The use of generative AI in grant writing can offer a wide range of benefits, including increased efficiency and the ability to generate a larger volume of grant proposals. By automating certain aspects of the process, such as generating the initial draft of a proposal or identifying potential funding sources, grant seekers can save valuable time and resources that can be allocated elsewhere.
However, there are also potential drawbacks to using AI in this process. One potential risk is that the proposals generated by AI may be too formulaic or lacking in creativity, which could decrease the chances of securing funding. Additionally, AI-generated proposals may not be able to capture the nuances of the grant seeker's organization or project, which could make the proposal less compelling to funders. To overcome these risks, it is extremely important to choose the right AI tools and to exercise careful oversight when deploying them.
When an organization is ready to apply for grant funding, and they have found a high-quality opportunity to pursue, the work of producing a grant application can begin. The most efficient organizations build upon existing work by repurposing language from recent proposals to produce new grant applications more quickly. Generative AI tools can supercharge this process by leveraging the incredible abilities of large language models to help locate and reformat grant proposal content in fractions of a second.
Manual scenario
You're working on a grant application and you come across a question that you know you've answered before. The problem is that you can't recall which of the dozens of recent grant proposals in your content library contains the previous answer. You proceed to check file after file, searching through shared drives and inboxes looking for keywords and clues to help you locate what you’re looking for. Finally, after a few minutes, or in the worst cases, several hours or days, you locate the passage, which you copy and paste into your new draft.
AI scenario
If you are working on a grant application and need to find a previously answered question, consider using Grantable, a generative AI grant writing software. Grantable can instantly search through all the content in your library and retrieve meaningful excerpts that you may wish to reuse. This saves you from having to go through each document and folder individually. You can search your entire library at once and either select excerpts yourself or allow the AI to recommend resources. The entire process usually takes only a few seconds.
Manual scenario
When crafting a grant proposal, it's common to reuse text from previous proposals. However, this can result in excess material that needs to be trimmed, updated, and adjusted to fit the new application question. Meeting vastly different length limits imposed by different application formats can be a significant challenge. For example, your source material may come from a proposal with a much longer format, requiring considerable effort to condense the text while preserving important information to meet a shorter word limit. The time required for this process can range from a few minutes to many hours, depending on the situation.
AI scenario
Grantable, a generative AI grant writing software, and other applications like ChatGPT excel at taking source material you provide and quickly reformatting it to address the various points in a grant application question and meet specific formatting requirements. These AI can also help revise and reformat the text to change details, perspective, tone, and more. Each of these processes takes only seconds, giving grant professionals more time and opportunities to curate and refine the overall proposal.
According to the 2023 State of Grantseeking Report produced by GrantStation, the top challenge reported by grant-seekers was insufficient time and capacity to pursue grant funding. Generative AI tools like Grantable can significantly reduce the time and energy required to produce high-quality grant applications, enabling organizations to apply for all the opportunities they may qualify for.
While there is no way to guarantee a successful funding outcome, grant-ready organizations that pursue well-aligned grant opportunities and submit as many qualified applications as possible are more likely to see success over time. By using generative AI tools like Grantable to store grant proposal content in a centralized and intelligent library, and leveraging generative AI writing features to reduce the amount of staff capacity per application, organizations can increase their chances of successful award outcomes.
In recent years, generative AI has gained more attention with the release of various tools like GPT-3 and ChatGPT. These tools have made it easier for people to experience the power of generative AI firsthand. The use of generative AI in grant writing is a relatively new field of exploration, and there are a few different tools available that are specifically designed for this purpose. In this section, we will compare some of the most popular AI grant writing tools and discuss their relative strengths and weaknesses.
ChatGPT is a free platform, which makes it accessible to all kinds of organizations. When you use ChatGPT, you will need to create an account on the platform's website. The sign-up process typically involves providing basic information such as name, email address, and password. Once you have created an account, you can log in and begin using the tool.
To create content using ChatGPT, you will need to provide a prompt or starting point for the AI to work with. This could be a question, a topic, or a general idea. ChatGPT will then generate a response based on the prompt, which you can review and edit as needed. The AI can also provide suggestions for related content or additional ideas to help you expand on your initial prompt.
Overall, the user experience of ChatGPT is designed to be simple and intuitive, with a user-friendly interface and helpful features to guide you through the content creation process.
Repeat steps 1 and 2 from above
Overall, ChatGPT is an incredibly powerful tool that can be a useful addition to anyone's toolkit. It is free, easy to use on both desktop and mobile devices, and extremely flexible in terms of its capabilities.
The biggest drawback of using ChatGPT for grant-seeking is the labor-intensive and repetitive process of providing context to the model before asking for outputs. ChatGPT is intended as a general-purpose interface to assist hundreds of millions of people worldwide with a wide range of queries and is therefore not optimized for understanding a specific organization or workflow. Feeding it context can significantly improve the focus and outputs of the tool, but it may still require searching through multiple files and folders to find the right content.
ChatGPT stores your interactions as chat threads, similar to text message conversations, which are excellent for managing conversations but inefficient for storing document information, such as grant proposals. Currently, there is no way to organize your different conversations with ChatGPT, which can make organization a challenge, especially if you're dealing with very complex grant-seeking programs.
As a chatbot, the interface is designed for people and ChatGPT to engage in back-and-forth dialogue. It is not structured for writing long-form documents, like grant proposals. There is no ability to edit or format like in a word processor, and it can be difficult to review a grant proposal when it has been rendered in the format of a conversation, rather than as a document with sections, prompts, and responses.
Grantable is a generative AI software designed specifically for grant-seeking. Similar to ChatGPT, it utilizes the latest large language models to provide comprehension and writing abilities. Additionally, it comes with a smart content library and a user-friendly word processing interface that simplifies grant content production.
To use Grantable, you must create an account using a valid email address. Many people start with a free account and upgrade later if they find the tool useful. Users must upload writing samples to their account, preferably a recently completed grant proposal, which Grantable processes as source material.
To begin working on a new grant proposal, create a new file in Grantable as you would in a typical word processor. Grantable's AI assistant can draft content at any point in the document and provide suggested source material snippets to respond to prompts. You can edit anything on the page, including the outputs of the Grantable assistant, and ask for revisions as well.
Grantable continuously updates all the content uploaded and created in an account, enabling it to locate the best source materials for a given task. With more content created, the system becomes better at drafting responses.
While both Grantable and ChatGPT are generative AI tools that can be used for grant writing, there are significant differences between the two.
One of the biggest advantages of Grantable is that it is specifically designed for grant-seeking, while ChatGPT is a general-purpose AI tool. This means that Grantable is optimized for particular work and information cycles in the grant-seeking process. As a result, Grantable is more likely to generate high-quality, coherent grant proposals that are tailored to the needs of the specific grant opportunity.
Another advantage of Grantable is its smart content library. Unlike ChatGPT, which requires users to search through multiple files and folders to find the right content, Grantable continuously updates all the content uploaded and created in an account, enabling it to locate the best source materials for a given task. With more content created, the system becomes better at drafting responses.
Finally, Grantable provides a user-friendly word processing interface that simplifies grant content production. This makes it easier for users to manage grant proposals and make edits to their content as needed, something that is not possible with ChatGPT.
While ChatGPT can be a useful addition to anyone's toolkit, its general-purpose nature means that it may not be the best choice for grant writing. If you are serious about grant-seeking and want to maximize your chances of success, using a purpose-built tool like Grantable is the way to go.
If you're considering incorporating generative AI into your grant-seeking process, there are several important factors to consider. Here are some key steps to take to ensure that you are making the best use of generative AI in your organization:
Keeping in mind the needs identified above (brainstorming, composition, formatting, content management and program/client management), consider using the follow resources periodically to learn about the latest AI tools for each situation.
Search engines
Use your the search engine of your choice (Google, Bing, etc.) to find tools to try by searching terms such as “generative AI tool for [INSERT YOUR NEED]”.
This website continually aggregates new AI tools and categorizes them by use. Search for the kind of tool or help you need and review the results.
We recommend the following podcasts as fun ways to stay up to date on the latest and most important discussions and trends in technology. This is a great way to hear about new tools and hone your ability to critique them.
When working with generative AI tools like Grantable, it's important to use them responsibly and collaboratively. Ensure that everyone on your team understands the limitations of the tool and how it should be used as a supplement to, not a replacement for, human expertise.
Grantable has endorsed the Framework toward Responsible AI for Fundraising developed by Fundraising.AI and we believe these guidelines can work well for grant professionals learning to add AI tools to their work.
The Fundraising.AI collaborative is a member-driven initiative supporting those working within the fundraising profession with the opportunity to collectively learn about Responsible AI, demonstrate their leadership around the subject, support best practices of Responsible AI applications, and support building a thriving charitable giving sector. The Framework for Responsible AI for Fundraising is intended to maximize the benefits of AI for fundraising purposes while minimizing the risk of damage to the hard-fought public trust of the nonprofit sector.
There are many different AI safety and responsibility frameworks being proposed, and we’ve chosen to share this one because it is specifically adapted for fundraising. Here is the framework:
Fundraising AI Actors must protect personal and sensitive data by following robust security standards within our respective roles, maintaining compliance with relevant data protection regulations, and respecting the privacy of donors, beneficiaries, and stakeholders.
These principles should be part of all phases of the AI system lifecycle, including;
Commit to ethical data collection standards, including, analysis and usage practices, ensuring that the data used is accurate, relevant, and collected with proper consent.
Actively address biases and disparities throughout the entire AI system lifecycle, by developing a framework to monitor, evaluate and design the AI systems through principles including;
Share accountability with Fundraising AI Actors for the AI applications that we develop, deploy, or utilize in the fundraising profession, ensuring that they align with our organization's or client's mission, values, and ethical principles that are;
Within reasonable efforts to safeguard corporate IP, will be transparent in the development, deployment, and utilization of AI technologies, providing, requiring or requesting clear explanations of AI methodologies, results, reporting, measurement, and potential impacts on participants. In addition, we will provide adequate visibility to consumers of our AI ecosystems outputs when autonomous AI has been utilized.
Commit to the responsible use of tested and untested resources while staying informed about the latest developments in Responsible AI, incorporating best practices into my work within the fundraising profession, and to share the responsibility of helping educate the broader fundraising community on Responsible AI best-practices.
Actively engage with and learn from my peers in Fundraising.AI, sharing my experiences, challenges, and successes in Responsible AI for fundraising.
Commit to being aware of, and abiding by, applicable laws, regulations, and best practices concerning AI development and operations pertaining to fundraising, data protection, and AI systems.
Strive to maximize the positive social impact of AI in fundraising while minimizing any potential harm by focusing on the needs of beneficiaries and communities.
Commit to considering the long-term sustainability and environmental impact of AI technologies and advocate for sustainable AI practices within my organization and the broader fundraising community.
When using AI for generating ideas and content, it's important to remember that it has its limitations. For instance, AI cannot replace the expertise and knowledge of a human researcher, and it is not a good research tool. Additionally, while the speed of content creation with AI can be useful, it can also create new opportunities to make mistakes, as the technology can generate large amounts of content quickly, but may not catch errors or inconsistencies. Furthermore, keep in mind that AI has no grasp of real-world context, which can lead to inaccuracies or irrelevant content. Finally, AI has no understanding of truthfulness or factuality, meaning that the content it generates may not always be accurate or trustworthy. As a result, it's important to use AI as a supplement to, rather than a replacement for, human expertise and judgment.
When you use generative AI to generate text, it's important to understand what's really happening behind the scenes. Essentially, the generative AI is making complex predictions based on probabilities. Large language models analyze enormous datasets to learn patterns and relationships between words and phrases. They then use this knowledge to generate new text that is similar to the input data. The models are trained on huge datasets and use complex algorithms to predict what words or phrases are likely to come next in a given context. The model generates output by choosing words or phrases that are likely to follow the input, based on statistical probabilities.
It's important to keep in mind that the outputs created by generative AI are not based on actual understanding or comprehension of the content, but rather on patterns and relationships learned from the large dataset. While this can be useful for generating new ideas or content quickly, it's not a good substitute for in-depth research or analysis. Generative AI lacks the ability to critically evaluate sources, synthesize information, or draw conclusions based on a nuanced understanding of the subject matter. Additionally, generative AI may not be able to recognize biases or inaccuracies in the data it is trained on, leading to outputs that perpetuate these issues. As a result, it's important to use generative AI as a supplement to, rather than a replacement for, your own expertise and judgment when it comes to research.
💡 It's important to keep in mind that the outputs created by the AI are not based on actual understanding or comprehension of the content, but rather on patterns and relationships learned from the large dataset.
Generative AI systems can produce pages of credible-sounding text in a matter of seconds. This is faster than most people can read, and a large quantity of AI-generated content requiring human approval can quickly pile up. Even those who initially review AI outputs vigilantly can be lulled into a false sense of security after a few minutes or hours of error-free products. It's crucial to remember that generative AI systems can inadvertently generate problematic content at any time.
A good way to think about writing with and without AI is to compare it to sewing by hand with a needle and thread versus using a sewing machine. When we write without AI assistance, we proceed only as quickly as the words come to us and we can type them. Similarly, with a needle and thread, we can only sew stitch by stitch, leaving us with plenty of time to guide the work.
Writing with AI assistance increases the speed of output exponentially, just like a sewing machine. You are now guiding a machine that moves much more quickly than a person can. The advantage, of course, is that you can complete a project much more quickly using a machine to assist, while the hazard is that even momentary lapses in focus can immediately create larger problems or lead you further away from the goal of your work.
Despite their impressive abilities to comprehend and respond to a wide range of queries and prompts, generative AI systems have no grasp of the realities they're writing about. Don't be fooled by skillful references to relatively current events or insightful analyses of complex issues — these are all the result of complex mathematics behind the scenes.
Generative AI systems have processed unimaginably large data sets of text, from which they've discovered patterns of probabilities that connect words, phrases, concepts, and structures of the writing they've ingested. When you prompt one of these systems, it breaks your query down into numbers and probabilities and predicts each word that comes next.
At no time is the model checking with any validated sources of information to ensure it is pulling correct information for you. It's not pulling information at all; it's just predicting the next most likely word in a sequence.
At the same time, it is not trying to understand your query in the way another person tries to process the meaning of your words and what actions should follow. Generative AI models are simply turning your words into numbers and predicting which ones should come next.
AI hallucination refers to when a generative AI system produces outputs that are illogical, nonsensical, or even offensive due to the limitations of the software. Real-life examples may include entirely fictional people, places, events, and source materials.
When producing these hallucinations, the AI will sound every bit as confident as when the information is factual and correct. It is incredibly important that human users remain vigilant for such hallucinations when relying on AI-generated content in their work.
💡 Grantable is far less likely to hallucinate because the AI model is limited to using an organization's existing writing samples as source material to guide its writing.
A hallucination is not a malfunction; the AI system is performing exactly as it has been programmed to do. It has simply made a text prediction that we experience as being nonsensical or false. To the AI model, the text it has generated comes from a correct calculation of the probability that this sequence of words is an appropriate continuation of the human user's input query.
On November 30, 2022, OpenAI released ChatGPT, a research prototype aimed at studying user engagement with its large language models (LLMs). Within five days, ChatGPT attracted over one million users (15X faster than Instagram) and surpassed 100 million users in two months. The chatbot was able to generate remarkable responses to complex textual input, leaving the world captivated, amazed, and unnerved.
Computing advances, investments, competition between tech giants, and global awareness of LLMs are driving the fastest technological revolution in human history. Bill Gates, co-founder of Microsoft, compared this moment to the introduction of the graphical user interface, which made computing accessible to everyone. Sundar Pichai, the CEO of Google parent company Alphabet, has called this technology "more profound than fire or electricity."
LLMs can approximate the work of highly skilled knowledge workers and creatives in a matter of seconds and at a cost approaching zero. They are becoming more capable by the hour, and the quality of their outputs is improving just as quickly.
It is impossible to fully imagine how these systems will transform the world, and predictions range from armageddon to utopia. In a 2021 essay titled, "Moore's Law for Everything," OpenAI's CEO, Sam Altman, envisions a positive post-AI future, writing "As AI produces most of the world's basic goods and services, people will be freed up to spend more time with people they care about, care for people, appreciate art and nature, or work toward social good."
While we can't clearly envision many specifics about this new era in technology, we can reasonably assume that change will come more quickly than in the past. This is because the world is increasingly digitally connected, the speed of innovation is accelerating, as more people, resources, and prior breakthroughs compound, and AI dominance is becoming a major economic and geopolitical goal for man.
Artificial intelligence, like the systems we've been studying in this course, and even more powerful systems with artificial general intelligence (AGI), which can exceed human intelligence and capabilities, are being developed. Such technology creates the possibility of reaching unimaginably positive and/or terrifyingly dire outcomes depending on how humanity builds and regulates the use of these systems.
The nonprofit sector, or as I like to call it, the purpose-driven sector, must play a leading role in helping to embed human, planet, and justice-centered values in the technology itself and infusing policymaking with the wisdom of the sector that has long sought to remedy the brokenness of existing socioeconomic systems.
In day-to-day work, nonprofit sector professionals should stay engaged with AI tools and discussions both to further the mission at hand and to be fluent in conversations about creating responsible and ethical norms and governance for AI.
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