Agile has reached maturity in many organisations, and the success of Artificial Intelligence (AI) makes its use unavoidable for organisations to evaluate and enhance the performance of Agile teams. While AI promises a data-driven, objective lens for analysing team dynamics, productivity, and outcomes, its application to evaluate project team performance raises profound ethical questions—particularly when viewed through the lens of the Project Management Institute’s (PMI) Code of Ethics and Professional Conduct. This blog post explores the ethical implications of using AI to compare Agile team performance, guided by PMI’s values of responsibility, respect, fairness, and honesty.
Responsibility: Ensuring Informed and Accountable Use
According to the PMI Code of Ethics, project managers are responsible for the decisions they make and their impact on stakeholders. The use of AI introduces a new layer of responsibility, as the algorithms and models deployed can influence critical business and technical decisions, team morale, and individual careers. Without a clear understanding of how AI systems operate, there is a risk of delegating accountability to technology, thereby abdicating human responsibility. Furthermore, Agile teams thrive on transparency and trust (Manifesto for Agile Software Development), and opaque AI models can undermine these foundational principles.
Respect: Preserving Dignity and Psychological Safety
The Agile Practice Guide and Manifesto for Enterprise Agility stress the importance of respect among team members. AI-driven comparisons risk reducing team members to data points, potentially diminishing their sense of value and psychological safety. This is especially concerning when performance metrics are derived from sources like code commits, velocity, or issue resolution rates, which may not capture the full scope of an individual’s or team’s contribution. Respecting people means recognizing their unique contexts, strengths, and the human factors that are not always quantifiable.
Fairness: Avoiding Bias and Ensuring Equity
PMI’s value of fairness calls for impartial and just decision-making. AI systems are only as unbiased as the data and assumptions upon which they are built. If training data reflects organizational biases or historic inequalities, AI may perpetuate or even exacerbate them. For example, if one team’s work is more client-facing and another’s more technical, or a team is developing new features while another is fixing defects, comparing them using the same metrics may yield unfair outcomes. The Manifesto for Agile Software Development emphasizes individuals and interactions over processes and tools, serving as a reminder that context matters—and fairness demands careful consideration of these differences.
Honesty: Transparency and Truthfulness in Reporting
Honesty, as defined by PMI, compels project managers to provide accurate and truthful information. The complexity of AI models can make it difficult to explain how conclusions are reached, leading to challenges in maintaining transparency. Agile values also highlight the importance of open communication and feedback loops. When AI-generated comparisons are presented without clear explanations, stakeholders may question their validity, eroding trust in both the technology and its advocates.
Blog post: Ethical aspects of using AI to compare the performance of Agile teams
ProjectManagement.com - The Agile Enterprise
