AI in Proposal Development

Past, Current, and Future

The market growth of AI Technology is surging. In 2019, the market size was valued at $27.23 billion, and by 2027, the market size is expected to reach 266.92 billion, as reported by Forbes.

“People fear what they don’t understand and hate what they can’t conquer.” Andrew Smith.

Artificial Intelligence (AI) is currently the most trending technology buzzword in America. Second is Machine Learning, a subset of AI. AI sparks our imagination and, for some people, invokes fear. Fear of the unknown. Fear of change. Fear of negative consequences.

“Nothing in life is to be feared; it is only to be understood. Now is the time to understand more so that we may fear less.” Marie Curie.

For others, the thought of AI causes excitement for new opportunities and greater improvement. I do not foresee technologies within the near future replacing the human element but augmenting one’s abilities to produce better and faster proposals. In other words, technology will help you focus on proposals that are more likely to win and ensure the submitted proposal is well received by the client.

The Past

When we think of AI today, we often think of Generative AI, such as Chat GPT. Generative AI is really a form of automation with some analysis. Automation performs tasks that would typically be done manually by humans.

Automation in proposal development did not start with AI; it started with the Request for Proposal (RFP) shredder/parser. In 1994, a software product called Proposal Software Solutions (PSS) by Life Cycle Technology Corporation (LCT) was released. PSS required users to choose which parsing patterns to use, and as a result, required a huge learning experience. In 2004, the first commercial fully automated RFP parser was released, called the RFP Analyzer by SoftRight Inc., which was then branded and sold by Privia until 2006. Years later, other companies released their own RFP shredders/parsers into the proposal development market.

A recent poll on APMP’s Official Discussion Group on LinkedIn showed that only 18% of the pollsters use RFP shredders/parsers (147 total votes). There are assorted reasons for low usage, such as cost, small documents, trust issues, and others. However, those who do use RFP shredders/parsers find the time savings extremely beneficial.


A recent survey by a well-known proposal development software company asked, “Are you leveraging AI in your proposal strategy.” The results: “22% are planning to use AI”, “28% Yes”, and “50% No”. This is rather telling in that one-half of the proposal teams are and will be using AI technology, and one-half won’t.

The various ways proposal development teams are using AI technology include:

  • Data Analysis and Research
    • Data collection, analysis, and research on possible clients and competitors.
  • Writing Support
    • Provide suggestions for using Plain Language, branding voice, grammar, and syntax, using Natural Language Processing (NLP) algorithms.
  • Proposal Evaluation
    • Automate the evaluation of proposals by comparing them against predefined criteria and scoring systems.
  • Proposal Generation
    • Automate the generation of proposals using predefined templates and proposal content libraries.
  • Collaborative Tools
    • Automate tasks: such as scheduling, document sharing, and color team reviews.

The Future

“You need imagination in order to imagine a future that doesn’t exist.” Azar Nafisi

Personally, I imagine a near future of AI in proposal development with huge improvements in automation and analysis for the AI technologies denoted in the previous topic area (Current). Based on a lesson from the past, as with the first RFP Parser (PSS) requiring users to select parser patterns, to the next level fully automated parser the RFP Analyzer.

A near-future is typically easier to predict than a long-term future, greater than five years. I see a large capability of advancement in proposal AI software solutions/platforms.

For example, imagine an AI technology (or platform) that provides a fully automated system: it will analyze newly drafted and released Government RFPs, and then determine which RFPs you should bid on, along with the probability of a win. Next, for those RFPs with high enough win probabilities and that match your criteria for bidding (e.g., available resources, alignment with your cost-to-profit ratio, and other factors), it generates your Win Themes and Discriminators, Proposal Outline, Compliance Matrix, and finally your Draft Proposal, all prior to you getting your morning coffee. And finally, it will send email notifications and schedule team meetings and reviews. This system would continue to learn what works best for wins and profit. While this fully automated system appears to replace humans, it won’t because it can’t. There will always be a need for human interaction and decision-making. There will be information that people know that an AI system won’t know, such as the client’s preference for a particular type of material or resource that isn’t defined in the RFP.

The need to win contracts will drive greater adoption of AI technologies. The current 50% of proposal teams not interested in AI may find themselves at a disadvantage compared to their competitors using AI technologies.

The question is: will the future of advanced AI technologies be affordable and accessible to most companies? If not, how will this division impact companies and their proposal teams?

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