AI beyond the buzzword

Artificial Intelligence is no longer a distant concept confined to sci-fi movies or elite tech companies. Today, some organisations 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.

AI is set to revolutionise how projects are executed and managed, from bolstering decision-making capabilities to optimising resource allocation. Here are some 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 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. For instance, Rolls-Royce uses AI to analyse data from its aeroplane 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. 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 prioritisation, Watson has reportedly improved productivity by up to 20%.
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%. In a different application, KPMG’s AI platform, 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.
Optimised 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%.
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 personalised, timely updates to team members, improving communication and collaboration.
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.

If we add 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 €1bn means potential savings and efficiencies worth €500m! 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.

Overcoming the challenges

With that said, let’s consider the hurdles that come with implementing AI. Organisations 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 roadmap to these challenges:

Data privacy and security: given that AI relies heavily on data, ensuring data privacy and security is paramount. Organisations should adopt robust cybersecurity measures, including encryption and secure networks, and adhere to data privacy regulations like the EU’s General Data Protection Regulation. Regular audits and data protection impact assessments should be conducted to ensure compliance. Furthermore, AI applications should be designed to minimise 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. Organisations 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 specialised skills that may not be present in existing teams. Organisations can address this gap through training programmes to upskill their current workforce or by hiring AI specialists. Collaboration 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, emphasising 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.