Leading AI Projects as a Non-Technical PM: A Practical Playbook

The Problem

As a project manager leading AI projects, you're the bridge between the data scientist and the CFO. But what changes when the project ships a model instead of a feature? You're no longer just managing timelines and budgets; you're now responsible for ensuring the model is transparent, explainable, and compliant with regulatory requirements. The stakes are higher, and the risk of missteps is greater.

Consider a scenario where your team has developed an AI-powered credit scoring model for a financial institution. The model is highly accurate, but it's also opaque, making it difficult to explain the reasoning behind its decisions. The CFO is concerned about regulatory exposure, while the data scientist is focused on improving the model's performance. As the project manager, you need to navigate these competing priorities and ensure that the project is delivered on time, within budget, and with minimal risk.

What the Research Says

Recent developments in AI have highlighted the need for project managers to be more involved in the development and deployment of AI models. Discussions on r/agile and LinkedIn posts from senior PMs note that traditional project management methodologies are no longer sufficient when dealing with AI projects. The use of kill-switch criteria, vendor management, and regulatory exposure are just a few areas where PMs need to be more proactive.

For example, a study by the Harvard Business Review found that AI projects are more likely to fail due to lack of transparency and explainability. Another study by McKinsey found that AI projects require a different set of skills and competencies than traditional IT projects. These findings highlight the need for project managers to be more aware of the unique challenges and risks associated with AI projects.

How LeadAI Academy Solves This

LeadAI Academy's role-specific training for project managers provides the necessary tools and expertise to navigate the complexities of AI projects. With coaches like Jordan/APEX, PMs can learn how to:

• Develop a comprehensive AI risk register that the CRO trusts
• Run a steering committee that balances business objectives with AI risk management
• Implement kill-switch criteria and vendor management best practices
• Ensure regulatory compliance and transparency in AI model development

For instance, LeadAI Academy's DocLab scenarios provide PMs with hands-on experience in developing and deploying AI models in a simulated environment. The scenarios cover various industries, including financial services, healthcare, and public sector, and are designed to help PMs develop the skills and competencies needed to manage AI projects effectively.

TL;DR & Next Steps

• Develop a comprehensive AI risk register to identify and mitigate potential risks
• Implement kill-switch criteria and vendor management best practices to ensure transparency and accountability
• Ensure regulatory compliance and transparency in AI model development to minimize exposure
• Run the 60-second Enterprise AI Readiness Assessment at /diagnostic to identify areas for improvement
• Start a DocLab session at /doclab to develop hands-on experience in AI project management