Managing AI Risks and
Compliance with Innovation
SECOND EDITION
By name Rizwan Amin Sheikh, Ph.D., PMP (USA)
Khalid Ahmad Khan, Ph.D., PMP (Pakistan)
Aamir Khalid Pirzada (Saudi Arabia)
The greatest risk in AI is not that machines will become too intelligent, but that we will deploy them without understanding their vulnerabilities.
Abstract
Artificial intelligence is transforming how projects are delivered, decisions are made, and value is created. Yet without proper governance, AI can expose organizations to risks, regulatory penalties, reputational damage, and operational failures. How do project leaders balance AI’s transformative potential against its inherent risks?
This paper introduces AI-CARVER—a practical, quantifiable framework for assessing AI risks adapted from the battle-tested CARVER methodology featured in Harvard Business Review. Originally developed for military target analysis and adopted by the Department of Homeland Security and Fortune 500 security directors, CARVER has been reimagined for the AI era. AI-CARVER enables project managers and executives to systematically evaluate AI systems across six critical dimensions: Criticality, Accuracy, Regulatory Exposure, Vulnerability, Explainability, and Recoverability.
Participants will gain hands-on experience applying AI-CARVER to real-world scenarios and leave with immediately actionable tools for AI governance, risk management, and compliance (GRC). The paper also explores insights from Harvard Business Review’s “8 Questions About Using AI Responsibly, Answered,” examines the NIST AI Risk Management Framework, and unpacks compliance requirements under GDPR and the EU AI Act—regulations now affecting global banks, healthcare organizations, pharmaceutical companies, manufacturers, technology firms, and others. Moreover, the authors will introduce a practical AI GRC Assessment Model that participants can immediately implement within their organizations.
Responsible AI is not about slowing innovation—it’s about building a sustainable foundation for AI systems that are ethical, secure, and compliant while remaining innovative and productive.
Learning Objectives
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- Discuss how companies can apply the AI-CARVER framework—a six-part risk assessment methodology adapted from Harvard Business Review—to systematically evaluate AI systems across Criticality, Accuracy, Regulatory Exposure, Vulnerability, Explainability, and Recoverability, enabling data-driven governance decisions.
- Explore how to use AI responsibly through eight key questions posed by Professor Tsedal Neeley of Harvard Business School.
- Learn how to utilize a practical AI GRC (Governance, Risk Management, and Compliance) Assessment Model that participants can immediately implement within their organizations to identify AI risks and compliance gaps, prioritize mitigation strategies, develop remediation roadmaps, and establish accountability structures.
- Examine the evolving regulatory landscape for AI, including the NIST AI Risk Management Framework, GDPR, and the EU AI Act, and assess how global organizations across banking, healthcare, pharmaceutical, and manufacturing sectors can manage AI risks and navigate compliance while pursuing innovation.
- Provide guidance to project managers on how to integrate responsible AI principles into project management practices, ensuring AI systems are designed, deployed, and monitored in ways that advance fairness, transparency, security, and ethical accountability without stifling innovation.
A number of management consulting organizations are reporting similar findings about the urgency of establishing responsible AI governance. According to Deloitte’s Global Boardroom Program (2025), nearly one-third of board members indicate that AI is not on their organization’s agenda, indicating a clear and urgent governance gap at the most senior level of leadership in organizations. In addition, Deloitte’s State of AI in the Enterprise report (2026) states that only 20% of organizations have developed mature governance models for autonomous agents. This represents a considerable gap in light of the rapid development of autonomous agents in every industry…
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Editor’s note: Second Editions are previously published papers that have continued relevance in today’s project management world, or which were originally published in conference proceedings or in a language other than English. Original publication acknowledged; authors retain copyright. This paper was originally presented at the 18th Project Management Symposium at the University of Texas at Dallas in May 2026. It is republished here with permission of the author and conference organizers.
How to cite this paper: Sheikh, R.A., Khan, K.A., Pirzada, A.K. (2026). Responsible AI: Managing AI Risks and Compliance with Innovation; Originally presented at the 18th Project Management Symposium at the University of Texas at Dallas in May, republished in the PM World Journal, Vol. XV, Issue VII, July. Available online at https://pmworldjournal.com/wp-content/uploads/2026/07/pmwj166-Jul2026-Sheikh-Khan-Pirzada-Responsible-AI-UTD-paper.pdf
About the Authors
Dr. Rizwan A. Sheikh, PhD
Author, Inventor, Professor
AI Governance Expert
Texas, USA
Dr. Rizwan A. Sheikh (“Dr. Riz”) is the Founder & CEO of Global AI Excellence (GAIX) and inventor of an AI Governance Model (patent pending). An AI strategist with over 30 years of experience, he specializes in AI governance, risk management, compliance, and digital transformation. As a former Deloitte executive, Dr. Riz has delivered over $300 million in cost savings for Fortune 500 companies. He has taught AI strategy and project management in executive programs at Harvard, MIT, and the University of Cambridge. He holds a Ph.D. from SKEMA Business School.
LinkedIn: linkedin.com/in/drrizwanasheikh
Khalid Ahmad Khan, PhD, PMP
Professor of Project Management and Governance,
Chief Data Scientist, AI Researcher
Lahore, Pakistan
Dr. Khalid Ahmad Khan is a professor, distinguished data scientist, AI researcher, and governance expert with over 30 years of experience spanning project management, AI, data science, risk management, and digital transformations across public and private sectors. Dr. Khan specializes in deploying AI-enabled analytics solutions for monitoring, document analysis, and executive decision-making. He advises governments on public investment management, portfolio planning, and project lifecycle governance, applying advanced decision-support tools including Monte Carlo simulations, real options analysis, and Earned Value Management. A global advocate for project management excellence, Dr. Khan is a founding member and President of the PMI Lahore Chapter and contributed to the PMI PMBOK Government Extension, Third Edition. His key clients include the World Bank, USAID, GIZ (German Aid Agency), and LUMS.
He holds a Ph.D. in Strategy and Project Management and is PMP certified by PMI.
Aamir Khalid Pirzada
Chief Information Officer
AI/Manufacturing Technology Leader
Al Khobar, Saudi Arabia
Aamir Pirzada is a transformative technology executive whose pioneering work in AI and machine learning has revolutionized manufacturing operations. As a seasoned Chief Information Officer (CIO), he has led organizations across manufacturing, oil and gas, retail, public services, and telecommunications industries.
Aamir specializes in leveraging AI-driven solutions for predictive maintenance, quality control, and data-driven decision-making—consistently delivering enhanced operational efficiency, significant cost savings, and superior product quality. His expertise spans enterprise system implementations including SAP S4HANA, Oracle e-Business Suite, and Microsoft Dynamics AX.
A recognized leader in cybersecurity, Aamir has established security protocols and led audits to safeguard critical manufacturing assets. His strategic vision centers on integrating AI, machine learning, and IoT to create smart, adaptive manufacturing ecosystems. With deep expertise in project management, business analysis, and solution design, Aamir continues to guide organizations toward technological excellence in the rapidly evolving digital landscape.
