Why Business and Academia Often View AI Differently
| From working in both academic research and industry, one observation is that artificial intelligence (AI) is often viewed very differently in these two environments. In academic research for business, AI is rarely the starting point of the study. When submitting research papers to journals, reviewers typically expect the research to begin with a clearly defined project problem, such as improving forecasting accuracy, identifying risk patterns, or optimizing decision-making. The researcher then uses theory and historical studies to evaluate analytical approaches to address that problem, and AI may be one of the methods considered. AI-based methods must withstand the rigour of comparison with other techniques, such as regression analysis, statistical models, or optimization methods. In other words, the research question comes first and the analytical process comes second. In business, however, the mindset is often quite different. Executives tend to view AI less as one analytical method among many and more as a strategic opportunity. AI has the potential to improve productivity, automate complex tasks, analyze large volumes of data, and generate insights faster than traditional approaches. Because of this, many organizations feel pressure to adopt AI quickly to avoid falling behind competitors. In practice, this leads to a very different starting point. Rather than asking, “What is the best analytical method for this problem?” business leaders often begin with the question, “How can we use AI to improve results?” This difference does not mean one perspective is right and the other is wrong. Academia emphasizes rigour, comparison, and methodological clarity. Business emphasizes speed, opportunity, and competitive advantage. The most productive path forward may lie somewhere between the two. Organizations benefit when they adopt AI thoughtfully, understanding both its potential and its limitations. At the same time, researchers can ensure their work remains relevant by studying real organizational challenges where AI is being deployed. Artificial intelligence is both a powerful tool and a strategic capability. Bridging the gap between academic research and business urgency may ultimately lead to better decisions in both worlds. |
Posted on: April 13, 2026 08:00 AM
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The Flaw with Human in the Loop
| Many organizations and AI frameworks use the phrase “human in the loop” to describe the interaction between individuals and AI. However, the formal definition of the phrase means being aware of current events. in the loop /ˌin T͟Hə ˈlo͞op/ idiom included in a group of people who are informed about or involved in something; aware of what is happening. — Oxford English Dictionary Being informed is not the same as having decision authority or responsibility for outcomes. When organizations implement AI systems, simply keeping a person “in the loop” may not provide the level of engagement necessary to ensure responsible decision-making. Instead, I encourage students and workshop participants to “collaborate” with AI. Collaboration implies working together to achieve an objective. If humans and AI processes work together, the outcome should improve, or at least be more fully understood in the context of making a decision. The distinction matters. Being in the loop suggests awareness. Collaboration requires involvement. If AI unknowingly implements a highly biased resource plan, is it enough for the project manager to be aware of it? A situation that requires corrective action necessitates a deeper level of understanding than merely being informed. Collaboration means understanding the process by setting the objective, developing a data collection plan, performing the analysis, and delivering an actionable output. Collaboration does not mean constantly monitoring AI. It means ensuring the process is designed for quality and robustness, not speed or productivity. AI-based algorithms can predict or classify, and they do so with an associated probability of accuracy. Being in the loop means you understand the concept. Collaborating means you are part of developing and perhaps approving the process. If you are simply in the loop and an AI-driven decision results in a significant financial loss, how responsible would you feel? Collaboration implies a different level of accountability. It requires understanding the risks and addressing them proactively, just as organizations would in any well-managed software deployment. Errors may still occur, but a collaborative approach makes them easier to detect, understand, and mitigate. Human oversight of AI is an ethical and governance necessity. How oversight is implemented will vary across organizations. Describing humans as “in the loop” may unintentionally suggest passive awareness when what is truly needed is more active involvement. The success of AI systems depends not just on technology but on how clearly we define the human role that governs them. |
Posted on: April 06, 2026 08:00 AM
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The Project Manager’s Role in Making AI Project Agents Successful
| Implementing AI project agents can dramatically improve project performance, but without proper controls, they can amplify errors, embed bias, and erode accountability. Project managers face a new responsibility to ensure these systems strengthen decisions rather than introduce new risks. This requires treating AI agents as decision-support systems and not autonomous decision-makers. A project management AI agent is an intelligent system that can access project data, perform analysis, and independently take appropriate actions. The agent actively supports and helps manage the project by detecting patterns, developing predictions, and optimizing decisions. In advanced processes, multiple AI agents work together, each specializing in areas such as scheduling, risk monitoring, budget tracking, or stakeholder communication. These types of agents share information, coordinate their actions, and collectively support the project manager as a collaborative support system. The agents can work in parallel, monitoring different project areas simultaneously or sequentially, where they collaborate in a step-by-step process to make decisions or take action as needed. In a construction project, one AI agent may monitor the schedule while another simultaneously tracks cost performance, working in parallel to provide real-time integrated reporting. In a separate sequential workflow, one agent can analyze the impact of a delay of a task, and a second agent uses the analysis to develop recovery options. Project managers should be aware that AI agents are only as reliable as the data and assumptions behind them, meaning poor data quality, outdated information, or incomplete inputs can lead to misleading analyses and flawed recommendations. From an ethics and governance perspective, project managers must ensure transparency in how agent recommendations are generated, maintain human oversight for the most consequential decisions, and protect sensitive project and personnel data from misuse or unintended exposure. Project managers can set up AI agents for success by taking a proactive and structured approach to how these tools are used within the project environment. In particular, they should focus on three core practices: 1. Maintain strong data discipline by ensuring project data is accurate, current, and complete, and by regularly checking that inputs still reflect real project conditions. 2. Apply informed human oversight by reviewing AI-generated insights for plausibility, comparing them with professional judgment, and adjusting thresholds or models as the project evolves. 3. Strengthen governance and ethics by documenting how AI tools support decisions, defining clear human approval points for major actions, and safeguarding sensitive project and personnel data. By embedding these practices into everyday project routines, project managers ensure AI remains a decision-support partner, reinforcing accountability, transparency, and stakeholder trust. |
Posted on: March 23, 2026 08:00 AM
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AI-Based Collective Project Intelligence
| Project management has traditionally been framed as a discipline of individual judgment. Even when supported by cohesive teams, most planning, staffing, and scheduling decisions ultimately flow through one project manager. Recent studies on using swarm intelligence for software project staffing and workforce deployment challenge this assumption by demonstrating how AI-based genetic algorithms using the Particle Swarm Optimization (PSO) method support project decisions in fundamentally different ways. Rather than searching for a single optimal plan, these approaches explore many feasible solutions simultaneously, revealing trade-offs that would be difficult for any single individual to discover. What makes swarm intelligence especially relevant to project management is its collective logic. These methods simulate the behavior of multiple autonomous agents, each exploring the problem space under different constraints and assumptions. In practice, this is closer to convening a roomful of experienced project managers with diverse perspectives. The algorithm does not decide for the manager. It expands the decision space available to them. Project failures are more often driven by overconfidence in a single plan. Swarm-based systems help counter these situations by externalizing judgment. They generate multiple staffing and scheduling alternatives, make skill-task mismatches visible, and allow managers to adjust priorities. The project manager remains accountable for the final choice, but that choice is informed by a richer set of possibilities. This opens an important future direction for project management. As multi-agent systems mature, project managers will increasingly act as orchestrators of intelligent agents rather than sole optimizers of plans. Genetic algorithms and swarm intelligence point toward a model where AI supports how decisions are made, not just what decisions are taken. In a profession defined by uncertainty, complexity, and competing priorities, that shift may prove more valuable than optimization itself. References Hameed, M., Khalid, H., Qamar, U., & Abass, S. K. (2017). Optimizing software project management staffing and workforce deployment processes using swarm intelligence. Proceedings of the Computing Conference 2017, London, UK. IEEE Oyekunle, A. A., Adebayo, O. O., & Afolayan, A. O. (2025). Swarm intelligence for project management and decision sciences. Open Science Journal, 10(1), Article 3708. https://doi.org/10.23954/osj.v10i1.3708 |
Posted on: March 09, 2026 08:00 AM
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AI is Getting Smarter: Two Emerging Project Management Careers
| According to an article published in Scientific American, ChatGPT scored 155 on an IQ test. This has a significant impact on career trajectories, if not employment itself. Although AI technology only has two capabilities, prediction and classification, it is the creative minds of people that continue to drive its development. For those starting their careers or repositioning for the future, two emerging areas are important opportunities. 1) AI Ethics and Governance Manager. As AI is deployed in all areas of organizations, specialists will be needed to oversee how these systems are used and to ensure they operate responsibly. This role includes a review of implementation, ongoing oversight of the process, from data usage to results analysis, and establishing response plans for breaches which could have internal and public implications. These issues can have serious operational, legal, and public-trust implications. Essential skills: learn how AI works, understand the field of explainable AI, and be knowledgeable about business and regulatory aspects of deployment. 2) AI-Driven Change Leader. Beyond becoming pervasive in enterprises, the speed of AI and scale of adoption will accelerate. Any new technology requires successful deployment, but AI will be more disruptive and require more emphasis on managing the change. Implementing AI is a project, so project management skills, combined with a strong understanding of change management processes, are essential for this role. As organizations adopt AI, the most durable career opportunities will center on managing the consequences rather than building the algorithms themselves. Roles focused on AI ethics and governance are essential to ensure responsible deployment, accountability, and trust. At the same time, strong change management capabilities are critical to help organizations manage change and translate AI investments into real value. These are opportunities where human judgment, oversight, and leadership will matter most in an AI-enabled future. |
Posted on: February 23, 2026 08:00 AM
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