What Does Trust in AI Look Like?
byIt seems as though AI developers are trying to make users so reliant on AI that they look past their reservations. That’s where the true risk is, and where project managers need to push back.
Conversations in Artificial Intelligence
Anonymous · Oct 15, 2026
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Alan Halley6 hours ago
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Michael Brinn · Feb 19, 2026
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Sonia Nagel8 hours ago
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Zakaria Botros · Oct 16, 2026
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Alan Halley11 hours ago
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Sarah Philbrick · Jun 18, 2024
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Rupi Jawandha20 hours ago
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Claudia Alcelay · Dec 6, 2023
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Ernesto Antonio Noya Carbajal21 hours ago
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Zakaria Botros · Oct 16, 2026
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Genevia GordonOct 17, 2026
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Jennifer Giaimo · Jun 9, 2025
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Abhiram GnyanijayaOct 17, 2026
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Claudia Alcelay · Jan 3, 2024
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Shelah MabeusOct 16, 2026
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Sarah Philbrick · Jun 6, 2024
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AnonymousOct 16, 2026
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Sarah Philbrick · May 23, 2024
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Jason DevinOct 16, 2026
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Top Influencers
Edivandro Conforto, Ph.D.
PMI Team Member
Management Scientist and Global Executive Advisor. AI, Technology
Strategist.| Humans in the Loop | E.C. Advisory & Insights Group
Campinas, São Paulo, Brazil
PMI Team Member
Keisha Lewis
Largo, Md, United States
Ronald Schmelzer
Founder & Chief Bot Wrangler, AI Columnist| Scalebrate & Forbes
United States, United States
Antonio Nieto
HBR Author | Director PMO | PMI Fellow & Past Chairman | Professor | Thinkers50 | Projects & Company
Brussels, Belgium
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AI Governance in Healthcare Transformation: A Project Leader’s Playbook for Responsible Delivery
Oct 22, 2026 9:00 AM EDT (UTC-4)
PREMIUM webinar
In healthcare, AI adoption cannot be treated as a simple technology rollout. It must be governed responsibly because project teams are often working with sensitive data, regulated workflows, complex stakeholder groups, clinical or operational risk, and high expectations for trust, transparency, and compliance.
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AI for Project Leaders: Practical Workflows, Guardrails, and Human Judgment
AI is quickly changing how project professionals draft reports, analyze risks, summarize meetings, and communicate with stakeholders. But the real leadership challenge is not simply learning a new tool. It is knowing which parts of the project work can be safely supported by AI, and which decisions must remain firmly in human hands.
The Orchestrated Enterprise: Aligning AI Productivity, SAFe® Governance, & Cloud FinOps
While generative AI tools promise unprecedented developer velocity, enterprise delivery leaders face a growing paradox: localized coding speedups rarely translate into predictable product delivery or bottom-line business value. Without structural governance, unchecked code generation pushes bottlenecks downstream into integration testing, security compliance, and uncontrolled multi-cloud infrastructure costs.
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GenAI and Agile: Amplifying Human-Centered Collaboration
byIn this PMI report, explore how GenAI strengthens agile teams by supporting trust, surfacing risks, and enabling breakthroughs—while keeping human judgment and collaboration at the center of project success.
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AI IQ
byTechnology offers an incredible opportunity to improve project performance. This blog shares the latest research and how organizations are implementing AI into their project methodology. Come with an open mind, increase your knowledge, share your concerns, and become a project manager with new skills to offer an organization.
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Learn From Others
Building Your AI-Powered Personal PM Operating System
byYou probably use AI for task-level improvements. But your greater opportunity is to use it to manage the entire flow of project information: from meeting to analysis to stakeholder communication to leadership action. Here's how.
How Structured Surveys Enabled High-Impact Summer Intern Projects
byBetter discovery leads to better project outcomes. This article highlights how Farm Credit Mid-America's PMO) guided its two summer interns in 2026 to deliver high-impact results using surveys in discovery to create actionable solutions.
From Project Documents to Project Intelligence: How I Use AI as a Project Knowledge Base
byProject managers generate an enormous amount of information. The challenge is not generating it; it is finding, connecting and using it when needed. Utilizing chatbots as a working project knowledge base can be invaluable.
The Intersection of Agile & AI: Proceed With Caution
byAgile is becoming an area where artificial intelligence is having a significant impact. But we need to be cautious about the implications, as critical and foundational decisions must remain in the hands of humans.
Design AI-Ready Processes Before Implementing AI
byOrganizations often try to automate processes that don’t really work. Simplify first, then automate, and you’ll get more value out of artificial intelligence. Here's how...
An Article About...a Watermark
byOne of the major AI engines just announced that it was embedding a watermark into every piece of content that it produces. This is very good news for project managers and the world at large, a step toward responsible AI.
Is AI Supplementing Your Skills…or Replacing Them?
byAI should be used to amplify our skills and experience. Instead, it is too often being used as a replacement. Not supplementing our skills and experience, but substituting them for its own version of the truth.
Is Your AI Risk Management Working?
byRisk is an area where artificial intelligence has a lot of potential, and where it can do at least as good a job as people without much effort. But it’s not a silver bullet solution.
Experience Decides: The PM’s Role in the Age of Artificial Intelligence
byAI is powerful, but it only works as well as the experience of the person using it. This practitioner has learned through experience that even with advanced tools, judgment remain critical to achieving the right results.
A (Possible) Day in the Life of a Future PM
byWhat will project management be like in the future? Are we at the point where most of the changes have already happened, or will there be ongoing reimagining of what project management is all about?
Unique Challenges With AI/ML Projects
byArtificial intelligence and machine learning projects have a lot of schedule uncertainty due to the time needed to train a model to perform subjective work. It's important to recognize the differences from managing a traditional software project.
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