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Building Empathetic Agility: The Leadership Muscle for Navigating Complexity with Purpose
Empathetic agility is the ability to adapt strategically and maintain momentum, while genuinely considering the experiences of others.
We are living through a pivotal moment in technological history. Artificial Intelligence (AI) is no longer a distant promise; it’s a present reality, reshaping how we work, communicate, and solve problems. For nonprofits, philanthropic organizations, and corporations alike, this moment offers both immense opportunity and profound responsibility.
Across sectors, AI is unlocking new possibilities. Nonprofits are using it to streamline operations and expand reach. Philanthropic organizations are exploring how AI can enhance impact measurement and equity analysis. Corporations are integrating AI into customer service, product development, and strategic planning.
Yet the significance of this moment goes beyond efficiency. It’s about reimagining how we think, how we collaborate, and how we build systems that are more inclusive, responsive, and future-ready. And let’s be honest: AI adoption can feel overwhelming. The pace of change is rapid, the tools are multiplying, and the expectations are high. Many organizations are asking, ‘Where do we start?’ ‘How do we do this responsibly?’ ‘How do we build staff capacity?’ ‘What governance do we need?’
These are some of the questions driving FSG’s own journey, which is underway and in some ways feels like it’s just beginning. Here’s what we’re learning through our real-time experimentation.
Rethinking Our Approach to AI
We’re learning that successful AI adoption starts with mindset. In systems change work, we know that the most visible changes, in this case, adopting new tools and reimagining some of the practices that guide our work, are only part of the picture. The deeper, less visible conditions—mindsets and mental models, power dynamics, and relationships—are often harder to identify and shift, yet they fundamentally shape whether AI adoption takes hold. For instance, underlying beliefs about risk and innovation shape how an organization holds the tension between moving carefully through policy-driven processes and learning quickly through experimentation.
This means that AI adoption is not just about having access to a seemingly endless array of powerful tools, ranging from generative models to predictive analytics. It’s about cultivating a culture of curiosity, experimentation, and ethical reflection. Organizations must ask: How do we think about AI? What are we trying to solve? How do we ensure that innovation aligns with our mission and values? The real value lies not in the tools themselves, but in why and how we use them.
Becoming an AI-enabled workforce means developing the skills and judgment to integrate AI meaningfully into our work. It’s about building capacity, not just capability.
AI Insights to Date
We’re still in the process of exploring how AI can best support our work. From conversations with practitioners, experts, colleagues, and tool developers, we’ve begun gathering a set of learnings that reflect what we’re discovering along the way. These evolving insights reflect the different levels at which AI adoption happens—from establishing clear policies and practices, to shifting how we work together, to examining the assumptions we have about AI.
Building a Foundation
Confirm client and partner guidelines first: Before initiating any AI-related work, ensure alignment with client expectations and values.
- Establish an AI governance framework: Build clear policies and oversight mechanisms to guide responsible use.
- Treat AI output as a first draft: AI-generated content is a starting point, not a final product.
Shifting How We Collaborate
- Democratize AI access: Make AI tools and knowledge accessible across teams and roles—not just technical experts.
- Keep a human in the loop: Ensure human oversight remains central to all decision-making processes.
- Disrupt systemic inequities: Use AI as a tool to challenge and correct imbalances, not reinforce them.
Examining Our Assumptions
- Encourage organic adoption: Promote usage and experimentation, allowing adoption to evolve naturally.
- Share what you learn: Promote transparency and collective growth by openly sharing insights and lessons.
Closing Thoughts: AI as a Systems Change Effort
Implementing AI is not simply a technical upgrade; it is a cultural transformation. Like any systems change effort, it requires intention, iteration, and a willingness to rethink how we work. To support this journey, we’ve assembled a cross‑organizational working group to help us navigate these challenges. This effort is not only about structural change; it also strengthens relationships and connections across our organization, an essential element of systems change. Most importantly, it calls on us to show up as humble learners.
Being a humble learner means acknowledging what we don’t yet know, staying curious in the face of complexity, and remaining open to evolving our assumptions. It means asking sharper questions, listening deeply to diverse perspectives, and recognizing that no one holds all the answers, not even the algorithms.
Technology alone won’t transform our organizations. People will. And the people who will lead this transformation are those willing to experiment, reflect, and grow. AI can amplify our capabilities, but it cannot replace human thought or moral judgment. The future of work isn’t about humans versus machines; it’s humans who are ready to learn, adapt, and lead alongside AI.
Let’s approach this moment not with certainty, but with humility. That’s how real change begins.
