Responsible AI and Opportunities for HR

AI has reached remarkable milestones in recent years: from the analysis of historical data (descriptive AI) to forecasts of future developments (predictive AI) and decision templates (prescriptive AI) to the creation of new content (generative AI). The future is clearly moving towards the autonomous execution of actions with the help of agentic AI.

Artificial intelligence is part of everyday (working) life for all employees and managers. However, its use in business practice is sometimes still rudimentary and limited to analytical applications. In-depth knowledge of AI and experience in its introduction and use have yet to be built up. This needs to happen while technical development is progressing at an ever faster pace, the range on offer is becoming unmanageable and the pressure to be efficient is increasing. Many companies are introducing AI in the same way they previously introduced software and tool solutions: with a view to (individual) functionalities that can be automated. The aim is to achieve greater efficiency and become more cost-effective.

Structural hurdles for AI

It is precisely at this point that they are now reaching their limits, as the structural requirements are not sufficiently in place: in order to truly exploit the potential of AI, the data basis must be correct, high quality, and transparent. Algorithms and tools must be understood and ethically thought through. And when individual functional solutions are replaced by AI agents, everything must be seamlessly connected.

In reality, however, countless tools and various databases and sources are in use. Automated and manual interfaces either exist side by side, or are missing altogether. Bad processes have been poorly digitized. Some historically grown IT landscapes consume enormous sums of money in maintenance, while functions remain opaque. On this basis, it is almost impossible to introduce AI solutions, or even agents, and truly generate the hoped-for benefits.

The path to comprehensive AI transformation and widespread use is a strategic one. Cloud-based platforms with standardized solutions are particularly powerful and show how AI-supported HR processes work—from the automation of administrative tasks to predictive talent development. However, without a viable data strategy and a critical review of the IT architecture, this potential will remain untapped. There are also completely new requirements for managers, because while management tasks can be automated, leadership tasks are gaining increasing importance.

Leadership on the test bench

Leadership in the age of AI is characterized by paradoxes: on the one hand, AI systems provide seemingly perfect analyses and decision bases; while on the other hand, empirical knowledge and contextual understanding are crucial. Managers also receive more ideas and decision templates and find it difficult to assess how trustworthy these are as long as they do not understand data sources, algorithms, and possible biases.

Another paradox is that managers should drive AI forward, even if this means questioning their own role or even the business model of the entire company. This requires courage, openness and a willingness to try out new things—but also critical questioning. Employees' and managers' AI skills do not automatically keep pace with rapid technological progress. Protected spaces where people can gain experience are important. Transparency is essential here, as employees are more likely to accept changes if they understand the goals and effects. The implementation of AI requires a well thought-out communication strategy that can be conveyed clearly and concisely in the face of increasing information density.

The use of AI is fundamentally changing the demands placed on HR and managers. It is not enough to rely on the "black box" of AI, the responsibility for ethically sound, value-based use is growing. "AI savviness", i.e. a profound understanding of technologies, is becoming a necessary key strength. This is the only way for managers to responsibly manage the impact on people and organizations. Values-based leadership and open dialogue with employees are becoming crucial management tasks in the digital age.

HR teams and managers must not only ensure the security of data and privacy, but also ensure fairness and transparency. This is especially true when dealing with sensitive personnel data and automated decisions, for example, in recruiting or performance management. Skills such as agility, scenario planning, and data analytics are becoming increasingly important. The successful use of AI requires strategic thinking, adaptability, and a continuous willingness to critically review one's own roles and team structures. Despite the advancing mechanization of the working world, the human factor retains its central importance: empathy, emotional intelligence, and the ability to build trusting relationships are key stremgtjs that cannot be automated. HR teams have a special role to play here. While HR must take the lead in managing administrative HR processes via AI, others are freed up to provide advice and coaching. The HR transformation towards a human-centric and technology-based organization is a first but crucial step.

Conclusion: HR excellence through AI

AI fundamentally changes work: from processes to roles and organizations to goals. Accordingly, it must be anchored as a strategic task at C-level. However, AI is not an end in itself, but a powerful tool for developing the full potential of people and organizations. Where the focus is on the organization of work and the cultural development of the company, it is the responsibility of HR to offer competent and courageous solutions, to moderate the process of transformation, and to introduce technology in a competent and ethically responsible manner. The future of successful HR work belongs to those who combine technology and humanity and have the courage to rethink leadership.

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