The decision seemed straightforward, at least on the surface. A leadership role opened after the successful delivery of a project, and several team members demonstrated strong performance, commitment, and clear growth potential. However, when the announcement was made the outcome surprised many. Not because the selected individual lacked capability, mainly because the process lacked clarity.

There were no transparent criteria, no visible evaluation process, and no opportunity for others to express interest. What was visible, however, was a prior relationship between the decision-maker and the selected individual. Intentionally or not, the perception of favoritism emerged immediately.

This is how favoritism and nepotism tend to show up in project environments, not as obvious violations, but as subtle departures from fairness. Favoritism occurs when personal preferences influence professional decisions. Nepotism goes a step further, granting advantage to family members or close connections. In both cases, the issue is not always about competence, but about whether decisions are made impartially, objectively, and free from competing self-interest.

From the perspective of PMI Code of Ethics and Professional Conduct, these situations directly challenge the core values: Responsibility, Respect, Fairness, and Honesty. Here is how each value comes into play:

- Responsibility is about ownership, not just of decisions, but of their consequences. Leaders are accountable for how decisions are made and for ensuring they align with the best interests of stakeholders. Avoiding structure or relying solely on personal judgment can unintentionally create ethical gaps.

- Respect goes beyond courtesy. It requires creating an environment where individuals feel valued, included, and able to contribute fully. When opportunities are not openly communicated, it limits participation and can undermine a sense of belonging within the team.

- Fairness is where the tension becomes most visible. The Code is explicit: decisions must be made impartially, and opportunities should be equally available to qualified individuals. It also clearly states that we must not reward or deny opportunities based on personal considerations such as favoritism or nepotism. Even the appearance of a conflict of interest must be treated with care and transparency.

- Honesty is about creating an environment where truth can be spoken and heard. This includes being transparent about how decisions are made and ensuring that information is complete, accurate, and not misleading.

The consequences of overlooking these values are not always immediate, but they are real. For example: the high performer who disengages, the colleague who stops applying, the meeting where fewer voices are heard. Trust does not disappear overnight, and it gets slowly replaced by doubt.

To be fair, leadership decisions are rarely black and white. Trust, experience, and working relationships matter. But ethical leadership requires more than good intent, it requires intentional processes. This means defining and documenting clear evaluation criteria before decisions are made, ensuring transparency in decision-making, involving multiple perspectives, and openly disclosing potential conflicts of interest when impartiality could reasonably be questioned. Even when decisions are ultimately sound, the absence of visible structure and transparency can weaken trust, create perceptions of bias, and discourage future engagement from team members who feel the process was not equitable.

Because ultimately, the question is not just whether the right person was selected. It is whether the process reflects the values we claim to uphold. As the Code reminds us, every choice matters, and collectively, those choices shape the credibility of our profession.

Have you ever experienced a situation where a decision felt influenced by favoritism, and how did it change the way you trust leadership?

Share your thoughts in the comments and let’s continue the conversation

References

Link to PMI’s Code of Ethics and Professional Conduct: https://www.pmi.org/about/ethics/guidelines

Link to PMI’s Ethical Decision-Making Framework (EDMF): https://www.pmi.org/ethics/ethical-decision-making-framework.pdf

Link to PMI’s Blog on Ethics “Ethics Bistro”: https://www.projectmanagement.com/blogs/365304/ethics-bistro

 

1. Enhanced Decision-Making: AI algorithms analyse massive datasets to predict outcomes and recommend actions, akin to a co-pilot guiding navigational decisions during a race. 
2. Efficiency Gains: AI automates repetitive tasks and improves processes, freeing project managers to focus on strategy—like how co-pilots manage tactical information during high-speed races. 
3. Risk Reduction: By identifying potential issues in advance, AI serves as an advisor, much like a racing co-pilot warning about challenging road conditions, enabling initiative-taking corrections. 

1. Over-Reliance on AI: Just as a driver must remain vigilant and not entirely dependent on the co-pilot, project managers risk deferring critical decisions to AI tools, potentially leading to a lack of accountability. 
2. Ethical Blind Spots: Racing ethics demand fair play and adherence to rules; similarly, ethical AI use in project management calls for attention to bias, transparency, and fairness. Overlooking these aspects can harm stakeholders or perpetuate inequitable practices. 

 

1. Unparalleled Precision: Autonomous AI can minimize human errors, akin to driverless cars maintaining perfect lane control or braking at precisely calculated intervals. 
2. Scalability: AI can manage complex, multi-layered projects beyond human capacity, like its role in optimizing traffic flows with autonomous vehicle networks. 

1. Loss of Human Judgment: Driverless cars highlight the drawback of removing human intuition, empathy, and situational awareness—a challenge mirrored in project management where human leadership and creativity are essential. 
2. Accountability Gaps: In a driverless car accident, responsibility is ambiguous. Similarly, with autonomous AI, project managers may struggle to allocate accountability for errors, raising ethical dilemmas. 
3. Ethical Concerns: Driverless cars must navigate moral conflicts (e.g., protecting passengers versus pedestrians). In project management, fully autonomous systems must grapple with potentially biased decisions affecting stakeholders, raising questions of fairness and inclusivity. 

 

• In shared responsibility (co-pilot), humans are ethically required to oversee and correct AI outputs, ensuring alignment with organizational values and stakeholder trust. Like racing, project managers retain control while benefiting from AI's support. 
• In autonomous systems (driverless cars), ethical concerns magnify as AI takes over critical decisions. Issues of fairness, inclusivity, and transparency emerge, demanding rigorous bias checks, accountability frameworks, and adherence to PMI’s Code of Ethics principles. 

 

Related discussion topic: Can project management run on AI autopilot?

• Embedding ethics training into all levels of the organization
• Applying the PMI EDMF v8 when facing ambiguous or high‑impact decisions
• Strengthening internal controls and access governance
• Encouraging employees to speak up when they perceive unethical conduct.
• Creating a culture where integrity is rewarded, not assumed.

1. Leadership buy-in is non-negotiable. 
2. Pilot projects are the safest way to prove value. 
3. Clear benefits must be communicated and demonstrated. 
4. Meaningful information is more important than flashy dashboards. 
5. Strategy alignment ensures AI adoption delivers stakeholder value. 
6. Simple wins build trust and momentum. 

• Bias and fairness: AI models can perpetuate discrimination if not carefully designed. 
• Transparency: Stakeholders must understand how AI reaches conclusions. 
• Accountability: Who is responsible when AI makes a wrong call? 
• Privacy: AI often relies on sensitive data—how is it protected? 
• Workforce impact: Automation may displace roles. How do we retrain and redeploy talent responsibly? 

• Embed ethics in project charters: Make fairness, transparency, and accountability explicit objectives. 
• Educate stakeholders: Provide training in AI’s capabilities and limitations. 
• Audit algorithms: Regularly check for bias and unintended consequences. 
• Prioritize human oversight: Ensure critical decisions involve human judgment. 
• Champion inclusivity: Use AI to augment, not replace, human talent. 
• Communicate openly: Share both successes and challenges of AI adoption. 

• How do we secure buy-in when AI alters workflows and job roles? 
• What pilot projects best demonstrate AI’s tangible benefits without overwhelming staff? 
• How do we balance efficiency gains with ethical responsibility? 
• How do we ensure transparency in AI-driven decisions? 
• What frameworks can help us retrain staff displaced by automation? 
• How do we measure cultural readiness for AI adoption?