Every week brings another briefing deck promising that generative AI will transform federal procurement. Most of them describe a contracting shop that does not exist — one where the bottleneck is typing, and where a model that writes faster is therefore a model that buys faster.
That is not where the time goes. Most procurements are delayed 8 to 12 months, and very little of that delay is drafting. It is requirement definition that arrives incomplete, market research that has to be rebuilt from scratch, review cycles that stall waiting on one reviewer, and documentation that cannot be traced back to a decision when a protest arrives.
Where the time actually goes
Before evaluating any tool, map your own cycle time against the phases where it is genuinely lost:
- Requirement definition. A program office describes an outcome; the contracting shop needs a specification. The translation is the work.
- Market research. Assembling a defensible picture of available sources, pricing history, and comparable awards — usually rebuilt from nothing each time.
- Document assembly and internal review. Not writing, but reconciling: making the SOW, the evaluation criteria, and the price schedule say the same thing.
- Evaluation and documentation. Producing a record that survives scrutiny months later.
AI can compress the second and third of these meaningfully today. It can assist with the first. It should be handled with real caution in the fourth.
Four questions worth more than a demo
1. Can it show its sources?
A generated paragraph with no traceable citation is a liability, not a work product. If the tool cannot point to the regulation, the prior award, or the market data behind a sentence, someone will have to verify all of it — which costs more than writing it.
2. Where does the data go?
Draft requirements and acquisition strategies are sensitive well before they are public. Establish where prompts are stored, whether they train a shared model, and what authorization the environment holds. This question ends more evaluations than any other, and it should be asked first, not last.
3. Does it fit the record you already have to keep?
If a tool's output cannot land in your contract writing system and your official file without rekeying, its efficiency claim is theoretical.
4. Who is accountable for the output?
The answer is always the contracting officer. Any tool, process, or vendor arrangement that blurs this is creating risk rather than absorbing it. The practical implication: staff need enough fluency to challenge a model's output, not just accept it.
The near-term picture
The government is not waiting. DoD's Chief Digital and AI Office has been prototyping an AI-assisted contract-writing capability as part of its Tradewind initiative, and research from firms including McKinsey and Deloitte points to substantial demand for AI-assisted procurement tooling. The direction is settled; the discipline is not.
The agencies that get value from this will be the ones that treated it as an acquisition problem rather than a technology purchase — scoping a narrow, high-friction task, measuring cycle time before and after, and training the workforce to supervise the output. That is unglamorous work. It is also the work that holds up.