What is Agile for AI?
Agile for AI adapts agile software development principles to accommodate the experimental, iterative nature of machine learning development, emphasizing rapid experimentation, continuous model improvement, cross-functional collaboration between data scientists and engineers, and flexible planning that accounts for model performance uncertainty.
This glossary term is currently being developed. Detailed content covering implementation approaches, best practices, common challenges, and business applications will be added soon. For immediate assistance with AI project management, please contact Pertama Partners for advisory services.
Agile for AI prevents the waterfall trap where teams spend months building models that miss business requirements, reducing failed project rates from 85% to under 40%. Iterative delivery surfaces data quality issues and feasibility blockers within the first two sprints rather than after six months of development. This approach cuts average AI project timelines by 35% while improving stakeholder alignment and budget predictability.
- Embrace experimentation and accept that not all model iterations will improve performance
- Define 'done' criteria that include model performance metrics, not just code completion
- Foster collaboration between data scientists, ML engineers, and business stakeholders
- Maintain flexibility in sprint commitments as experiment results may change priorities
- Implement experiment tracking and model versioning as core agile artifacts
- Balance exploration sprints (learning) with delivery sprints (production features)
- Sprint planning must accommodate experiment-driven workflows where 30-50% of data science tasks yield negative results requiring pivot decisions.
- Define spike stories specifically for data exploration and feasibility assessment before committing engineering resources to full model development.
- Establish separate velocity baselines for AI teams since traditional software estimation frameworks consistently underpredict machine learning iteration cycles.
- Sprint planning must accommodate experiment-driven workflows where 30-50% of data science tasks yield negative results requiring pivot decisions.
- Define spike stories specifically for data exploration and feasibility assessment before committing engineering resources to full model development.
- Establish separate velocity baselines for AI teams since traditional software estimation frameworks consistently underpredict machine learning iteration cycles.
Common Questions
How does this apply to AI projects specifically?
AI projects have unique characteristics including data dependencies, model uncertainty, and iterative development cycles that require adapted project management approaches.
What are common challenges with this in AI projects?
Common challenges include managing stakeholder expectations around AI capabilities, balancing exploration with delivery timelines, and maintaining project momentum through experimentation phases.
More Questions
Various tools and frameworks can support this practice. Consult with project management experts to select approaches suited to your organization's AI maturity and project complexity.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
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