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. |
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 |
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 |
Understanding How Gen AI Makes Decisions: The Role of Explainability
| As Generative AI becomes more integrated into decision-making, from content generation to project insights, it is essential to understand how it arrives at its outputs. This requires emphasis on explainability, which refers to our ability to understand why and how an AI system makes a specific decision. Unlike traditional algorithms that follow clear, rule-based logic, Gen AI models such as ChatGPT operate based on patterns learned from vast amounts of data. These models use probabilities to predict the most likely next word, sentence, or outcome, based on context and training. But this process can lack transparency. To address this, researchers and developers employ techniques such as attention mapping, input attribution, and model visualization to identify the factors influencing the AI’s outputs. For example, explainability tools can highlight which words in a prompt had the most influence on the model’s response or indicate how confident the model is in its predictions. Some of these capabilities are built into generative AI platforms, such as ChatGPT, through conversational prompting and token probabilities. Others, such as LIME and SHAP, are standalone Python libraries designed to interpret traditional machine learning models and provide insights into model behavior. Why does this matter? In high-stakes environments such as healthcare, finance, or project management, stakeholders require transparency to trust and validate AI-driven insights. Explainability supports accountability, helps detect bias, and ensures that decisions align with ethical and legal standards. It bridges the gap between powerful AI capabilities and responsible human oversight, ensuring that Gen AI serves as a reliable partner in decision-making. |
Ethical AI in Project Management: Building a Framework for Gen AI Use
Categories:
AI
Categories: AI
| As generative AI (Gen AI) becomes increasingly embedded in project management processes, organizations must develop a clear and responsible framework for its use. Gen AI, such as ChatGPT, can create schedules, draft procurement strategies, assess risks, and even suggest team configurations. However, employees require guidelines to reduce risks that may undermine both project and organizational integrity. Adhering to ethical considerations when interacting with Gen AI requires a framework that aligns with the organization's ethical policies, reflecting its values, regulatory requirements, and fairness. Here are three components to a good framework.
Organization Responsibility:
Training Objectives:
Data Imperatives:
The consequences of ethical violations when using Gen AI can be significant (Hagerty & Rubinov, 2019). Poor decisions and breaches of ethical policy can not only have serious repercussions but may go unnoticed for some time. Accountability is necessary from all parts of the organization involved. Employees must acknowledge their use of Gen AI and understand the associated level of risk. Ethical lapses in the use of Gen AI can also have cascading effects, from poor project outcomes to reputational damage. Accountability must be built into every layer: policy, practice, and personnel. Organizations should not only empower employees to utilize Gen AI but also equip them to be stewards of its ethical application. The technology offers incredible opportunities for organizational efficiency, which must be balanced by defining and implementing a framework that upholds ethical standards. Is your organization ready to integrate Gen AI responsibly?
Some references to check: Dignum, V. (2019). Responsible artificial intelligence: How to develop and use AI in a responsible way. Springer. Hagerty, A., & Rubinov, I. (2019). Global AI ethics: A review of the social impacts and ethical implications of artificial intelligence. AI & Society, 36(1), 55–66 |
