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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Using AI to Improve Team Communication (Without Losing Trust)
| Artificial intelligence is starting to reshape how project teams communicate. There is software that can summarize meetings, draft updates, translate languages, and even flag when messages may be unclear or misaligned. Used well, AI can reduce noise, improve clarity, and help teams stay aligned in fast-moving environments. But communication is also about trust, context, and human judgment. That makes ethical considerations important. If team members are unaware that AI is generating or shaping communication, trust can erode. People may question whether messages reflect genuine intent or automated output. Being open about when and how AI is used helps maintain credibility. Another challenge is bias and tone. AI systems are trained on existing data, which may include biased or overly formal communication patterns. This can result in messages that unintentionally exclude, misrepresent, or misinterpret meaning, especially across cultures. Project managers need to review AI-generated content to ensure it aligns with the whole project team’s values and context. There is also the issue of privacy. AI-based software can analyze communication patterns or summarize conversations may process sensitive information. Teams need clear boundaries on what data can be used, where it is stored, and who has access to it. Without this, efficiency gains can come at the cost of confidentiality, and AI may be viewed as invasive. Perhaps the most subtle risk is over-reliance on AI processes. If teams begin to depend on AI to interpret, summarize, and respond, then critical thinking and direct communication can decline. Misunderstandings may go unnoticed because the human layer of reflection has been reduced. The role of the project manager is to balance these dynamics. AI should support communication, not replace it. This means setting clear expectations, reviewing outputs, and ensuring that important conversations still involve human engagement. AI can make communication faster, clearer, and more consistent. However, the process still needs to be guided by a collaborative effort from the project manager and the project team. |
Posted on: June 01, 2026 08:00 AM
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Start with AI, not a Project Framework.
| Most organizations still begin with the wrong question: What is the most effective project process: waterfall, agile, or a hybrid? That thinking reflects a pre-AI mindset. If artificial intelligence is going to reshape how projects are planned and delivered, then it should not be layered onto existing frameworks. It should be the starting point. The Project Management Institute (PMI) currently positions AI as a tool within established processes. In this view, project managers continue to follow familiar structures, simply enhancing them with AI capabilities. That sounds reasonable, but it misses the critical point that those frameworks were not designed for an AI-enabled environment. They were built for human-driven planning, sequential decision-making, and limited data processing. The results speak for themselves. Across industries, project success rates have remained stubbornly inconsistent for decades. Cost overruns, schedule delays, and unmet benefits are not rare exceptions but persistent patterns. If the frameworks were truly effective, there would have seen meaningful improvement by now. Instead, we continue to optimize within systems that were never designed for the level of insight, speed, and adaptability that AI provides. This is not the first time organizations have faced this challenge. When enterprise resource planning (ERP) systems were introduced, companies quickly learned that simply automating existing processes led to poor outcomes. Real value came only when processes were redesigned to align with the capabilities of the technology. The same principle applies today. AI changes how decisions are made and enables continuous analysis rather than periodic review. It surfaces patterns and risks that traditional methods cannot detect. It enables dynamic planning rather than static baselines. Trying to force AI capabilities into rigid frameworks limits their impact. The path forward is clear. Start with AI. Design your project approach around what AI can do, then determine which processes support that reality. This elevates the project manager's role as the focus shifts from managing process steps to orchestrating intelligent decision-making. The question is no longer which framework to use. The question is how to build a project environment where AI can deliver full value. |
Posted on: May 25, 2026 08:00 AM
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