Project Management and the Regulation of AI

By George Pitagorsky

Regulating artificial intelligence (AI) in organizations is a strategic necessity. A systems-oriented leadership approach aimed at optimal performance requires a robust framework for AI governance—one that balances innovation with responsibility and is rooted in awareness and continuous improvement.

Project managers and project management office leaders must be aware of the reality of AI as a major factor in project management. Using AI for estimating, resource management, and other planning activities is a reality. Projects to streamline the organization’s systems and procedures will undoubtedly contain an AI component. Additionally, there will be programs and projects focused on implementing AI governance.

Quality Management – Trust and Verify

AI governance is concerned with AI’s accuracy and biases. Leaders must not make the mistake of relying on AI without making sure that the data being used and the AI’s programming and tuning are valid and exactly what you want them to be. Inadvertent and purposeful biases skew AI-produced results.

Regulation is needed to ensure testing, documentation, and monitoring to avoid bias and promote fairness in areas such as lending, hiring, and health care, and mastery in decision-making. Transparency and Accountability are needed to create trust that AI-powered outcomes are understandable and justifiable.

AI’s reliance on large-scale data collection and analysis makes data accuracy and privacy a critical issue. Data governance standards, enforcement, and security frameworks are needed to protect sensitive information from misuse.

Creating an AI governance process is complex. Values such as the need for responsible and ethical application of technology are a guiding factor. The process is to be integrated into the tech environment, organizational culture, and architecture. A big project.

The State of AI Regulation

Organizations are using AI in areas as diverse as project planning and control, hiring, making financial decisions, composing reports, marketing and correspondence copy, and providing direct services to clients and employees. Unregulated AI presents significant organizational risks. Algorithmic biases in planning, hiring, or promotions require managing sensitive data and making critical business decisions.

Biased and hallucinating AI can damage reputations and result in costly legal and financial consequences. On a broader level, individuals are at risk because they are increasingly relying on AI to make decisions and to remain informed.

Many organizations are crafting and establishing AI policies and procedures, but many lack depth, organization-wide execution, and formal governance. There is no comprehensive federal law regulating AI, and President Trump signed an executive order in January 2025 aimed at removing regulatory barriers. Other executive orders have required agencies to implement unbiased principles and guidance, though not necessarily in the form of enforceable regulations. States are enacting laws to focus on transparency, safety, and consumer protection. The EU is using a risk-based model, and China, Japan, and other countries are enacting their own restrictions.

In other words, it’s like the wild west. Organizations must actively monitor the evolving legal and regulatory landscape. There is no comprehensive global model. Most governments and many organizations require documentation, risk assessments, human oversight for high-risk systems, and clear notices when users interact with AI. particularly in areas affecting safety, rights, or critical infrastructure.

How Best to Regulate AI

Effective AI regulation requires a practical, systems-oriented, risk-based approach that is integrated into existing policies and procedures. Assess the degree to which your organization has addressed the following

AI policies, covering what is allowed and what isn’t
AI Governance Officer, or AI Governance Office with AI-savvy staff
Cross-Functional Leadership Engaging IT, legal, HR, and ethics teams in policy development, echoing the insight that effective leaders integrate self-awareness, values, and collaboration.
Educate people as to the way AI works and how to use it effectively in their jobs to avoid resistance based on misinformation and myths. Provide regular AI education, training, and coaching for stakeholders at all levels on the development and use of AI systems, including implications of AI strategy on workforce changes, tools and techniques, regulations, and industry standards.
Formal process for staying on top of evolving regulations and standards
Monitoring and testing systems for accuracy, biases, and misuse
Formal incident reporting and remediation
Regular analysis to determine which conventional systems can be augmented or replaced by AI-based systems and which AI systems are targets for upgrade and replacement as tools evolve.

Adopt a Risk-Based Framework: Use models like the EU AI Act that categorize AI by risk level, imposing stringent requirements for high-risk systems while allowing more flexibility for benign applications.

Ensure Human Oversight: Keep people “in the loop” for critical decisions—mandating human review for AI-generated outcomes that affect jobs, finance, or safety.

What is Getting in the Way?

These obstacles to effective use and governance of AI lead to an inability to keep up with this fast-changing field

Denial of the reality of AI as a fact of life
Lack of sponsorship at the executive level
Lack of knowledge within teams and project management offices
No budgeted resources
Denial of the need for regulation
Rushing to market without paying needed attention to regulation, testing, governance, and impact on clients.

Conclusion

AI is a concern for project managers. On the surface, the project management process can use AI to enhance the capacity to estimate, schedule, and manage risk, changes, and resources. Doing that effectively requires regulation to ensure the data being used and the “tuning” of the AI agents are correctly reflecting reality in their output. If you have accurate historical project data, you are ahead of the game. As a project management professional, if you haven’t done so yet, explore the way AI and Project management interact.

Many projects will have AI components, and managing organizational change projects will become more complex if resistance to AI emerges. AI-focused quality assurance and control will be required. Projects that use AI agents to streamline design, coding, authoring, administrative processes, customer contact, and decision-making are mission-critical.

Regulating AI is about cultivating a mindful, resilient organizational culture that sees advanced technology as both an opportunity and a responsibility, with risks and rewards. Adopt transparent, context-sensitive regulation frameworks and prioritize critical thinking, ethical behavior, and continuous learning to leverage AI for sustainable success while safeguarding people, principles, and success.

How is AI in the context of project management being addressed in your organization?

Source

https://pacific.ai/2025-ai-governance-survey/

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