Enhancing Queen’s IRC Methods and Tools with AI

Organization Development


Artificial Intelligence (AI) is revolutionizing organizational change, offering practitioners tools to enhance data analysis and streamline transformations. According to PwC’s 2024 AI Business Predictions, [1] Generative AI not only sharpens the competitive edge but also redefines various organizational roles. By automating data collection and analysis, AI facilitates quicker adaptations to emergent change and puts Queen’s IRC methods and tools “on steroids”, ensuring organizational transformation remains anchored in leading practice.

The following sections will delve deeper into how AI can be leveraged to tailor work that is deeply aligned with organizational goals and stakeholder expectations. This strategic integration of AI facilitates a more nuanced understanding of stakeholder dynamics. It accelerates the transformation process, ensuring practitioners are not merely reacting to changes but actively shaping their organizations’ future.

Overview of AI in Organizational Transformation

AI profoundly transforms organizational capabilities, enhancing decision-making, increasing efficiency, and improving impact analysis with a level of detail and accuracy not previously achievable. AI tools enable leaders to make predictive, data-driven decisions and automate routine tasks, freeing up time for strategic thinking. The advent of custom GPTs from OpenAI in November 2023[2] and evolved from OpenAI’s Code Interpreter (now called Data Analysis) released in the summer of 2023 allows for the automation of detailed impact assessments, offering deep insights into organizational trends and specific stakeholder impacts, thus effectively mitigating potential risks.

AI can tailor change management interventions to the needs and preferences of individual stakeholder groups/employees, increasing engagement and reducing resistance to change. Persona prompts are a powerful tool for personalizing a given source document for different audiences. “Write me a 2-page summary for the CEO…Write me speaking notes for the CEO to engage with front-line employees at a town hall…write a follow-up communique to reinforce key messages from the town hall”.

Augmentation Through Document Provision

A key area where AI significantly augments the change management process is the provision and analysis of various contextual documents. AI systems can ingest various organizational documents—such as strategic plans, HR reports, and past change management initiatives—to gain a nuanced understanding of the organization. This technique enhances the AI’s contextual awareness, offering more tailored and precise recommendations for each phase of the transformation process. In some ways, you can think of it as additional training provided to a large language model (LLM) to tailor its outputs to your needs!

AI’s Role in the Change Management Process

AI significantly enhances each phase of the change management process, providing a more data-driven and responsive strategy.

  • Define Phase: AI uses sentiment analysis and trend prediction to identify necessary changes, maps organizational networks to spotlight key stakeholders, and supports the strategic planning of change initiatives.
  • Discover Phase: AI analyzes current organizational data to assess performance and capabilities and uses this data to forecast future trends and scenarios, aiding in strategic foresight and planning.
  • Design Phase: AI aids in creating and refining the blueprint for the future state, using data insights for strategic planning and employing simulation technologies such as digital twins[3] (virtual models of physical objects or systems that mirror real-world conditions and behaviors) and triplets[4] (advanced virtual models that integrate additional data sources to provide even more detailed simulations and predictions) to test and optimize plans.

These contributions ensure change management strategies are innovative and effectively aligned with organizational goals.

As plans move to Implementation and Sustainment, AI ensures strategies are executed efficiently:

  • Training Analysis and Development AI can take documented change impacts and inform training needs analysis, learning objectives, syllabus design, course development and even gamified simulations, and related learning activities to enrich the learning experience. AI Avatar software can be used to develop studio quality training videos without needing a video crew and actors/actresses.
  • Monitoring and Adjustment: AI systems can continuously monitor the progress of change initiatives and suggest real-time adjustments. This includes tracking key performance indicators (KPIs) and providing alerts when deviations from the plan are detected. Using the OpenAI Data Analysis GPT, practitioners can load their data, receive suggested analytics, and have these insights rendered visually. This is all done by the AI system writing Python code in the background on behalf of the user!
  • Feedback Loop Integration: AI facilitates a continuous feedback loop by analyzing the effectiveness of change initiatives and integrating employee feedback. This helps to make iterative improvements to change strategies, ensuring they remain relevant and practical. New and emerging AI “Swarm” intelligence[5] AI technologies show tremendous promise to replace conventional survey-based approaches with real-time workforce insights to capture employee concerns, questions, and innovation opportunities.

ERP Case Study and Practical Applications

In this section, we will bring the above concepts together and delve deeper into practical applications with an Enterprise Resource Planning (ERP) case study that exemplifies how AI can be integrated into various phases of change management.

Leveraging Meeting Transcripts with AI

When combined with AI technologies, electronic meeting transcripts are a rich data source that can provide deep insights into organizational dynamics and stakeholder perspectives. Here’s how AI enhances the utility of meeting transcripts (your new best friend!):

  • Automated Transcript Analysis: AI tools with natural language processing (NLP) capabilities can analyze meeting transcripts to extract key points, identify themes, and detect sentiment. This enables practitioners to quickly grasp the essence of potentially multiple discussions held each day without manually sifting through hours of recordings or feeling like their entire work weeks are consumed by meetings.
  • Alignment with Queen’s IRC Models and Tools: AI can apply the principles and tools recommended by Queen’s IRC, such as stakeholder mapping and gap analysis, directly to the transcripts’ content. For example, AI can identify mentions of key stakeholders, map their influence and interest, and assess the gaps between current practices and desired outcomes as discussed in meetings.
  • Strategic Insight Generation: AI can provide strategic recommendations directly aligned with organizational goals by integrating insights from meeting transcripts with established Queen’s IRC models. This may include identifying areas lacking stakeholder engagement, proposing interventions, and predicting potential impacts of suggested changes. It also excels at taking documented impacts and proposing project milestones, deliverables, and tasks to advance business and people readiness.

ERP Case Study and Practical Applications

In this ERP case study, a multinational corporation upgraded its ERP system to enhance global operations, tackling the project’s scale and complexity by leveraging AI.

Case Study Summary:

  • Challenge: Due to the vast scope, key stakeholders couldn’t attend all meetings, making comprehensive insight capture essential.
  • Solution: AI was deployed to analyze meeting transcripts, ensuring alignment with strategic ERP goals and adapting to stakeholder needs.
  • Process: Strategic meetings were transcribed and analyzed by AI, using NLP to extract key insights and align with Queen’s IRC frameworks.
  • Outcome: AI’s transcript analysis streamlined the ERP upgrade, ensuring the team remained informed and could address arising challenges effectively, aligning stakeholder engagement through tailored interventions. Imagine having detailed impacts for each main ERP sub-group and understanding the cross-cutting trends in hours versus weeks!

Ethical Considerations and Regulatory Compliance in AI-Enhanced Change Management

Integrating AI into change management introduces important ethical considerations, including:

  • Data privacy: Ensuring the confidentiality of organizational and personal data processed by AI.
  • Bias and fairness: Monitoring AI systems for unbiased operations and equitable insights.
  • Transparency and explainability: Maintaining clarity on AI’s decision-making processes.
  • Accountability: Establishing clear responsibilities for outcomes influenced by AI.

Regulatory Compliance

On March 14, 2024, the European Union adopted the Artificial Intelligence Act[6], establishing one of the first comprehensive frameworks for regulating AI systems. The Act identifies specific prohibited uses of AI, including “emotional recognition in workplaces and schools, social scoring systems, predictive policing based solely on profiling, and technologies that manipulate human behavior or exploit vulnerabilities”.

Inspired by the EU’s initiative, Canada is considering similar legislation under Bill C-27[7], which aims to regulate AI systems to protect privacy and ensure responsible development. As this bill progresses, it will be crucial to monitor any provisions like the EU’s Act, especially the use of AI in workplaces and broader societal contexts.

Given these developments, employing AI for sentiment analysis during organizational change management must be cautiously approached. The EU’s AI Act restricts the use of AI for emotion inference in settings like workplaces unless explicitly authorized for specific purposes. This raises important questions for applying such technologies in organizational change management: Is it permissible to use sentiment analysis to categorize stakeholder groups broadly? This question underscores the need to align AI practices with emerging legal standards to avoid potential legal and ethical issues.

Implementing Ethical AI in Change Management

Effective implementation of ethical AI in change management involves:

  • AI ethics governance: Establishing an ethics board to oversee AI implementations, ensuring they meet both internal and regulatory standards.
  • Compliance audits: Regularly auditing AI systems for adherence to ethical norms and regulatory compliance.
  • Training: Educating change practitioners on ethical AI usage, focusing on understanding biases and maintaining data privacy.

Adopting these measures ensures AI contributes positively to organizational transformation, maintaining trust and compliance.


AI is a Tool to Augment Collective Intelligence in Change Management

AI significantly enhances organizational transformation by streamlining processes and bolstering decision-making with robust data insights. Yet, its true value lies in augmenting human capabilities, not replacing them.

AI assists in managing complex data, enabling practitioners to focus on strategic and human-centric aspects of change. This synergy between AI and human intelligence fosters more effective and ethically sound organizational changes.

AI supports human decision-makers by uncovering patterns and insights from extensive data sets, aiding in strategic planning and risk management. Ethical governance and ongoing training in AI use are crucial, ensuring that AI’s integration respects privacy, avoids bias, and remains transparent, thus facilitating continuous improvement and adaptation.

Looking Forward

In many respects, what this article describes is just a sample of what’s to come. As we move into the future, practitioners will hear more and more about AI Agents — tools that coordinate other AI tools to deliver complex, integrated responses. Practitioners will increasingly use AI Agents to access and analyze data from various sources like ERP systems, corporate databases, and the web, integrating these into cohesive insights.

The focus here is on large language models and generative AI, which empowers organizational transformation practitioners. Explore numerous specialized AI tools at websites like https://whataicandotoday.com/ for specific tasks such as creating impactful infographics.

AI is a critical tool for change management professionals, enhancing human efforts vital to successful transformations. As AI integrates into change management, it’s essential to maintain ethical standards and augment human intelligence, ensuring AI and humans drive strategic organizational change collaboratively.

 About the Author

Stuart MacMillan, CHRL, is a bilingual digital change management, strategic human resources, and AI consultant with extensive experience leading technology-enabled projects across various sectors, including healthcare, insurance, defence, and ERP implementations. An alumnus of Queen’s University Industrial Relations Centre (IRC), Stuart integrates AI and emerging technologies to optimize organizational processes and achieve strategic objectives. Committed to driving operational efficiency and innovation, Stuart has recently upgraded his expertise with advanced AI and digital transformation studies at MIT, Stanford, Vanderbilt, and Cornell. His approach combines cutting-edge knowledge with practical experience, delivering impactful organizational change.



[1] PwC. (n.d.). 2024 AI Business Predictions. Retrieved May 6, 2024, from https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html.

[2] OpenAI. (n.d.). ChatGPT Release Notes. Retrieved May 6, 2024, from https://help.openai.com/en/articles/6825453-chatgpt-release-notes. Note: OpenAI introduced updates to GPT models on November 6, 2023.

[3] Martin, S. (2022, September 16). What is a digital twin? NVIDIA Blog. Retrieved May 6, 2024, from   https://blogs.nvidia.com/blog/what-is-a-digital-twin/

[4] Gutiw, D. (2024, April 23). Digital triplets: Extending digital twins to create an AI-powered virtual third-party advisor | CGI Blog. Retrieved May 6, 2024, from https://www.cgi.com/en/blog/artificial-intelligence/digital-triplets-extending-digital-twins-to-create-ai-powered-virtual-advisor

[5] Use cases. BrainE4 . (n.d.). Retrieved May 6, 2024, from https://braine4.com/en/case-studies/

[6] High-level summary of the AI Act. EU Artificial Intelligence Act. (n.d.). Retrieved May 6, 2024, from https://artificialintelligenceact.eu/high-level-summary/

[7] Research publications. Legislative Summary of Bill C-27: An Act to enact the Consumer Privacy Protection Act, the Personal Information and Data Protection Tribunal Act and the Artificial Intelligence and Data Act and to make consequential and related amendments to other Acts. (n.d.). Retrieved May 6, 2024, from https://lop.parl.ca/sites/PublicWebsite/default/en_CA/ResearchPublications/LegislativeSummaries/441C27E#a2-5-1


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