Writing Smarter: The Role of Artificial Intelligence in Winning Proposals

Ashley (Kayes) Floro, CPP APMP • February 23, 2026

The use of generative artificial intelligence (AI) tools is becoming almost commonplace in our daily lives, much in the same way that we use cell phones and the internet without a second thought. And why not—it’s so easy to enter a prompt into ChatGPT and use the response to start an email, thank you letter, or social media post. So, it’s not surprising that the explosion of generative AI tools like ChatGPT, Claude, and others has sparked major interest—and debate—among those in the proposal community. 


AI promises so many benefits to our proposal process: it can save time and increase productivity, it can help brainstorm win themes and messaging, it can improve readability and clarity, and it can serve as a knowledge assistant. This can help reduce burnout on proposal teams since AI is saving the team time doing the “grunt work” so that proposal team members can focus on higher-value tasks. 


However, AI use does not come without potential risks, including confidentiality and data security risks. Inputting sensitive content—like solution content, past performance content, pricing, client names, etc.—into public AI tools can breach Non-Disclosure Agreements (NDAs) or contracts, expose proprietary or competitive information to third parties, or violate privacy regulations. Knowing the potential benefits, as well as the risks, you and your teams may be asking how you can responsibly use AI to draft sections, develop win themes, or tailor boilerplate content. And how can you do so without introducing AI generated errors? Let’s dig into it!


How You Can Responsibly Use AI to Draft Sections, Develop Win Themes, or Tailor Boilerplate Content

If used responsibly, you can use AI prompts to suggest creative ways to frame your team’s differentiators and benefits, help align your solution with the customer’s priorities and hot buttons, and provide alternative ways to express key messages to keep language compelling. You can also use AI to help rewrite dense, jargon-heavy technical text into clear, persuasive, customer-focused content. Here are some ways you can minimize risk while using AI to support proposal development:


  • Set a policy and train your team 
  • Never input confidential or proprietary information into public AI tools
  • Always treat AI output as a draft, never a final deliverable


Set a policy and train your team

Before you allow teams to start using AI to support proposal development, you should create a written guideline or policy. The policy should define when and how team members may use AI, which tools are approved, and how to mitigate risks while using AI. The policy should define clear boundaries for AI use, considering where it adds value and where it doesn’t. Remember, AI is good for things like:


  • Drafting boilerplate content, like company overviews or standard capabilities statements (after it is fed with source materials)
  • Generating ideas for win themes based on key differentiators and customer hot buttons you provide (note that capture still has to provide this information!)
  • Helping tailor boilerplate to meet solicitation requirements
  • Checking readability and suggesting stylistic improvements
  • Summarizing content


AI is not good for:


  • Making compliance decisions: it may miss mandatory requirements
  • Factual accuracy: it can invent or misstate facts
  • Interpreting ambiguous solicitation language: human judgement is still needed for this!


Once you set the guidelines, train your team! Make sure everyone knows and understands the guidance. Be sure to highlight the data privacy risks and concerns. For many companies, entering proprietary data into a public AI tool may be grounds for termination. 


Never input confidential or proprietary information into public AI tools

Although we have already touched on this point, it really deserves some additional attention. But the bottom line is that you should never paste client-sensitive content, proprietary solution information, or internal pricing into tools like ChatGPT unless your company has a private, secure instance. Many public AI tools store user inputs and use them to further train their models. That means your sensitive information could remain on their servers indefinitely—and possibly reappear in responses to other users. Even with private, secure instances, some companies may be concerned with data breaches or cyber-attacks. If this is the case, consider developing a policy to redact or anonymize sensitive names and figures before asking AI to help tailor content, even in your private instance of the tool.


Always treat AI output as a draft, never a final deliverable

While I was at the APMP Bid and Proposal Conference in Nashville earlier this year, I heard a story about a team that was thrown out of competition because they used AI to write their proposal and then didn’t tailor it. The customer told them that another team submitted the exact same response. I am not sure I was able to hold back the level of shock that a team would submit content without adjusting it—but after hearing that story, this really needs to be said. For so many reasons, you should always review, revise, and tailor the content you receive from your AI tool. The best advice I have heard is to treat AI like a junior writer or assistant—its suggestions still need review, fact-checking, and editing by your experienced proposal team. Just like we have always done as part of our proposal best practices, have a human team member review all your content for compliance, accuracy, your specific proposal style guide, and tone—especially the content developed initially by AI.


How Can You Avoid Introducing Errors or “Hallucinations” When Using AI?

In the context of generative AI tools, a hallucination happens when the AI generates output that is factually incorrect or fabricated, but it still presents the information confidently, as though it were true. Examples of common AI hallucinations include:


  • Inventing a certification your company doesn’t hold
  • Citing a law, regulation, or standard that doesn’t exist
  • Referencing past performance examples or customer names that aren’t real
  • Providing made up statistics or figures 


Hallucinations happen because AI models don’t actually know or understand facts—they predict likely sequences of words based on patterns they’ve seen during training. When they can’t find the answer in the data they were trained on or in what you provided in your prompt, they sometimes simply generate something that sounds plausible. 


This is why, especially when teams are using AI, it is critical for proposal managers and Subject Matter Experts (SMEs) to actively stay involved throughout the process. Here are some steps you can follow to help avoid hallucinations and errors when using AI:


Always start with a compliance matrix

Get back to the basics: build your compliance matrix first and track every requirement explicitly. You can have AI generate a first cut, but then you need to go back and add in everything that the tool may have missed. Next, just as best practice has always told us to do, have a human peer review and validate the matrix. Then use the matrix as the source of truth, and verify that every section written, whether by AI or by a human, maps back to the correct requirement. Remember, AI can help you phrase responses, but it doesn’t reliably recognize all mandatory instructions, page limits, or formatting rules.


Use AI as a support tool, not a decision-maker

Remember that AI can draft, suggest, and rephrase, but it doesn’t understand the legal or contractual weight of a solicitation. We’ve mentioned this already, but always have a SME and/or compliance lead review every section of the response. Don’t throw out your best practice review cycles!


Fact-check everything

Again—another “old school” best practice that has become ever more important in the age of AI. Because AI is prone to hallucinations, it may confidently invent product specifications, certifications, client names, or achievements. To catch these errors, first make sure you provide the AI with accurate source material. Then as part of your review process, require the reviewers to check all the information, including data points, dates, names of agencies or organizations, and references to laws, standards, or regulations.


Feed AI verified content: don’t let it guess

Don’t ask AI open-ended questions, such as “What are the key benefits of our solution?” unless you also give it source content to work with. Instead, first give the tool your actual product or solution specifications, differentiators, and past performance examples and ask it to organize, rephrase, or summarize those into proposal language. Remember, only do this if you are using a paid/private version of the tool. 


Leverage secure, organization-approved AI tools

If you are going to introduce AI tools into your proposal process, it’s best to use private or enterprise-grade AI systems that can be fine-tuned on your approved boilerplate and style guides. Public tools are trained on general data and will be less aligned with your standards—and they also pose serious confidentiality and data security risks if not used carefully. 


Final Thoughts

As generative AI tools continue to evolve and become more embedded in our workflows, proposal teams have an opportunity to harness them responsibly and effectively to produce efficiencies in our winning proposal processes. By understanding both the benefits and the risks, and by establishing clear policies, training, and review processes, you can use AI to enhance productivity without compromising compliance, accuracy, or confidentiality. Ultimately, AI should serve as a supportive assistant, not a substitute for human judgment, expertise, and quality control. Remember:


  • Always fact-check AI output against trusted sources
  • Provide the AI tool with accurate, complete input material (don’t let it guess)
  • Have SMEs review content for accuracy
  • Don’t let AI generate sections from scratch without oversight


With the right balance, AI can help your team work smarter, reduce burnout, and deliver stronger, more competitive proposals!


Shows requests for information (RFIs) becoming more important
By Ashley (Kayes) Floro, CPP APMP May 5, 2026
In government contracting, the pre-solicitation phase is where requirements are shaped, vendor relationships get established, and acquisition strategies get set. Experienced teams know this, which is why they invest heavily in customer engagement, competitive intelligence, and capture planning long before a Request for Proposal (RFP) is released. But one pre-solicitation activity has historically been undervalued: the Request for Information (RFI). That's changing fast, and contractors who haven't noticed are already behind. RFIs: No Longer Just Market Research The traditional view of RFIs is simple: agencies issue them to gather information about industry capabilities before drafting a solicitation. That definition no longer captures what's happening in the current procurement landscape. Today, agencies use RFIs to define and refine requirements, test the feasibility of solutions, identify capable vendors early, and reduce the risk of poorly structured procurements. RFIs aren't just gathering information anymore, they're shaping the acquisition itself. Why Agencies Are Leaning into RFIs Several forces are pushing agencies toward deeper pre-solicitation engagement with industry, with a noticeable emphasis on RFIs. Increasing complexity. Emerging technologies and evolving mission needs mean agencies often don't know what the right solution looks like. The Government needs industry input to understand what's possible. Budget pressure. With tighter budgets and greater oversight, agencies must justify acquisition strategies earlier. RFIs let the Government validate assumptions before committing funds. Risk reduction. Poorly defined requirements lead to protests, delays, and costly rework. Getting it right before the RFP saves the Government time, money, and credibility. The result: more consequential work is happening before the RFP is ever released, and RFIs are holding more weight. The Strategic Opportunity Contractors Are Missing For contractors, this shift changes when and how opportunities are won. Responding to an RFI is no longer a courtesy or a branding exercise. It's a chance to shape how the problem is framed, introduce alternative approaches, position your capabilities as the benchmark, and influence evaluation criteria before they're finalized. Organizations that engage early often help define the playing field. By the time the RFP drops, those who sat out may find themselves reacting to requirements that already favor someone else. The Bar Is Rising RFIs are also becoming more structured. Agencies increasingly use standardized response templates, form-based submissions, and structured data collection—making it easier to compare vendors side by side. This raises the stakes for how you respond. Vague answers and marketing language don't land in structured formats. Clear, specific, well-supported responses stand out and are far more likely to influence the outcome. What To Do About It Organizations serious about win rates need to rethink how they treat RFIs: not as optional, but as strategic. This means being selective but intentional about which RFIs to pursue, aligning RFI responses with your broader capture strategy, and focusing on insight rather than just information. The goal isn't simply to answer the questions being asked: it's to shape the questions that will appear in the RFP. Final Thoughts RFIs are not new, but their role in government contracting is changing in meaningful ways. RFIs have become a critical touchpoint where agencies and industry collaborate to define problems, explore solutions, and reduce acquisition risk. For contractors, they represent one of the earliest, and most valuable, opportunities to influence an outcome. The organizations that recognize this, and act on it, are the ones best positioned to win.
By Ashley (Kayes) Floro, CPP APMP March 30, 2026
When was the last time your team truly examined why you won—or lost—a proposal? Every submission your team makes, win or lose, contains a roadmap for doing better next time. Yet many organizations treat each proposal as a standalone event, moving quickly from one bid to the next without pausing to reflect on what worked, what didn't, and why. This is a costly mistake. A structured lessons learned program, built into every stage of the business development lifecycle, is one of the most powerful tools a company can use to sharpen its competitive edge. What many organizations overlook is that some of the most valuable lessons come directly from the customer. Internal assessments are important, but they only tell part of the story. Customer debriefs provide a rare opportunity to understand how the Government evaluated your solution, what aspects of your proposal resonated, where evaluators perceived risk, and how your approach compared to the competition. When combined with internal lessons learned efforts, debriefs can uncover strategic insights that dramatically improve future capture and proposal performance. Conducting Lessons Learned Conducting lessons learned after each proposal submission is a critical part of the business development lifecycle. It helps companies understand where they are excelling and where they need to improve. To ensure the experience is fresh in everyone's mind, each member of the proposal team should document their impressions—both positive and negative—within the first week after submission. Sample questions to consider include: Was the proposal development schedule reasonable and realistic? Why or why not? Were there any bottlenecks or major issues? If so, what were they, and how could they be mitigated in the future? Did the team work well together? If not, how could team dynamics have been improved? How effective was communication among the team? What went well? What could have been improved? Did any unexpected problems occur during proposal development? If so, how could they be mitigated going forward? Did the team stay within its B&P budget? If not, what could have been done differently? What worked best during the capture and proposal effort? What areas require improvement? A practical way to gather and analyze this feedback is to send a survey to each team member using an automated tool, which makes it easier to collate and compare responses. Just as importantly, organizations should ensure lessons learned discussions extend beyond proposal operations and include capture, pricing, solutioning, staffing, and customer engagement activities. Many proposal losses are rooted in issues that occurred long before writing ever began. Request a Customer Debrief Too often, companies only request debriefs after a loss, missing valuable opportunities to learn from wins as well. Winning debriefs can reveal why the customer selected your solution, which strengths carried the most weight, and where evaluators still saw weaknesses or risk despite awarding your team the contract. Those insights can be invaluable for recompetes and future pursuits. When requesting a debrief from the Government, professionalism matters. Requests should be submitted promptly, kept concise, and focused on understanding the evaluation rather than challenging it. The objective is not to argue with the customer, but to gain insight into how the proposal was perceived and evaluated. During the debrief itself, the most productive teams ask thoughtful, strategic questions such as: What proposal strengths had the greatest impact on the evaluation? Were there areas where the Government perceived increased risk? How well did the proposed approach align with the agency’s priorities? What differentiated the winning proposal? Were there areas where the proposal appeared compliant but not sufficiently tailored? Did the Government identify any gaps in customer understanding? The best debriefs often reveal issues that are not immediately obvious in evaluation scores alone. A technically compliant proposal may still fail because it lacked clear differentiation, did not sufficiently address customer pain points, or failed to inspire confidence in execution. Those insights are essential for improving future capture and proposal strategies. Equally important, organizations should approach debriefs as relationship-building opportunities. Agencies remember contractors that engage professionally, ask intelligent questions, and demonstrate a genuine commitment to improvement. After Action Report Once the feedback has been gathered, the Proposal Manager should review the trends and prepare an After Action Report that details lessons learned, root causes, and recommended next steps. This report should be shared with the full proposal team to ensure that insights are carried forward into future efforts. The most effective After Action Reports do more than document tactical issues. They identify whether the team’s overall strategy aligned with the customer’s priorities. Did the proposed discriminators truly differentiate the solution? Did the proposal clearly communicate customer understanding? Were there areas where the team assumed the customer valued something that ultimately had little impact on the evaluation? Answering these questions helps organizations move beyond surface-level process improvements and make meaningful strategic adjustments. Lessons Learned Session Additionally, after contract award is announced, the team should conduct a formal Lessons Learned Session to document and discuss observations, findings, and conclusions—win or lose. This session should incorporate both internal feedback and customer debrief information whenever possible. By understanding where the team encountered roadblocks, where the customer found gaps in the response, and what elements of the solution resonated most strongly, organizations can strengthen both their processes and their competitive positioning. Equally important: identify what the team is doing well and make sure those practices are preserved and repeated. Analyzing Trends and Updating Standard Operating Procedures (SOPs) Conducting lessons learned after each proposal is valuable, but the benefit compounds when you step back and look at the bigger picture. On an annual basis, review your After Action Reports, customer debrief notes, and lessons learned sessions as a body of work, and analyze them for recurring themes and patterns. As the year wraps up, whether you follow a corporate fiscal year or the calendar year, ask your team: What challenges keep surfacing? Where does the team consistently perform well? What issues repeatedly appear in customer debriefs? Are evaluators consistently identifying the same weaknesses or risks? Are there gaps in capture execution, customer engagement, staffing, or pricing strategy? Discussing and sharing these trends with your team creates a culture of transparency and accountability, and helps focus improvement efforts where they matter most. More importantly, translate those findings into action by updating your business development and proposal SOPs. If internal feedback shows the team is consistently scrambling during production, adjust your SOPs to launch the production process earlier. If customer debriefs repeatedly cite a lack of customer understanding, take a hard look at your capture process and strengthen your call plan execution. If evaluators consistently identify transition risk or staffing concerns, revisit your solution development and recruiting strategies. Continuously refining your processes in response to real data, especially direct customer feedback, is one of the clearest paths to improved performance and more wins. Final Thoughts Every organization in this industry wants to win more, and win rates are often cited as the headline measure of a business development organization's health. While they are a useful starting point, win rates alone don't tell the whole story. Too many variables influence any single outcome. What matters more is building the discipline to learn from every effort, regardless of the result. A consistent lessons learned program, paired with customer debrief analysis, annual trend reviews, and a willingness to update your processes, creates a feedback loop that makes your team sharper over time. The companies that win consistently aren't just the ones with the best writers or the biggest budgets, they're the ones that systematically learn faster than their competitors. Every proposal, whether it results in a win or a loss, provides an opportunity to improve. The organizations that capitalize on those opportunities are the ones that steadily build stronger capture strategies, stronger proposals, and ultimately, stronger win rates over time.
By Ashley (Kayes) Floro, CPP APMP March 25, 2026
Tight page limitations are continuing to be a challenge as contracting officers streamline their acquisition processes. When faced with tight page restrictions, we often find ourselves struggling with trimming five pages of material into two pages of allocated space. However, sometimes the content we are working with is so long because it is simply overly wordy. In this article, I present six tricks for eliminating waste. 1. Use Active Voice With active voice, the subject of the sentence comes first and performs the action in the sentence. Active voice is more straightforward and concise than passive voice. It typically results in shorter, sharper sentences. So not only does it take up less real estate, it flows better and is easier to understand. Passive: It was decided by the Program Manager to streamline the program. Active, Strong Verb: The Program Manager streamlined the program. 2. Eliminate Redundancies Remove redundancies that take up extra space and don’t add value. I present some examples below.