TABLE OF CONTENTS
AI Strategy: Case Study of How We Helped a $6 Billion Foundation
Avoid AI hype with a people-first AI strategy. Case study with a major foundation (billions in assets).
This is part of a series on problem solving for high-impact innovations
1. Strategy & Approach
The Regret Minimization Framework: A Practical Checklist
What is a Calculated Risk? An Essential Guide
AI Strategy: Case Study of How We Helped a $6 Billion Foundation
Philanthropic Strategy: Three Easy-to-Miss Tips for Impact (One Pager)
2. Goals and Metrics
Tech-Enabled Services Versus SaaS Business Model: Key Metrics
How To Achieve Financial Sustainability Without Sacrificing Impact
Founder Market Fit: The Overlooked Path to Your Ideal Client Profile
Tech-Enabled Services Versus SaaS Business Model: Key Metrics
How To Achieve Financial Sustainability Without Sacrificing Impact
Founder Market Fit: The Overlooked Path to Your Ideal Client Profile
AI Strategy: Cut Through The Noise
For mission-driven organizations, the constant roar around Artificial Intelligence presents a confusing mix of possibility and pressure.
Is AI a shortcut to greater impact, or another complex, resource-draining detour that could leave vulnerable communities further behind?
Caught between FOMO and the justifiable fear of costly missteps with already precious, limited resources, many leaders feel stuck. Traditional tech support often falls short too, with generic tools or training that don't translate.
This case study with a major foundation (billions in assets) details how a practical, people-centered AI strategy helped one group navigate this landscape and progress on a critical unmet need.
🪴 Joyful Ventures helps you win funding & contracts for lasting community impact through program discovery, positioning, and optimization, fusing Harvard PhD insight with Silicon Valley agility
Phase 1: Grounding the AI Strategy – Going Broad Before Going Deep
An effective AI strategy starts with understanding human needs, not chasing technological trends. Recognizing that past tech initiatives often stumbled without adequate groundwork, the first step was casting a wide net to understand the full landscape of challenges and potential opportunities before zeroing in.
- Defining the Focus: First, we clarified who we were serving: primarily leaders and staff at diverse nonprofits. To ensure we captured a representative range of experiences, we stratified our outreach based on key criteria like organizational size, role, technical capacity, and resource levels, actively seeking perspectives from those often least served by complex tech initiatives.
- Unearthing Needs: We started simply, analyzing existing data (quantitative and qualitative) to identify trends and form initial hypotheses rapidly. Then, we used mixed methods – broad quantitative surveys scanning common challenges followed by deep qualitative interviews – to validate and refine. This revealed crucial insights: top organizational needs often centered on core operations (like donor outreach, financial sustainability), while specific AI-related interests focused less on specific tools and more on practicalities like seamless integration into existing workflows, help identifying the right tools, and support using data for insights. We also surfaced common AI adoption challenges: integration difficulties, decision-making paralysis amidst options, internal skills gaps, and securing organizational buy-in.
- Exploring Solution Directions: Even at this early stage, we didn't just explore problems; we co-explored potential solution formats using simple methods. We asked "What would truly help?" and sketched out possibilities together—actionable guides, targeted workshops, simplified tool concepts—allowing us to test core ideas and validate the most promising type of assistance without committing major resources prematurely.
Phase 2: Diagnosing Deeply & Validating Solutions in Your AI Strategy
Having gone broad to identify top priorities (both problems and promising solution types), the next crucial step was going deep to diagnose root causes and rigorously validate potential solutions.
- Understanding Workflows & Past Lessons: We learned from nonprofits' past tech initiatives — what genuinely helped, what became shelfware — and dug into specific workflow bottlenecks related to the prioritized need (fundraising/grant writing). This provided critical context for designing something truly useful.
- Validating Interest Early: We got deeper feedback on the top-rated solution formats identified in Stage 1, exploring value propositions, how they would fit into daily work, and potential barriers. Testing true interest via simple waitlists and identifying potential partners willing to contribute helped ensure solutions met key, user-defined requirements for relevance (addresses my core task), concreteness (actionable, fits my workflow), and safety (reliable, unbiased, demystified), leading us to pivot confidently towards more targeted support.
Phase 3: Activating the AI Strategy with Accessible, Trusted Support
With validated needs (fundraising help) and solution concepts (practical guidance), the focus shifted to delivering impact efficiently by actively looking for existing "bright spots" – assets, tools, or relationships already working well.
- Start with Quick Wins & Trusted Partners: Addressing the grant writing struggle, a key bright spot was a trusted frontline fundraising expert known to the community. We partnered with her, leveraging existing trust and expertise. Instead of introducing complex new tech, we co-created an actionable donor segmentation guide integrating responsible AI prompts using accessible tools likely already familiar or easy to adopt, fitting it into their existing workflow.
- Earn the Right to Go Bigger: This approach, building on the bright spot of the expert partnership and familiar workflows, delivered immediate value and built crucial trust. Enthusiastic feedback affirmed this method. This positive experience created the essential foundation for bigger, responsible experimentation, now grounded in proven value
Key Lessons for Your AI Strategy
This case study highlights actionable principles:
- Go Broad, Then Deep: Scan widely to prioritize needs and potential solution types first, then dig deep to understand root causes and validate specific approaches.
- Validate Relentlessly with Users: Test concepts early and often. Define requirements like relevance, concreteness, and safety through their lens. Be ready to pivot.
- Build on Bright Spots: Leverage existing tools, workflows, and trusted relationships (like expert partners) to deliver value quickly and meet nonprofits where they are. Solve immediate problems well using accessible and proven methods first; this builds the trust needed for more ambitious AI exploration later.
AI Strategy as Partnership and Progress
Developing a successful AI strategy is less about procuring technology and more about fostering a human-centered partnership for progress. By prioritizing deep understanding, co-creation, practical application, and trust-building, nonprofits and their supporters can move beyond the hype. Together, we can ensure AI becomes a reliable tool that genuinely empowers organizations to amplify their vital work and achieve meaningful outcomes for the communities they serve.
Made with Bullet