AI excellence in a team

Artificial intelligence (AI) gives companies the opportunity to raise productivity and innovative strength to a whole new level. At the beginning of 2025, McKinsey estimated the long-term global potential of AI at up to 4.4 trillion US dollars, but just around 1% of companies today feel truly “ready for AI.” One of the main reasons for this discrepancy is not the lack of technology or talent, but a leadership philosophy that enables the targeted use of AI and enables teams to develop their full potential.

Superagency and AI-First Leadership

This is where the term “superagency” comes into play. This term describes the ability of humans and machines to achieve more together than either side could on their own. Influenced by Reid Hoffman in his book “Superagency: What Could Possibly Go Right with Our AI Future,” he stands for an optimistic vision in which AI systems do not replace people, but expand scope for action and unlock creative potential. For managers, this means consciously creating framework conditions in which teams and AI tools can work together in real co-creativity.

Anyone who wants to live a superagency should therefore not only see AI as a tool in the toolbox, but must also use it as a strategic element that works from vision to everyday working life. This is exactly where the concept of “AI-First Leadership” comes in. Being an AI-first leader primarily means learning how to create an environment in which people and AI tools work together synergistically and across the entire level.

In practice, this means that managers

• First create a common understanding of superagency. They explain how AI-based assistance systems can be used in processes and what new working methods result from this.

• develop a binding roadmap that states which areas will be selected first for AI pilot projects, what the AI strategy should look like and which goals (e.g. increasing efficiency or reducing errors) should be achieved as a result.

• invest in targeted continuing education initiatives, such as basic workshops on data literacy or co-thinking sessions, in which employees work together on real case studies and thus increase acceptance of change at the same time.

AI-first leadership also means consistently questioning your own role as a driver of transformation and not simply nodding off AI initiatives. Instead, managers should put regular “AI reviews” in their calendar, in which they systematically check ongoing AI projects based on four criteria:

• Strategic fit
• added value
• scalability
• risk management

Based on these reviews, it is possible to see which projects actually contribute to the superagency vision and where need to be readjusted (McKinsey 2025).

Only when these AI-first foundations have been laid can teams really grow into their role as superagents. The next step is therefore to equip and empower the operational units or areas that have been agreed upon with clear goals and the freedom to choose methods and tools themselves to implement the AI pilot projects defined in the roadmap. This creates a seamless transition from strategic vision to actual implementation and makes the concept of superagency tangible in daily teamwork.

Best practices for sustainable AI excellence

For AI initiatives to really work in the long term, strategic goals, corporate structure and lived culture must go hand in hand. Managers can promote this by paying particular attention to the following points:

Establish cross-functional collaboration

Instead of pushing AI projects into an IT team in isolation, leaders should adapt corporate structures in such a way that mixed, multidisciplinary teams can be formed in which developers, subject matter experts, product managers, etc. can work together on AI use cases from the start. It is also worthwhile to set up a small core team, which meets once a week to incorporate direct feedback from the specialist areas.

Fostering a culture of trust

Since a trusting culture forms the basis of successful AI initiatives, it is important to create a collaborative environment in which all employees feel safe and involved. For this, it is important that managers establish an open error culture, in which missteps are understood as a learning opportunity and are discussed openly in “retroperspectives.” Regular feedback rounds and transparent dissemination of information help to ensure that everyone is always on the same level of knowledge. The active involvement of employees in decision-making processes is also an important aspect in order to further strengthen not only trust in the company but also responsibility.

Strengthen change management and skill building

In order to facilitate the introduction of these new processes, it is worthwhile for Leaders to introduce structured change programs with workshops on AI basics and at the same time set up a mentor network, where more experienced colleagues can act as a point of contact for questions and best practices. In order not to overburden mentors, micro-learning modules or “lunch & learn” sessions can be offered regularly, in which AI applications are tested directly in everyday working life and newly acquired knowledge is immediately applied. As a result, fears of contact are reduced, specialist skills are built up as needed and the willingness to actively use AI tools grows sustainably.

Conclusion

Managers are required to consistently combine AI strategy, organizational structure and corporate culture. By promoting AI-first leadership, empowering multidisciplinary teams, implementing rapid pilot projects and establishing a culture of trust and responsibility, they lay the foundation for sustainable competitive advantage. In this way, the vision of a “superagency” in everyday working life becomes a tangible reality for the benefit of employees, customers and the entire company.