Webinar Recap: The Evaluator Has Changed. Your Capture Strategy Should Too.

Procurement Sciences recently joined GovConWire and Executive Mosaic for a webinar on one of the most consequential shifts in government contracting right now: AI is no longer just a tool for writing proposals. It is increasingly being used to evaluate them. And if your capture strategy has not caught up to that reality, your proposals are likely leaving points on the table.

Bruce Feldman, AI Platform Strategist at Procurement Sciences, led the session. With thirty-five years of government contracting business development experience spanning capture management, proposal management, and executive leadership, Bruce brings a practitioner's perspective to how this shift changes what winning actually requires.

The Question Has Already Changed

Five years ago, AI-assisted proposal evaluation was essentially nonexistent in the federal government. The question at that time was whether it would ever happen.

That question is settled. The question now is whether your team is prepared to write proposals that score well in an environment where both human and AI evaluators are involved.

The shift happened faster than most people expected. When the SOUP 6 solicitation appeared in 2024 with a clause requiring disclosure of AI use in proposal development, it created significant anxiety across the industry. Many companies interpreted it as a warning signal and began asserting in their proposals that no AI had been used.

More recently, a CDAO solicitation included language stating that the government may use AI and machine learning to assist in the administrative review and technical assessment of proposals. That is not a future possibility. It is a present reality. And the agencies moving forward with AI-assisted evaluation are not moving backward.

Why the Government Is Moving This Direction

Understanding why the government is adopting AI in evaluation helps frame what is actually happening and what to expect next.

The acquisition workforce has shrunk significantly. Fewer contracting officers are being asked to manage the same volume of acquisitions, with less time and more complexity from FAR revisions, executive orders, and new acquisition mechanisms. AI offers a practical solution to that workload pressure: automate the repetitive, time-consuming parts of evaluation so human evaluators can focus on judgment.

The government is not adopting AI uniformly. There is no federal standard mandating a specific toolset. Agencies are piloting and experimenting independently, which means the specific tools being used vary. But the direction is consistent across the board. Bruce's observation after working with thousands of government contractors: no agency appears to be moving away from AI. The trajectory is in one direction only.

One important caveat worth internalizing: the government is under no obligation to tell you what tools they use to evaluate proposals. They must disclose evaluation criteria and methodology, but not the specific AI systems, prompts, or outputs used in the evaluation process. A recent protest attempt that tried to challenge AI-assisted evaluation was abandoned for exactly this reason: the government had no obligation to disclose, and without that disclosure, there were no grounds to protest on.

How AI Reads a Proposal Differently Than a Human Does

Before getting into what to change about capture, it helps to understand what changes about evaluation when AI is involved.

A human evaluator gets tired. Reading four or five proposals over a week, attention fluctuates with energy levels and biorhythms. AI does not. It applies the same rubric consistently from the first page of the first proposal to the last page of the last one.

That consistency has a specific implication for your proposal: weaknesses that might have been glossed over by a fatigued human evaluator will now be identified with the same rigor as your strengths. The floor has risen. Compliance is no longer a differentiator. It is the minimum required to stay in the competition.

AI also does not respond to tone, style, or prose quality the way a human reader does. It converts text into mathematical representations and looks for similarity to the evaluation criteria, not for graceful articulation. A beautifully written sentence asserting that your team brings unmatched expertise will not score. A specific claim tied to a measurable outcome and traceable to documented past performance will.

Generic language is essentially invisible to AI evaluation. When every proposal in the pile uses phrases like best-in-class approach, proven track record, and comprehensive methodology, those phrases cluster together in the AI's semantic model and become indistinguishable from each other. Specific, quantified, evidence-backed claims stand out precisely because they do not look like everything else.

There is one more dimension worth noting: AI can read all volumes of your proposal simultaneously. Human evaluators typically divide responsibility by volume. AI does not. Inconsistencies between your technical, management, and past performance volumes that might have gone unnoticed when different reviewers read different sections will be surfaced by AI. Consistency across volumes is now a requirement, not a nice-to-have.

What Needs to Change in Capture

The implications for proposal development are real, but the deeper problem is that most of the content that scores well, or fails to, is determined during capture. If capture has not done the homework, the proposal room cannot manufacture a winning solution on deadline.

Shape the evaluation criteria early and deliberately.

The government is increasingly using AI to draft solicitation documents. That means contracting officers are often starting from a repository of prior solicitations from the same office. So are you. Mining historical solicitations from the same contracting office for evaluation criteria used on similar requirements is one of the most valuable things a capture team can do.

Beyond research, shape actively. When you meet with contracting officers and program managers, introduce the metrics and KPIs you believe should govern successful performance. Frame them as being in the government's interest, not yours. If your language shows up in the final solicitation, that is a strong signal your shaping worked. It also means your solution is already structured around criteria the evaluator cares about.

Build proof before you need it.

One of the most common capture failures is discovering too late that there is no substantive evidence to support a key capability claim. AI evaluation makes this problem worse, not better. An assertion without proof will not score. Period.

Early in pursuit, when you are forecasting likely evaluation criteria, assess honestly whether your company can provide documented, quantified evidence of its ability to deliver against each criterion. Past performance references need to include specific contracts, measurable outcomes, and verifiable results. If you cannot muster that evidence, it is a meaningful signal about whether to pursue the opportunity at all.

Avoid language like similar scope and complexity in your past performance references. It is vague, it will not differentiate you, and AI evaluation will treat it as generic filler. Give the AI something concrete: a contract citation, a specific metric, a documented outcome.

Build your solution against the evaluation rubric, not around your capabilities.

Many proposals are built from the inside out: here is what our company does, here is how we will apply it to this requirement. That approach produces proposals full of capability descriptions that do not map cleanly to what the evaluator is scoring.

The alternative: build your solution from the evaluation criteria outward. What specifically will the evaluator score as a strength? What proof do you have that your approach will deliver that outcome? How is your solution measurably better than the baseline requirement, not just compliant with it?

This framing work happens in capture, during the months and years before the RFP drops. By the time the clock starts in the proposal room, the core of the solution should already exist.

Validate Your Capture When the Draft RFP Arrives

The draft RFP stage is one of the most underused validation checkpoints in the capture process.

When the draft arrives, compare it systematically against the evaluation criteria you forecasted. Did your shaping land? Are the specific metrics and KPIs you introduced with the customer reflected in the document? Are the discriminators you built your solution around actually being evaluated?

If language you worked to introduce during shaping does not appear in the solicitation, that is meaningful information. It may mean your shaping did not take. It may mean the discriminator you built your solution around will not score with this evaluator. Either way, it is better to know before the final RFP than after.

Also compare the draft solicitation to historical solicitations from the same office using AI. Look for how the evaluation criteria are evolving. What is new? What has been carried forward? That analysis gives you a picture of how this contracting officer approaches evaluation and how it may continue to develop.

A Practical Starting Point

In the first thirty days: pull your last five losses and use AI to map the weakness and risk scores you received back to what happened during capture. Identify where the gaps were created. Use AI to score a typical loss against the evaluation criteria and compare the results to your debrief. Start building an understanding of how this customer evaluates.

In the next thirty days: apply that evaluation model to incoming RFPs. Begin building past performance profiles with hard data, specific contracts, measurable outcomes, named proof. Expand your research into the customer's solicitation history and mine it for evaluation trends.

After sixty days: continue refining. Track where you score strengths consistently and where you do not. Identify patterns across customers. Make researching the government's use of AI a standing task on your capture agenda, and be willing to ask your customer contacts directly about their experience with AI-assisted evaluation.

The Fundamentals Have Not Changed. The Stakes Have.

The core of what it takes to win has not changed. You still need to understand your customer deeply, build a solution that delivers measurable outcomes, and make a compelling case for why your offer is better than the competition.

What has changed is the precision required. AI evaluation does not grade on a curve. It does not have patience for vague assertions or unmeasured claims. It will score your proposal factor by factor against the rubric, and it will do so with a consistency that human evaluation never fully achieved.

The companies that will pull ahead are the ones that build their capture process around that reality now, before every competitor has caught up. Capture is where proposals are won or lost. AI evaluation has made that truth harder to paper over than it has ever been.

Want to see how Procurement Sciences supports capture teams with AI-driven research, opportunity intelligence, and proposal preparation? Book a demo to talk through your specific use cases.