As many organizations struggle with the complexities of utilizing AI, understanding the optimal ways and pace to adapt their working methods is critical. Inspired by “The Agile Manifesto” and my journey of learning AI through hands-on experience, I propose an AI Experimentation Manifesto.

This manifesto serves as a high-level guide for those exploring AI, aiming to find the best path for their organization. As I continue to develop advice for AI Policies, I composed this manifesto as a starting point.  

The AI Experimentation Manifesto

  1. Individuals and Interactions over Processes and Tools: Give precedence to skilled professionals and collaborative interactions in AI experimentation, valuing the human element over rigid adherence to specific processes or tools.
  2. Iterative Learning over Extensive Planning: Prioritize rapid, iterative cycles of experimentation and adaptation, responding in an agile manner to feedback, new learnings, and data rather than relying on extensive upfront planning.
  3. Collaborative Experimentation over Individual Expertise: Favor collaborative efforts in AI experimentation, promoting team interactions and knowledge sharing instead of isolated individual research.
  4. Cross-Disciplinary Collaboration over Specialized Approaches: Foster collaboration across various fields of expertise to enrich AI experimentation, drawing from Agile’s emphasis on team collaboration and breaking down silos.
  5. Ethical Considerations over Extreme Efficiency: In AI experimentation, ethical considerations should outweigh the optimization for extreme efficiency.
  6. User-Centric Experiments over Technology-Driven Trials: Concentrate on experiments that uncover real user needs and challenges rather than those driven by technological capabilities.

The original Agile Manifesto for software development says: “That is, while there is value in the items on the right, we value the items on the left more.” The same applies to my manifesto. 

With this manifesto, I wish to emphasize the importance of user-centricity, risk mitigation, and ethical considerations which I see as fundamental for responsible AI development. I also want to point out the crucial roles of transparency, the integration of continuous feedback into the process, and adaptability to change, especially now at the beginning of your learning path.

By remaining agile and responsive in the rapidly evolving field of AI, we can enhance our capabilities as individuals and foster growth and innovation within our organizations.

Riitta 🌟  

The original Agile Manifesto for Software Development can be found here: