For months, the Staffing Advisors team has been wrestling with how to prepare our nonprofit and association clients for the inevitable impact of artificial intelligence (AI) on the leadership landscape. A recent article from the Nonprofit Alliance emphasizes the responsibility that leaders have to explore these tools, “Simply saying ‘no’ to AI is no longer viable; dismissing it as too overwhelming or complex undermines the potential for considerable mission advancement and societal impact.”
It’s coming. AI—specifically generative AI (GenAI)—will soon be a part of many operational ecosystems. And that means you’ll need to consider AI competencies when hiring for top leadership roles.
But here’s the thing: because AI is so new to many industries, you aren’t likely to find many candidates with in-depth experience in this area. So, what do you look for when hiring C-suite executives to lead through AI adoption and beyond? Here’s how to begin prioritizing which skills matter most.
(If you’re looking for a deeper dive into how to interview for these skills, stay tuned. We are developing those frameworks and will share them soon.)
Strategic Vision: Level Up
Strategic thinking or vision is a standard job competency for most C-suite roles, but AI adoption takes it up a notch (or five).
With this rapidly evolving technology, tested use cases for associations and nonprofits are not yet known. There could be significant risk to privacy and stakeholder trust if not managed properly. (A 2024 article from the International Association of Privacy Professionals outlines a risk assessment framework here.) And there are countless interdependencies—how AI adoption could affect different parts of an organization, from member relations to internal communication—that require a holistic, systems-oriented view.
A 2023 McKinsey report underscores this idea, “Leaders should view the situation through an ‘attacker’s lens.’ They should consider all the primary, secondary, and even tertiary effects of gen AI: Which business use cases are highest priority now—and which might be candidates for gen AI enablement in six months, 12 months, and so on?”
Creating a strategic vision in the era of AI will require more than critical thinking about an organization’s mission, value in the sector, and competition in the market. Leaders will also need an understanding of emerging AI technologies, how to navigate ethical considerations, and the capability to set clear, data-informed goals while educating and bringing stakeholders along—including board members.
It’s a tall order.
Learning Agility: The Key to Embracing Change
According to a 2020 report from the Center for Creative Leadership, learning agility is “a mindset and corresponding collection of practices that allow leaders to continually develop, grow, and utilize new strategies that will equip them for the increasingly complex problems they face in their organizations.” While not a new concept in organizational psychology, learning agility is more important than ever.
The status quo isn’t going to cut it anymore.
Leaders who are adept at absorbing knowledge and gathering input from varied sources to adapt to change—often discarding approaches that are no longer relevant—possess learning agility. This is more than learning new domain knowledge, although that is part of it. It involves learning from others, challenging assumptions, learning from difficult situations and failures, being comfortable moving forward despite ambiguity, and a willingness to take measured risks and learn from those.
Data Literacy: Understanding the Language of AI
The AI revolution is driven entirely by data—data literacy will no longer be a “nice to have” leadership competency.
In a 2023 MIT Sloan article on data literacy and modern leadership, the author shared, “Leaders need to understand data enough to make their best decisions, drive literacy throughout the organization, and create a culture of trust in data.” This spans from being able to critically review AI outputs to understanding how data is collected, structured, and shared across an organzation.
And for many nonprofits and associations, data is the foundation of intellectual property that drives revenue generation. In his whitepaper Leading Through the AI Revolution, Greg Starling, AI Expert and Head of Emerging Tech at DesignDATA (our technology partner), emphasizes, “Treat your data as a competitive edge that needs safeguarding. The importance of rigorous encryption, access controls, and regular audits are even more critical with the introduction of AI. Data that may have been buried deep in old files becomes instantly accessible.”
Your next leader may not need a degree in data science, but they will need to know the right questions to ask data analysts and understand which metrics are important and why. Defining KPIs, creating systems of accountability, and using those insights to adjust approaches should be among your next leader’s strengths.
Governance: Navigating a Complex Ethical Landscape
Nonprofits and associations are public good organizations. Leaders are tasked with managing resources responsibly, keeping the needs and values of the communities they serve at the heart of strategic choices.
When AI is involved, this also includes ensuring technology practices follow strict compliance and ethical use guidelines. Using AI to support different job functions and service delivery also requires a keen understanding of state laws and regulations, the potential for bias and error, and the need for appropriate transparency.
A 2023 article from the Stanford Social Innovation Review outlines some common concerns, “Generative AI raises a host of ethical questions and complexities. Large learning language models like ChatGPT are trained on content from the internet, including sites like Wikipedia, Twitter, and Reddit. Because this data includes overrepresented, white supremacist, misogynistic, and ageist views, the tools trained on it can further amplify biases and harms.”
And that doesn’t begin to touch on the potential ethical issues related to job redesign and organizational restructuring that may be the logical outcome of adopting AI.
Leaders will need to establish clear governance frameworks and policies that account for growth and scale. Those with experience working closely with boards on sensitive matters like changing bylaws, creating membership guidelines, developing codes of conduct, etc., will likely be able to oversee the ethical deployment of AI.
Change Management: Sustaining Human Culture
AI adoption could require major changes in processes and systems, and from a human resources perspective, it may fundamentally change how employees work. That can be overwhelming for staff. Leaders will need to create an environment where individuals feel secure in expressing concerns and uncertainties.
In a 2023 joint research report from the Burning Glass Institute and SHRM, researchers predict that “GenAI is expected to have a broad impact across nearly all categories of white-collar work … reskilling and upskilling workers will become increasingly important as GenAI becomes central to business operations.” From training sessions to ongoing education and engagement opportunities, all levels of an organization will need to understand how AI works, its benefits, and its challenges.
Methodical and human-centered change management will be critical to leading AI-driven transitions smoothly with clear communication and empathy. How someone led their teams through shifts spurred by the pandemic could be a good indicator of their expertise. Every leader had to manage change during that time, but some implemented more effective strategies than others.
Bridging the Gap
Here are a few examples to show how existing leadership experience in these areas could transfer to meet the demands of an AI-driven future:
| Skill Area | Existing Experience | AI Application |
|---|---|---|
| Strategic Vision | Developing Long-Term Growth Strategies | Crafting a strategy for how AI could support revenue diversification, enhance efficiency, and open new avenues for service delivery. |
| Learning Agility | Cross-Sector Collaboration | Leveraging partnerships across sectors to solve complex problems through shared data and models. |
| Ethical Governance | Handling Conflict of Interest | Developing policies that proactively manage potential conflicts of interest, ensuring impartial and transparent decision-making around AI initiatives. |
| Data Literacy | Overseeing Impact Assessments | Analyzing AI program effectiveness and adapting approaches based on KPIs of mission impact. |
| Change Management | Leading Org Restructuring | Aligning existing workforce skills with AI objectives and filling gaps with training opportunities or collaboration across departments. |
An Evolution of Effective Leadership
As AI reshapes the workplace, what we expect from leaders must evolve, too. Making sound decisions about why, when, and how to leverage these technologies internally and with stakeholders requires an emphasis on systems-thinking, process-oriented skills. Modern leadership may require these competencies to some degree, with or without AI, but AI puts a new spin on how deep the expertise should be.