Go/No-Go Decision Criteria for AI-Enabled Proposals

Most capture teams already have go/no-go decision criteria: customer relationship, past performance, competitive position, price. Those still matter. But once a solicitation asks for an AI-enabled capability, a new class of disqualifier appears, and most rubrics have no line for it.

The question is no longer only "can we win?" It is also "can we run AI under these terms, show our work to an evaluator, and keep it safe after award?" This article shows how to add that layer to your decision, and where RiseLab Platform can supply evidence for it.

Why AI changes your go/no-go decision criteria

Traditional criteria assume the delivery risk is mostly people and schedule. AI adds risks that are harder to see at the capture stage:

• The model or data path may not be acceptable to the customer.
• Outputs may need a named human accountable for them.
• Safety claims may be requested as evidence, not assurances.
• Run costs can erode margin on a fixed-price award.

If you score these as ordinary additive items, strong past performance can hide a fatal gap. Treat the AI stack the way you treat a missing clearance: a gate that runs before any scoring.

Gate one: five pass/fail questions about your AI stack

Run these before you tally a single point. A "no" on any of them ends the review or forces a written mitigation with an owner and a date.

1. Can we run it where the solicitation says it must run? Write down the environment the customer will accept, then check your platform against it. RiseLab trains and serves models on AWS SageMaker, GCP Vertex AI or Azure ML. It also fine-tunes and serves open-weight models such as Llama, Mistral, Qwen, DeepSeek, Gemma and Phi, or calls GPT, Claude and Gemini for inference, switchable per workload. That flexibility helps when a requirement restricts which models you may use. It does not replace reading the clause. Confirm the specific accreditation or residency language in the solicitation yourself, and do not assume a vendor's capability list satisfies it.

2. Can we control and audit who touches the system? Evaluators and security reviewers will ask about identity and traceability. RiseLab supports SSO, role-based access control, audit logs, and encryption at rest and in transit. Map each of those to the control language in the solicitation, line by line.

3. Is a human accountable for consequential outputs? If the AI touches a decision that affects a person, a payment, or a record, your technical volume should name who approves it. RiseLab routes critical AI decisions through configurable human approval workflows with an audit trail for every approval. Your gate question is whether you have actually defined which decisions count as critical.

4. Can we show safety evidence, not just describe it? This is where proposals get thin. RiseLab tests models against adversarial attacks, including jailbreak and prompt injection testing and bias and toxicity probes. It applies guardrails for content filtering and PII detection. It also publishes a public trust scorecard that runs a fixed 94-probe adversarial corpus across 8 categories against its deployed input filter, and the same corpus gates every release. A public, repeatable test is easier to cite in a proposal than a paragraph of reassurance. Cite it for what it is: results against that defined corpus, not a blanket guarantee.

5. Do we know what it costs to run? On a fixed-price vehicle, unpredictable inference spend is a margin risk. RiseLab tracks AI infrastructure cost in real time with budget alerts and usage analytics by user and project. If you cannot forecast run cost per workload before you bid, treat that as an open gate item.

Score what survives the gates

Once the gates pass, use your normal weighted rubric. A simple version scores five dimensions from 1 to 5, with two evaluators scoring independently:

1. Customer and mission fit
2. Competitive position, including incumbency
3. Technical and past-performance credibility
4. Price-to-win headroom
5. Capture cost against expected contract value

Set thresholds for no-go, conditional go, and go before you look at the opportunity, so the bar does not move to fit the pursuit. Write scoring anchors down in advance, and require a one-sentence justification for any top score. The point is a record a reviewer can read after a loss debrief, showing who scored what and why.

For AI-enabled bids, add one habit: when you score technical credibility, count only capabilities you can demonstrate. If a feature is on a roadmap, it is a risk, not a strength.

Use the proposal itself as evidence

AI-enabled bids often require you to show how the system will ground its answers in the customer's own material. RiseLab builds retrieval pipelines over documents, images and video with source attribution and citations. In a technical volume, that gives you something concrete to describe: an answer that points back to its source, so a reviewer can check it.

The same logic applies to your internal capture process. If your team uses AI to draft or check compliance matrices, require that every generated claim traces to a source paragraph in the solicitation or your own past proposals.

Operational reliability belongs in the decision

A bid that promises agentic automation should also consider what happens when things go wrong mid-run. Recent platform changes show the kind of behavior worth asking any vendor about. In October 2026, RiseLab changed agent tools that act outside the system, such as sending an email or writing to a CRM, so each runs only once per run; a retry or provider fallback gets the original result instead of acting twice. The same month, every scheduled background job began alerting a person when it fails or misses its expected run time.

You do not need those specific features to make the point. In your gate review, ask what prevents a duplicate action and who is told when a job stops. If nobody can answer, that is a delivery risk to price in.

Re-run the gates when the solicitation drops

Your first decision rests on assumptions. The final solicitation replaces them. When the draft or final RFP arrives:

• Re-read the AI, data handling, and security language against your gate answers.
• Check whether evaluation factors have shifted weight toward technical approach or safety evidence.
• Re-score with the real criteria, not the ones you expected.
• If a gate now fails, document it and decline. A disciplined no-bid costs far less than a compliant proposal that cannot be delivered.

Keep the roles separate. The capture lead recommends, an executive sponsor decides, and the written scores go in the file.

Next step

Take your current go/no-go template and add the five AI gates above as a pass/fail section that runs before scoring. Then check each answer against what the solicitation actually requires. To see how RiseLab's access controls, human approvals, adversarial testing, guardrails, and cost tracking map to those gates, review the RiseLab Platform page and bring your gate list to the evaluation.