AI: Beyond the Buzzword
Project - The official journal of the Association for Project Management (APM)
London - Association of Project Management – July 2023
Ricardo Viana Vargas, Ph.D.¹
As a senior project sponsor, you oversee a complex portfolio of projects with a combined budget exceeding $50 million. One of these is a high-stakes transformation project to modernize your company's production facilities. As you sit down for the weekly status update meeting, you're greeted by an AI-driven bot. It seamlessly synthesizes information from multiple projects into actionable insights, providing a clear snapshot of your entire project portfolio.
The AI system presents a health check of your transformation project. A clear visual representation of progress against project deliverables and timelines greets your eyes, along with a comprehensive forecast of upcoming tasks and associated risks. One risk, a crucial piece of equipment with a 60% probability of failure in the next three months, stands out. If ignored, it could result in project delays costing upwards of $500,000.
This risk is even more critical because two other projects in your portfolio rely on the same equipment. The AI system takes this interdependency into account and suggests a proactive solution. A preventive maintenance schedule, driven by machine learning analysis of maintenance logs and equipment performance data, could reduce the risk of equipment failure to less than 5% with minimal disruption to all projects' timelines.
Simultaneously, the AI bot anticipates a 20% spike in resource demand across your portfolio in the next quarter. This is partly due to the transformation project entering a critical phase and two other projects ramping up.
However, the AI bot doesn't just present a problem—it also offers a solution. By reallocating resources from less critical tasks and adjusting timelines, it proposes a plan that could meet the increased demand without overstretching your resources or inflating your budget. Implementing this plan could save $1 million across the portfolio, avoiding last-minute resource hiring or expensive rush charges.
These AI-powered, data-driven insights put you not just one but several steps ahead, enabling you to anticipate and manage complex interdependencies and resource challenges across multiple projects before they escalate into crises.
Current Utilizations of AI in Project Management
This scenario is no longer a future dream. Artificial Intelligence is no longer a distant concept confined to sci-fi movies or elite tech companies. Today, some organizations are already leveraging AI to navigate their unique project management challenges. AI-powered tools address issues from resource allocation to risk mitigation, ushering in a new era of efficiency and effectiveness. Here are some examples that demonstrate the transformative power of AI across different sectors:
- Construction: Autodesk and DEWALT have teamed up to create an AI-driven platform for construction sites (1). Their system uses image recognition to monitor construction sites, identify safety hazards, and ensure project compliance. The result is a decrease in safety incidents by up to 5%, potentially saving hundreds of thousands of dollars in safety-related costs. Additionally, using machine learning algorithms to analyze project schedules, the system predicts potential delays, reducing them by up to 20%. This predictive capability has resulted in tangible savings in project budgets and schedules.
- Healthcare: Mount Sinai Health System has implemented an AI algorithm to manage its hospital resources (2). The algorithm uses historical data and real-time patient influx to forecast beds, staff, and equipment needs. The AI system's predictions have proven accurate, leading to a 10% improvement in resource allocation efficiency. This translates into reduced patient wait times, improved patient care, and substantial operational cost savings.
- Energy: General Electric's Predix platform represents one of the most significant applications of AI in project management in the energy sector (3). This platform uses AI to predict potential equipment failures, optimizing maintenance schedules to prevent project delays. The AI's predictive analysis has resulted in up to 20% fewer project delays and significant cost savings. Moreover, the platform's ability to optimize power generation and distribution has increased energy efficiency, substantially impacting GE's carbon footprint.
- Manufacturing: Siemens has implemented an AI-based system to streamline its manufacturing processes (4). Their system utilizes AI to optimize production schedules and workflows based on real-time data, leading to increased efficiency and decreased waste. The company reports a 15% increase in productivity due to implementing this AI system.
- Retail: Walmart has been pioneering the use of AI in managing its complex supply chain (5). The AI system analyzes vast amounts of data to predict product demand, manage inventory, and streamline logistics. The results include improved customer satisfaction due to better product availability, reduced warehousing costs, and fewer out-of-stock scenarios.
These examples illustrate the exciting and diverse ways AI is currently utilized in project management across different sectors. As we explore the transformative power of AI across various sectors, let's shift our focus toward the broader benefits soon to be witnessed in this AI-driven Project Management revolution.
The Compelling Benefits of Adopting AI in Project Management
We have seen how AI is set to revolutionize how projects are executed and managed, from bolstering decision-making capabilities to optimizing resource allocation. Let's now look deeper into these benefits, with concrete examples and data, underscoring the potential and tangible value that AI can bring to the project management field.
- Enhanced Decision-Making: AI's ability to handle vast amounts of data at high speeds allows for generating real-time insights and predictive analyses. Accenture reported that 79% of executives agree that AI is instrumental in creating new insights and better decision-making processes (6). For instance, Rolls Royce uses AI to analyze data from its airplane engines, enabling it to anticipate problems and make informed decisions that could save millions in potential repairs and downtime.
- Increased Efficiency: AI's automation capabilities can handle routine tasks and free up team members to focus on more strategic tasks. According to a report by McKinsey, AI has the potential to automate about 50% of the activities employees are paid to do, leading to significant time and cost savings (7). In project management, IBM has been leveraging its AI platform, Watson, to automate routine tasks and project monitoring activities. By employing natural language processing to handle communication and documentation and machine learning algorithms for risk prediction and task prioritization, Watson has reportedly improved productivity by up to 20% (8).
- Improved Risk Management: AI's predictive capabilities can identify potential risks ahead of time, allowing for proactive risk mitigation. A report by PwC suggested that AI could reduce project cost overruns by up to 10% (9). In a different application, KPMG's AI platform, known as KPMG Clara, uses machine learning to perform risk assessments. This helps identify financial irregularities and other potential risks before they escalate, allowing teams to mitigate them and proactively reduce project overruns (10).
- Optimized Resource Allocation: AI can forecast future resource needs, leading to optimal resource allocation. An illustrative example can be found in the construction sector, where ALICE Technologies has developed an AI-driven platform. This platform uses AI to plan, schedule, and manage complex construction projects, predicting the resources needed for different tasks and phases of construction. This predictive capability enables more effective resource allocation, with users reporting efficiency improvements in resource deployment of up to 15% (11).
- Enhanced Stakeholder Communication: AI can generate tailored, up-to-date reports for different stakeholders. This capability enhances transparency and communication, significantly improving stakeholder satisfaction and trust. Microsoft's Project Cortex, for instance, uses AI to provide personalized, timely updates to team members, improving communication and collaboration (12).
- Learning and Continuous Improvement: AI's ability to learn from past projects and continuously improve future performance promises a step change in project outcomes. AI-powered project management tool ClickUp has a feature that learns from past task estimates to predict future task durations, enabling better planning and scheduling (13).
If we sum up all these potential savings and improvements, we could estimate that implementing AI in project management could lead to a 50% overall improvement in project efficiency and cost reduction. A portfolio of 1 billion euros means potential savings and efficiencies worth 500 million euros!
It's important to note that this is a highly simplified assumption. Actual savings and efficiencies would depend on numerous factors, including how effectively the AI is implemented, the nature of the projects, and the current operational efficiency of the company.
These potential savings underscore the tremendous potential AI offers in the realm of project management. With that said, let's consider the hurdles that come with implementing AI.
Overcoming Challenges: The Road Ahead
While the benefits of AI are enticing, the journey isn't without challenges. Organizations often encounter hurdles such as data privacy and security, ethical considerations, integration with existing systems, lack of skilled personnel, and resistance to change. Here's a road map to these challenges:
- Data privacy and security: Given that AI relies heavily on data, ensuring data privacy and security is paramount. Organizations should adopt robust cybersecurity measures, including encryption and secure networks, and adhere to data privacy regulations like the EU's General Data Protection Regulation (GDPR). Regular audits and data protection impact assessments should be conducted to ensure compliance. Furthermore, AI applications should be designed to minimize data collection and retention, aligning with the principles of Privacy by Design.
- Ethical considerations: AI should be used responsibly, considering ethical implications such as bias and transparency. Biased AI systems can lead to skewed outcomes and unfair practices. Organizations can develop an AI ethics policy and invest in bias-detection tools to ensure fairness. Moreover, AI systems should be designed to be explainable and transparent, helping stakeholders understand how AI makes decisions.
- Integration with existing systems: Implementing AI in project management often requires significant changes to existing systems. This can be a complex and time-consuming process. Change management strategies should be employed to ensure smooth integration and user adoption. This can include training sessions, regular communication of benefits, and staged implementation. Engaging a cross-functional AI implementation team that includes IT, project managers, and end-users can also facilitate a smoother integration process.
- Lack of skilled personnel: Implementing and managing AI systems requires specialized skills that may not be present in existing teams. Organizations can address this gap through training programs to upskill their current workforce or by hiring AI specialists. Collaborations with universities or tech firms can also be a way to access AI expertise.
- Resistance to change: AI implementation often means a significant shift in workflows, which can lead to resistance among team members. Leaders can address this by fostering a culture of change, emphasizing the benefits of AI, and involving team members in the implementation process. Providing adequate support during the transition is crucial, such as additional training or resources.
Indeed, successfully integrating AI into project management can be an uphill battle. However, with a strategic approach, organizations can overcome these hurdles and unlock the full potential of AI.
At a glance
Implementing Artificial Intelligence into project management marks a revolutionary shift, offering transformative potential. With AI, strategic decision-making reaches unprecedented accuracy and efficiency, as demonstrated by Rolls Royce's engine maintenance or IBM's project monitoring with Watson.
The potential for a 50% increase in project efficiency and cost reduction, akin to the operational enhancements at Siemens or Walmart, could translate into hundreds of millions in savings for more extensive portfolios. These savings signal a significant shift in the value proposition of project management within organizations.
Moreover, AI propels project management into a strategic role within organizations, driving innovation and sustainable growth. As we've seen with General Electric's Predix platform or Mount Sinai Health System's resource management, AI is a potent tool for future-proofing organizations in an evolving world.
Adopting AI in project management is a critical step into a future of unparalleled efficiency, innovation, and value creation. The question for organizations now is not whether to embrace this revolution but how swiftly they can harness its immense potential.
About the Author
Professor Antonio Nieto-Rodriguez, is founder Strategy Implementation Institute founder, and author of the HBR Project Management Handbook (HBR, 2021).
Ricardo Viana Vargas is former Director for infrastructure and project management at the United Nations and author of Project Management Next Generation (Wiley, 2022).
- "AI in Construction Market Report 2021", Cision PR Newswire, 2021.
- "AI in Healthcare: Leadership Report 2021", Mount Sinai Health System.
- "IBM Watson: Transforming Businesses Across Industries," IBM case study, 2022.
- "Artificial Intelligence in Construction Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026)", Mordor Intelligence, 2021.