Many people from the e-commerce industry, fear the impact of artificial intelligence technologies on their professional future. It seems to me that today it’s quite clear that machines will replace some people working in certain professions — especially at lower levels and/or those that don’t require creativity and special skills. In many professions, an effective partnership between humans and machines will exist:
The copilot relationship between humans and AI is not just a technological upgrade, but a profound change in how we perceive work: the worker becomes a process manager, insight selector, and innovation leader — together with a digital partner that frees up time, improves decision-making and expands capabilities. The success of this transition depends on the ability of workers and organizations to adopt continuous learning, adapt to changes, and harness AI as an empowerment tool rather than a replacement. In this context, it’s worth referring to another term: “Human in the Loop” (HITL) — a term describing an approach where humans are actively integrated into the workflow of artificial intelligence (AI) and machine learning (ML) systems, especially in critical stages such as development, training, testing, and operation of models. HITL emphasizes the importance of combining human expertise with smart automation, and ensures that AI systems remain safe, reliable, and adapted to professional and ethical needs.
Key principles:
- Humans provide feedback, correct errors, tag data, and control system outputs.
- The integration allows AI systems to better handle complex, sensitive tasks or those requiring professional judgment, understanding of nuances and context, or ethical considerations.
Implementation examples:
- Professional translation — human editor checks and corrects automatic translation.
- Tagging medical, legal, or financial data — experts mark data for training models.
- Oversight of automated decisions in critical systems (such as medicine, autonomous vehicles, security).
Benefits:
- Improved accuracy, reliability, and adaptation to reality.
- Reduction of biases and ensuring fairness.
- Ability to handle exceptional or complex cases that a machine alone would not identify.