Guessing is not a strategy: How to build decision velocity with AI and real-time data
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AI Optimizers: The Hidden Ethics Risk in Project Software
| Artificial intelligence is increasingly being added to project management software. Schedule compression engines, resource-leveling algorithms, portfolio ranking systems, and forecasting models now operate in the background of many project platforms. While these AI optimizers promise efficiency and consistency, they introduce a growing ethical challenge. When optimization logic is embedded inside software, bias becomes harder to detect, question, or govern. Across the project management software landscape, vendors increasingly use AI-based algorithms to determine prioritization, forecasts, resource allocation, risks, and workflow optimization. AI-enabled software is promoted under the banner of productivity. In practice, it has become challenging to identify any mainstream project management software application that does not claim to leverage AI in some aspect of planning, coordination, or decision support. None of this implies wrongdoing, but it does raise the important governance question: whose values are embedded in these optimizers? Bias in project AI rarely appears as overt discrimination. Instead, it emerges structurally. Algorithms may favor projects that resemble past successes, penalize innovative or unconventional initiatives, or prioritize cost efficiency at the expense of safety, resilience, or social impact. Because these assumptions are encoded inside mathematical models and training data, they remain invisible to users. The result is an illusion of objectivity, as decisions appear neutral because they are based on a statistical process. Three ethical risks are especially relevant for project managers:
As AI becomes standard inside project software, ethics will depend on whether project managers can still see, question, and justify the decisions being made. This efficiency, in the form of productivity, may obscure the responsibility for ethical practices. |
Posted on: January 05, 2026 12:51 PM
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Emotion & AI
| When people argue that humans possess superior interpersonal skills compared to AI, I challenge that assumption. Anyone who has worked for a manager with anger issues, a habit of taking personal credit for team accomplishments, or a tendency to deflect blame knows that human interpersonal skills are far from guaranteed. On the other hand, AI can deliver a refreshingly honest, unbiased perspective on your value to the organization and your career potential. As project managers, we have to learn to be better people managers and motivators. For many of us, it is not a natural ability. AI is becoming more common in project management, reducing the administrative demands on project managers. As technology improves and becomes more sophisticated, AI’s emotional intelligence may surpass that of an average human. While not evidence of AI superiority, a 2024 study found that AI-based chatbot interventions produced “substantial improvements” in depressive and anxiety symptoms. Many similar studies confirm AI's ability to successfully manage people’s emotional well-being. Is there anything that AI cannot do? Yes. AI cannot love someone. Love is the ultimate differentiator because fake love can be detected. As we approach the end of another year, I encourage everyone to think about the ones you love and the ones who love you. That is a gift that AI cannot replicate. Happy holidays, and wishing everyone a joyous new year. |
Posted on: December 18, 2025 01:01 PM
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Agentic AI or Automating Clicks?
| Agentic AI can automate workflows, analyze data, and fire off emails, but it still can’t pour concrete or dig a tunnel. Agentic AI lives in the digital world and operates entirely through software, APIs, data, and cloud tools. Until project robots arrive, AI agents are only automating the actions we take in front of our computer. Of course, creating a status report, managing emails, and examining resource issues are all important and become more efficient using AI. Projects exist in the physical world, changing reality through infrastructure projects or triggering consequences in the real world such as improved cost, quality, or work efficiency. That’s the main disconnect: projects happen in the physical world, while agentic AI operates entirely in the digital one. Project managers are the ones who bridge that gap. How much can agentic AI really help with construction projects where physical materials and manual work are essential to achieving milestones? AI-based predictions of impending problems and clear recommendations for a resolution are valuable, especially in a dynamic and uncertain environment. This is dependent on ensuring the underlying data is structured, complete and timely. That’s a problem for many projects (Decker et al., 2022). If even one team member fails to update a critical task, the status report becomes inaccurate, leading to misinformed stakeholders. For agentic AI to be effective, it needs more than accurate data. In the dynamic environment of a project, agents need access to reliable data which is a critical responsibility for project managers who want to rely on AI agents. The future belongs to PMs who know how to combine real-world judgment with digital assistants that can automate the busywork and amplify insight. Reference Decker, D., Edelman, D., & Sharma, A. (2022). How data can help tech companies thrive amid economic uncertainty. McKinsey & Company. |
Posted on: December 04, 2025 04:13 PM
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Debunking 3 AI Myths for Project Managers
Categories:
AI
Categories: AI
| With the growing interest in how AI is changing our world, some articles have emerged that are not based entirely on reality. It is fascinating how stories circulate and become exaggerated through social media. The truth is, we cannot predict the future, but we can understand the current facts. Myth 1. You need a lot of data to make AI work. This most likely applies to the field of medicine, where errors can be costly. In project management, I conducted an academic literature review and identified 8 project management studies that used machine learning to establish statistical correlations. The range for the number of projects was 22 to 692, and the range of features (project characteristics) was from 4 to 44. Myth 2. AI requires a lot of energy to function. This is probably based on recent headlines about the race to build data centers needed to make large language models (LLMs) more accurate and effective across a broader range of topics. There is more to AI than LLMs. I run a Python-coded machine learning clustering algorithm on my laptop. I use the neural network function in IBM SPSS software on my desktop computer with 108 project datasets containing 17 variables. This myth might be more accurate if stated as “some” AI-based apps might need a lot of energy. As for the future, many creative people are working to improve the performance of both hardware and software. Myth 3. AI is contributing to climate change. This might be true if the major data center providers were running on fossil fuels. AWS invested in solar and wind energy projects and, in 2023, reached the goal of matching 100% of the electricity used in its global operations with renewable energy (AWS, 2025). Google claims they are the champion of clean energy. They achieved 100% renewable energy matching in 2017 and target 100% carbon-free emissions by 2030 for their data centers (Corio, 2022). Microsoft Azure, which hosts OpenAI, has LEED-certified data centers and plans to be carbon-neutral by 2030 (Microsoft, 2025). AI is changing how organizations function and how project management works. Reliable information should always guide our progress. References Amazon Web Services. (2025). AWS Cloud – Sustainability: Our progress. Retrieved November 8, 2025, from https://sustainability.aboutamazon.com/products-services/aws-cloud sustainability.aboutamazon.com Microsoft Corporation. (n.d.). Powering sustainable transformation. Microsoft Data Centers. Retrieved November 8, 2025, from https://datacenters.microsoft.com/globe/powering-sustainable-transformation/ Peterson Corio, A. (2022, June 23). Five years of 100% renewable energy – and a look ahead to a 24/7 carbon-free future. Google Cloud Blog. https://cloud.google.com/blog/topics/sustainability/5-years-of-100-percent-renewable-energy |
Posted on: November 17, 2025 10:00 AM
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Why do AI Projects Fail?
Categories:
AI
Categories: AI
| There is a widely circulated claim that a Gartner report stated 85% of AI projects fail. In fact, the original Gartner press release was a forecast that from 2018 through to 2022, 85% of AI projects would deliver erroneous outcomes due to bias in the data, misaligned algorithms, or project team implementation. Setting aside the misinterpretation, organizations that succeed in deploying AI tend to do four things differently: · Redesign processes instead of automating bad workflows · Provide training that explains what makes AI successful · Establish governance to realize benefits and avoid pitfalls · Lead change intentionally through structured change management In my view, the most significant factor is that AI projects fail because organizations don’t incorporate AI into their project processes. It is inconsistent to expect successful AI deployment without integrating AI into the very processes that manage its implementation. AI projects do not fail because of the technology, but because organizations don’t embed AI into their project management methodology. For AI projects to succeed, organizations need to redesign project processes, provide targeted training, and reinforce governance and change management to sustain adoption. Reference Gartner. (2018, February 13). Gartner says nearly half of CIOs are planning to deploy artificial intelligence. Gartner Newsroom. https://www.gartner.com/en/newsroom/press-releases/2018-02-13-gartner-says-nearly-half-of-cios-are-planning-to-deploy-artificial-intelligence |
Posted on: November 07, 2025 10:43 AM
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