Artificial Intelligence and the Future of Nonprofit Organizations: A Paired Review

As nonprofit organizations confront intensifying resource constraints, rising accountability demands, and increasingly complex social problems, artificial intelligence (AI) has emerged as both a promise and a provocation. While practitioner discourse has rapidly embraced AI as a set of practical solutions, systematic academic and empirical research has lagged behind this acceleration, leaving key questions about organizational consequences insufficiently examined. Across fundraising, volunteer management, program evaluation, and internal operations, AI technologies are increasingly seen as tools to enhance efficiency, personalization, and strategic insight, while also helping nonprofits offset persistent resource constraints related to staff capacity, time, and expertise. At the same time, scholars have cautioned that technological solutions introduced into mission-driven fields may reshape accountability, discretion, and power in unintended ways (e.g., Bryson, 2010; Eubanks, 2018). Yet the rapid diffusion of AI into the nonprofit sector has outpaced systematic reflection on its organizational, ethical, and governance implications, creating a growing need for empirical research that moves beyond advocacy and experimentation to examine how AI is actually integrated, governed, and experienced within nonprofit organizations.

Two books published by Wiley in 2025—AI for Nonprofits: Putting Artificial Intelligence to Work for Your Cause by Darian Rodriguez Heyman and Cheryl Contee, and Nonprofit AI: A Comprehensive Guide to Implementing Artificial Intelligence for Social Good by Nathan Chappell and Scott Rosenkrans—seek to address this gap from complementary, but distinct, vantage points. Taken together, these volumes offer a timely snapshot of how AI is currently being imagined, operationalized, and normalized within nonprofit organizations at a moment when scholarly understanding of these processes remains fragmented and underdeveloped. While both books are oriented toward practitioners, they differ markedly in how explicitly they engage with the organizational, governance, and accountability implications of AI adoption—differences that open distinct avenues for future empirical inquiry.

Heyman and Contee’s AI for Nonprofits is firmly practitioner-oriented. Drawing on insights from more than 50 nonprofit leaders, consultants, and technology professionals, the book is structured as a pragmatic guide to “putting AI to work” across core nonprofit functions. Rather than advancing a unified theoretical framework, the authors organize the text around functional domains—fundraising, marketing and communications, program delivery, operations, and leadership—each populated with concrete examples of AI-enabled practices. This orientation reflects a broader trend in nonprofit management discourse that privileges adaptive problem-solving and operational responsiveness over formalized planning models (Bryson & George, 2024).

The book’s primary strength lies in its accessibility. For nonprofit managers who may feel overwhelmed by the pace of technological change or uncertain about where to begin, AI for Nonprofits lowers the barrier to entry. AI is presented not as a disruptive force requiring fundamental organizational restructuring, but as an incremental extension of existing practices. In this sense, the volume functions as a field guide, translating emerging technologies into actionable steps aligned with day-to-day nonprofit work. However, this operational emphasis also circumscribes the book’s analytic reach. Ethical considerations and governance issues are acknowledged, but remain secondary to implementation concerns, offering limited insight into how AI tools become routinized over time or how responsibility and decision authority shift between human actors and algorithmic systems—questions that merit closer scholarly attention. Chappell and Rosenkrans’s Nonprofit AI engages these more profound questions more directly by positioning AI as an organizational capability rather than a collection of tools. Structured into 18 chapters, the book advances a holistic vision in which leadership, culture, and governance are central to responsible AI adoption. Rather than treating AI as a technical add-on, the authors conceptualize it as a layer of organizational intelligence that must be intentionally aligned with mission, values, and stakeholder expectations. This perspective resonates with growing scholarly concern over the “responsibility gap” created by algorithmic systems, particularly in public- and mission-oriented organizations (Mittelstadt et al., 2016) and invites future research to examine how such alignment is negotiated, contested, or stabilized in practice.

One of the book’s most notable contributions is its sustained attention to responsible AI. Issues of data stewardship, transparency, accountability, and ethical alignment are woven throughout the text rather than isolated in a discrete chapter. By foregrounding these concerns, Nonprofit AI underscores the distinctive obligations nonprofits face when deploying AI systems, given their reliance on public trust and moral legitimacy. AI is consistently framed as a decision-support infrastructure that should augment, rather than replace, human judgment—an approach that aligns with broader critiques of technological solutionism and normative arguments emphasizing democratic accountability in social-purpose organizations (Reich, 2019). Future research could build on this normative framing by empirically examining how accountability mechanisms evolve once AI systems are integrated into nonprofit governance structures.

Despite its strengths, Nonprofit AI also exhibits some limitations. While the book aspires to comprehensiveness, its engagement with established nonprofit and organizational theory remains largely implicit rather than explicitly theorized. In addition, although the authors emphasize ethical responsibility and mission alignment, the analysis remains largely abstracted from variations in organizational scale, resource endowment, and institutional context. As a result, the book offers limited insight into how AI adoption may unfold differently across nonprofits facing chronic resource constraints or uneven digital infrastructure—an issue that has long concerned scholars of nonprofit capacity and organizational resilience (e.g., Lecy & Searing, 2015; Young, 2022), and one that calls for comparative research across organizations with differing levels of capacity and institutional support.

Read together, the two books are best understood as complementary rather than competing contributions. AI for Nonprofits excels in translating AI into actionable practices that organizations can adopt in the short term, while Nonprofit AI provides the strategic and ethical scaffolding necessary to evaluate whether—and how—those practices should be pursued. The former answers the question of what nonprofits can do with AI right now; the latter raises deeper questions about organizational identity, governance, and public responsibility. Their juxtaposition highlights a central tension in contemporary nonprofit management: the pull between rapid experimentation and the need for institutional reflection.

Both volumes also offer clear pedagogical value, albeit in different ways. AI for Nonprofits is well suited for applied courses in nonprofit management, fundraising, or social entrepreneurship, particularly at the master’s level or in executive education programs. Its concrete examples and tool-oriented structure lend themselves well to case discussions and practitioner-focused workshops. Nonprofit AI, by contrast, is better positioned as a supplementary text in courses on nonprofit strategy, governance, or digital transformation, where its emphasis on leadership and ethics can foster more critical discussion. For researchers, the two books together underscore the need for empirical work that moves beyond documenting tool adoption to examine how AI reshapes decision-making, accountability, and power within nonprofit organizations. In particular, future research would benefit from longitudinal and comparative designs that trace how AI systems are embedded into organizational routines over time, how responsibility is redistributed between humans and algorithms, and how these dynamics vary across nonprofits with differing levels of capacity, scale, and institutional support. Such work would help bridge the gap between practitioner-oriented guidance and theory-driven understanding of AI in nonprofit settings.

These books reflect a pivotal moment in the evolution of nonprofit management, as AI shifts from speculative innovation to increasingly normalized organizational practice. While neither volume offers a definitive theoretical account of AI in the nonprofit sector, their combined contributions lie in clarifying the questions that now demand sustained and systematic empirical inquiry. As AI becomes more deeply embedded in nonprofit work, the central challenge will be not only to use these technologies effectively, but to govern them in ways that reinforce—rather than erode—the values and public trust on which the sector depends.