Philanthropic Submission
How an AI Director and specialist agents developed a portfolio of tailored philanthropic submissions in weeks, not months — with client involvement limited to strategic review and guidance.
The Context
A health research institute with a small leadership team and deep domain expertise needed to develop multiple tailored philanthropic submissions for priority funding prospects within a compressed timeline. The target was to secure philanthropic capital that would unlock significantly larger government co-investment.
The Opportunity
The institute's leadership team was simultaneously managing multiple strategic projects across partner organisations. A traditional approach to developing tailored philanthropic submissions — typically requiring several weeks per submission — would not simply have been slow; it would have been impossible to undertake alongside existing commitments. The strategic window for these funding approaches demanded a fundamentally different model.
The target funders represented diverse philanthropic philosophies — from legacy-focused, trustee-led foundations to global-scale, data-driven organisations. Each required fundamentally different positioning, tone, and submission architecture. A generic approach would not work.
The Approach
Phase 1: Knowledge Synthesis
The project began by ingesting the institute's core source materials — master narrative, context documentation, and exemplar project descriptions. These were synthesised into a modular content architecture with funder-type modules, enabling rapid customisation while maintaining narrative consistency. This knowledge asset became the foundation for all subsequent work, demonstrating the “capacity multiplier” principle: invest once in structured knowledge, then deploy it across multiple contexts.
Phase 2: Deep Funder Intelligence
AI research agents conducted comprehensive intelligence gathering on each target funder, producing detailed profiles covering strategic priorities, decision-making patterns, governance structures, and optimal positioning. The intelligence work went well beyond surface-level research — agents mapped investment philosophies, identified precedent grants, uncovered governance sensitivities, and recommended tailored framing strategies. This intelligence was consolidated into a comprehensive strategic decision paper covering portfolio analysis, sequencing strategy, risk scenarios, and positioning architecture.
Phase 3: Tailored Submission Development
Multiple distinct submissions were developed, each calibrated to funder requirements:
- Governance partnership model — framed around sovereign capability and safety infrastructure, with milestone-gated investment
- Systems-change positioning — aligned with the funder's existing framework, addressing equity and workforce resilience
- Concise expression of interest — brief format per funder requirements, with clear partnership invitation
- Legacy partnership brief — long-horizon framing around talent retention, precinct competitiveness, and multiplier effects
Each submission used distinct language, narrative structure, and investment framing — not variations on a template, but genuinely differentiated documents built from shared knowledge foundations.
Collaboration Model
A critical feature of this engagement was the minimal time required from the client's leadership team. Their involvement was focused almost entirely on high-value activities: providing strategic context, sharing institutional knowledge, reviewing drafts, and confirming positioning direction. They were not involved in research, drafting, or document production.
This meant the leadership team could integrate a major new fundraising initiative into their existing workload without displacing other commitments. They added this campaign on top of their concurrent projects rather than trading off against them.
Value Delivered
Capacity Transformation
The most significant outcome was not simply efficiency — it was feasibility. The leadership team could not have undertaken a traditional multi-submission development programme given their existing commitments. The AI-enabled approach transformed an otherwise impossible initiative into one that required a fraction of the typical client time, focused entirely on activities where their expertise was irreplaceable.
Deliverables Produced
- A comprehensive, reusable knowledge asset (modular content architecture)
- Deep intelligence briefs for each target funder
- A strategic decision paper covering portfolio sequencing and positioning
- Multiple tailored philanthropic submissions, each with distinct funder-specific framing
The Multiplier Effect in Action
Traditional model
Senior leadership time consumed by document production. Sequential development. Knowledge lost between submissions.
AI-enabled model
Senior leadership focused on strategy. Parallel development across all funders. Reusable knowledge infrastructure.
Key Takeaway
AI-enabled workflows transformed what would have been an impossible project into an achievable one. Multiple differentiated, funder-specific submissions were produced in weeks rather than months, with client involvement limited to strategic review and guidance.
Senior leaders contributed where only they could — providing institutional knowledge, setting direction, and exercising quality judgement — while AI agents handled research, synthesis, and production. This is the model at its clearest: technology that amplifies human capability rather than replacing it.
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