Use AI to Improve Stakeholder Communication
| Over the years and many projects, I learned that stakeholders do not all need the same information. An executive may want a concise view of progress, risk, and decisions. A technical stakeholder may need detailed information about an issue. A customer may be primarily concerned about impacts and outcomes. Yet project communication is often created once and distributed to everyone. AI provides an opportunity to change that. Section 3.1.3 of PMI’s Standard for Artificial Intelligence in Portfolio, Program, and Project Management encourages using AI to tailor messages for different stakeholder groups and communication channels. For most project professionals, a chatbot offers a simple way to put this idea into practice. I suggest using the following Stakeholder Communication Prompt Template. Give the chatbot the project information and specify:
An important value of AI is its ability to help us communicate more effectively with each stakeholder. |
Posted on: October 05, 2026 08:00 AM
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AI Makes Data Quality a Project Leadership Issue
| Artificial intelligence is changing the importance of data in project management. For years, project teams have worked with misaligned schedules, inconsistent cost information, incomplete risk registers, and retrospective progress reports. People learned to recognize these weaknesses and compensate for them through experience and judgment. AI changes that relationship. An AI system can analyze thousands of data points and produce a forecast in seconds. However, greater analytical capability does not compensate for poor project data. In fact, it can amplify the problem by producing sophisticated outputs based on incomplete, inconsistent, or inaccurate information. PMI’s Standard for Artificial Intelligence in Portfolio, Program, and Project Management emphasizes that reliable and actionable AI outcomes depend on data quality. For project leaders, this makes data quality more than a technology issue. It becomes a management responsibility. Before relying on an AI-generated forecast or recommendation, project professionals should ask:
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Posted on: September 28, 2026 08:00 AM
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AI Changes How We Manage Project Risk
| Project risk management has traditionally relied on identifying risks, assessing probability and impact, developing responses, and reviewing the risk register periodically. Artificial intelligence creates an opportunity to make that process more continuous. AI can analyze changing project data, detect patterns, identify anomalies, and reassess risks as conditions change. A risk that appeared relatively insignificant last week may become important when schedule performance, resource availability, cost trends, or external conditions begin moving in the wrong direction. PMI’s Standard for Artificial Intelligence in Portfolio, Program, and Project Management recognizes this shift by emphasizing continuous monitoring and reassessment of AI-related risks as new data, technologies, and internal or external conditions emerge. The same thinking can be extended to how AI supports project risk management. Instead of asking only “What are our current risks?”, project professionals can increasingly ask “How is our risk exposure changing?” That is an important development. AI makes it possible to move risk management closer to the project's actual pace. Emerging patterns can be identified earlier, assumptions can be continually tested, and potential responses can be evaluated before a risk becomes an issue. The future of project risk management is not simply a better risk register. It is a continuously updated view of where the project may be heading. |
Posted on: September 21, 2026 08:00 AM
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Before You Use That Chatbot Response
| For the many project professionals I contact, using AI does not mean implementing a sophisticated AI system. It means opening a chatbot and asking it to analyze a risk, summarize a document, draft a communication, review a schedule, or recommend an action. The ease of getting an answer creates a new challenge: deciding whether the answer should be used. PMI’s Standard for Artificial Intelligence in Portfolio, Program, and Project Management emphasizes the importance of interpreting AI outputs critically and recognizing when generated content does not align with the original intent, factual accuracy, or business policy. Before using a chatbot response in a project, I recommend a simple check: Chatbot Response Checklist
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Posted on: September 14, 2026 08:00 AM
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Five Ways to Make AI More Reliable on Your Projects
| The recently published PMI Standard for Artificial Intelligence in Portfolio, Program, and Project Management emphasizes that quality and reliability should be designed into AI from the beginning, not added later. Having had the opportunity to review and suggest edits to the standard, I found that many of its principles align closely with my own research and practical experience applying AI to project management. The ideas below combine guidance from the PMI standard with my insights from research and real-world implementation. Organizations are investing heavily in artificial intelligence, yet many are disappointed with the results. The problem is rarely the AI itself. More often, it is how AI is implemented. If you want more reliable AI recommendations, start with these five practices. 1. Improve your project data. AI learns from historical information. If schedules are incomplete, costs are inconsistent, or risks are poorly documented, AI will produce unreliable recommendations. Better data almost always leads to better AI. 2. Never accept AI recommendations without validation. Before acting, ask whether the recommendation reflects the project's current reality. Have priorities changed? Are new risks emerging? AI cannot always recognize recent events that are not represented in its data. 3. Keep humans involved in important decisions. AI should support project decisions, especially when they are critical to project results. However, strategic trade-offs, stakeholder concerns, and organizational priorities require human judgment. 4. Measure how well AI performs. Track AI predictions against actual project outcomes. Compare schedule forecasts, cost estimates, and risk predictions with what actually occurs. This allows you to identify where AI adds value and where improvements are needed. 5. Continuously improve the system. Projects evolve, and AI should evolve with them. Update project data, retrain models when appropriate, and incorporate lessons learned into future recommendations. AI is not a one-time implementation. It is an ongoing capability. Reliable AI is built through disciplined project management, not just advanced technology. Organizations that combine quality data, human oversight, performance measurement, and continuous improvement are far more likely to achieve meaningful results from AI. |
Posted on: September 07, 2026 08:00 AM
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