IBA editorial team Artificial intelligence is shifting everyday office work from a human–human division of labour towards a human–machine–human model. Collaboration becomes faster, more data-rich and more dynamic, while simultaneously requiring new rules, responsibilities and a culture of critical engagement.
Here, author Birgit Gebhardt draws an insightful comparison. She summarises the transformation potential of AI as a cross-cutting technology for the division of labour and collaboration, and then examines the extent to which a societal consensus on the responsible use of AI remains viable under these changed conditions. The pre-read prompts a closer look: how will AI reshape collaboration within organisations, and which framework conditions must be redesigned to ensure that people remain at the centre and continue to develop?
COLLABORATION
AI does not merely expand the range of available tools; it becomes an active partner in collaboration. Where documents were once shared, tasks distributed and meetings coordinated, AI agents can now co-shape work processes. They structure information, summarise discussions, remind teams of open issues and suggest next steps. In doing so, they relieve teams of routine tasks and promise space for strategic and creative work – at least in theory!
In practice, however, organisations under pressure to increase efficiency and performance will not automatically free up this space, especially as AI increasingly takes on strategic and creative tasks itself.
Nevertheless, there are several reasons why organisations and employees alike need this space and clear rules for the new interplay:
1. People remain responsible for decisions involving AI and require protected spaces for critical reflection and mutual feedback.
2. Organisations will increasingly manage both AI agents and human employees and must design the division of labour in a way that not only reduces costs, but also enables learning opportunities, upskilling and role mobility.
3. To develop judgement, role flexibility and trust, experience and mistakes must be permitted. With AI, this often occurs in simulated application contexts. In office environments, learning offers real multisensory advantages that should be utilised.
4. To keep pace with the creativity of AI, people must be allowed to play. Birgit Gebhardt therefore describes the office as a real-world “sandbox”.
5. Above all, employees must learn to recognise and apply the strengths of their natural intelligence. For these qualities to be recognised and supported by organisations, verifiable rules and extended criteria are required, rather than purely quantitative performance metrics.
In the pre-read, Birgit Gebhardt draws on cognitive research to explore how the next generation learns, identifying key insights within the gaming community.
LEADERSHIP
AI is also changing the rules of leadership. Automated analyses, dashboards and forecasts already support management decisions and are intended to relieve leaders of routine tasks.
HOWEVER, REALITY SHOWS: When leadership relies too heavily on AI-generated metrics, judgement and accountability risk erosion. A counter-movement is therefore required:
- Managers must learn to critically question AI outputs, recognise bias and ensure transparency.
- As they often co-decide at departmental level whether AI or humans are deployed, they need a deeper understanding of their employees’ potential and must foster it more deliberately.
- They must create time and space for dialogue in order to strengthen critical capability, enabling teams to review, contextualise and challenge AI results.
- They must establish a culture in which people have the courage to challenge AI-based routines when errors are identified.
- Leadership increasingly means orchestrating humans and machines intelligently, clearly separating responsibilities and strengthening trust in team collaboration.
Leadership therefore evolves from decision-making authority into an authority of responsibility, moderating between humans and machines. Where such frameworks must be clearly defined, space itself takes on a new role. The pre-read uses the metaphor of a control centre to describe this emerging form of collaboration.
KNOWLEDGE TRANSFER
In KNOWLEDGE TRANSFER AI appears to achieve what has long been difficult within organisational systems: breaking down departmental knowledge and reducing barriers between silos, while making data, code and specialist knowledge accessible in natural language. Employees can generate content, retrieve summaries or automatically create entire learning pathways. AI systems store, link and disseminate knowledge faster and more broadly than ever before. For employees, knowledge transfer increasingly means not only sharing information, but also training judgement and assessing the validity, relevance and responsibility of AI-generated knowledge.
However, practical application also reveals risks. When AI appears able to answer every question, when its knowledge is universally accessible and results are always faster than human solutions, people risk losing the ability to develop, evaluate and contextualise information themselves. The danger of deskilling increases, confidence in one’s own abilities declines and AI takes over.
this, too, requires clear framework conditions:
1. Organisations must deliberately create spaces for human learning experiences, such as joint reflection, critical review and practice-based projects.
2. Spaces are needed for multi-perspective exchange and multisensory rituals that bring different viewpoints together on a level of resonance.
3. Predefined process steps allow contributors sufficient space for individual deep work, while also providing clarity about where human input is essential and which environments support it, helping teams find solutions step by step with AI.
The pre-read illustrates this in the R&D context using the example of an innovation funnel. Here, Semih Aridogan, Academy Director at the innovation agency Dark Horse, outlines how integrating AI is changing human–machine collaboration within the design thinking process.
Further inspiration will follow at the end of October 2026 at the Wherever Whenever Work Culture Festival at ORGATEC, including the final ‘ew Work Order’study on collaboration with AI.
