Surprising Ways that Innovation is a Challenge with AI
📅 Date: April 14, 2026
🕒 Time: 7:00 pm - 8:30pm EST
🌐 Venue: via Zoom
🎟 Registration: Open to members and the public for a fee
Speaker Bio:
Michael Palladino is an Agile, Program Management and Operational Excellence professional with broad experience, including AI, across several industries. He was recently the Director of Enterprise Agility at Bristol Myers Squibb Pharmaceuticals. Mike is an adjunct professor at Columbia University and Villanova University teaching courses in Agile and Project Management. He is a PMI ATP trainer. He is a Past President of PMI-DVC and an author for Data Management University Magazine. Mike is a professional speaker, having presented in many countries for over two decades.
Presentation Summary:
What if the decisions we believe we control have far wider consequences than we realize? And what if innovation stalls, not because of technology, but because we underestimate the human side of change?
In this lively and thought-provoking session, we explore four interconnected themes at the intersection of project management, leadership, and artificial intelligence. First, we examine the often-overlooked world of unintended consequences, including secondary and tertiary effects that emerge long after a decision is made. Next, we contrast two fundamentally different mindsets: the logical, engineering-driven approach that values precision and predictability, and the people-centric approach, where understanding human behavior becomes the true engine of innovation.
We then explore how managerial expectations, perceived likability, and the universal human desire to “fit in” can subtly but powerfully shape the decision-making process and influence AI adoption.
Through engaging examples and a touch of humor, this session reveals how technical choices, human behavior, and leadership mindset interact to either enable or quietly undermine innovation.
Learning Objectives:
- Recognize unintended consequencesof project, leadership, and AI-related decisions, including delayed secondary and tertiary effects.
- Distinguish between engineering-driven and people-centric mindsets and understand how each shapes innovation and decision-making.
- Assess how leadership expectations and social dynamics influence AI adoption, team behavior, and organizational outcomes.
