Leveraging Technology in Proposal Writing

When I talk to my colleagues and graduate students about how they are using AI tools, I realized that they are missing out on some important use cases that I've found extremely valuable. I wanted to share some of these below and look forward to hearing your thoughts on other unconventional ways you've applied these tools! ✅ Iterative Proposal Refinement – Used ChatGPT to evaluate a revised grant proposal in the context of reviewer comments, identifying gaps, strengthening arguments, and ensuring all weaknesses were addressed. This mimics an outside reviewer’s perspective before submission. ✅ Logic and Flow Checks – AI can analyze argument coherence, detect missing connections, and suggest clearer phrasing in technical documents, making research papers and proposals more compelling and concise. I will prompt to ask for what information is missing to enhance understanding or to identify areas that were unclear and need more explanation. ✅ Cutting the Fluff – Academics love long paragraphs, but reviewers don’t. I ask the LLMs to identify areas of redundancy or areas of varying detail between different parts of a proposal. ✅ Comparative Feedback Analysis – Given multiple drafts, ChatGPT can compare versions, pinpointing what improved and what still needs work—saving time in manual cross-referencing. ✅ Visualization Gaps & Idea Generation – Beyond writing, LLMs can help brainstorm visualization strategies, high priority areas where figures can benefit understanding, or suggest charts or tables to ease understanding. Happy to share prompting strategies I've been using that have been successful - please feel free to leave a comment. 💡 How are you using LLMs in your research? Would love to hear about unconventional ways you've integrated AI tools into your academic workflow!

6 months ago, this consultancy wasted 20 hours on ONE proposal and thought it was normal. Today, it takes 4 - and they're on track to 2X ARR without a single extra salesperson. Here's how the @Incepteo team built the agentic AI solution that drove their success: In October 2024, I got an email from John (name hidden), the sales director of a London consultancy who specializes in public sector cost reduction. PwC and Deloitte – his primary competitors – were integrating AI into their sales and operations. He wanted to know how he could leverage it too. To help, I took him through our signature @Incepteo process: 1) Hosted a Discovery Workshop I hosted a quick discovery workshop to help John identify areas in his business workflow that could leverage AI. As we were brainstorming ideas, I asked him how much time his team spent on proposals. John told me it had been a huge pain point for years: • Each proposal required over a month to complete • Every word was written manually If AI could help them create high-quality proposals faster, they could focus more on closing deals. Using this information, we… 2) Built the System My team and I built a custom agentic AI proposal writing system with the aim of taking off the majority of his team's workload. This task wasn't something that simple GPT could handle. GPT (and other LLMs) can talk and generate content, but they struggle to • Process large, complex business documents • Understand structure & context beyond generic training data To fix this, we used Retrieval-Augmented Generation (RAG), a method that helps LLMs pull answers from specific knowledge (in this case, business documents). We trained the system on 5 years' worth of John's best proposals to ensure accuracy and consistency. Then, we added an agentic approach on top —a multi-step AI workflow where specialized AI agents work together to handle critical steps:   → Researcher – Gathers insights, analyzes documents, and understands requirements. → Writer – Structures the proposal, aligns it with the right templates, and drafts the content. → Reviewer – Reviews, validates, and flags inconsistencies in a checklist. → Finalizer – Organizes everything into a Word document for human review.   It's like having a team of clever assistants working together! 3) The Result John has just gone live with this system. Using this system, his company: • Cut proposal creation time from 1 month to 1 week • Reclaimed 50% of their time for their sales team • Gained potential to 2x revenue AI didn't replace his writers. It made their lives easier so that they can build relationships & work on high level tasks. This project even inspired us to rebuild our own proposal system. More on that soon :)

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

I read hundreds of grant proposals every year, and I think it is okay to use AI to write your proposal. But I can also tell if it's AI-written. Nonprofits are often strapped for time and resources, so if AI can help speed up the grant writing process, go for it. Tools like ChatGPT or Claude can help you organize your ideas, structure your proposal, and even suggest clearer ways to communicate your goals. But here's the catch: AI should be your co-pilot, not your ghostwriter. Before you hit "submit," ask yourself: 🔸 Have I given the AI enough context about my project and the foundation’s priorities? 🔸Does this proposal clearly tell our story, in our voice? 🔸Is the language repetitive or robotic? (AI also loves to overuse the em dash.) 🔸Have I actually answered each question in full or just filled space? (If there are questions presented.) 🔸Most importantly, have I proofread the proposal like a human? The best proposals strike a balance between clarity and authenticity. They feel intentional and personal, not generic or auto-generated. AI can absolutely make your process more efficient. But don’t let it strip away the passion, nuance, and mission only you can bring to the page. Have you used AI for grant writing yet?

RFPs/tenders can be a headache. 🤯 Here's how AI tools can help: I'm building an AI agent that structures RFP criteria and helps you write and check your proposals against them! 1. You give it all the RFP/tender documents (PDFs, Word, Excel files, even scanned images). 2. It extracts all the content, then translates it into English (or Romanian, or any other language). 3. Then, it extracts all the relevant criteria, removes duplicates, and organizes them by categories, keeping the exact wording in the original tender documents. It works like a charm up to this point! I've tested it on a tender with 7+ different documents in multiple languages. It extracted and cleaned all the information in 5 minutes. It would have taken me a full day to do that. --- Next steps: 4. Generate the proposal structure (chapters, subchapters, addendums) based on the RFP/tender requirements. 5. Populate the proposal with content that answers the exact requirements, based on some ideas and initial drafts I give the agent. 6. Check the resulting proposal against all the RFP criteria, then come up with suggestions for completion and compliance. 7. Validate that the budget includes items for all the lines in the RFP. 8. Validate the final proposal against the scorecard for the RFP and simulate a possible score. --- If you are working on something similar or know tools that do this better, let me know in the comments!

Thank you to Richard Campbell and Carl Franklin for having me on the DotNetRocks podcast! - In our conversation, I shared lessons from our two-year journey with our Gen AI startup - pWin.ai—a purpose-built GenAI assistant for professional proposal writers. Listen here: https://lnkd.in/ea-gz3Nj We talked about the differences between generic AI chatbots (ChatGPT, Claude), productivity assistants (M365 CoPilot, Google Gemini), and domain-specific solutions like pWin.ai. Unlike general models, pWin.ai is tailored to the complex, highly orchestrated business process of proposal development. Our design philosophy: Map GenAI support to critical orchestration steps, attach measurable KPIs and ROIs to each, and drive real business value. We also addressed important challenges: How do you build trust in AI? (Hint: Responsible AI—transparency, traceability, and accuracy are core to pWin.ai -https://lnkd.in/eFuTB37s How do you handle the high variability in RFX documents? How do you organize and tag content for AI models? (Read our blog on how to manage content store) How do you produce persuasive, compliant text at scale? One innovation we discussed is our patented "object-based writing" approach. Rather than stringing together snippets, pWin.ai helps users define a cohesive strategy and then, using a Shipley methodology-powered writing engine, generates a unified, review-ready proposal that reflects that strategy across all sections—saving weeks off traditional timelines. pWin was recently recognized by Gartner in a case study about how AI helped expedite proposal drafting time by 93%! Read the full case study here: Transforming Proposal Generation: pWin.ai Recognized by Gartner® - pWin.ai https://lnkd.in/e6RFy7Mw