AI is good at the mechanical parts of an SF330 and bad at the parts that win it. It can reformat a resume, summarize a project record, and draft a first pass from material you already have. It can't pick the right projects, write a credible key-personnel narrative from nothing, or decide what a Section H should promise. The firms getting value from AI are the ones who know which side of that line a task falls on.
This is the question proposal teams are actually asking in 2026, now that the speed gains are obvious and the win-rate gains aren't: not "should we use AI," but "what should we use it for."
Where AI Actually Helps
The green-light tasks share one trait: the source material already exists, and a human reviews the output. Inside that boundary, AI saves real time without much risk.
- Reformatting existing content. Taking a resume you already have and cutting it into Section E format, or a project writeup into Section F layout. The facts come from you; AI handles the shape.
- First-pass project descriptions from a real record. Turning structured facts (role, scope, dates, outcome) into Section F prose you'll edit. The draft is a starting point, not the submission.
- Summarizing long source material. Compressing a ten-page past-performance writeup into a usable paragraph, so the relevant detail surfaces faster.
- Drafting boilerplate variations. A first version of a methodology or QA/QC paragraph that you then tailor to the pursuit.
- Consistency checks. Flagging where a name, title, or number doesn't match across sections before a color team catches it the hard way.
Every one of these starts from something true and ends with a person reviewing it. That's the safe zone.
Where AI Doesn't
The red-light tasks share the opposite trait: they need judgment, or facts the AI doesn't have. Hand these to a general AI tool and it fills the gap with what's plausible, which is how proposals get into trouble.
- Inventing firm history. If it isn't in your records, AI makes something up that reads well. That's the mechanism behind hallucinations in a submittal: not random nonsense, but confident overstatement of real people and projects.
- Writing a key-personnel narrative with no source data. Without the person's actual record, AI promotes the role, rounds the years, and adds a certification the role usually carries. The narrative is exactly where evaluators probe.
- Selecting which projects go in Section F. This is the single biggest win-rate decision in the form, and it's judgment, not generation. The right ten projects for this scope and this agency is a call a person makes.
- Finalizing Section H commitments. Staffing levels, methodology, and technology promises need a licensed professional's sign-off, not an AI's confidence. After April 24, an unverified commitment is also a certification exposure, not just a credibility one.
Why the Speed Gains Haven't Moved Win Rates
A 2026 industry benchmark put AI adoption among A/E firms at roughly 75%, up about 20 points in a year. In the same survey, only about a third of firms tied that adoption to a higher win rate, and fewer than a third said they had confidence in their own data. Those three numbers are the whole story. We covered why in depth in the piece on AI adoption and flat win rates, but the short version connects directly to the green-and-red split: most firms point AI at the green-light tasks. Drafting and reformatting save hours, and hours show up in margin, not on the evaluator's scoresheet. A proposal that came together in four days instead of seven, from the same project sheets and the same Section H language, is a faster proposal, not a more competitive one.
What Actually Moves Win Rates in an SF330
The variables that decide shortlists sit on the red-light side of the line, where AI can't reach without your judgment and your data:
- Relevance and recency of Section F projects. The right recent work for this scope and agency, not the firm's biggest or most familiar projects.
- Team continuity in Section G. The people who actually did the work are the people proposed to do it again.
- A tailored Section H. A narrative that answers this agency's specific problem, instead of a polished approach that could front any pursuit.
- The key-personnel match. Named staff whose real experience lines up with what the agency said it wants.
None of these is a drafting-speed problem. They're selection and judgment problems, which is why faster drafting doesn't touch them. The lever is evidence, not adjectives, and AI that writes faster produces more adjectives, not more evidence.
The Prerequisite: Structured Source Data
Here's the thread that ties both lists together, and it explains the firms that don't trust their own data. The green-light tasks are only safe when AI has your records to work from, and the red-light tasks stay human but get faster when the human can pull the right material in seconds instead of digging for it for days. Both depend on the same thing: staff and project qualifications stored as structured, current data rather than scattered across PDFs and shared drives.
That's the difference between records-grounded AI and a blank chat box. A general tool with no view of your firm guesses; a tool working from your actual records can only describe what's recorded. RFPM.ai generates resumes and project sheets from a firm's own structured data for exactly this reason, so the content describes what's true and the human stays in the selection seat. The point isn't the tool. It's that AI is worth using on an SF330 in direct proportion to how good the data underneath it is.
The firms doing this well aren't avoiding AI, and they aren't trusting it with the judgment calls. They've sorted the work into the tasks AI can take and the tasks it can't, and they spent their effort on the data layer that makes the first list safe and the second list fast. For the pursuits that earn the effort, that's how you respond to more of them without adding staff.
Frequently Asked Questions
What can AI do well on an SF330?
AI works well on tasks where the source material already exists and a human reviews the output: reformatting an existing resume into Section E layout, drafting a first-pass Section F description from a real project record, summarizing long past-performance writeups, and checking consistency across sections. The common thread is that AI shapes content you already have, rather than inventing content you don't.
What should you not use AI for on an SF330?
Don't use AI to invent firm history, write a key-personnel narrative with no source record, select which projects go in Section F, or finalize Section H commitments. The first two cause hallucinations because AI fills missing facts with plausible ones. The second two are judgment calls and sign-offs that decide win rate and carry certification exposure, so they stay with a person.
Can AI write SF330 Section E resumes?
It can draft and reformat them, but only safely when it works from the person's actual record. A general AI tool with no source data will upgrade roles, round years, and add certifications the person doesn't hold. AI grounded in structured staff records can reformat what's true without inventing what isn't, but a human should still verify every resume before it goes in.
Does AI improve proposal win rates?
Not on its own. About 75% of A/E firms now use AI on proposals, yet only around a third connect it to a higher win rate, because most firms use AI for drafting and reformatting, which save time but don't change what evaluators score. Win rates move on project relevance, key-personnel match, and a tailored approach, which are judgment and data problems, not drafting-speed problems.
Is it safe to use AI on a federal proposal?
Yes, on the right tasks and with verification. AI is safe for reformatting and first drafts from your own records, followed by a human review. It's risky for anything that requires facts it doesn't have or commitments a licensed professional must stand behind. After April 24, an unverified AI overstatement in a federal submittal is a certification issue, so the verification pass matters more than ever.