High uncertainty projects
High rates of change, complexity, and risk require techniques that enable adaptability, rapid response times, and frequent feedback loops.
What is Agile?
Agile is a mindset when approaching and executing a project. It uses multiple methods and frameworks depending on the project's goals and alignment with the Agile Manifesto. Four values align with the manifesto: individuals and interactions over processes and tools, working software over comprehensive documentation, customer collaboration over contract negotiation, and responding to change over following a plan (Project Management Institute, 2017). The highest priority is to deliver continuous value to the customer at short and regular intervals. These shortened work process cycles help mitigate risk, reduce waste, and enable quick responses to change, enabling continuous improvement.
Benefits of AI
Artificial intelligence (AI) helps teams quickly process and organize large amounts of data. AI can also assist teams with discovery by finding patterns, providing insights, and organizing them into categories. AI can also support backlog creation with user-centered artifacts such as personas and journey maps. Teams can then focus on high-value analysis instead of expending effort on mundane, time-consuming processes. AI is also capable of developing hypotheses for further exploration and consideration, while ensuring solutions tie back to the problem or need.
According to Matt Sigelman, incorporating AI into project management aids employee retention and creates an upskilled employee cohort; mitigates risk through more profound organizational expertise; and benefits the organization financially by enabling it to execute more projects (Project Management Institute, 2025, p. 9). Utilizing AI in the agile process makes sense, as it aligns perfectly with the values on which agile operates; speeding up analysis, enabling teams to operate in shorter increments, and delivering continuously. Teams can discover faster, work more collaboratively, and therefore focus on the customer.
(McKinsey, 2025)
Nearly two-thirds of organizations are still in the experimentation or piloting phase of scaling AI across the enterprise, with sixty-two percent experimenting with AI (McKinsey, 2025). Therefore, many organizations find themselves still in the pilot phase of embedding AI across workflows and are inadvertently leaving gains and benefits on the table. Respondents who use AI across at least one business function reported improvements in company-wide qualitative outcomes. The top three are innovation, employee satisfaction, and customer satisfaction, with competitive differentiation in close fourth place (McKinsey, 2025).
Future of AI
Agentic AI is a goal-oriented AI system that can plan, decide, and act independently to achieve organizational objectives using large language models. Developers of Agentic AI systems claim it provides “cost efficiency, revenue growth, and the ability to unlock the full potential of their talent” (Deloitte, 2025). Organizations are urged to strategize by adopting AI early and incrementally, so teams can adapt to implementing these autonomous systems into their daily tasks, thereby upskilling the workforce.
Twenty-three percent of organizations are scaling agentic AI across some part of the business (McKinsey, 2025). Moving forward, organizations need to ensure the systems they implement align with the enterprise at each step of the phase. This can be done by assessing the organization's readiness to adopt, designing a rollout strategy for each business area, and creating metrics to measure the implementation process's success. Utilizing agile in the adoption and continuous growth of AI within the organization provides a competitive advantage, as enterprises can maintain quality, scale, empower workers, and prepare for ongoing changes with regularity and adaptability. These capabilities will be critical in maintaining quality, trust, and cost as organizations shift from Gen AI to agentic AI.
Risks and Considerations of AI
Incorporating AI into work processes is not without issues. First, bias in AI training must be recognized, as it can lead to hypotheses and suggestions that may not be best suited to the organization or project at hand. AI assumptions must be challenged, and decisions must be made as to which interventions take priority to be genuinely beneficial. Organizations should implement practices that encourage employees to report bias in AI algorithms (Shestakova, 2021, p. 10). Therefore, teams should be familiar with their data so that any mistakes made by AI can be identified and misdirection avoided. Organizations must remember that AI is a tool for human teams to utilize, not a replacement for human capital.
Second, organizations must consider privacy concerns. AI uses inputs to train further, which can expose sensitive information elsewhere as it shares what it has learned with external organizations. This information can include strategic information such as new product ideas and long-term market plans. AI can also be susceptible to external malicious attacks aimed at gaining access to private information. Organizations must provide employees with resources to understand how to use AI responsibly and ensure that the platforms they implement that handle sensitive data have safeguards to protect it.
Another consideration is the impact that using AI has on human morale and momentum. Employee engagement and employee task performance outcomes are well documented as positively correlated (Obuobisa-Darko, 2020, p. 16). Therefore, organizations need to be mindful of how AI is introduced and to what extent it is used in employee tasks. Too much reliance on AI outputs can lead to lowered motivation to perform and overall output levels. Approaching implementation in phases is recommended to offset adverse outcomes.
Click the image to be redirected to the Artificial Intelligence Risk Management Framework (AI RMF 1.0) developed by the National Institute of Standards and Technology, part of the U.S. Department of Commerce.
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References
Deloitte (2025). Agentic enterprise 2028: A blueprint for cost savings, job creation, and faster growth through agentic AI. https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2025/agentic-ai-enterprise-2028.pdf
McKinsey (2025). The state of AI in 2025: Agents, innovation, and transformation. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai?utm_source=chatgpt.com#/
Obuobisa-Darko, T. (2020). Ensuring employee task performance: Role of employee engagement. Performance Improvement 59(8), 12-23.
Project Management Institute (2017). Agile Practice Guide.
Project Management Institute (2025). Enabling project management transformation with GenAI: The need for organizational support. https://www.pmi.org/-/media/pmi/documents/public/pdf/learning/genai_the-need-for-organizational-support_final-fix.pdf?rev=a1e6d5d9748b46d4b2a1db32f9c1c833
Shestakova, V. (2021). Best practices to mitigate bias and discrimination in artificial intelligence. Performance Improvement, 60(6), 6-11.
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