As AI becomes part of daily BD and capture operations, leaders need a clear view of how it affects team structure, pricing workflows, and overall throughput. This final installment builds on Part 1: What’s Working in Federal Contracting and Part 2: How Growth Teams Are Using it to Win Smarter. Here, we focus on where AI meaningfully improves capture and proposal performance, what stays firmly in human hands, and how organizations can prepare for a full-stack AI environment that supports every stage of the growth lifecycle.

The questions below came directly from executives who attended our Deltek webinar on practical AI for government contractors.

Team Impact

Question: What kind of impact might this have on a typical pursuit and capture team relative to the number of team members needed?

AI will not replace capture and proposal professionals, but it will help to redistribute their time. It should be used to automate tedious, mechanical work so teams can spend more time on strategy, customer engagement, and solution design. The structure of your team will mostly likely remain the same but how they allocate their time will differ based on your growth goals and objectives.

Shifting Priorities

• Expect about 20–40% fewer drafting hours across capture and proposal sections when your content library is clean and prompts are tuned.
• Time savings should show up in storyboarding, bulleting sessions, editing, and compliance prep.
• Oversight and fact-checking workloads may increase since teams review more output in less time. Without GenAI, capture and proposal resources are spending more time generating ideas and content and less time reviewing/assessing the information.

Core Roles Should Stay the Same

Companies will still need a capture leads, solution architects, proposal managers, technical writers, and pricing analysts. What you are adding with GenAI are three (or more) roles intended to supplement your efforts:

• A market researcher who can search online for federal market and agency context, historical data, and trends
• A technical SME who understands tools, technologies, and solutions
• A content librarian/AI lead who maintains reusable material, prompt libraries, past strengths, model context.

The key is to understand that the information they provide will not always be accurate, hence the need for human oversight.

Throughput and Cycle Time

Teams can handle more bids with the same staff when processes are structured and prompts are consistent. The key is a disciplined process. Use structured libraries, consistent prompts, and review checkpoints. GenAI may streamline some capture and proposal functions, but it doesn’t mean a company can pursue more work with fewer resources.

Human Oversight is Mandatory

In addition to having capture and proposal professionals oversee GenAI outputs, corporate leadership need to have responsibility on what is produced and eventually submitted. Compliance and judgment calls should not be outsourced to GenAI, especially if it places your company at risk of non-compliance or ineffective capture approaches. That’s also consistent with federal guidance under the NIST AI Risk Management Framework, which emphasizes human accountability for AI-assisted work.

AI and Pricing

Question: Are there tips/tools useful for cost and pricing for BD. Any recommendations on how to utilize AI for Pricing?

AI can help with various aspects of proposal pricing including collecting external pricing information, organizing pricing data, analyzing trends, and drafting narratives. However, companies still need financial and pricing expertise internally to disseminate through the data, double check for accuracy, and determine pricing rationale. As with being overly reliant on GenAI for proposal development, companies will face challenges if they trust AI to own the final numbers and overall pricing strategy.

Where AI Adds Value

• Organizing price-to-win (PTW) scenarios (assuming you are feeding it proper inputs), mapping labor categories, normalizing rate cards, checking escalation assumptions, and generating draft basis-of-estimate (BOE) language.
• Structuring pricing data so pricing leads can focus on validation instead of formatting.

Stay Anchored to the Rules

Always reconcile AI outputs against FAR 15.404-1 (proposal analysis techniques) and DFARS 215.404-71 (Weighted Guidelines) to ensure you are following proper price evaluation requirements. You can have your model draft short narratives that cite these references, but verify the outputs before submission.

A Practical Workflow

1. Start early. Use AI for preliminary pricing scenarios including labor mixes, wrap-rate ranges, and fee posture. It can be a good sounding board assuming you’re using it to test hypotheticals versus treating the results as the actual PTW.
2. Run key prompts.
   • Historicals → ranges: “From public award data (Use actual sources. FPDS/USAspending, wage determinations, GSA CALC, market surveys. Require quotes or file+page), produce low/target/high rate bands for these roles at [agency/vehicle]. Provide verification and proof from other sources that these pricing ranges are valid.”
   • Labor mapping: “Map our labor categories to the solicitation’s categories. Flag SCA or WD applicability and anomalies. Provide proof that the mapping is accurate based on keywords, experience, and education requirements.”
   • WGM narrative: “Draft a Weighted Guidelines narrative that notes performance and contract-type risk factors for negotiation prep.”
3. Structure the data. Use pricing tools like ProPricer to run scenarios while AI drafts the supporting language.
4. Draft and refine. AI can generate first-cut BOE language and a pricing checklist such as assumptions, escalation, ODCs, and fee caps. This is assuming you are customizing the prompts to include all pricing inputs, risks, and clauses applicable to your BOE. Have your pricing team can review, validate, and finalizing the BOE language.

Use AI to help organize, analyze, and develop a pricing story, but never to produce or sign off on the final numbers. It is important for pricing staff, financial analysts, and your key leadership to stay in control of the numbers, rationale, and compliance because DCAA, auditors, and contracting officers will check.

What’s Next in AI?

Question: Looking into the future, do you envision there will be a toolset that enables the BD process from cradle to grave?

Yes. We will see many toolsets in the near future that will enable the BD process from start to finish. It will initially look more like a stack, as opposed to a single product. While there are specific tools and technologies that support an individual piece of the BD lifecycle (i.e., proposal AI software), companies like Deltek will likely be on the forefront offering a unified solution that encapsulates your data, prompts, and results.

What We Can Expect

• AI-supported pipeline discovery tied to your CRM with alerts on incumbents, budgets, vehicles, and due dates.
• Autmoated capture and win strategies linked to past performance and technical experience libraries, teaming suggestions based on gaps, and competitive analysis generation to name a few. Human review and sign-off remain required
• Draft outlines, content, and reviews (with human oversight) tied to agency-specific RFPs.
• A governed content library with retrieval that returns the exact paragraph, file, and page.
• Pricing companions for BOEs, rate comparisons, and PTW scenarios.
• Post-award learning that pulls lessons, CPARS, and win/loss notes back into the library so the next bid is smarter.

Why this Matters Now

As agencies continue to publish AI playbooks and run governed pilots, industry should continue to investigate how the government is evolving their federal procurement methodologies. It will be important to assess these end-to-end AI toolsets to match evolving procurement methods.

How to Start (and Scale)

Companies can begin the process now by either beginning to build their own GenAI solution using an enterprise level GenAI or purchasing an AI toolset to support specific elements of the BD lifecycle. These are good first steps before deciding to go all-in on an end-to-end system.

The key is to master AI basics before purchasing a system that claims it can do everything for you. As you increase your experience with AI, you’ll be able to also try new tools that help to complete the full stack of technologies to support your BD process.

Series Conclusion

Across this three-part series, one theme has held true: AI doesn’t replace experience, it amplifies it. Government contractors that approach AI with discipline, compliance, and human oversight are already gaining a competitive edge.

Get Up to Speed on the Full Series

AI in Action: What’s Working in Federal Contracting? (Part 1 of 3)

AI in Action: How Growth Teams Are Using It to Win Smarter (Part 2 of 3)

If you want to move from experimenting with AI to operationalizing it across your growth lifecycle, Red Team can help. Our advisors can assess where AI fits into your BD, capture, proposal, and pricing processes, develop a compliant governance model, and build a practical roadmap your team can execute.