By Bazila Fayaz
AI in government contracting works for reading, sorting and drafting, and fails at facts and citations. Here is the split, and the contract terms now in play. AI reads and drafts well and invents facts badly. This guide splits the jobs that hold up from the ones that only look finished, and covers the OMB and GSA terms now reaching contractors. AI In Government Contracting AI in government contracting works for reading, sorting and drafting. It fails at facts, citations and judgment. That split is the whole answer, and it holds across every tool on the market today. Use a model to shred a long solicitation, build a requirement matrix and produce a first draft. Keep a person on every number, every FAR cite and every win theme. The rules moved as well. Agencies buying AI now carry procurement terms that did not exist two years ago, and contractors who run AI systems on a contract face new disclosure duties. Shaavir uses Claude in its own proposal work and says so plainly. What follows separates the jobs these tools finish from the jobs they only appear to finish. What Works Right Now Four jobs hold up under pressure. Reading at volume comes first. A model digests a 300-page solicitation faster than any team can. Feed it Section L, Section M and the performance work statement, then ask for every "shall" as a numbered row. You still verify the output line by line. You start from a draft rather than a blank page. Structuring comes second. Turning a shred into an annotated outline works well, and so does mapping each heading to the factor that scores it. Our post on Section L and Section M (/blog/section-l-and-section-m) explains why that crosswalk decides page budgets. Third comes first-pass prose. A model writes a serviceable 400-word section from an outline, a proof point and a format rule. The draft reads flat and generic. A writer then earns the fee by making it specific. Fourth comes summarising sources you already hold. Point a model at a CPARS record, an agency strategic plan or a prior volume, and ask what it says. That saves hours and carries little risk, because you own the source and can check it. ! Three columns showing which bid tasks a model may draft, which tasks a person must own, and what never gets pasted into a model (https://mhhifytmrlyksfrjacvi.supabase.co/storage/v1/object/public/blog-images/2026/10/ai-in-government-contracting-figure.png) Keep the speed where the source is yours and the judgment where a fact has to be true. Where AI Still Fails Hallucinated citations top the list. A model will produce a FAR paragraph number that looks right and does not exist. It will name a clause an agency rescinded, or quote a threshold from an older rule. Every regulatory cite in a proposal needs a person opening the actual text on acquisition.gov or eCFR. Past performance facts fail the sam
AI reads and drafts well and invents facts badly. This guide splits the jobs that hold up from the ones that only look finished, and covers the OMB and GSA terms now reaching contractors.
AI In Government Contracting AI in government contracting works for reading, sorting and drafting. It fails at facts, citations and judgment. That split is the whole answer, and it holds across every tool on the market today. Use a model to shred a long solicitation, build a requirement matrix and produce a first draft. Keep a person on every number, every FAR cite and every win theme. The rules moved as well. Agencies buying AI now carry procurement terms that did not exist two years ago, and contractors who run AI systems on a contract face new disclosure duties. Shaavir uses Claude in its own proposal work and says so plainly. What follows separates the jobs these tools finish from the jobs they only appear to finish. What Works Right Now Four jobs hold up under pressure. Reading at volume comes first. A model digests a 300-page solicitation faster than any team can. Feed it Section L, Section M and the performance work statement, then ask for every "shall" as a numbered row. You still verify the output line by line. You start from a draft rather than a blank page. Structuring comes second. Turning a shred into an annotated outline works well, and so does mapping each heading to the factor that scores it. Our post on Section L and Section M (/blog/section-l-and-section-m) explains why that crosswalk decides page budgets. Third comes first-pass prose. A model writes a serviceable 400-word section from an outline, a proof point and a format rule. The draft reads flat and generic. A writer then earns the fee by making it specific. Fourth comes summarising sources you already hold. Point a model at a CPARS record, an agency strategic plan or a prior volume, and ask what it says. That saves hours and carries little risk, because you own the source and can check it. ! Three columns showing which bid tasks a model may draft, which tasks a person must own, and what never gets pasted into a model (https://mhhifytmrlyksfrjacvi.supabase.co/storage/v1/object/public/blog-images/2026/10/ai-in-government-contracting-figure.png) Keep the speed where the source is yours and the judgment where a fact has to be true. Where AI Still Fails Hallucinated citations top the list. A model will produce a FAR paragraph number that looks right and does not exist. It will name a clause an agency rescinded, or quote a threshold from an older rule. Every regulatory cite in a proposal needs a person opening the actual text on acquisition.gov or eCFR. Past performance facts fail the sam