How do we write an RFP for an enterprise AI project?

A good enterprise AI RFP defines the business problem and success metric before describing any specific technology, then asks vendors to propose their own approach rather than dictating a particular tool, since the fastest way to receive generic, unhelpful proposals is to specify the solution before the problem is fully understood. Structure the document around a clear problem statement and current-state baseline showing what the process costs or takes today, the data sources and systems involved, required integrations such as SAP, Salesforce, Microsoft 365 or ServiceNow, compliance and security requirements including any data residency constraints, a realistic timeline, and the evaluation criteria that will actually be used to score responses. Ask each vendor to include a reference architecture, a staffing plan naming actual team members rather than generic titles, and a fixed-scope quote for a first phase instead of an open-ended estimate for the entire program. Request evidence of comparable production deployments rather than a capability statement alone. Keep the document to roughly ten to fifteen pages, since longer RFPs tend to produce boilerplate responses rather than genuine engagement from serious vendors. Nanobase AI responds to this kind of RFP with a named team, a phased fixed-scope proposal and references from comparable deployments rather than a generic capabilities deck.

Start with the problem, not the solution

The fastest way to receive generic, low-effort proposals is to specify a particular tool or architecture in the RFP before the problem is fully understood. Describing the desired outcome and constraints, and asking vendors to propose their own approach, produces responses that reveal how a vendor actually thinks about the problem, which is far more useful for evaluation than responses that simply confirm they can deliver whatever was already specified.

An RFP that dictates the technical solution instead of describing the problem tends to attract vendors willing to say yes to anything, rather than vendors who will push back on a flawed assumption before it costs real money.

Section-by-section structure

What to require from every vendor response

Ask each vendor to include a reference architecture specific to the stated problem, not a generic capability overview, and a staffing plan naming actual team members with relevant experience rather than generic role titles copied across every proposal a vendor sends out. Require a fixed-scope quote for a first phase, discovery or a pilot, rather than an open-ended estimate for an entire multi-year program, since a fixed first phase is easier to evaluate honestly and gives both sides a natural checkpoint before committing further budget.

Require evidence of comparable production deployments as a mandatory response element, not an optional attachment, since vendors who lack this evidence will otherwise omit it and hope the gap goes unnoticed during scoring.

Keeping the document usable

1. Limit the RFP to roughly ten to fifteen pages; longer documents tend to produce boilerplate responses rather than genuine engagement from serious vendors.
2. State the evaluation criteria and their relative weight directly in the document, so vendors know what to emphasize and internal reviewers score consistently.
3. Set a realistic response window, typically two to four weeks, since a shorter window favors vendors who reuse generic proposals over those who genuinely engage with the specifics.
4. Include a point of contact for clarifying questions and publish the answers to all vendors, not just the one who asked, to keep the process fair.

Scoring responses without bias toward polish

A well-designed evaluation rubric weights the substance of a response, evidence of comparable work, technical soundness of the proposed architecture, staffing quality, over the visual polish of the document itself, since polish correlates with proposal-writing resources more than with delivery capability. Scoring each response against the same weighted criteria used across all vendors, rather than an overall gut impression, produces a fairer and more defensible outcome, particularly if the decision needs to be justified to a board or procurement committee afterward; see how to choose the right enterprise AI company for the broader evaluation criteria this scoring should draw from.

Frequently asked questions

Should an AI RFP go to a wide list of vendors or a narrow shortlist?

A narrower list of five to eight well-matched vendors, screened informally before the RFP goes out, typically produces better responses than a broad blast to every vendor a search engine returns. Vendors invest more effort in a proposal when they sense a genuine, competitive shortlist rather than a mass solicitation.

How specific should the budget range be in the RFP?

Including an approximate budget range, even a wide one, generally produces more realistic and comparable proposals than omitting it entirely, since vendors otherwise guess at the buyer's expectations and either underscope to win or overscope to protect margin. A stated range narrows that guessing.

Is it appropriate to ask vendors for a proof of concept as part of the RFP process?

For higher-stakes or higher-cost decisions, a small paid proof of concept from the top two or three finalists, evaluated before final selection, can surface real differences that a written proposal alone cannot. This adds time and cost to the process, so reserve it for decisions where the added confidence justifies the delay.

What is the biggest mistake companies make when writing an AI RFP?

Specifying the technology or vendor category before fully understanding the problem, which narrows the field before evaluation even begins and often locks in an approach that a more open RFP would have shown to be the wrong fit. A close second is omitting a current-state baseline, which makes it impossible to evaluate proposed ROI claims against a real starting point.

How Nanobase AI helps

Nanobase AI, a Silicon Valley enterprise AI engineering company, responds to this kind of RFP with a named team, a phased fixed-scope proposal, and references from comparable deployments rather than a generic capabilities deck. The team is also available to review a draft RFP directly and flag where it may be inadvertently narrowing the field of qualified responses.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.