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?

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? 

• Accountability and responsibility: When AI systems are used for decision-making, it is challenging to assign accountability if something goes wrong. AI agents are not members of the project team; they are a tool that should augment human capabilities. Project managers need to establish clear lines of responsibility for the outcomes of AI-driven projects.
• Bias and fairness: AI is still in its infancy, and finding large volumes of good-quality data that can be used to train AI models is difficult. AI models can inherit biases from the data they are trained on, leading to unfair or discriminatory outcomes in areas like task assignment or performance evaluation. These biases can reinforce existing societal prejudices related to factors like gender, race, or socioeconomic status, potentially leading to workplace discrimination and legal penalties.
• Transparency and explainability: The "black box" nature of some AI algorithms makes it difficult to understand how they reach a decision. This lack of transparency can erode trust and make it hard for project managers to oversee, troubleshoot, or validate AI-driven recommendations.
• Over-reliance on AI agents and lack of human oversight: At any point in the project, the control should remain with humans and avoid over-reliance on AI. Lack of knowledge and practice can lead to a decline in critical thinking and human judgment among team members.

• Contextual application: The principles and the code are designed to be applied within the context of project work. Ethical dilemmas are often encountered when balancing conflicting needs, and the framework provides guidance for decision-making.
• Performance domains: Ethical dilemmas can arise in any of the performance domains (e.g., Stakeholders, Delivery, Performance). The principles and the code provide the tools for navigating these situations and making responsible choices.
• Focus on value: Ethical considerations are a crucial part of focusing on long-term value, rather than just short-term outputs, ensuring that projects are conducted in a responsible and sustainable way. 

• The principles in the PMBOK 8th Edition align with and reinforce the values in the PMI Code of Ethics and Professional Conduct, which are honesty, responsibility, respect, and fairness.
• Project managers are expected to apply these principles in their daily work to make ethical choices that lead to positive results and maintain trust. 
• The PMI Code of Ethics and Professional Conduct remains the primary source for detailed ethical guidelines.
• ProjectManagement.com offers webinars that discuss the connection between PMBOK 7 principles and the Code of Ethics. 

It was a rainy Tuesday when the red flag popped up. The AI tool, designed to optimize resource allocation across our project portfolio, had flagged three critical projects for delay. The model’s recommendation? Shift half the team from Project Titan to Project Eclipse to balance out workloads.

At first glance, it seemed logical. The resource allocation maps, and velocity graphs supported the reallocation. But something did not sit right.

I had collaborated closely with Titan’s team leads for months. They were on the verge of a breakthrough with a critical client deliverable. Moving people now, even with Eclipse falling behind, could cause a domino effect across our most valuable account.

I called a huddle.

“Why did the model deprioritize Titan?” I asked the AI SME.

“It is based on risk scoring from delivery variance, budget utilization, and resource burn. Titan looked stable, so it pulled from there.”

“But it does not know the client conversation we had last week. Titan’s ‘stability’ is built on momentum we cannot afford to interrupt.”

That was it. The AI had the data but not the context.

We chose not to follow the recommendation. Instead, we manually adjusted scope and brought in temporary support for Eclipse. It was a tough call, but three months later, Titan delivered on time and exceeded client expectations. Eclipse caught up too—without derailing the portfolio.

That experience taught me something: AI is brilliant at pattern recognition, but it does not see what you know. It does not read nuance. And it does not carry responsibility.

So, when should project managers trust AI—and when should we intervene?

Trust AI when:

• You need unbiased, data-driven insights fast.
• The decision space is clearly defined and repeatable.
• You are analyzing trends across massive datasets where human bias or oversight might creep in.

But intervene when:

• The stakes involve human relationships, trust, or reputational risk.
• The model’s logic lacks access to critical context.
• The recommendation “feels wrong” and your intuition is backed by experience not fear.

AI is like a junior analyst with infinite memory and no emotional baggage. But it lacks judgment, and judgment is where leadership lives.

As project managers, we are not just responsible for outcomes; we are stewards of values. According to the PMI Code of Ethics, we are bound to act with responsibility, respect, fairness, and honesty. Blindly following AI no matter how accurate without human oversight may compromise all four.

Use AI like a compass not a map. Let it guide your thinking, but do not let it override your wisdom.

Because when things go south, the algorithm will not be in the room explaining the outcome you will.

So next time your AI flags a decision, pause. Ask: Does this align with what I know, what I have seen, and what matters most?

If the answer is no, trust yourself and intervene.

Reference:

PMI Code of Ethics

Webinar: Ethical Project Leadership in the digital age

Webinar: When to Trust AI and When to Intervene

Sunday, 23rd of March 2025 – A recent segment on 60 Minutes Australia highlighted growing concerns around the ethical use of artificial intelligence (AI) in digital platforms. In the episode titled “Defiant: Former Executive Takes on Facebook” (Watch here), a former Facebook (Meta) executive raised questions about how AI systems influence content delivery and the potential consequences for individuals and society.

This conversation echoes earlier testimonies from Frances Haugen, a former employee who publicly shared concerns about how content ranking algorithms may contribute to broader societal challenges (2021, 2024). These discussions are part of an important global dialogue on the ethical design and use of AI technologies not only in social media, but across all sectors.

As AI becomes increasingly embedded in business solutions, it’s vital that organizations consider its ethical implications during project planning and delivery. To address this, the inclusion of an AI Management Plan in project management plan and governance should be adopted as  best practice to ensure ethical alignment, regulatory compliance, and responsible innovation.

AI systems are driven by algorithms and data both of which can reflect the limitations and biases of their sources. When ethical considerations are not built into the design and deployment of AI, the technology can inadvertently reinforce inequalities or deliver unintended outcomes. This risk is amplified in high-impact areas such as recruitment, finance, law enforcement, and content moderation.

In recent years, several public examples have highlighted the complexities involved. For instance, facial recognition systems have led to wrongful arrests, particularly in the United States. One notable case involved Robert Williams ( https://www.abc.net.au/news/science/2023-11-01/ai-facial-recognition-robert-williams-crime-prison/103032148), who was wrongfully arrested in Detroit due to a false facial recognition match.

In the hiring domain, Amazon discontinued an AI recruiting tool  after it was found to show bias against female applicants (Reuters Article).

These examples underline the importance of proactively managing AI-related risks within the project lifecycle. The broader public discussion around AI use highlighted by media programs and individual testimonies shows that innovation must be balanced with responsibility. AI has the potential to deliver significant benefits, but only when developed and deployed with care and Ethics at the forefront. This can be achieved by embedding an AI Management Plan into project delivery, then organizations can demonstrate a commitment to ethical practice and risk mitigation. This proactive approach not only ensures compliance but also enhances transparency and trust in the solutions being delivered. In an era where AI is rapidly evolving, taking a structured, ethical approach isn’t just good practice it’s becoming essential. Building trust in AI starts with responsible project delivery, and that starts with planning for ethics from day one.

Question?

What are your thoughts on including an AI Management Plan as part of project delivery? What key sections or considerations do you believe should be included to ensure ethical and responsible AI implementation?

Follow our AI and Ethics articles below

(https://www.pmi.org/learning/ai-in-project-management) 

PMI x AI | PMI Blog | PMI

ProjectManagement.com - Ethics Bistro