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Addressing the Challenges of AI Adoption: What Project Managers Need to Know
Addressing the Challenges of AI Adoption: What Project Managers Need to Know
During the webinar “AI Use Cases”, hosted by PMI Romania on 30-Jul-2024, we explored many aspects of artificial intelligence, from its history to use cases and the development of our own GPTs. However, despite the excitement about AI, there are critical challenges that need to be addressed when deploying AI at scale. In this article, we will discuss five major challenges: AI hallucinations, alignment, bias, lack of long-term context, and decisional transparency. We will also explore the recommended solutions, inspired by the courses promoted by PMI, which are essential for project managers who want to effectively manage these aspects.-
AI hallucinations
Hallucinations are situations where AI models generate information that appears correct but is actually erroneous or fictitious. This can have serious consequences, especially in areas such as health or finance, where decisions are based on precise data, but also in project management. The solution involves not only improving data sets and algorithms, but also applying sound prompt engineering principles, such as those described in PMI’s “Talking to AI: Prompt Engineering for Project Managers” course. By correctly formulating requests and adjusting the feedback received, project managers can minimize the risks associated with hallucinations and improve the accuracy of AI results. -
Alignment: Alignment of AI Goals with Human Values
AI alignment is about ensuring that models respect human intentions and values. Without proper alignment, AI could make decisions that, while effective, are contrary to organizational ethics or intent. Here, project management can play a crucial role. The course “Generative AI Overview for Project Managers” emphasizes the importance of collaboration between developers and stakeholders to set clear and ethical goals. Project managers must collaborate with multidisciplinary teams to continuously verify AI outputs and ensure constant alignment with project goals. -
Bias and Impartiality
AI bias is a well-known problem that occurs when machine learning models perpetuate or amplify existing biases in the data they were trained on. This problem can affect decisions made in critical processes such as hiring or performance evaluation. Recommended solutions involve diversifying datasets and applying rigorous methods for evaluating model performance, discussed in the “Data Landscape of GenAI for Project Managers” course. Project managers must be aware of sources of bias and implement continuous auditing and verification processes to ensure fair decisions. -
Lack of Long-Term Context
One of AI’s weaknesses is its inability to retain and use long-term context. In complex or long-term interactions, AI models tend to lose sight of relevant details from previous conversations. This is a challenge for tasks that require continuity and deep understanding. The solution lies in improving memory algorithms and creating workflows that integrate continuous input from human users, as suggested in the course “Talking to AI: Prompt Engineering for Project Managers.” Thus, project managers can adjust AI strategies to maintain long-term coherence and relevance. -
Lack of Decisional Transparency (Explainability)
Decisional transparency or explainability is the AI’s ability to explain how it reaches a certain decision. In the case of complex AIs, such as deep learning models, this transparency is often lacking, which can diminish confidence in their results. Project managers need a balance between performance and transparency. According to the Generative AI Overview for Project Managers course, it’s critical to adopt tools that can provide clear explanations for AI decisions. This helps gain stakeholder buy-in and ensures AI decisions are understood and justified.
Recommended Solutions: How Can Project Managers Address These Challenges?
AI adoption is not only about integrating new technologies, but also about managing risks and ensuring clear alignment with organizational goals. To deal with these challenges, it is essential that project managers follow a process of continuous learning. PMI courses such as “Generative AI Overview for Project Managers,” “Data Landscape of GenAI for Project Managers,” and “Talking to AI: Prompt Engineering for Project Managers” provide a solid foundation for developing the skills needed to manage AI projects.By understanding challenges such as hallucinations, bias, or lack of transparency, and applying the solutions recommended in these courses, project managers can ensure successful and accountable AI implementations. Also, cross-departmental collaboration, constant auditing of models, and rapid adaptation to change are key to staying competitive and ethically embracing this revolutionary technology.
AI adoption comes with great responsibilities. It is essential to approach these challenges not just as obstacles, but as opportunities for continuous improvement. In our next webinar, we will explore in more detail how we can manage these risks and how to make the most of the power of AI.
The courses mentioned above can be found here: https://www.pmi.org/explore/ai-in-project-management
