Data-Driven Grant Proposals

🔹 Why do some grant proposals win, while others fail? Over the years, I’ve reviewed, written, and secured funding for organizations across Africa seeking non-equity financing or grants. I’ve seen what works, what doesn’t, and where most applicants go wrong. If you’re struggling with grants, here’s a simple framework I use for winning proposals: ✅ 1. A Problem That Keeps Funders Up at Night Most proposals get tossed aside because they are organization-centered, not problem-centered. A great proposal starts with a compelling, data-backed problem statement that aligns with what funders care about. ✅ 2. A Game-Changing Solution, Not Business as Usual Funders are not looking for routine projects. They want innovation, sustainability, and impact. Answer these questions clearly: What makes your solution different or scalable? How will it continue beyond the grant period? ✅ 3. A Budget That Makes Sense Many proposals lose credibility with budgets that either lack justification or seem unrealistic. A winning budget: Is cost-effective but not underestimated Clearly links every expense to the intended impact Shows co-funding or sustainability plans ✅ 4. A Story That Moves, Not Just Data Facts and figures are critical, but funders connect with stories of impact. Your proposal should bring the problem to life with real-world examples. If it doesn’t inspire, it won’t get funded. Working on a proposal? Drop a comment if you have questions! 🚀

My colleague, Susan Mernit, has a terrific blog post describing how she used not one, but two GPT to develop a grant proposal in a human-centered, ethical way. The grant proposal was a large and complex RFP and she used the focused AI tools to help prepare and edit the grant, along with a team from the organization submitting the grant. The first GPT was named The Brilliant Organization Brain and uses information based on the organization's work, such as impact statements, program descriptions, and mission and vision. She uses it to analyze data, reports, and proposals through the lens of the organization's programs, impact, budget, and mission. The second GPT was called "Funder Focus," and was dedicated to this RFP and reflects the foundation's mission, vision, and goals in awarding and administering this grant program. It knows the grant rules and requirements and can analyze materials and assess strengths and weaknesses. She uploaded the application, announcement, FAQ, and information about five early grantees for this GPT. Creating these custom tools involved fine-tuning pre-existing GPT models with relevant data and defining clear objectives and a GPT-4 pro account. The development costs were minimal compared to the potential benefits, as the tools could be reused for future grant proposals. Her pieces described in detail how to iteratively worked with the two GPTs to draft, edit, and revise the grant proposal. She also describes the process for integrating human feedback from the team into the drafts, and using the GPTs to help revise and focus the description. She also describes in detail a "Human in the loop," process - how she fact-checked or audited for hallucinations, etc. Susan's work flow is one of two approaches for using these tools, dubbed "Centaur and Cyborg" by Ethan Mollick. The Centaur, half-person and half-animal, way of working is let the AI do a first draft and human completes it. The Cyborg is when you work flow is intertwined with the AI. Which approach to use? When doing a simple writing task like drafting an email or brainstorming subject lines, the Centaur works. For more complex projects and to incorporate rounds of human reviews, the Cyborg is the way to go. What you learn by working with the AI is knowing when to leverage your human intelligence and when to let the AI do its thing. Also, what role the AI will play - thought partner or intern. I think it is important to start with a few simple writing tasks to internalize a work flow and then build on it for more complex writing projects. https://lnkd.in/gEZdApE7

I published a paper showing that grant applications can be automatically generated from a one-pager idea, they meet all donor requirements and achieve 50% success rate, surpassing human success rates of 10-20%. AUTOMATION OF GRANT APPLICATION WRITING WITH THE USE OF CHATGPT Purpose: This paper examines the integration of generative AI, specifically ChatGPT, into grant application writing, evaluating its impact on efficiency, quality, and equity in research funding. The study aims to address systemic challenges in grant writing, such as high time investment, low success rates, and inherent biases against underrepresented groups. Design/methodology/approach: The research analyzes the development and submission of four grant proposals to public and private funding bodies in the U.S. and EU. ChatGPT was employed to automate key components of the process, including generating proposal structures, drafting content, and formatting team qualifications. The outcomes were compared in terms of time efficiency, success rates, and the quality of applications. Findings: The use of ChatGPT reduced the average grant preparation time from 30–50 days to 3–5 days while achieving a 50% success rate, significantly exceeding typical success rates of 10–20%. The findings highlight ChatGPT’s potential to enhance the inclusivity of funding processes by mitigating biases and lowering entry barriers for junior faculty and underrepresented groups. Research limitations/implications: The study is limited by the small sample size of four grant applications and the inherent variability of AI-generated outputs. Future research should explore scalability, reproducibility, and the ethical implications of AI use in academic and professional settings. Practical implications: The adoption of AI in grant writing can streamline the application process, allowing researchers to focus on substantive project development. Funding bodies are encouraged to adapt evaluation standards to distinguish between human-authored and AI-generated content, ensuring fair assessments. Social implications: By reducing biases and increasing accessibility, AI-driven grant writing can democratize research funding opportunities, fostering greater equity and diversity in academic and scientific communities. Originality/value: This study provides the first empirical evaluation of ChatGPT’s application in grant writing, offering insights into its transformative potential for academia, policy, and research funding practices. It is valuable to researchers, funding organizations, and policymakers seeking to leverage AI for more inclusive and efficient grant processes. Link: https://lnkd.in/dQ2CPygM #generativeAI #ChatGPT #grants #automation #AI