Catalyst Collective: A Fictional Case Study

Welcome to a grounded, narrative walkthrough that brings the Trail Map AI Maturity Model to life.

This case study explores how a fictional nonprofit—Catalyst Collective—navigates the real-world challenges, opportunities, and ethical questions of integrating AI into mission-aligned work.

Why This Case Study?

To tell a story – Stories make ideas stick. This one helps you visualize what AI maturity looks like in practice.
To show the Trail Map in action – It’s not just theory. You see it unfold stage by stage.
To reflect real results – While the org is fictional, the strategies, tools, and challenges are based on real-world experience.

Why a Fictional Org?

Most organizations are early in their AI journey. Few have completed a full arc of exploration, strategy, and implementation.

This fictional case offers a reference story—an adaptive, multi-dimensional journey mapped to the Trail Map framework.

Meet Catalyst Collective

Catalyst Collective is a values-driven nonprofit empowering local changemakers through leadership, storytelling, and capacity-building.

They serve grassroots partners with a focus on equity, inclusion, and a hybrid (in-person + digital) model.

Org Core Docs

To understand their journey, here are Catalyst’s foundational materials—serving as their internal source of truth:

Strategic Overview & Governance Programs, Impact & Risk Management Housing & Infrastructure Community & Marketing Strategy Audience Analysis & Donor Persona Report

(*All files open in a new tab and are downloadable PDFs*)

Where They Started

The story starts with curiosity—and pressure. Leadership had questions, funders had expectations, and no one knew where to begin.

From that uncertainty, Catalyst began an evolving relationship with AI—exploring, learning, and building aligned systems over time.

Starting with Applications

Catalyst Collective began like many others—with a mix of curiosity, uncertainty, and a growing sense of urgency. They’d heard the buzz around AI, but weren’t sure if it was relevant, ethical, or realistic for their work.

After encountering the Trail Map AI Maturity Model, they found what they were looking for:

A clear, honest overview of the AI journey—without the hype.
A simple tool to assess where they were and what to do next.
Free resources, practical prompts, and grounded recommendations.

Most importantly, the Trail Map didn’t just describe the path. It pointed them to their next step: explore applications.

Deciding to Experiment

According to the model, experimenting with practical AI applications—tools that save time, money, or energy—is one of the safest and most accessible starting points. Awareness is important. But it shouldn’t be a stopping point.

The Trail Map taught them how to do this safely:

Use tools that don’t require uploading sensitive data.
Run internal, low-stakes experiments—no public-facing outputs.
Look for clear value: time savings, efficiency, clarity, or better storytelling.

With that, Catalyst Collective began using ChatGPT—a $20/month tool—to test a few core use cases. They focused on grant narratives, meeting summaries, donor thank-you drafts, and planning prompts.

The Trail Map guided them through:

Brainstorming promising use cases
Tracking experiments and measuring time saved
Reflecting on what those time savings could enable

What follows is a simulated version of those early experiments. In under one hour, we replicate the types of things they tried. Each screencast shows the actual prompts and outputs generated along the way.

This video demonstrates how to create a Custom GPT designed for nonprofit consulting. Instead of using a generic AI assistant, we build a system that understands nonprofit-specific challenges by feeding it key documents and giving it precise personality traits, expertise, and response guidelines.

AI Experiment Results

How AI compared to manual work in terms of time savings, cost efficiency, and performance over time.

AI vs. Manual Time Savings (in Hours)

Cost Savings Breakdown

AI Efficiency Growth Over Time

Documenting Dilemmas

One of the reasons Catalyst Collective chose to follow the Trail Map was the alignment they saw with TrailGuide’s core values. This wasn’t just a framework for reaping AI’s benefits—though those were clear and measurable. It was also a space to ask harder questions. Questions about people. About power. About what truly serves.

The Trail Map didn’t just say “experiment.” It made space for reflection. It invited them to document not only their wins, but their dilemmas—the tensions, discomforts, or emerging risks they encountered as they tested new AI use cases.

What surfaced during experimentation?

During the early AI experiments (primarily around fundraising and donor communications), staff and leadership noted several concerns—some technical, some ethical, some interpersonal. Here are a few of the tensions they logged:

✉️ Emails felt “too personal” or emotionally manipulative.
💰 Segmentation favored wealthier donors.
🙏 Guilt-based appeals crept into messaging.
🧾 Disclosure uncertainty around AI-authored content.
🧠 Hallucinated facts in generated summaries.
📉 AI outputs used without review, risking quality control.

These dilemmas weren’t showstoppers—but they couldn’t be ignored. And thanks to the Trail Map, Catalyst Collective didn’t panic. They paused.

From confusion to clarity: Red, Yellow, Green

The Trail Map introduced a simple way to sort discomfort: a traffic-light system for ethical triage.

🟢 Green Light

Easily resolved. Example: if tone feels off, build tone checklists or use pre-approved language libraries.

🟡 Yellow Light

Needs deeper discussion. Example: Should you disclose AI authorship in grant materials? No clear policy existed yet.

🔴 Red Light

Trigger pause or escalation. Example: If AI reinforced bias or manipulated donors, it was paused and reviewed.

The core challenge of AI wasn’t technical—it was human. AI is communication. Good AI is just good communication, done faster. And that speed amplifies the very dynamics it automates.

This reflection marked a turning point. Catalyst was ready to define their non-negotiables and draft living principles to guide ethical AI use moving forward.

Up Next:

From Reflection to Principles — Catalyst begins designing its AI ethics framework to support long-term trust and alignment.

From Dilemmas to Principles

At this stage, Catalyst Collective found itself surrounded by an array of ethical questions—many of which they’d never been trained to answer...

The Trail Map taught them that discomfort isn’t a dead end—it’s a signpost...

This was the beginning of values-based leadership...

Catalyst Collective’s 5 Guiding Principles for AI

👥 People First

AI should amplify—not replace—human connection, judgment, and creativity.

🔍 Transparency Always

AI involvement in content or decisions must be clearly communicated to stakeholders.

⚖️ Equity by Design

AI tools should be regularly tested for bias and used to promote fairness across programs.

👁️ Human Oversight

All AI-generated outputs must undergo human review before implementation or publication.

🎯 Mission Alignment

Every use of AI must reinforce the organization's mission and contribute to its intended impact.

Principles in Action

These didn’t come from a think tank. They were born from Catalyst’s real tensions...

🟢 Green Light: Tone & Voice

Dilemma: Emails written by AI felt too polished or emotionally charged.
Resolution: The “People First” principle led to tone guidelines and pre-approved phrases...

🟡 Yellow Light: AI Authorship

Dilemma: Should they disclose when content is AI-assisted?
Resolution: “Transparency Always” became policy...

🔴 Red Light: Donor Bias

Dilemma: AI segmentation favored higher-income donors.
Resolution: “Equity by Design” required a pause...

These weren’t abstract values. They became tools. Conversation starters. A way to move forward...

Up next: Catalyst Collective begins designing their AI Strategy...

The Strategy Trail: Adaptive Leadership & AI

As Catalyst Collective moved deeper into their AI journey, they reached a crucial realization: the challenges they were facing weren’t technical—they were adaptive. It wasn’t just about learning tools. It was about shifting mindsets, changing behaviors, and redefining culture.

This distinction—between technical and adaptive challenges—is at the heart of adaptive leadership, a concept pioneered by thinkers like Ronald Heifetz. Catalyst began reframing AI integration using these core principles:

Adaptive challenges are people challenges – They need new habits and shared learning, not just expertise.
Leadership is everyone’s responsibility – It’s about participation, not position.
Growth requires discomfort – Progress comes from surfacing tension without letting the system burn out.

The Trail Map got them experimenting. But transformation required deeper work: creating space for conflict, building resilience, and re-centering human flourishing as both compass and goal.

AI Strategy as Adaptive Strategy

From there, Catalyst built a living, learning-centered AI strategy—more than a tech roadmap, an adaptive operating system:

Quarterly AI Review Cycles – Reflection on what’s working and what needs attention.
Multi-role Ownership – Including leads, ethical reviewers, communication stewards, and test pilots.
Ongoing Stakeholder Feedback – Invitations to voices across the org and community.
Single Source of Truth – Transparent documentation of prompts, results, and decisions.
Ethics & Equity Roundtables – Monthly sessions to apply principles to real tensions.

Eventually, this mindset spread beyond “AI work.” It shaped how Catalyst led overall—surfacing other cultural gaps in collaboration, decision-making, and communication.

A Human Problem

Key Insight

AI maturity isn’t about mastering tech—it’s about building the human capacity to adapt, align, and lead in change. AI isn’t a tech problem—it’s a people problem.

What began as a series of tech experiments had transformed into something deeper: a strategy rooted in trust, ethical clarity, and human-centered learning. It embraced the complexity of AI while honoring the humanity at its heart.