From Agent to Expert: The New Role of AI in Organizations
- Jakob
- Aug 4
- 2 min read
Many organizations are currently building AI agents. They answer questions, automate processes, or support information retrieval. But while agents can handle individual tasks efficiently, they often lack integration into broader organizational contexts.
The transition from agents to true virtual team members is therefore a crucial step – and it requires a fundamental shift in how we approach artificial intelligence.
🤖 What exactly are AI agents – and what’s still missing?
AI agents are typically described as autonomous software units that pursue defined goals and take actions based on rules, models, or data (IBM, 2024).
In practice, most agents today are designed to perform clearly defined tasks or sub-steps within structured workflows – particularly in automation, information processing, or standardized interactions.
They are highly specialized, but not built to work flexibly or as long-term contributors within teams and processes.
Moreover, many agents are still used in deterministic ways – rule-based, reactive, and limited to simple environments.
As a result, the true potential of modern AI often goes untapped. Agents are useful tools for narrow tasks, but their role and capacity for action remain highly restricted.
⚠️ The Agency Gap: Why agents hit their limits
The so-called AI Agency Gap describes exactly this issue. Gartner (2024) defines it as the gap between what AI systems and agents can do today and the agency of human employees. While deterministic chatbots are limited to simple, reactive tasks in stable settings, more advanced LLM-based assistants and agents offer greater flexibility – but still fall short of human-level autonomy.
The agency gap becomes especially evident when it comes to acting independently, proactively, and handling complex tasks in dynamic environments (see illustration).
🧠 Artificial Experts: A new generation of virtual professionals
Artificial Experts bridge this gap. They represent a more advanced approach to using AI productively in the workplace. Unlike traditional agents, they’re not conceived as tools – but as virtual employees.
They possess:
a permanent role and identity
a stable, long-term memory
skills and capabilities
multimodal communication across channels
collaboration with humans, systems, and other AEs
the ability to learn from experience and grow continuously
Thanks to AI-powered training paths and their learning capacity, Artificial Experts can be trained quickly and deployed effectively. Instead of completing isolated tasks, they take on complex areas of responsibility and act proactively – unlocking the full potential of modern AI.
🎓 Training is the key: From tool to workforce
One of the core differences between traditional agents and Artificial Experts lies in the nature and quality of their training.
Artificial Experts are:
trained through structured learning paths
supported by AI-based trainers (like Emma)
continuously developed within a dedicated virtual coworking space
The evolution from AI agents to Artificial Experts marks a fundamental paradigm shift in how we work with AI systems.
Agents are tools that execute tasks efficiently – mostly deterministic, limited to specific processes. Artificial Experts are virtual colleagues who take on defined roles and grow with their responsibilities.
With them, a new form of collaboration emerges: AI no longer acts only in the background as a technical tool - it becomes a true virtual workforce.
