1. Data Quality and Availability

Evaluating the quality, volume, and accessibility of data, since data is essential for AI models.

How would you rate the quality, accuracy, and completeness of your organization's core data (e.g., donor, program, volunteer data)?

Emerging Data is often incomplete, inaccurate, or in different formats. Processes are informal and just beginning. Developing We have some defined processes for data quality, but they are applied inconsistently. We're aware of the need to improve. Strategic Our data is proactively managed, regularly audited for quality, and well-documented. It's a trusted asset.

Is your data centralized and easily accessible for analysis, or is it siloed in different spreadsheets and systems?

Emerging Data is highly siloed in personal spreadsheets or systems. It's very difficult to get a complete view. Developing Some data is centralized (e.g., in a CRM), but other key data is still siloed. It requires manual effort to combine. Strategic Our data is in a 'single source of truth' (like a data warehouse or well-integrated CRM) and is easily accessible.

2. Infrastructure and Technology

Reviewing whether existing hardware, software, and network resources can support AI workloads.

Can your current technology stack (e.g., CRM, cloud services, computers) support modern data analysis tools?

Emerging Our systems are mostly legacy, on-premise, or not integrated. They cannot support modern analysis tools. Developing We have a mix of old and new systems. Some are cloud-based, but we face limitations and workarounds. Strategic Our tech stack is modern, cloud-native, and well-integrated (using APIs), ready to support new AI tools.

How scalable is your IT infrastructure to handle larger datasets or more complex computations (e.g., use of cloud)?

Emerging Our infrastructure is fixed and not scalable. A large increase in data or users would be a major problem. Developing We have some cloud resources and can scale in certain areas, but it's often a manual or slow process. Strategic Our infrastructure is highly scalable (e.g., auto-scaling cloud services) and can handle complex workloads.

3. Workforce and Skillsets

Assessing if the organization has the necessary AI expertise, and identifying skill gaps.

How would you rate the overall data literacy (comfort with data, charts, basic analysis) of your team?

Emerging Our team is generally uncomfortable with data. Decisions are made almost exclusively on intuition. Developing Some team members are data-savvy and use basic reports, but data literacy is not consistent across the organization. Strategic Our team is data-literate, confidently uses dashboards to ask questions, and values data in decision-making.

Do you have access to (or a plan to train/hire) individuals with specialized data analysis or AI skills?

Emerging We have no specialized data/AI skills in-house and no current plan to acquire them. Developing We have 'data champions' or are planning to train/hire, but we don't yet have dedicated, advanced AI expertise. Strategic We have dedicated data/AI specialists (in-house or via a strong partner) who lead and mentor others.

4. Organizational Alignment and Culture

Gauging leadership support, company culture, and openness to innovation for AI adoption.

Does your organization's leadership (e.g., Board, E.D.) actively support and champion data-driven initiatives?

Emerging Leadership is skeptical or not engaged with data initiatives. It's not seen as a priority. Developing Leadership offers passive support. They are 'open' to ideas but don't actively champion or fund them. Strategic Leadership actively champions data-driven initiatives, allocates resources, and ties them to our core mission.

How open is your team culture to experimenting with new technologies and changing existing processes?

Emerging Our culture is very resistant to change. New technologies are seen as a burden, and we prefer 'how it's always been done'. Developing Our team is neutral or cautiously optimistic. Some are open to new tools, while others are resistant. Strategic Our culture is eager to innovate. We embrace experimentation, learn from failures, and are excited by new tech.

5. Process Optimization

Identifying workflow inefficiencies that AI can improve.

Have you clearly identified specific, repetitive tasks or workflow bottlenecks (e.g., data entry, donor acknowledgments)?

Emerging We haven't formally identified or documented our repetitive tasks or bottlenecks. We just 'get the work done'. Developing We have a general, informal understanding of our bottlenecks, but they are not well-documented or analyzed. Strategic We have clearly documented our key workflows and have pinpointed specific, high-impact areas for optimization.

Is your team actively looking for ways to automate or improve inefficient processes?

Emerging We rarely or never look for ways to automate. Most repetitive work is done manually. Developing We sometimes look for automation (e.g., using mail merge), but it's an occasional effort, not a consistent priority. Strategic Process improvement and automation are a core, consistent priority. We actively seek out and implement efficiencies.

6. Governance, Security, and Compliance

Ensuring AI practices comply with regulations and maintain data security.

Do you have clear, written policies for data governance, privacy, and security, especially for sensitive constituent data?

Emerging We have no formal, written policies for data governance, privacy, or security. It's an unaddressed risk. Developing We have some informal or outdated policies, but they are not comprehensive or consistently enforced. Strategic We have clear, modern, and actively enforced policies for data governance, privacy, and security.

Are you aware of and prepared for the ethical considerations and potential biases in using AI with your community's data?

Emerging We are not aware of or have not discussed the ethical implications or potential for bias in using our data. Developing We are vaguely aware of the issues. We've had informal discussions but have no formal framework or checklist. Strategic We have a formal ethical framework and actively monitor for bias. This is a key part of our review process.

7. Financial Viability

Evaluating budget and expected return on investment (ROI) for AI projects.

Have you allocated (or are you planning to allocate) a specific budget for data and AI initiatives (including tools and training)?

Emerging We have no budget for data/AI initiatives and no plan to secure one. We rely on free tools only. Developing We are exploring grant opportunities or have a very small, experimental budget for data projects. Strategic We have a dedicated, approved budget line for data infrastructure, tools, and team training.

Does your leadership have a realistic understanding of the potential costs AND long-term mission-impact (ROI) for AI projects?

Emerging Leadership has an unrealistic (e.g., 'it's a magic bullet') or no understanding of the costs and mission-impact (ROI). Developing Leadership has a vague idea of costs and benefits but hasn't seen a detailed business case or ROI model. Strategic Leadership has reviewed a well-researched case for AI, understanding both the costs and the mission-based ROI.

8. AI Strategy and Roadmap

Defining clear AI goals and creating a phased plan for implementation.

Does your organization have a clear, written strategy that connects AI adoption directly to your core mission and goals?

Emerging We have no AI strategy. Any efforts are random, 'shiny object' experiments. Developing We have some verbal ideas or a rough draft of a strategy, but it is not formally written or tied to our main goals. Strategic We have a clear, written strategy that directly connects AI implementation to our core mission and strategic plan.

Do you have a phased plan or roadmap for testing and implementing AI, starting with small, high-impact pilot projects?

Emerging We have no plan. We don't know where or how to start with AI projects. Developing We are in the brainstorming phase, identifying potential pilot projects, but don't have a formal, phased roadmap. Strategic We have a clear, phased roadmap that starts with small, high-impact pilots and scales up. We are executing this plan.

Nonprofit AI Readiness Assessment

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