The Impact of AI within Project and Change Management

I have been researching, studying and writing about the impact of AI on the project management and change management profession for over five years. So, it was a great pleasure to organise a panel discussion recently on this topic in collaboration with Glen Curristan and the team at Morgan McKinley in Dublin.

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It was an honour to bring together three of Ireland's brightest minds in project management, change management, and data science for a lively and insightful discussion - Peter Glynne , Bronwyn Hall McLoughlin and James Garza MSc . I have summarised our panel discussion in this article.

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Gartner predicts that by 2030, 80% of project management tasks will be automated. A Goldman Sachs report echoes this, suggesting AI could replace 300 million full-time jobs by then.

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In a more humorous take, comedian Jerry Seinfeld remarked at Duke University that AI is "the most embarrassing thing we've ever invented" and joked that Chat GPT's slogan should be the opposite of Nikes' - "You just can't do it!". This highlights our often-unrealistic expectations about AI. One critic quipped, "It's like we've found a talking dog and are now criticising its grammar!"

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Despite these views, I am optimistic about AI's impacts. Change and project managers are ideally suited to lead organisations through this significant technological and societal shift. Our roles emphasise the people impacts of technology changes, ensuring effective governance of AI initiatives. Both professions already embrace AI tools like PMI Infinity and Prosci's Kaiya, showing the potential of AI to enhance our work.

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The panel's optimism about AI's impact on society and our profession was palpable, though nuanced. I believe AI can enhance productivity significantly. However, a primary concern lies in the reliance on generative AI as a factual information source, blurring the lines between fact and fiction. This will challenge both professional integrity and societal trust.

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AI has existed for decades but only recently became widely accessible. For our profession, its potential is immense. AI can automate tedious tasks such as documentation, analysis, reporting, and communications, freeing us to focus on people-centric activities like listening, co-creating, and connecting. This shift could enhance the quality of our interactions and the outcomes of our projects.

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Regarding societal impact, opinions are mixed. While AI, like previous technological advances, offers great promise, it also brings risks. We are entering an age of misinformation, where AI could exacerbate the spread of false information. Regulatory frameworks and safeguards lag, and there are both positive and negative environmental and social implications. Despite these concerns, AI holds the potential to solve major global issues and improve human performance.

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Overall, the panel is optimistic about AI's transformative impact on the project management profession. A recent Ireland Chapter of PMI survey revealed that 74% of respondents see AI driving innovation in project management. Though we are in the early stages, the opportunities for our profession are substantial and exciting.

The panel are regular users of AI. Bron mentioned that she even recently used AI as an executive coach in the absence of having a real-life therapist or coach! The responses were thought-provoking and helped her approach the issue more effectively. The panel also uses AI for business development proposals, identifying business opportunities, designing web pages, creating content plans, and recording and summarising conversations with Otter.ai .

Large Language Models (LLMs) v Small Language Models

James explained that Large Language Models (LLMs) are AI systems trained on vast amounts of text data, requiring substantial resources and energy. With billions to trillions of parameters, LLMs are generally designed to answer a wide range of questions.

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In contrast, Small Language Models (SLMs) are tailored to an organisation's specific data, needing fewer resources for training. They focus on producing outputs relevant to fields like accounting or legal services. SLMs have millions, sometimes billions, of parameters based on dataset size. This difference in resource requirements is crucial for organisations considering AI integration.

How Change Managemnt is Responding

The Change Management (CM) profession is responding to AI with two main opportunities: assisting others in adopting AI and incorporating AI within CM practices. Despite the promise, adoption varies widely. A 2023 Prosci study revealed that less than 20% of Change Managers use AI extensively, while 32% haven't adopted it. Most use it for tasks like brainstorming and drafting communications or training materials.

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The CM field is at a critical juncture. Many CM models are outdated, lagging behind in the digital transformation seen in other functions. AI offers a chance to modernise practices, allowing change managers to move away from low-value, high-effort tasks. Currently, AI aids traditional tasks such as communications, training, impact assessments, and sentiment analysis. AI tools can also support coaching change sponsors and champions by providing tailored prompts.

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However, the profession must balance AI's benefits with potential risks. AI might miss nuances in webinars or emails, reducing interactive experiences. Over-reliance on AI could stifle innovation and creativity. Therefore, leveraging AI for personalisation is crucial while maintaining human-centric experiences. Training AI for empathy, culture, values, and tone of voice is essential to ensure storytelling and real human experiences remain at the core of change management.

The Project Management Profession Response to AI

The profession responds very positively to both the opportunity and the requirement for change. It is still early in the AI journey, and the real power will come to the fore when individual organisations use language models to leverage their project data and drive decision-making. In the recent Ireland Chapter of PMI member survey on AI, 70% stated that AI will transform the project management industry and change project management practices forever.

PMI is investing heavily in AI and has launched its AI hub on the website. The PMI Infinity AI tool is free to PMI members, and they are also offering eLearning to develop a core AI skillset across the profession. There will be convergence between the role of the project manager and the change manager as both skill sets move closer. What this looks like will emerge over the coming years.

How the Education Profession is Responding to AI

The advent of AI in education is transforming traditional teaching methods. While AI cannot make cognitive leaps like humans, such as understanding the process of making a cup of tea from various ingredients, it has significant potential in personalising education. Historically, education has evolved from familial trades to industrial-era classrooms with a single teacher addressing diverse students. Educators have had to focus on the majority, often leaving the needs of above-average and below-average students unmet.

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AI offers a revolutionary change by providing personalised tutoring to individual students, enabling them to learn at their own pace and in their unique way. This individualised attention can lead to exponential learning growth. Furthermore, AI is being integrated into education by requiring students to present their work and answer questions, demonstrating their understanding and knowledge. Just as calculators and Google searches have become educational tools, AI represents the next step in this technological evolution, with the education profession already adapting to its capabilities.

Barriers to Greater AI Adoption

Peter highlighted several barriers that hinder the widespread adoption of AI, including a lack of governance within organisations, insufficient "how-to" knowledge, and building trust in the technology. Additionally, regulatory compliance demands significant resources, particularly under the EU AI Act and similar legislation. Companies like Apple and Meta have adjusted their AI offerings to meet stringent EU regulations, while other regions, like the US, have opted for less rigorous approaches.

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Despite these challenges, AI presents substantial opportunities for project managers, influencing both the execution of work and new career paths. As AI adoption accelerates, organisations must decide whether to innovate or react. Those that innovate and adopt AI early will gain a competitive edge. Conversely, 80% of organisations may only react as their competitors implement AI, while only 20% will be proactive and ahead of the curve. Overcoming these barriers requires strategic planning, investment in AI literacy, and fostering a culture of innovation.

Environmental and Social Impacts of AI?

AI has profound environmental impacts. Google's recent 48% increase in emissions and data centres in Ireland consuming 21% of the country's energy, surpassing residential usage, are significant concerns. This surge in energy consumption, primarily attributed to AI, threatens climate targets. Additionally, data centres require substantial water for cooling. However, nearly all the energy used for cooling is released as warm air, which could be repurposed for district heating. In Dublin, this method is starting to provide low-cost, low-carbon heat, crucial in an era of rising living costs. AI models also offer environmental benefits by predicting wind power availability, aiding climate change mitigation efforts.

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Socially, AI presents challenges related to data bias. For instance, ChatGPT is trained on Wikipedia data, 95% of which is written by men, leading to potential bias. Similarly, pharmaceutical trials predominantly involving white men in the US and Europe result in skewed medical advice. Ensuring the accuracy and diversity of data used in AI training is crucial. Workers in Kenya are often employed to filter out incorrect or dangerous information, sometimes under poor conditions, affecting their mental health. It's vital to avoid outsourcing these issues, similar to first-world waste and fast fashion dumps. Equitable access to AI tools is essential to prevent further widening the gap between advantaged and disadvantaged communities.

Looking Beyond LLMs

While generative AI and large language models (LLMs) dominate the current discourse, other AI forms offer significant value to businesses. One such example is reinforcement learning, which operates on the principle of positive reinforcement to improve decision-making.

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Consider the simple act of deciding to carry an umbrella. Factors like weather forecasts, current conditions, seasonal trends, and geographical location influence this decision. Similarly, reinforcement learning uses a decision function incorporating various factors to make optimal decisions. It requires input data and a reward function to evaluate outcomes and learn from them, much like a human would. Reinforcement learning can mimic the decision-making process of a project manager. Analysing input data can make informed decisions, helping project managers become more efficient, reduce costs, and complete projects faster. Unlike humans, AI lacks emotions, eliminating biases like the "hot hand" theory, where recent successes or failures unduly influence future expectations. AI's unemotional, data-driven approach ensures consistent and objective decision-making.

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Additionally, AI can perform exhaustive data analysis and provide decision recommendations, enhancing productivity without fatigue or emotional interference. Ultimately, the project manager still makes the final decision, but with AI's support, those decisions are better informed and more efficient. Exploring and implementing AI technologies like reinforcement learning can offer substantial benefits beyond the capabilities of generative AI, driving innovation and efficiency in project management and other business functions.

Preparing Ourselves and our Organisations

To prepare for AI, we must approach it as a tool that enhances productivity, much like cars or public transport, to streamline our daily commutes. Here are the key steps:

• ?Education and Curiosity: Educate yourself on AI applications and their potential to boost work efficiency. Stay curious and proactive when exploring AI tools.
• Testing and Learning: Conduct trials with AI tools to understand their benefits and environmental impacts. This hands-on experience is crucial for practical learning.
• Data Preparation: Identify and prepare the data you need for AI training, such as project success metrics and adoption drivers. Clean, relevant data is essential for effective AI implementation.
• Building Trust: Ensure transparency and trust in your data. Follow the example of the Ireland COVID app, which gained global trust through open-source code and transparent data sets. Apply similar principles to avoid misinformation and bias in AI.
• Innovation and Opportunity: Embrace AI as an opportunity for innovation. The nature of roles, especially in project management, will evolve, presenting new opportunities for growth and efficiency.

Historically, resistance to new technologies, like the protests against calculators in the 1970s, has proven unfounded. Similarly, embracing AI can bring significant advancements and benefits for individuals and organisations.