Federal contractors should be paying close attention to a growing issue in government procurement: the use of shadow AI and generative artificial intelligence by agency evaluators during proposal evaluations.
As federal agencies increasingly experiment with AI tools in procurement and acquisition processes, evaluators may be using generative AI platforms to summarize proposals, identify strengths and weaknesses, assess compliance, draft technical findings, and assist with source selection decisions. In many procurements, however, the solicitation does not disclose the use of AI-assisted evaluation tools. This creates significant legal and bid protest risks.
When agencies rely on undisclosed or poorly governed AI tools during federal procurement evaluations, contractors may have grounds to challenge the procurement at the Government Accountability Office (GAO) or the U.S. Court of Federal Claims (COFC). AI-assisted evaluations can introduce factual inaccuracies, unstated evaluation criteria, inconsistent analysis, and irrational or non-independent decision-making into the procurement record.
For government contractors, debriefings are becoming increasingly important because they may provide the first indications that evaluators used generative AI during the source selection process.
What Is Shadow AI in Government Contracting?
Shadow AI refers to the unauthorized or unofficial use of artificial intelligence tools by employees without formal agency governance, approval, or oversight. In federal contracting, shadow AI may involve agency evaluators using publicly available or internal AI systems to review proposals or assist with technical evaluations. Evaluators may use generative AI tools to summarize lengthy proposals, compare offerors, draft consensus reports, generate evaluator narratives, identify performance risks, or recommend strengths and weaknesses.
Although evaluators may view these tools as efficiency aids, AI-generated outputs can significantly affect the fairness and legality of a procurement evaluation. Unlike traditional evaluation methods, generative AI systems can hallucinate facts, misunderstand technical content, omit critical proposal details, compress distinctions between competing proposals, and generate standardized language that masks individualized evaluation judgments. These risks become particularly significant where the solicitation never informed offerors that AI-assisted evaluation methods would be used.
Why AI-Assisted Proposal Evaluations Create Bid Protest Risk
Federal procurement law requires agencies to evaluate proposals fairly, rationally, and consistently with the solicitation’s stated evaluation criteria. If agency evaluators use generative AI tools during proposal evaluations, several traditional bid protest grounds may arise. For example, AI-generated analysis may introduce unstated evaluation criteria by emphasizing concepts not contained in the solicitation. AI tools may also inaccurately summarize proposal content, causing evaluators to overlook strengths, misunderstand technical approaches, or assign weaknesses based on fabricated or distorted information. In addition, agencies may face difficulty defending procurements where evaluators cannot adequately explain how conclusions were reached or whether evaluators independently validated AI-generated findings. These issues are likely to become increasingly important in GAO bid protests and COFC litigation as federal agencies expand AI adoption across procurement operations.
Why Debriefings Matter More Than Ever
Contractor debriefings may provide critical clues that agency evaluators relied on generative AI during the evaluation process. Because agencies are unlikely to voluntarily disclose shadow AI usage, contractors should carefully scrutinize debriefing materials and evaluation narratives for signs of AI-generated analysis. Potential warning signs include overly generic evaluator language, repetitive phrasing across multiple evaluation findings, summaries that oversimplify technical solutions, references to concepts not contained in the proposal, unexplained factual inaccuracies, and evaluation conclusions disconnected from the solicitation criteria.
Contractors should also pay attention to evaluation narratives that appear unusually formulaic, synthesized, or abstract compared to the proposal’s actual language and structure. In many cases, AI-generated summaries fail to capture technical nuance, especially in complex procurements involving cybersecurity, cloud computing, software development, engineering services, defense technologies, artificial intelligence systems, or highly specialized professional services.
Signs Agency Evaluators May Have Used Generative AI
Although agencies may not openly acknowledge AI-assisted evaluations, certain indicators may suggest evaluators relied on generative AI tools during the procurement process.
- Evaluation Narratives Misstate Proposal Content
One of the most common indicators is when evaluation findings inaccurately describe proposal language or technical approaches. Generative AI systems frequently distort nuanced concepts when summarizing long or highly technical proposals. If evaluators appear to misunderstand core proposal features that were clearly explained, contractors should consider whether AI-generated summaries influenced the evaluation. - Repetitive or Formulaic Evaluation Language
AI-generated writing often relies on repetitive phrases and generalized conclusions. Evaluation findings that repeatedly state a proposal “lacks sufficient detail,” “does not clearly demonstrate capability,” or “presents performance risk” may reflect AI-assisted drafting rather than individualized evaluator judgment. When multiple offerors receive nearly identical narrative language, contractors should scrutinize whether the agency conducted an independent and reasoned evaluation. - Strengths and Weaknesses Do Not Match the Solicitation
Generative AI systems may emphasize issues or themes that were never part of the solicitation’s evaluation criteria. This can create a classic unstated evaluation criteria problem, which remains one of the most common and successful bid protest arguments in federal procurement litigation. - Evaluators Cannot Explain Their Conclusions
In bid protests, agencies generally must produce a contemporaneous record supporting their evaluation decisions. If evaluators relied heavily on AI-generated outputs, they may struggle to explain how findings were developed, whether AI-generated content was independently verified, what prompts or instructions were used, or whether all offerors were treated consistently. These gaps may undermine the agency’s ability to defend the procurement record. - The Administrative Record Contains AI-Related Artifacts
Internal evaluation documents may sometimes contain indicators of AI usage, including abrupt stylistic shifts, inconsistent terminology, generic summaries, copied boilerplate language, unusual formatting artifacts, or prompt-like text embedded in evaluator notes. Such issues may become important during litigation involving administrative record challenges, document preservation disputes, or allegations that the agency failed to maintain a complete procurement record. - Discovery and Litigation Issues Involving AI Evaluations
As agencies continue integrating artificial intelligence into procurement operations, future bid protests are likely to involve disputes regarding whether AI use was authorized, whether offerors were entitled to disclosure, whether AI outputs became part of the evaluation record, and whether agencies properly preserved AI-generated materials. Contractors may also challenge procurements where agencies fail to preserve prompts, AI-generated summaries, evaluator interactions with AI systems, or internal communications regarding AI-assisted evaluations. These issues may raise significant questions regarding administrative record completeness, spoliation, transparency, and whether the procurement decision was arbitrary and capricious.
Practical Steps for Government Contractors
Government contractors should begin incorporating AI-related scrutiny into proposal evaluations, debriefings, and bid protest strategies.
During debriefings, contractors should ask detailed questions regarding the evaluation methodology, the preparation of evaluator narratives, and the basis for specific weaknesses or deficiencies. Contractors should closely analyze whether evaluation findings accurately reflect proposal content and whether evaluators appear to have independently engaged with the proposal.
During protest analysis, contractors should compare evaluation language for signs of standardized or machine-generated phrasing, identify factual inaccuracies consistent with AI hallucinations, and assess whether the agency relied on unstated evaluation considerations.
Contractors should also assume that AI-assisted summarization tools may increasingly influence evaluations and draft proposals accordingly. Clear proposal organization, precise terminology, and highly structured technical explanations may reduce the risk of evaluator misunderstanding or AI-driven distortion.
The Bottom Line on AI in Federal Procurement Evaluations
Artificial intelligence is rapidly entering the federal procurement process, including proposal evaluations and source selections. But long-standing procurement law principles still apply. Agencies must conduct rational, fair, and solicitation-consistent evaluations supported by a defensible administrative record. When shadow AI or generative AI tools influence evaluation outcomes without transparency, oversight, or proper safeguards, contractors may have strong grounds for filing a bid protest. As agencies continue experimenting with AI-assisted procurement evaluations, contractors and government contracts counsel should carefully examine whether the “human judgment” reflected in evaluation records is truly human at all.
If you have any questions about the foregoing or otherwise require assistance, please do not hesitate to contact Aron Beezley or Gabby Sprio.