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Embracing Self-Organizing Teams and Empiricism: Conquering Jobs That AI Can’t Do

In today’s rapidly evolving technological landscape, artificial intelligence (AI) has become an indispensable tool for automating tasks and enhancing productivity across various industries. However, not all jobs can be wholly automated by AI, as some tasks require the unique capabilities of small self-organizing teams and the essence of empiricism.

Research from Hun Lee at Ohio State’s College of Business has gathered data to support this position. “Say you’re listening to a song, we know I can listen to a song in my headset or my computer, maybe even better quality. But we like to feel alive. We want to feel the vibe, in-person.” said Lee.

In this blog post, we explore how these human-centric aspects contribute to tackling challenges that AI cannot fully address.

The Complexity of Human Interaction

While AI excels in processing vast amounts of data and executing repetitive tasks with accuracy, it falls short in replicating the complexities of human interaction. Jobs that involve empathy, emotional intelligence, and interpersonal communication rely on the nuanced understanding of human behavior, which remains a distinctly human attribute. Small self-organizing teams, comprising individuals with diverse perspectives, foster collaboration and creativity, enabling them to navigate intricate social landscapes with finesse.

Adaptability and Creativity

Jobs that require adaptability and creativity often lie beyond the reach of AI’s programmed capabilities. Small self-organizing teams possess the power of collective intelligence, as they can quickly respond to changing circumstances and devise innovative solutions. Empiricism, with its focus on learning from real-world experiences, enhances this trait further by encouraging teams to experiment, iterate, and refine their approaches based on actual feedback.

Unstructured Problem-Solving

AI is highly proficient in addressing problems with clear structures and well-defined rules. However, when it comes to unstructured or ambiguous problems that demand contextual understanding and intuition, human intervention proves vital. Self-organizing teams thrive in such scenarios as they can leverage their collective expertise, creativity, and adaptability to unravel intricate challenges.

Ethical Decision Making

One of the most critical aspects where AI often falls short is in ethical decision-making. Ethical dilemmas often require consideration of societal values, moral reasoning, and empathy, which can’t be fully replicated by algorithms. Self-organizing teams can collaboratively assess ethical implications, leveraging multiple perspectives to make responsible and humane choices.

Continuous Learning and Improvement

Empiricism lies at the heart of Agile methodologies, and small self-organizing teams embrace this concept fervently. They focus on delivering small increments of value while consistently gathering feedback from stakeholders and users. This constant learning loop allows teams to adapt, pivot, and improve, enhancing their ability to address complex challenges that go beyond AI’s capabilities.


Artificial intelligence undoubtedly revolutionizes the way we work and opens doors to unimaginable possibilities. However, there are certain aspects of work that demand uniquely human attributes. Small self-organizing teams, driven by the principles of Agile and empowered by empiricism, are essential in tackling jobs that AI can’t handle alone. By harnessing collective intelligence, adaptability, and creativity, these teams stand as a testament to the immense potential of human collaboration in an increasingly AI-driven world. Embracing the human element alongside technological advancements is the key to thriving in the future of work.