By Mehdi HassanWhat do enterprise buyers look for on an AI vendor's website?
Enterprise buyers look for evidence that an AI vendor will pass their security, legal and procurement review. The business owner wants the outcome. Everyone else in the buying group wants to know what happens to their data, which models touch it, who else processes it and what the contract says. An enterprise AI website answers those people too.
This checklist is mapped to the questions those reviewers actually ask, not to design taste. Use it to audit your current site. For the broader structure of an AI startup site, see our hub on AI startup website design.
Who reads an enterprise AI website, and what do they check?
An enterprise AI website is read by five groups, and each one checks something different. Most AI sites are written only for the first.
Reader | What they check | Page they need |
|---|---|---|
Business owner | Outcome, workflow fit, proof | Homepage, use cases, customer stories |
Security reviewer | Data flow, training, certifications, subprocessors | Trust center or security page |
Legal and privacy | DPA, terms, retention, residency, AI-specific terms | Legal and privacy pages |
IT and identity | SSO, SCIM, roles, audit logs, deployment | Enterprise or admin features page |
Procurement | Pricing model, contract terms, vendor details | Pricing and enterprise contact page |
Our guide on selling to technical and non-technical buyers on the same page covers how to keep the homepage readable for the business owner while routing reviewers to the detail.
What is the enterprise AI website checklist?
The checklist below covers what enterprise reviewers ask most often, in roughly the order they ask it. Each item should be answerable from a public page or from a document a visitor can request in one step.
- Training policy: a one-sentence answer to "Is our data used to train your models?", with any opt-in or opt-out terms.
- Model providers: which third-party or open models you use, for which features, and whether they retain data.
- Subprocessors: a named list with purpose and region, and how customers are told about changes.
- Certifications and reports: SOC 2, ISO 27001 or others you actually hold, with the report period and how to request it.
- Data retention and deletion: how long prompts, outputs and files are kept, and how deletion works.
- Data residency: where data is stored and processed, and which regions customers can choose.
- Identity and access: SSO, SCIM provisioning, role-based permissions and audit logs.
- Human oversight and limits: where people review AI output, and what the product shouldn't be used for.
- Availability: a public status page and uptime history, plus support hours and response targets.
- Contracts: a DPA, security addendum and terms that address AI output ownership, available on request or published.
- Accessibility: an accessibility statement and, if you sell to the public sector, a conformance report.
- Security contact: a way to report vulnerabilities and a named route to start a security review.
How should an AI vendor explain data use and model training?
An AI vendor should explain data use and model training in one plain sentence at the top of the trust page, then in detail below. The short version is what reviewers paste into their notes; the detail is what legal checks against the contract.
Cover four things: whether customer data trains your models or any third party's, how long inputs and outputs are kept, who can access them inside your company, and how customers can delete them. If the answer differs by plan, say so in a table. Our AI trust page guide has the full structure for this page.
Hypothetical example: a data-use summary that passes review
For example, suppose an AI contract-review vendor writes this at the top of its trust page:
"We don't use customer documents or outputs to train our models or any third party's. Documents are processed by our model providers under zero-retention terms, stored encrypted in your chosen region (US or EU), and deleted within 30 days of removal from your workspace. Access by our staff requires customer approval and is logged."
Every clause in that summary maps to a questionnaire item, and every number would need to be true and backed by the contract. That's the point: the website becomes the first draft of the security review.
Where should enterprise AI trust content live?
Enterprise AI trust content should live on a dedicated trust center or security page, linked from the main navigation and the footer, and summarised near every enterprise call to action. Many vendors use a trust subdomain with certifications above the fold, a subprocessor list and a document request flow.
Put certifications and the training answer above the fold
Reviewers want the two facts that decide whether to keep reading.
Group the detail by reviewer
Security, privacy, AI and compliance sections, so each reviewer jumps to theirs.
Gate reports, not answers
Put audit reports behind an NDA request if needed, but keep the answers to common questions public.
Date everything
Show when each section was last reviewed. A stale subprocessor list is a finding in itself.
Assign an owner
Give the page a named owner and a review schedule, tied to your vendor and model changes.
For the generic version of this page, see trust center design, what to include on a SaaS security page and the wider set of trust pages every B2B website needs.
How should an enterprise AI website handle pricing and contracts?
An enterprise AI website should explain the pricing model even when the price is custom. Procurement wants to know the unit, such as seats, usage or platform fee, what's included at the enterprise tier and how overages work, before they enter a negotiation.
- Name the enterprise features: SSO, SCIM, audit logs, data residency, dedicated support, custom retention.
- Say how usage is billed if it's usage-based, so finance can forecast.
- Offer contract documents such as the DPA and security addendum through a clear request path.
- Give a direct enterprise contact route that skips the generic demo form.
Clear structure helps AI answer engines too. Buyers increasingly ask chatbots to compare vendors, and pages that state pricing models, security posture and features plainly are easier to quote. Our guide to answer-first content structure explains how to write for that.
How do you keep an enterprise AI website current?
Keep an enterprise AI website current by storing trust facts as structured CMS content with owners and review dates, not as paragraphs buried in page designs. AI vendors change model providers, regions and features often, and every change can make a public claim false.
Tie website updates to the same process that updates your subprocessor notices and security documentation. When a new model provider goes live, the trust page, subprocessor list and any feature page that mentions it should change the same week. A practical test: once a quarter, have someone outside the security team answer your own questionnaire using only the website, and fix every gap they hit.
