Guessing is not a strategy: How to build decision velocity with AI and real-time data
June 10, 2026 | Live Webinar
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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Will the PMO Become the Center of AI Adoption in Organizations?
| Artificial intelligence is reshaping how organizations plan, prioritize, and manage projects. While much of the discussion focuses on new technologies, the real organizational question is governance, and determining how AI is used effectively and responsibly in project environments. Increasingly, that responsibility will fall to the Project Management Office (PMO). Project portfolio management has always been about making informed decisions under constraints such as limited resources, competing priorities, and uncertain outcomes. AI strengthens this process by analyzing historical and operational data to identify patterns, predict risks, and support portfolio prioritization decisions. Machine learning models, for example, can estimate project value, forecast potential risks, and highlight relationships between projects that may create strategic interactions. When combined with optimization methods, these insights support better portfolio selection and resource allocation decisions. Research shows that while AI technologies such as machine learning, expert systems, and decision-support tools are increasingly applied to project environments, organizations often lack structured frameworks for integrating these technologies into management processes. The PMO is uniquely positioned to act as the organizational bridge between technology capabilities and decision-making processes. Rather than treating AI as a purely technical tool, the PMO can integrate AI insights into portfolio governance structures. For example, AI can support project prioritization, resource allocation, scheduling, and risk analysis across the portfolio, while the PMO ensures that decisions remain aligned with strategic objectives. Governance, interpretability, and accountability remain essential components of responsible AI adoption. In practice, this means the PMO will need to take on several new responsibilities. 1. Establish governance frameworks that define how AI models are used in project decision processes. 2. Ensure transparency and interpretability so that portfolio decisions remain explainable to executives and stakeholders. 3. Help build organizational capability by promoting data standards, training project managers to work with AI tools, and ensuring that models are used appropriately. As organizations continue to implement artificial intelligence, the PMO's role will evolve from administrative oversight to strategic orchestration. In an AI-enabled environment, the PMO will manage the interaction between human judgment, data, and intelligent systems to improve the quality of project decisions. |
Posted on: May 18, 2026 08:00 AM
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Project Manager Accountability in the Era of AI
| Artificial intelligence is rapidly reshaping how projects are planned and delivered. Today’s AI solutions optimize schedules, allocate resources, predict risks, and recommend budget adjustments with impressive speed. The results can look precise, data-driven, and highly convincing, but there is an important condition that is easy to overlook. Is AI optimizing part of a project without considering unintended consequences to other aspects? Projects normally have many constraints, and when one element is optimized, others can be affected, sometimes in subtle ways. Scope, schedule, and cost are standard project constraints. When one of these changes, there is likely to be an impact on one or both of the others. A resource optimization model might improve utilization but overload key team members, reduce quality, or increase risk. A cost reduction plan may inadvertently limit scope or reduce stakeholder value. These are not failures of AI. They are the natural consequences of optimizing without project manager oversight, which maintains the overall project-level perspective. Projects may also have interdependencies with other projects in a portfolio or with external factors that may interact dynamically as the project is delivered. Skill availability, environmental concerns, or new industry regulations may all contribute to change. AI can only be considered a comprehensive solution if it accounts for potential unintended consequences. Otherwise, AI-based results must be treated as inputs into a decision process. AI analysis should occur within a wider perspective of the total project and project environment. Project managers need to be leaders who take responsibility for ensuring AI processes benefit the project. |
Posted on: May 11, 2026 09:54 AM
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Reference Class Forecasting Depends on How You Define “Similar”
| Reference Class Forecasting (RCF), popularized by Bent Flyvbjerg, is used to address optimism bias in project cost and schedule estimation. The logic is simple: compare your project to similar completed projects and adjust expectations based on their actual outcomes. It is one of the most influential forecasting innovations in project governance. But a foundational question that requires more attention is: how is similarity defined in the first place? In practice, reference classes are often formed using broad administrative categories such as “light rail,” “metro,” or “heavy rail.” Yet these similarity criteria are frequently underspecified, even though they determine the empirical distribution from which percentile uplifts are calculated. Before any statistical adjustment occurs, a methodological decision has already shaped the forecast. In my recent study using a dataset of U.S. mass transit projects, I examined the sensitivity of RCF to alternative reference class formation. Rather than relying on predefined categories, I applied unsupervised clustering techniques to construct alternative reference classes based on structural attributes such as track length, number of stations, underground proportion, and rolling stock. Cost and schedule outcomes were excluded to preserve the outside-view logic. The results showed that changing similarity groupings altered cost and schedule distributions. Percentile-based uplifts shifted, dispersion patterns changed, and the implied contingency requirements varied across alternative clusters. In other words, RCF outcomes proved structurally contingent on how similarity was operationalized, not merely on statistical adjustment. This does not undermine RCF. The behavioral foundations established by scholars such as Daniel Kahneman and Amos Tversky remain essential. The outside view is still one of the strongest correctives to optimism bias in major projects. However, the findings suggest that the credibility of RCF depends not only on selecting the appropriate percentile (P50, P80, etc.), but also on transparently justifying how the reference class was formed. For practitioners and governance bodies, this has important implications. Reference class formation should be treated as a methodological decision requiring documentation and testing. Specifically:
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Posted on: May 04, 2026 08:00 AM
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