Artificial Intelligence
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Collaboration between humans and AI

Rather than taking over jobs, which is the way that AI is regularly framed, AI is becoming a partner at work and in our daily lives. In certain ways, AI is already taking over routine tasks to allow humans to focus on complex and creative work.

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Next to routine tasks, AI is already considered a companion to turn to for therapy, identifying one’s purpose, and resolving life issues. This AI partnership will be further strengthened by the growth of multi-agent systems.

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Agentic AI, which has been in development since the late 1990s, promises to significantly enhance human-computer interaction through its natural language interfaces. Additionally, other interfaces are emerging in collaborative robots (cobots) and humanoid robots, enabling more human-like interactions.

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Furthermore, AI is being integrated as ambient intelligence into various tools and devices. Consider wearables with optics and microphones that can analyse the wearer’s surroundings and assist in the same manner as a personal assistant or AI companion. A noticeable sentiment about this development relates to the use of LLMs from big tech companies for running the applications.

Maturity

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More diversified access to large AI models Increase in co-evolution between hardware and AI Collaboration between humans and AI Democratisation of XR content creation using generative AI Public investments in XR continue to increase Standardisation of data space architectures for secure and trusted data sharing Growing significance of augmented data management Trusted digital recognitions are emerging Digital Trust Frameworks: from hierarchical to distributed trust Emerging dualuse of AI in cybersecurity More accessible privacy-enhancing technologies to share data Rising pressure to prepare for post-quantum cryptography Emergence of measures for secure Internet of Things The growing need for security protocols to work in the cloud Growing investments in the European cloud infrastructure & sovereignty More AI-driven hardware in data centres to manage workloads in the cloud Increasing need for heterogeneous and hybrid computing platforms Trade-offs in accuracy with approximate computing Fixed wireless access Fibre optic networks: strategic infrastructure for the knowledge economy Enhancing quantum computing robustness and potential Quantum sensing integration to other domains

Drivers

Impact

education

Education

  • Changes are necessary in the curriculum to meet the new ways of collaborating with AI in professions, and all without compromising public values.
  • Agentic AI may reduce teacher workload by automating assessments, feedback, and lesson planning – but raises questions about student agency, bias in feedback, and surveillance concerns.
  • As workplace collaboration between humans and AI has a profound impact on jobs, students will need adaptability and a change in mindset as crucial skills for the future.
  • Students, and teaching staff are becoming a data source for big tech, as the availability of AI tools become more widespread and accessible.
Research

Research

  • Researchers could benefit from AI agents to handle repetitive tasks (e.g., formatting, summarising, and literature scanning).
  • Assuring reproducibility, data privacy, and authorship attribution are more complex for AI agents to handle.
  • By streamlining data analysis, supporting experimental design, and managing laboratory tasks through robotic and predictive systems, AI promises increased automation and efficiency.
  • Ownership of research data and associated questions are exposed with the use of publically available LLMs.
Operations

Operations

  • AI can increase efficiency and productivity, but care needs to taken with ethical judgements. Importantly, the main goal of education is not to lower costs, but to create a meaningful learning environment for students, as well as a safe and inspiring place to work for educational professionals.
  • Institutions risk vendor lock-in and rising IT costs due to their reliance on embedded AI tools.
  • AI-powered services could optimise scheduling, student support, and administration, lowering operational costs.
  • Human-AI collaborative systems will be fed with huge amounts of physical and personal data, necessitating strict privacy regulations and ethical considerations to safeguard users and promote responsible AI practice.
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