Nonprofit organizations attempting to cut costs through new technology and AI are discovering that decades of poorly maintained data infrastructure are blocking their progress, according to a new analysis published by RubinBrown partner Colleen Guillen on InformationWeek. As inflation drives up expenses and demand for services increases, nonprofits face mounting pressure to operate more efficiently. But the report finds that years of treating technology systems as an afterthought have left many organizations unable to take advantage of the very tools designed to help them save money.
The analysis traces how budget-conscious nonprofit leaders historically avoided investing in systems not directly tied to program work, instead relying on vendors to provide stripped-down versions of enterprise software used by private companies. While these tools typically cost less and offer simpler interfaces, they were often layered onto existing digital infrastructure rather than serving as foundational systems. The result is data trapped in silos and scattered across multiple platforms, with development teams working in donor CRM systems that don't sync with the software used by operations staff. Instead of maintaining one authoritative source of information, nonprofits store data throughout the organization, frequently kept only in the institutional knowledge of a handful of key leaders rather than in unified platforms that can support growth.
The report states that organizations of all sizes and stages are being held back in their ability to fully use AI, largely because of inadequately designed data systems. According to the analysis, countless organizations remain stuck in basic AI functionality, unable to move beyond large language models like ChatGPT and Claude, which merely touch the surface of AI's potential for organizational use. The report notes that as organizations adopt more sophisticated predictive AI models, the fundamental requirement becomes cleaner data—while LLMs can process messy human speech patterns, embedded AI tools, API-integrated solutions, and retrieval-augmented generation all require clean, structured data to train models and verify accuracy. Beyond AI implementation, the analysis finds that even fractional executives—senior leaders hired part-time as a cost-saving measure—spend months sorting through disconnected databases, inconsistent reports, and mismatched processes.
The analysis recommends that nonprofits abandon small tactical fixes in favor of a comprehensive approach to data infrastructure. Organizations need to work with their boards and leadership teams to define what they're trying to accomplish, then determine whether AI can assist—whether by accelerating intake processes or identifying potential partnership opportunities. Without a fundamental grasp of strategy and its metrics, any cost-saving initiative cannot be accurately measured, the report argues. The analysis points to companies like Accenture rolling out Copilot to nearly 750,000 employees with specific use cases already defined, not waiting to see what happens. Nonprofits can do the same for their teams, establishing clear, actionable process improvements while simultaneously cleaning up data for more advanced applications. Organizations that have already centralized records, clarified ownership, and unified disconnected systems can move faster when leadership opportunities or new tools emerge, while those who deferred that work are still trying to establish a baseline before strategic decisions can even begin. The choice between reactive catch-up and proactive readiness will determine which nonprofits can actually deliver on the efficiency promises their boards are demanding.