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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Accomplish More with AI-Powered PMI Infinity
| PMI Infinity has evolved into much more than a capable project assistant. The latest enhancements show a clear direction, with the GenAI application having moved beyond question-and-answer interactions toward an AI workspace designed specifically for active project professionals. The overall user experience has improved with a cleaner interface, support for multiple document uploads, and faster access to PMI knowledge. The platform feels like a valuable project workspace. Here are 3 significant productivity features. 1) Folders. As more project managers rely on GenAI, conversations quickly become difficult to organize. Folders allow you to organize chats by project, client, or topic, making it much easier to return to previous work instead of starting from scratch each time. 2) Templates. Rather than repeatedly prompting AI to create a project charter, risk register, stakeholder plan, or communications plan, users can leverage structured templates aligned with PMI standards to achieve more consistent results. Standardized outputs also make it easier for PMOs to maintain common project documentation across multiple projects. 3) Agents. Instead of writing lengthy prompts, project professionals can launch agents designed for specific deliverables such as project charters, RACI charts, stakeholder management plans, and risk management plans. This reduces prompting effort while producing outputs that align with recognized PMI practices. Another valuable capability is document analysis. Users can upload project documents and ask PMI Infinity to summarize content, identify risks, review quality, compare documents, or generate new deliverables based on existing information. This can save significant time when reviewing lengthy project documentation. My biggest takeaway is that PMI Infinity is becoming more than a source of project management knowledge. It can support day-to-day project work by organizing conversations, creating standardized documentation, analyzing project information, and applying PMI guidance throughout the project lifecycle. For project leaders, the opportunity is to rethink how this GenAI workspace can be integrated into everyday project delivery. For more details, I recommend the recent webinar "Use AI to Level Up Your Project Work." https://www.projectmanagement.com/videos/1217206/Use-AI-to-Level-Up-Your-Project-Work |
Posted on: August 31, 2026 08:00 AM
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False Confidence in Using AI in Projects
| AI is becoming part of everyday project management. Project teams use it to analyze schedules, identify risk, forecast costs, recommend resource allocation, and summarize project performance. These activities can now be performed in seconds, not hours or days. However, reliance on AI can introduce the risk of overconfidence in the outputs. The danger is believing that AI output is more accurate than it really is. Several factors can make AI less reliable. Data quality. If the data is incomplete, inconsistent, or biased toward dissimilar projects, the recommendations will reflect those weaknesses. Model training. Every project environment is different, and no model can fully account for all aspects of project type or organizational strategy. Probability theory. AI is based on estimating what is most likely to happen. When probabilities are interpreted as guarantees, project professionals can become overly optimistic. Human behavior. When AI consistently produces useful recommendations, project teams may stop questioning the results. Assumptions and decisions go unchallenged because they are system-generated. False confidence gradually replaces critical thinking. Project leaders can reduce the risk of overconfidence through disciplined oversight. AI recommendations need to be continuously validated and monitored. AI offers exceptional decision-support capabilities, but the responsibility for interpreting results remains with the project leader. Organizations that gain the greatest benefit from AI will be those that implement governance structures and understand when to question AI results. |
Posted on: August 24, 2026 08:00 AM
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How Projects Can Start Strong
| The most significant improvements to the methodology for managing projects can be found in the book How Big Things Get Done by Bent Flyvbjerg and Dan Gardiner. The research findings were mainly targeted for megaprojects, although some aspects surely apply to all projects. One insight is to get off to a good start. This seems intuitive, yet many project plans are rushed or planned for updates as the project progresses. Once funding is in place, it can be difficult to wait before starting a project, especially with stakeholders who expect immediate, visible progress. Being proactive and creating comprehensive, accurate project plans takes time. However, with AI-based solutions, the ability to get off to a good start changes this. A project manager can use AI to validate the project budget, duration, and risks. The project scope can be analyzed for gaps and inconsistencies. If set up properly, AI analysis can take minutes or hours instead of days or weeks. Getting off to a good start means that within the first months of the project everything runs according to plan. Research suggests that a strong start increases the likelihood of overall project success. Maintaining that momentum serves as an incentive for project professionals to continue using AI to predict risks and minimize other potential variances. As always, using AI requires knowledge, data collection, and effective implementation. Although I often promote an overall integrated project solution for applying AI in project management, using AI for individual project documents is a reasonable approach to get your project off to a good start. |
Posted on: August 17, 2026 08:00 AM
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