI starts with governance, ethics and technology
At BI2run, we accompany companies on the path to trustworthy AI. Together with our technology partner IBM, we rely on a holistic approach that combines ethical principles, clear governance structures and specialized technologies. For us, the focus is on implementing responsibility in day-to-day business – supported by tools such as watsonx.governance for transparent management processes along the entire AI lifecycle.
This is supported by scalable workflows for data management, data protection and model responsibility – without inhibiting our customers’ innovative strength. Our role here is to ensure targeted implementation, guarantee the necessary transparency and create trust – from technical implementation to strategic consulting.
Responsibility, safety and education as the basis for scalable AI
Our AI consulting services also aim to combine ethical responsibility with entrepreneurial added value. We not only help companies to “do AI right”, but also to “do the right AI”. This includes practical methods such as transparency analyses, action chain evaluations, safety guidelines against hallucinations or unintentional data disclosure and the use of explainable AI.
Human control is particularly important to us: with the “human-in-the-loop” principle, we ensure that feedback and assessments by humans are actively integrated into AI processes. At the same time, we promote skills development through training courses, e.g. on IBM watsonx, or through our enablement program for customers.
With BI2run, companies not only gain access to state-of-the-art AI technologies, but also an experienced partner who consistently thinks about responsibility, security and scalability.
