In this latest instalment of the Trinity Impact Series, we are delighted to feature Peter Glynn, a distinguished Programme Management Leader and Adjunct Teaching Fellow at Trinity Business School. With a wealth of expertise in programme leadership, Peter works with organisations to drive meaningful change, while also serving as the President of the Ireland Chapter of PMI (Project Management Institute)

In this engaging interview, Jonathan Totterdell, Head of Communications for Trinity Business Alumni, speaks with Peter about the transformative role AI is playing in project management. Peter shares his insights on the integration of AI tools to enhance project outcomes, including automating resource-intensive tasks, improving risk management, and fostering collaboration within distributed teams.

1. How do you see AI transforming traditional project management methodologies, and what specific AI-driven tools have you integrated to improve project outcomes?

In the first instance, AI is automating the more technical and resource intensive activities across project management such as document creation, risk analysis or project planning. This enables project professionals to focus on higher value activities with a greater focus on Power Skills such as leadership, communication and empathy. In the 2024 AI survey by the Ireland Chapter of Project Management Institute (PMI), 70% of respondents stated that AI will have a transformative effect on the project management industry and change practices forever.  Significant value will come from the implementation of specific in-organisation language models which will enable unstructured data from their projects to become a valuable source of automated intelligence.  It will still be some time before this will be available to all organisations. As AI technology matures the use cases will become significantly more advanced and the value more profound.

2. What are the key challenges you’ve faced when implementing AI in project management, and how have you addressed issues like team resistance or data quality?

As with any new initiative or technology, there is an adoption curve. Whilst most organisations are using GenAI to support information-based activities there is not yet widespread integration of AI functionality into project management software. Also, there is not yet widespread use of organisation specific language models to leverage the power of unstructured project data to drive decision making. This is happening, but it will take more time to become truly embedded into everyday activity.

In the 2024 Ireland Chapter of PMI survey 83% of respondents say that AI lacks governance in the sector. In addition, 65% cite lack of a clear organisational strategy as a barrier to success.  There is also the topic of ethics and how AI could be used for illegal or unethical activity. In time, the maturity curve will drive organisations to ensure that strategy, governance and ethics align.  In some cases, this may be a reactive response to what the rest of the sector is doing rather than an early adopter strategy. The EU AI Act is bringing a step change in the governance of AI across organisations recognising that a fine of up to €35m can be imposed.

The key to success is to bring people on the change journey; educate the team on AI, what it means for them and the benefits for the organisation. Everyone knows what AI is; not necessarily what it can do for them. PMI has a number of fabulous eLearning certificate courses on AI and these are free to members.

3. In what ways do you believe AI enhances predictive capabilities in project management, particularly around risk management, resource allocation, or timeline forecasting?

AI frees up project professionals to focus on areas of higher value such as what PMI refers to as Power Skills, namely collaborative leadership, communication, problem-solving and strategic thinking.  This enables project professionals to enhance their careers through new opportunities for enhanced learning and growth, driving a seismic shift in skills.

The automation of the more technical activities around risk management, resource allocation or timeline forecasting will be enhanced with the availability of widespread internal and external reference data along with greater data relevance and accuracy. The key aspects are quality of data, automation of workflows and the ability to sense check AI led decision making.

The ability for AI to help mitigate project risks will be enhanced with the technology. The ability to analyse similar risks and their mitigations across internal and external project data enables AI to present highly relevant mitigations for consideration. This becomes even more powerful when sector specific data is used e.g. possible mitigations presented based on similar risks across the pharma sector.

4. How do you balance the role of AI in automating tasks with the need for human judgment and creativity in project management?

There will always be a need for human judgement and creativity in project management. The shift to Power Skills creates the opportunity for managers to become project leaders as the focus moves away from the more technical aspects. Human judgement is a critical aspect of this.  Strong governance and responsible ethics are critical elements of the legislative and regulatory environment within organisations, particularly when many projects break new ground with leading developments.

Human oversight across project management is particularly important as organisations ultimately exist for shareholder or societal value. It is important to sense check the quality of AI generated information and decision making however it is even more critical when it comes to governance and ethics.

5. How do you integrate AI-driven analytics into real-time project decision-making, and what impact has this had on project timelines and outcomes?

The most immediate benefit of AI in project management is quality information at your fingertips to support timely decision making. The other is automation of the more technical aspects of managing a project thereby saving time and effort e.g. automated reporting.  Overall, this enables project professionals to focus on Power Skills such as leadership, strategic thinking and communication thereby improving project success rates. The ability to develop a standardised project plan using AI driven activity planning is a great time-saving benefit particularly where this takes account of lessons learned on similar projects. Whilst the plan will require sense checking and adjustment it automates a lot of the heavy lifting.  The availability of strong internal or external reference data to automatically build the project plan is a major benefit.

6. What role does AI play in improving collaboration and communication among project teams, especially in large or distributed teams?

GenAI is great for drafting project communications as it saves considerable time developing high impact messaging; however, the prompt in ChatGTP or a similar tool needs to be carefully structured for maximum benefit. Clearly there is always a need for human sense checking before any comms are issued. The sharing of robust information using AI driven analytics can improve collaboration across project teams though more timely information and a higher level of confidence in the data.  Other examples include AI translating information into different languages to enable a richer and more timely experience for distributed project teams. As AI technology for project management develops, we will see significant further improvement across team collaboration and communication.

7. What are the performance indicators for AI in project management?

There are a number of important indicators highlighting how an organisation is performing across AI and project management. In the earlier stages of AI adoption across an organisation these include the:

• Number and percentage of project professionals trained in AI
• Adoption rate (number of active project management users / total project management users)
• User satisfaction rate with AI tools in project management

Other KPIs relate to the effectiveness of AI in improving project management and are more subjective. Examples include:

• Prediction accuracy rate on project or activity completion time (AI predicted time / actual completion time)
• Risk reduction rate (total no of risks predicted v total no of risks mitigated)
• Project decision accuracy rate (correct AI decisions made / total AI decisions made)

There are many other performance indicators that could be deployed around AI in project management. As AI technology matures so will be standard set of performance indicators enabling organisations to benchmark across sectors and geographies.

8. Finally, what advice do you have for alumni looking to level up in their project careers?

Recognise that project management roles will change significantly over the next few years with a much greater focus on collaborative leadership, communication, problem-solving and strategic thinking.  Arguably, there will be convergence with change management as similar technological advances in AI bring the two domains closer together. Project professionals must take the lead in ensuring project success not just supporting it; AI will help enable this. There is an exciting journey ahead. Stay ahead of the curve, embrace the power of AI and focus on the Power Skills that make a real difference.