The Growing Gap Between Project Complexity and Project Management Capability
| Projects are becoming increasingly complex. Modern projects operate within interconnected environments involving multiple stakeholders, evolving requirements, global supply chains, regulatory constraints, and rapidly changing technologies. Information moves faster than ever, risks emerge unexpectedly, and decisions made in one area can have significant consequences elsewhere. Yet many project management practices remain structurally unchanged. Project managers still rely heavily on periodic reporting, static schedules, manual analysis, and governance processes that were developed for a different era. While these approaches have served organizations well, they were designed for environments where change occurred more slowly and project information was less dynamic. The result is a growing gap between project complexity and the ability to manage it effectively. As projects become more interconnected, the volume of information often exceeds the capacity of individuals to analyze and interpret it in a timely manner. Important signals can be missed, risks may be identified too late, and corrective actions become increasingly reactive rather than proactive. Artificial Intelligence (AI) offers an opportunity to narrow this gap. Project managers can use AI to continuously analyze project data, identify emerging risks, detect patterns, and provide insights that would be difficult to discover through manual methods alone. Rather than replacing project professionals, AI can enhance their ability to understand complex environments and make more informed decisions. The challenge facing organizations is not simply adopting new technology. It is recognizing that project complexity has evolved and that management approaches must evolve with it. Organizations that successfully combine human judgment with AI-enabled insights will be better positioned to manage increasingly complex projects and improve project outcomes. |
Posted on: July 06, 2026 08:00 AM
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How AI Can Improve Executive Confidence in Major Projects
| Major projects involve enormous complexity, multiple stakeholders, evolving risks, and constant pressure to deliver results. Whether the initiative is a transportation system, a hospital, an energy facility, or a digital transformation program, executives are often required to make critical decisions with incomplete information. Artificial Intelligence (AI) offers an opportunity to improve executive confidence by providing greater visibility into project performance, earlier awareness of emerging issues, and stronger decision support. AI can continuously analyze project data to identify trends, detect anomalies, and highlight areas requiring attention. Rather than relying solely on periodic status reports, executives can receive near real-time visibility into project performance and potential concerns. This allows leaders to focus on issues while corrective action remains possible. One of AI's greatest strengths is its ability to identify patterns that may not be apparent through traditional project controls. By analyzing historical and current project data, AI can reveal indicators associated with delivery challenges, resource constraints, quality issues, and other emerging risks. These insights support more informed decisions and help organizations direct attention where it is needed most. AI does not eliminate uncertainty, nor does it replace executive judgment. However, it can significantly enhance situational awareness and provide an additional layer of analysis to support leadership decisions. For executives responsible for major projects, these capabilities can strengthen governance, improve confidence, and increase the likelihood of successful outcomes. |
Posted on: June 29, 2026 08:00 AM
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Will AI Change the Need for Project Managers?
| Globally, there are an estimated 16.5 million project managers. A forecast by PMI suggests another 25 million new project managers will be needed by 2030. That’s good news for all my project management students, but some organizations may adopt AI to address the challenge of hiring more project managers. Organizations are likely to use AI to expand the span of control of project managers in two ways. 1. Add AI to manage specific processes independently and include a dynamic exception warning. This can allow a single project manager to manage many more projects at the same time. 2. Allow AI to be the project manager for smaller, simple projects within specified constraints. This can be performed with an AI-based agent that completes the project independently, unless exceptions are identified that require attention. AI is already capable of performing a growing range of project management activities. In addition to organizing meetings and preparing status reports, AI can assist with planning, scheduling, budgeting, risk identification, forecasting, resource allocation, and performance monitoring. Many project documents can be generated, reviewed, or validated by AI. These capabilities may reduce demand for some project coordination, scheduling, and support roles as an increasing number of project management activities are automated. The role of the project manager increasingly shifts toward oversight, exception management, organizational alignment, and ensuring that AI-supported decisions align with project objectives. AI will not determine whether the demand for project managers rises or falls. Organizations will make that decision based on how they redesign project work. The future is likely to involve AI performing a growing share of project management activities, while project managers focus increasingly on leadership, judgment, stakeholder engagement, and accountability. |
Posted on: June 22, 2026 08:00 AM
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Is ChatGPT Lying or Are We Asking the Wrong Question?
| It’s easy to slip into thinking that generative AI behaves like a person. We say things like “it lied,” or “it doesn’t understand.” That’s anthropomorphism: assigning human traits to something that isn’t human. It seems natural because these systems communicate in a conversational way, but that doesn’t mean there is any real understanding behind the response. Generative AI does not lie. Lying requires intent and awareness. AI has neither. The output is based on the data it was trained on, the prompt it receives, and the algorithms generating the response. When something is incorrect or misleading, it is not deception. It is a limitation of the system. This is where things get blurred. Many GenAI tools seem to have personalities. They adjust tone, respond smoothly, and often sound confident. That personality is part of the design, not evidence of human-like thinking. Treating it as real creates a subtle but important trap. When we assume human qualities, we stop questioning properly. Instead of asking what data or assumptions led to an output, we ask why it misled us. That shift reduces critical thinking and can result in missed errors or poor decisions. At the same time, a single incorrect answer can cause people to dismiss the technology entirely. Expectations move from perfect to useless, which is not realistic. Think about your smartphone. You expect it to work, and when it doesn’t, it’s frustrating. But you don’t assume intent. You troubleshoot the issue. GenAI should be treated the same way. It is not human and not perfect, but it is a powerful tool that requires judgment, curiosity, and better prompts. |
Posted on: June 15, 2026 08:00 AM
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Ethical Lag: A Hidden Risk in AI Adoption
| A significant challenge in AI adoption is what can be described as ethical lag, the gap between what technology can do and what organizations are prepared to manage responsibly. AI capabilities are advancing rapidly, enabling faster decisions, deeper insights, and greater automation. However, ethical frameworks, governance structures, and decision accountability are not evolving at the same pace. This creates a misalignment that introduces real risk. Ethical lag is not simply about extreme scenarios or misuse. It appears in everyday project decisions. Algorithms may optimize for efficiency at the expense of fairness. Predictive models may reinforce historical bias embedded in data. Automated recommendations may be accepted without sufficient scrutiny because they appear objective or data-driven. In these situations, the issue is not the technology itself, but the lack of readiness in how it is applied, interpreted, and governed. For project leaders, this gap is especially important. Projects are where strategy becomes reality, and increasingly, where AI is deployed in practical ways. If ethical considerations are not embedded into project processes, risks are amplified at scale. Decisions made quickly by intelligent systems can have lasting consequences, particularly when accountability is unclear. Addressing ethical lag requires a shift in focus. It is not enough to implement AI tools or integrate advanced analytics into workflows. Organizations must build ethical capability alongside technical capability. This includes establishing clear governance structures, defining accountability for AI-supported decisions, and ensuring that project professionals are equipped to question, interpret, and validate outputs. Ethical readiness is not a constraint on innovation. It is what enables innovation to be sustained, trusted, and aligned with long-term value. As AI becomes more embedded in project environments, closing the ethical lag will be essential to delivering outcomes that are not only effective but also responsible. |
Posted on: June 08, 2026 08:00 AM
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