Benchmarking Your AI Maturity Against Similar Organizations
Understand where your nonprofit stands in AI adoption compared to peers. Learn to use maturity frameworks, identify gaps, and create a roadmap for strategic improvement that aligns with your mission and resources.
In 2025, 82% of nonprofits use AI in some capacity, yet only 10% have formal policies governing its use. This striking gap reveals a fundamental challenge: most organizations are adopting AI tools without a clear understanding of where they stand relative to their peers or what "good" AI implementation actually looks like. Without benchmarking, nonprofits risk either falling behind organizations that have strategically invested in AI capabilities or wasting resources on initiatives that don't align with their actual maturity level.
Benchmarking your AI maturity isn't about keeping up with the latest technology trends—it's about making informed decisions. When you understand how similar organizations approach AI adoption, you gain perspective on what's realistic for your budget, staff size, and mission. You can identify which investments will have the highest impact and avoid the common mistake of jumping to advanced applications before building foundational capabilities. Perhaps most importantly, benchmarking helps you communicate with stakeholders—from board members to funders—about where you are and where you're headed.
The challenge is that AI maturity isn't a simple metric. It spans strategy, data infrastructure, organizational culture, governance, and technical capabilities. A nonprofit might be advanced in using AI for fundraising personalization but behind in data governance practices. Another might have excellent AI policies but struggle with actual tool adoption. Understanding these nuances requires a structured framework and honest self-assessment.
This guide walks you through the process of benchmarking your nonprofit's AI maturity. You'll learn about established maturity frameworks designed for organizations like yours, how to conduct meaningful self-assessments, where to find peer comparison data, and how to translate findings into actionable improvement strategies. Whether you're just beginning your AI journey or looking to optimize existing capabilities, understanding your maturity level is the essential first step toward strategic progress.
Understanding AI Maturity Models
AI maturity models provide structured frameworks for assessing an organization's capabilities across multiple dimensions. Rather than asking the oversimplified question "Are you using AI?", these models examine the depth, breadth, and sophistication of AI integration throughout an organization. They help you understand not just what tools you're using, but how effectively you're using them, how well they're governed, and how prepared you are to scale.
Most maturity models divide the AI adoption journey into stages—typically ranging from three to five levels. At the lowest level, organizations are experimenting with AI in isolated pockets, often without formal strategy or governance. At the highest levels, AI is deeply integrated into operations, continuously improving, and driving strategic decisions across the organization. The journey between these stages isn't linear; organizations often advance faster in some dimensions than others.
MITRE AI Maturity Model
Comprehensive framework available free to nonprofits
MITRE grants a non-exclusive, royalty-free license for nonprofits to use their AI Maturity Model. The framework evaluates 20 dimensions across six critical pillars, providing metrics by which an organization can qualitatively determine its progress in AI adoption.
- Ethical, Equitable, and Responsible Use: How well you ensure AI doesn't cause harm
- Strategy and Resources: Alignment of AI initiatives with organizational goals
- Organization: Structure, roles, and change management
- Technology Enablers: Infrastructure and technical capabilities
- Data: Quality, accessibility, and governance of data assets
- Performance and Application: Actual AI deployment and outcomes
SEI/Accenture AI Adoption Maturity Model
Industry-developed framework with five maturity levels
Developed by the Software Engineering Institute and Accenture, this model divides AI capabilities into eight core dimensions and defines five maturity levels that organizations progress through.
- Exploratory AI: Initial experimentation with limited coordination
- Implemented AI: Working applications in specific areas
- Aligned AI: AI initiatives connected to organizational strategy
- Scaled AI: AI deployed broadly with consistent governance
- Future-Ready AI: Continuous innovation and adaptation
Choosing the right maturity model depends on your organization's needs. MITRE's model is particularly well-suited for nonprofits because of its emphasis on ethical and responsible use—critical considerations when serving vulnerable populations. The SEI/Accenture model, while developed for broader enterprise contexts, provides a clear progression path that many nonprofits find intuitive. Some organizations use elements of multiple models, creating a hybrid approach that addresses their specific concerns.
Regardless of which framework you choose, the key is consistency. Using the same framework over time allows you to track progress and demonstrate improvement to stakeholders. If you're working with peer learning networks, agreeing on a common framework enables meaningful comparison and knowledge sharing.
The Five Dimensions of AI Maturity
While different maturity models use varying terminology, most assess organizations across similar core dimensions. Understanding these dimensions helps you identify where your nonprofit excels and where it needs development—essential knowledge for prioritizing improvement efforts and making meaningful peer comparisons.
1. Strategic Alignment
Strategic alignment measures how well AI initiatives connect to your organization's mission and goals. At lower maturity levels, AI adoption happens opportunistically—staff members try tools that seem useful without coordinating with organizational strategy. At higher levels, AI investments are driven by strategic priorities, with clear connections between specific AI applications and mission outcomes.
Key indicators of strategic alignment maturity include: having a documented AI strategy (which only 24% of nonprofits currently have), leadership involvement in AI decisions, budget allocation specifically for AI initiatives, and metrics that connect AI activities to mission impact. Organizations that have developed comprehensive strategic plans for AI typically score higher in this dimension.
Self-Assessment Question: Does your AI adoption follow a documented strategy, or do teams adopt tools independently based on individual needs?
2. Data Readiness
Data is the foundation of effective AI. Data readiness encompasses the quality, accessibility, governance, and integration of your organization's data assets. Many nonprofits struggle here: according to Gartner's 2025 report, 34% of leaders from low-maturity organizations cite data availability and quality as their top AI implementation challenge.
Lower maturity organizations typically have data siloed across multiple systems with inconsistent formats and limited documentation. Higher maturity organizations have established data governance policies, integrated data sources, and processes for ensuring data quality. They understand what data they have, where it lives, who can access it, and how it can be used—considerations that become even more critical when managing organizational knowledge with AI.
Self-Assessment Question: Could you easily pull a unified dataset combining donor, volunteer, and program participant information? How confident are you in the accuracy of that data?
3. Organizational Capacity
Organizational capacity examines whether your people, processes, and culture can support AI adoption. The statistics here are sobering: 40% of nonprofits report that no one in their organization is educated in AI, and 92% feel unprepared for AI implementation. These gaps manifest as resistance to new tools, inconsistent adoption, and inability to troubleshoot problems.
Building organizational capacity requires investment in training, identification of AI champions who can lead adoption efforts, and change management strategies that address staff concerns. Higher maturity organizations have training programs, clear roles for AI oversight, and cultures that embrace experimentation while managing risk appropriately.
Self-Assessment Question: What percentage of your staff has received formal AI training? Do you have designated individuals responsible for AI adoption?
4. Governance and Risk Management
The governance gap is perhaps the most significant finding in recent nonprofit AI research: 82% of nonprofits use AI, but only 10% have formal policies. This creates substantial risk—from data privacy violations to reputational damage from biased outputs. Governance maturity reflects how well an organization manages AI-related risks while enabling innovation.
Lower maturity organizations have no policies or ad hoc guidelines that aren't consistently followed. Higher maturity organizations have comprehensive AI policies, risk assessment processes, and mechanisms for ongoing oversight. They've addressed questions about what data can be input into AI systems, who reviews AI outputs, and how errors are handled. Creating an appropriate AI governance framework doesn't require a legal team, but it does require intentional effort.
Self-Assessment Question: Do you have a documented AI policy that staff are aware of and follow? What happens if an AI tool produces a problematic output?
5. Operational Integration
Operational integration measures how deeply AI is embedded in actual workflows and how effectively it's delivering value. Many organizations have access to AI tools but haven't integrated them into day-to-day operations. The gap between "using AI" and "achieving value from AI" is substantial—BCG found that 74% of companies haven't seen real value from their AI investments.
At lower maturity levels, AI use is sporadic and often limited to individual productivity tools used by tech-savvy staff. At higher levels, AI is integrated into core processes—from donor journey automation to grant compliance tracking—with measurable impact on efficiency and outcomes.
Self-Assessment Question: Which core organizational processes have AI embedded in standard workflows? Can you quantify the impact AI has had on those processes?
Conducting Your Self-Assessment
A meaningful AI maturity assessment requires honest reflection and input from multiple perspectives across your organization. The goal isn't to achieve a high score—it's to accurately understand where you are so you can make informed decisions about where to invest. Organizations that inflate their self-assessments end up pursuing initiatives they're not ready for, while those who underestimate themselves may miss opportunities.
Assessment Process Best Practices
- Gather multiple perspectives: Include leadership, IT staff (if you have them), program managers, frontline staff, and development professionals. Each group has different visibility into AI adoption and challenges. A leader might believe AI is well-integrated while frontline staff report frustration with tools.
- Use structured tools: The ISG AI Maturity Index offers a 15-minute assessment that provides benchmarking against peers. MITRE provides detailed rubrics for each dimension. Using a structured tool ensures you consider all relevant factors rather than focusing only on obvious strengths or weaknesses.
- Document evidence: For each dimension, identify specific evidence supporting your assessment. Don't just rate your data readiness as "medium"—note what data systems you have, known quality issues, and specific integration challenges. This documentation becomes valuable for planning and for demonstrating progress over time.
- Be specific about scope: Are you assessing the entire organization or specific departments? AI maturity often varies significantly across functions—your fundraising team might be advanced users while your program team has barely started. Decide whether you need an organization-wide view or department-level assessments.
- Establish a baseline and reassess periodically: Your first assessment establishes a baseline. Plan to reassess at regular intervals—annually at minimum, or after major initiatives. Tracking changes over time demonstrates the impact of your investments and helps you adjust strategies.
When conducting your assessment, resist the temptation to compare yourself to tech-forward for-profit companies or large foundations with substantial resources. Your benchmark should be organizations similar to yours in size, budget, and mission area. A small community food bank shouldn't measure itself against a multi-billion-dollar health system, even if both are technically "nonprofits."
Consider engaging neutral external support if resources allow. Consultants who work with multiple nonprofits can provide perspective on where you actually stand relative to peers—perspective that's difficult to gain from inside your own organization. However, if external support isn't feasible, the structured assessment tools mentioned above can help ensure rigor.
Finding Peer Comparison Data
Knowing your maturity level is only valuable if you understand how it compares to relevant peers. Fortunately, several resources provide nonprofit-specific benchmarking data, though availability varies by organization size and sector.
TechSoup AI Benchmark Report
TechSoup's 2025 State of AI in Nonprofits Benchmark Report provides comprehensive data from over 1,000 nonprofits. The report breaks down adoption rates, barriers, and outcomes by organization size, offering relevant benchmarks for comparing your situation.
Key findings include that larger nonprofits (budgets over $1 million) adopt AI at nearly twice the rate of smaller organizations, and that 76% of nonprofits lack a formal AI strategy—useful context for understanding where your organization stands.
Peer Learning Networks
Perhaps the most valuable benchmarking comes from direct peer connections. Industry associations, coalitions, and informal networks often facilitate AI knowledge sharing among similar organizations. These connections provide context that published reports can't—insight into what's working, what's failing, and what peers are planning.
Consider joining or forming a peer learning cohort with organizations of similar size in your sector. Even informal conversations with counterparts at similar organizations can reveal where you're ahead, where you're behind, and what you might learn from others.
Key Nonprofit AI Benchmarks (2025-2026)
Reference points for contextualizing your assessment
Adoption Metrics
- 82% of nonprofits use AI in some capacity
- 66% of large nonprofits ($1M+ budget) actively use AI tools
- 34% of small nonprofits (under $500K) actively use AI tools
- 21% of organizations have achieved company-wide AI integration
Readiness Metrics
- 24% have a documented AI strategy
- 10% have formal AI governance policies
- 60% express uncertainty and mistrust about AI
- 30% cite financial limitations as primary adoption barrier
When interpreting benchmark data, remember that averages can be misleading. If 82% of nonprofits use AI "in some capacity," that includes organizations where one person occasionally uses ChatGPT alongside organizations with sophisticated integrated systems. Look for data that segments by organization size, budget, and sector when available, and focus on the metrics most relevant to your strategic priorities.
Also consider the direction of change, not just current state. If your organization has moved from ad hoc experimentation to having a formal strategy and designated AI champion in the past year, that trajectory matters—even if you're still below average on some metrics. Understanding your rate of progress helps you set realistic expectations and communicate momentum to stakeholders.
Interpreting Your Results
Once you've assessed your maturity across dimensions and gathered peer comparison data, you need to make sense of the findings. The goal isn't simply to identify weaknesses—it's to understand patterns, prioritize effectively, and develop a realistic improvement roadmap.
Look for Pattern Disconnects
The most actionable insights often come from finding disconnects between dimensions. If your strategic alignment is high but operational integration is low, you have plans that aren't translating into action. If your data readiness scores well but governance is weak, you're exposed to risks that could undermine your AI investments. If organizational capacity is strong but operational integration is weak, there may be process or tool barriers preventing capable staff from applying their skills.
These disconnects suggest specific interventions. Strong strategy with weak execution often indicates a need for better change management or clearer ownership. Good data with poor governance calls for policy development. High capacity with low integration might mean your tools don't fit your workflows, pointing toward better AI stack integration.
Identify Prerequisite Gaps
Some dimensions are prerequisites for others. Advanced operational integration without governance is risky. Sophisticated AI applications without data readiness will underperform. If your assessment reveals that you're advanced in some areas but behind in prerequisites, you may need to pause expansion and shore up foundations.
For example, if your fundraising team has adopted multiple AI tools but your organization lacks data governance policies, the priority might be establishing those policies before expanding AI use—even if adoption is going well. The costs of AI failures are often highest when organizations scale before they're ready.
Recognize Strategic Strengths
It's equally important to identify what you're doing well. Areas where you exceed peer benchmarks represent potential competitive advantages and opportunities for thought leadership. If your governance is stronger than most peers, you might be well-positioned to expand AI use more quickly and with less risk. If your organizational capacity is high, you can tackle more ambitious AI initiatives than similarly-sized organizations.
Strengths also indicate assets you can leverage. Staff with strong AI skills can mentor others. Well-organized data can enable applications that peers with messier data can't pursue. Robust policies can accelerate adoption by providing clear guidelines that reduce decision paralysis.
Creating Your Improvement Roadmap
Assessment without action is merely interesting. The real value of benchmarking comes from translating findings into a focused improvement plan. This doesn't mean addressing every weakness—it means prioritizing strategically based on your mission, resources, and peer context.
Prioritization Framework
When deciding where to focus improvement efforts, consider these factors:
High Priority: Foundation Gaps
Address these first—they limit everything else:
- Data quality issues blocking AI effectiveness
- Missing governance creating unmanaged risk
- Lack of any AI champion or ownership
Medium Priority: Capacity Building
Important for sustainability but can develop incrementally:
- Staff training and skill development
- Process documentation and standardization
- Tool selection and integration planning
When creating your roadmap, be realistic about organizational capacity for change. Addressing every gap simultaneously leads to burnout and incomplete initiatives. Most nonprofits can meaningfully advance in one or two dimensions per year while maintaining progress in others. Choose priorities that offer the best combination of impact and feasibility.
Also consider how your priorities connect. Improving data readiness enables better operational integration. Building organizational capacity supports governance implementation. Look for synergies where investing in one area accelerates progress in others, creating a coherent improvement trajectory rather than disconnected initiatives.
Sample Improvement Objectives by Maturity Level
Exploratory Stage → Implemented Stage
- Identify and empower an AI champion
- Create basic AI use guidelines
- Implement AI in one core workflow with documented process
- Conduct basic data inventory across key systems
Implemented Stage → Aligned Stage
- Develop formal AI strategy connected to organizational goals
- Establish comprehensive AI policy with staff training
- Implement data governance framework
- Expand AI to 2-3 additional workflows with measurement
Aligned Stage → Scaled Stage
- Integrate AI across all major organizational functions
- Establish AI metrics and continuous improvement processes
- Build internal AI training capacity
- Contribute to peer learning networks and sector knowledge
Communicating with Stakeholders
Your maturity assessment provides valuable material for stakeholder communication—with your board, funders, staff, and partners. However, different audiences need different messages, and how you frame your findings matters as much as the findings themselves.
Board Communication
Boards typically want to understand where you stand relative to peers, what risks exist, and what investment is needed. Frame your assessment in terms of governance responsibilities—ensuring the organization is neither falling dangerously behind nor pursuing initiatives beyond its capacity.
- Present peer comparison context showing where you stand
- Highlight governance gaps and risk implications
- Propose prioritized roadmap with resource requirements
- Connect AI maturity to mission impact and strategic goals
Funder Communication
Funders increasingly expect nonprofits to be thoughtful about technology. Your assessment demonstrates that thoughtfulness—showing you're approaching AI strategically rather than haphazardly. Frame it as evidence of organizational maturity and responsible stewardship.
- Demonstrate structured approach to AI adoption
- Show alignment between AI investment and impact goals
- Highlight governance and ethical considerations
- Provide context for technology funding requests
For staff communication, focus on what the assessment means for their work and development. If you've identified organizational capacity as a weakness, this becomes an opportunity to discuss training investments. If governance is a priority, involve staff in policy development. Transparency about where the organization stands builds trust and helps staff understand why certain initiatives are prioritized.
In all communications, balance honesty about gaps with recognition of progress. The goal is realistic optimism—acknowledging where improvement is needed while demonstrating a clear path forward. Stakeholders respond better to "we have gaps and here's our plan" than to either denial of challenges or overwhelming lists of deficiencies.
Making Benchmarking Continuous
AI maturity benchmarking isn't a one-time exercise—it's most valuable as an ongoing practice that tracks progress, identifies emerging gaps, and adapts to the rapidly evolving AI landscape. Building benchmarking into your organizational rhythm ensures you maintain strategic awareness of your AI capabilities relative to peers and opportunities.
Establishing Your Benchmarking Rhythm
- Annual comprehensive assessment: Once per year, conduct a thorough assessment across all dimensions using a consistent framework. This provides the baseline for tracking year-over-year progress and should inform strategic planning cycles.
- Quarterly check-ins: Each quarter, briefly review progress against your improvement roadmap. Are you meeting milestones? Have new gaps emerged? This lighter-touch review keeps AI maturity on the leadership agenda without requiring extensive resources.
- Peer benchmark updates: When new sector reports are published (like TechSoup's annual survey), update your peer comparison context. Understanding how the broader landscape is evolving helps you calibrate whether your progress is keeping pace.
- Post-initiative assessments: After completing major AI initiatives, assess their impact on your maturity profile. Did implementing a new AI tool improve operational integration? Did developing policies strengthen governance? Connect specific investments to maturity outcomes.
Keep in mind that benchmarks themselves evolve. What constitutes "high maturity" today will be baseline expectations in a few years. The organizations achieving the best outcomes aren't those who reach a certain maturity level and stop—they're those who build cultures of continuous improvement, constantly raising their capabilities even as the bar rises across the sector.
Finally, remember that maturity is not the end goal—impact is. Higher AI maturity should translate into better service to your community, more efficient operations, and stronger mission outcomes. If your maturity is improving but impact isn't, something is disconnected. The ultimate benchmark is whether your AI capabilities are helping you better serve your mission, and that measure should remain central even as you track more technical maturity metrics.
Taking the Next Step
Benchmarking your AI maturity against similar organizations transforms vague concerns about "keeping up" into concrete understanding of where you stand and where to focus. The process itself—gathering input across your organization, comparing against peer data, identifying pattern disconnects—generates valuable insights regardless of what the final scores show. You learn not just how mature you are, but how your organization thinks about AI and where its assumptions might be wrong.
The nonprofit sector's AI maturity is rising rapidly, with adoption rates jumping from 55% to 82% in just two years. This pace of change means that standing still is effectively falling behind. But thoughtful organizations that understand their current capabilities—and systematically address gaps—can navigate this evolution strategically rather than reactively. They make investments that build on strengths, address critical weaknesses, and position them for success as AI becomes increasingly essential to effective nonprofit operations.
Start with an honest assessment using one of the frameworks described in this guide. Gather perspective from across your organization. Compare your findings to peer benchmarks. Identify the one or two priorities that will have the greatest impact given your current maturity level. And build benchmarking into your ongoing practices so that AI maturity remains a strategic focus rather than a one-time exercise. The organizations that thrive in the AI-enabled future will be those that approach it with both ambition and self-awareness—understanding clearly where they are as they chart a course toward where they need to be.
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