Understanding the Five Stages of AI Maturity

Most organizations I talk to are optimistic about AI’s impact on the sector, but they’re struggling to determine how AI drives real, measurable impact and ROI. They also want to ensure that they’re adopting AI responsibly.

That’s why maturity models matter. They give us a way to step back, see the full picture, and make more intentional decisions about how we move forward to make real impact.

The Responsible AI Institute, one of our partners in the AI Coalition for Social Impact, has created a clear roadmap of the stages of AI Maturity. Their maturity model outlines how to move from experimental, ad-hoc tactics into intentional, governed AI strategies.

What follows is a simple way to understand those stages, and what they look like in practice.

The 5 Stages of AI Maturity

The Responsible AI Institute’s model describes a progression most organizations are already moving through, whether they’ve named it or not.

Progress through the stages is rarely linear, and different teams often sit at different points simultaneously. The five stages below describe what AI use looks and feels like at each level.

1. Aware: AI is happening, but it’s not coordinated

At this stage, AI use is informal and often driven by individual initiatives.

You might see:

• Staff experimenting with tools on their own
• Small pilots or one-off projects
• No clear policies or shared standards
• Uncertainty about what’s allowed—especially around data

There’s energy here, and often some early wins. But it’s inconsistent. What works in one team isn’t repeatable across the organization. Most critically, it illustrates a gap in regulations and best practices.

How it feels: promising, but unclear—and more than a little risky.

2. Active: patterns start to form

As organizations spend more time with AI, they begin to recognize the need for more structure.

At this stage, you’ll typically see:

• Early conversations about acceptable use
• Draft policies or guidelines starting to take shape
• A few repeatable use cases that teams come back to
• Growing awareness of risks like bias, accuracy, and data exposure

This is often where leaders step in more actively. Putting guardrails in place to make sure the organization is moving in a consistent direction.

How it feels: more coordinated but still evolving.

3. Operational: governance becomes real and visible

This is where responsible AI starts to move from intention to practice.

Organizations at this stage have:

• Clear policies that guide how AI can be used
• Defined roles and accountability (who owns what)
• Standard practices for reviewing outputs and managing risk
• Alignment across teams, rather than isolated experimentation

Importantly, governance becomes something people can actually use—not just a document that exists somewhere.

How it feels: clearer, more confident, easier to move forward.

4. Systemic: AI is integrated into how the organization works

At this stage, AI is no longer a side effort. It’s part of core workflows.

You’ll see:

• AI supporting key functions (fundraising, engagement, operations, etc.)
• Consistent processes for evaluating performance and impact
• Stronger data practices to support reliable outputs
• Ongoing training so staff know how to use AI appropriately

Decisions about AI are no longer reactive. They’re planned and tied to organizational priorities.

How it feels: stable, repeatable, and increasingly effective.

5. Transformational: AI is aligned to strategy and mission

At the most advanced stage, AI is fully embedded in how the organization delivers on its mission.

This includes:

• AI use that is intentionally designed around mission outcomes
• Proactive governance that evolves alongside new capabilities
• High confidence in data, processes, and oversight
• Clear communication—internally and externally—about how AI is used

Organizations at this stage aren’t just using AI well. They’re using it in a way that strengthens trust and long-term impact.

How it feels: aligned, intentional, and purpose-driven.

How to use this model

The goal isn’t to “reach the final stage” as quickly as possible.

Most organizations will operate across multiple stages at once. One team may be experimenting, while another has more defined practices in place.

What matters is being able to answer a few honest questions:

• Where are we today?
• Where are we seeing inconsistency or risk?
• What would help us move forward with more clarity?

Progress usually looks like small, practical steps:

• Turning informal practices into shared guidance
• Making incremental but compounding improvements to data, governance, and transparency
• Agreeing on where AI should—and should not—be used

Why maturity matters

There’s a lot of pressure to adopt AI quickly. But what we’re seeing across the sector is that adoption alone doesn’t lead to better outcomes.

Organizations that see meaningful results are the ones that:

• Use AI intentionally
• Put guardrails in place early
• Connect AI use back to real organizational goals

In other words, they build maturity over time.

That’s especially important in social impact work, where trust, accuracy, and human judgment are core to the mission.

A shared foundation: build your AI Policy

If you’re early in this journey, you’re not behind. You’re where most of the sector is.

What matters now is building a shared understanding of what responsible, effective AI looks like—and taking the next step from there.

Maturity models don’t solve everything. But they give us a practical starting point.

And right now, starting with clarity is one of the most valuable things we can do.

Want to take the next step?  The Responsible AI Institute’s resource library, specifically the AI Policy Template is the perfect place to start establishing governance and moving towards getting more from your AI use.