Enterprise AI RFP Template Considerations for Australian Procurement Teams

A procurement team issues an enterprise AI RFP. Twelve vendor responses come back. The responses are thorough, professionally formatted, and largely incomparable. Each vendor has answered a slightly different question. Some have addressed data residency in passing. Others have reframed the use cases. Several have included pricing structures that cannot be compared because they measure different things. The evaluation panel spends three weeks trying to normalise responses that were never designed to be normalised.

This is what happens when an enterprise AI RFP is built by adapting a generic software procurement template. The questions are often too broad. The response format is uncontrolled. The evaluation criteria were typically not designed to expose the things enterprise AI requires.

This article provides a practical enterprise AI RFP template for Australian procurement teams: the sections that commonly structure an enterprise AI RFP, the questions that typically belong in each section, the non-functional criteria that typically gate everything else, and illustrative weighting guidance that can be adapted to organisational priorities, risk appetite and deployment context. It is a working resource, not a structural philosophy. For the structural philosophy, the Enterprise AI RFP Blueprint for Australian Organisations covers the design rationale in detail.

This template is intended primarily for vendor platform procurements rather than custom AI development projects. It assumes the organisation has already determined that procuring a platform is the right approach. Organisations still working through that question may find enterprise AI build vs buy considerations a useful prior step.

Why Generic RFP Templates Fail for Enterprise AI

Standard ICT RFP templates are typically designed around software that behaves deterministically. Inputs produce predictable outputs. Features either work or they do not. Pricing is commonly per-seat or per-transaction. Governance is largely a deployment-time activity.

Enterprise AI often exhibits characteristics that differ from this model. Outputs are probabilistic, meaning the same input can produce different outputs at different times or across different model versions. The underlying model can be updated by the vendor without the organisation's input, changing output behaviour across workflows that depended on specific output structures. Commercial exposure scales in ways that are not visible from base pricing. And governance obligations rarely end at deployment. They commonly intensify as usage grows, use cases expand, and regulatory scrutiny increases.

A generic RFP template often approaches enterprise AI as though it were deterministic software. The questions it generates may not surface these differences effectively. A vendor can respond truthfully to every question in a generic template and still be unsuitable for enterprise deployment. Where templates fall short, it is often less about vendor evasiveness and more about questions that were not designed to expose enterprise AI-specific risks. Before engaging the market, it is also worth reviewing what to resolve before vendor evaluation begins, as RFPs issued before operating model clarity is established produce responses that cannot be properly assessed.

Enterprise AI RFP Template: Sections and Structure

One common approach is to organise questions into sections that mirror the evaluation dimensions used to score responses. When sections correspond directly to weighted evaluation criteria, vendor responses tend to map more cleanly to scores. When they do not, evaluation typically requires more interpretation.

The sections below form the core of a practical enterprise AI RFP template. Each section is followed by questions commonly associated with that evaluation dimension.

Section 1: Vendor Profile and Market Context

This section establishes commercial baseline. It is not an evaluation section in itself, but the responses inform due diligence and vendor stability assessment.

Questions to consider:

• Provide an overview of the organisation, including year of founding, ownership structure, and Australian entity status.
• Describe the vendor's enterprise AI product portfolio and identify which product or configuration is being proposed in response to this RFP.
• How many enterprise customers (500+ users) are currently operating the proposed platform in production?
• How many customers are operating the proposed platform within Australian regulated industries (financial services, government, health)?
• Describe any material changes to the product's ownership, investment structure, or strategic direction in the past 24 months.
• Describe the organisation's funding structure and any material dependencies on continued external investment. What is the vendor's primary revenue model for the proposed platform?
• Provide contact details for three enterprise reference customers of similar business size who are willing to be contacted directly.

Distinguishing current capability from roadmap commitments is a common evaluation challenge in enterprise AI procurement.

• Which material capabilities described in the proposal are currently available versus planned for future release?
• Identify any proposed capability that is dependent on future product releases, planned functionality, or services not yet generally available.

Section 2: Functional Capability

Functional requirements in this section are typically assessed against the organisation's defined use cases rather than vendor demonstration materials. Questions in this section commonly name specific use cases and ask vendors to address them directly.

Questions to consider:

• For each of the following use cases [insert organisation's defined use cases], describe how the platform addresses the requirement and provide example outputs generated from representative inputs.
• Describe the platform's approach to context retention across multi-turn interactions. What is the effective context window for document-heavy use cases?
• How does the platform handle conflicting information within a document set or knowledge base?
• What mechanisms exist for reducing hallucinated outputs in document summarisation and retrieval use cases?
• Describe the platform's knowledge integration capability. Does retrieval-augmented generation operate over live data sources, indexed data, or both?
• What administrative controls exist to restrict output type, topic, or format by user group or use case?
• Describe how output quality is maintained or monitored over time, particularly as the underlying model is updated.

Evaluation methodology questions surface how vendors assess and maintain output quality across model versions.

• Describe the methodology used to evaluate model performance and output quality internally.
• What benchmarks, test suites, or evaluation frameworks are used to assess model performance before release?
• Can customers create and maintain their own evaluation datasets or benchmark suites against which platform performance can be measured?

Customer-controlled guardrails become relevant as usage scales and consistent policy enforcement across users becomes a governance consideration.

• Can organisations define custom instructions, policies, or guardrails that apply consistently across users or use cases?
• Can prohibited topics, actions, or output types be restricted centrally rather than at the individual user level?

Section 3: Non-Functional Gating Requirements

In most enterprise AI procurement models, these function as pass/fail questions assessed before weighted evaluation begins. Vendors that cannot meet these requirements typically do not proceed to scoring, regardless of functional capability or commercial attractiveness.

Questions to consider:

• Where is user data processed during inference? Confirm whether processing occurs within Australia, and if not, specify the jurisdiction.
• Where are organisational inputs, outputs, and conversation history stored? Confirm storage jurisdiction and applicable data sovereignty controls.
• Does the vendor use organisational inputs, outputs or interaction data to train or fine-tune foundation models? Describe the terms under which organisational data may be used for model improvement purposes, and what controls or opt-outs are available.
• Does the platform support SAML 2.0 or OIDC-based single sign-on? Which identity providers are natively supported?
• Does the platform support role-based access control at the user group level?
• Describe audit logging capability, including the fields captured per session, retention period, and available export formats.
• What is the platform's committed availability SLA? How is downtime calculated, and what remedies apply for breach?
• Can the organisation export all organisational content, conversation history, and generated artefacts at contract termination? In what format, and at what cost?
• Does the platform hold ISO 27001 certification? Does the Australian entity or the operating entity hold this certification?
• Is the platform certified under the Australian Signals Directorate's Information Security Registered Assessors Program (IRAP), or is such certification underway?

Agent controls are relevant where platforms support or are expected to support agentic or automated workflow execution.

• Does the platform support configurable human approval workflows before AI agents can execute actions in external systems?
• Can autonomous actions be restricted by workflow, user group, or system?
• Are agent actions fully auditable and attributable to an individual user or service account?

Data deletion timelines vary considerably between vendors and are increasingly common in enterprise data governance requirements.

• Can organisational data be permanently deleted upon request or at contract termination?
• What deletion timelines apply across production environments, backups, and disaster recovery environments?

Section 4: Commercial Model and Total Cost of Ownership

The hidden costs in enterprise AI budgets are a commonly observed procurement risk. This section is designed to make those costs visible before commitment.

Questions to consider:

• Describe the complete pricing structure for the proposed configuration, including all components that contribute to total cost.
• Identify every feature, capability, or governance function included in the proposed configuration that would require a tier upgrade or add-on purchase at additional cost.
• Provide a complete cost model for 250, 500, and 1,000 users, incorporating licence costs, usage-based components, integration costs, and support fees.
• What consumption-based cost components apply to the proposed configuration? How are consumption thresholds metered, and what occurs when thresholds are exceeded?
• Describe the conditions under which pricing increases. What advance notice is provided before price changes take effect?
• What are the total costs associated with contract termination at 12, 24, and 36 months, including data extraction, transition assistance, and any penalty provisions?
• Are there minimum commitment requirements for contract term or consumption volume? If so, describe them.
• Do unused consumption credits or seat allocations carry forward between billing periods, or do they expire? Describe any provisions governing unused capacity in the proposed commercial model.
• What is included in the proposed support tier, and what support functions require upgrade or separate purchase?
• What migration or transition assistance is available at contract termination and how is it priced?
• What dependencies would require replacement if the organisation moved away from the platform?
• What data export formats are available and are there any restrictions on the usability of exported data?

Section 5: Architecture and Integration

Questions to consider:

• Provide an architecture diagram showing how data moves from user input through processing, storage, and output generation.
• List all third-party subprocessors involved in delivering the proposed platform, including their function, location, and the data they access.
• Which integrations with [list the organisation's existing platforms] are natively supported? For each, describe the integration type, maturity, and any dependencies.
• Which integrations require third-party tools, middleware, or custom development? Provide an estimate of the implementation effort involved.
• Describe how data is isolated between organisational tenants. Is multi-tenancy at the infrastructure level or the application level?
• What controls exist to prevent organisational data being exposed across tenants?
• Has the vendor experienced any material tenant isolation or data exposure incidents in the past 36 months? If so, describe the nature and resolution of those incidents.
• What does the vendor's model update and deprecation process look like in practice? How much advance notice is provided before a model version is retired?
• Can the organisation pin to a specific model version? If so, for how long, and under what conditions?

Foundation model strategy is an increasingly distinct consideration from platform capability, particularly where platforms support multiple underlying providers.

• Which foundation models are currently supported, and how does the vendor manage changes to the supported model set?
• Can the organisation select between multiple models for different use cases or user groups?
• What occurs if a supported foundation model is retired or materially changed by the underlying model provider?
• Does the platform support a multi-model strategy across multiple foundation model providers?

Rate limiting and capacity behaviour vary considerably between platforms and can affect performance at scale.

• Are any usage, concurrency, throughput, or rate limits applied to the proposed configuration?
• How is platform capacity allocated during periods of high demand, and what recourse is available if capacity constraints affect service quality?

Section 6: Governance and Lifecycle Model

Governance questions are often the weakest section in AI RFPs because most teams have not yet encountered the lifecycle governance failures that make these questions critical. They become critical between 12 and 24 months into deployment, when model updates have altered workflow outputs, when compliance teams are requesting audit data that was not logged at the required granularity, and when the organisation has no contractual mechanism to require a remediation timeline from the vendor.

Questions to consider:

• Describe the vendor's process for notifying customers of model updates. How far in advance is notice provided, and what change information is included?
• What testing or validation is the vendor obligated to perform before a model update is pushed to production? What is the organisation's right to defer an update?
• What audit logging is available at the administrator level? Can the organisation query audit logs, or must they be exported for external analysis?
• Describe the process for reporting and resolving incidents where AI outputs caused material errors, compliance concerns, or reputational impact.
• Does the platform provide tools for monitoring output consistency over time, or detecting degradation in output quality relative to a baseline?
• What product roadmap commitments apply to governance features specifically: audit logging, admin controls, model transparency, and data handling?
• Describe the vendor's incident response obligations, including response time commitments and escalation paths for critical events.

Explainability and traceability are particularly relevant for retrieval-augmented generation deployments and use cases where output provenance affects trust or compliance.

• Can users identify the sources used to generate a response?
• Can administrators review the retrieved content or evidence used in generating specific outputs?

Agentic AI governance is an emerging area as platforms extend beyond conversational interfaces into workflow execution and system integration.

• What controls exist to govern AI agents performing actions within enterprise systems?
• Can approval requirements be configured to gate agent actions before they are executed?
• How are agent decisions, actions, and outcomes logged for audit purposes?
• What controls exist to prevent unintended or recursive workflow execution?

Output ownership and intellectual property considerations are increasingly common in enterprise AI legal reviews.

• Who owns the outputs generated using the platform?
• Does the vendor claim any rights over outputs generated by the organisation?
• What contractual protections apply regarding third-party intellectual property claims relating to generated outputs?

Section 7: Australian-Specific Requirements

This section covers considerations that are specific to Australian enterprise deployment and are less likely to be addressed adequately by a standard global vendor response. These questions commonly surface gaps in vendor readiness for the Australian market.

Questions to consider:

• Does the platform process and store all data within Australia? If not, which jurisdictions apply and what contractual controls govern data flows to those jurisdictions?
• Describe how the platform supports compliance with the Australian Privacy Principles (APPs) under the Privacy Act 1988 (Cth). Specifically address APP 8 (cross-border disclosure) and APP 11 (security of personal information).
• If the organisation operates in a regulated sector (health, financial services, government), describe the platform's capability to support sector-specific compliance obligations including [insert relevant obligations].
• Is the vendor listed on any applicable Australian Government procurement panels, including the Digital Marketplace or relevant state-based panels? Provide panel details.
• What Australian-based support resources are available? Describe the support model, including the location of support staff and response time commitments during Australian business hours.
• Does the vendor have an Australian entity capable of executing contract terms under Australian law?
• Has the platform been assessed under any Australian Government security frameworks, including IRAP? Provide details of the assessment scope and outcome.
• How does the vendor formalise Australian data sovereignty and APP compliance obligations? Describe whether these are reflected as contractual terms, policy statements, or other mechanisms, and how they are enforced.

Section 8: Pilot and Proof of Concept

Pilot support varies considerably across vendors and is often not addressed in RFP responses unless specifically asked. Including these questions surfaces practical differences in how vendors approach evaluation engagements, what resources the organisation is expected to contribute, and what evidence the pilot is designed to generate.

Questions to consider:

• What pilot or proof-of-concept support is included in the proposed engagement, and are there any cost implications?
• What resources are typically required from the customer organisation during a pilot?
• How does the vendor recommend measuring pilot success, and what baseline or benchmarking approach is proposed?
• What data sets or content can be used during a pilot, and what restrictions apply to the use of production data in a testing environment?
• What artefacts or reporting will be provided at pilot completion, and in what format?
• What specific, measurable outcomes does the vendor propose as evidence of pilot success?
• What baseline measures should be established prior to pilot commencement to enable meaningful comparison of outcomes?

Non-Functional Gating: What It Means in Practice

The non-functional gating section above is not a weighted evaluation dimension. In most procurement models, it functions as a filter that operates before evaluation begins. A common approach is to structure gating questions so vendors are asked to respond with a clear yes or no, supported by evidence. Many organisations treat vendor responses that address gating questions with qualified commitments, roadmap references, or policy statements rather than current capability as insufficient to satisfy the requirement.

The practical test is this: if a vendor cannot meet a gating requirement at the time the RFP is issued, can the organisation wait for them to meet it? In most cases, the answer is no. Contractual commitments to future capability are generally not treated as equivalent to current capability in a well-structured procurement process. If data residency in Australia is a requirement, a vendor whose Australian-region hosting is a roadmap item would not typically satisfy that requirement. If audit logging is required at session level, a vendor whose audit logging operates at the platform level would not typically satisfy that requirement.

In many cases, organisations that soften gating criteria because a vendor is otherwise attractive may be deferring a governance issue that is likely to surface later in deployment. The Enterprise AI Vendor Evaluation Scorecard sets out how gating criteria interact with the weighted scorecard process.

Evaluation Weighting Guidance

Once non-functional gating has been applied and compliant vendors identified, weighted evaluation typically follows. Weighting is commonly set before vendor responses are reviewed. Weighting set after responses are reviewed tends to reflect the vendor the organisation is already inclined to select.

Weighting approaches vary considerably between organisations. Industry, regulatory obligations, risk appetite, deployment objectives and organisational priorities can all influence how evaluation criteria are weighted. The ranges below illustrate one possible approach rather than a universal model.

Indicative weights for an Australian enterprise AI procurement:

• Functional capability: 20–25%. Shortlisted vendors typically meet minimum functional requirements. Differentiation on functional grounds is narrower than organisations expect.
• Governance capability: 25–30% in some enterprise AI procurement models. This is often one of the dimensions most closely associated with post-deployment governance and operational challenges. Governance gaps can be expensive and difficult to remediate after contract signature.
• Commercial model and total cost of ownership: 20–25%. This dimension tends to be more informative when assessed against full cost exposure, including consumption-based components and exit costs, rather than headline licence pricing.
• Architecture and integration fit: 15–20%. The questions in this section reveal lock-in risk, integration complexity, and lifecycle sustainability.
• Australian context and compliance: 10–15%. Organisations in regulated industries and government commonly weight this dimension more heavily. The questions in Section 7 are specifically designed to differentiate vendors on this dimension.
• Vendor stability and support: 5–10%. Useful for due diligence, but difficult to assess objectively from vendor responses alone. Reference checks are more informative than RFP responses for this dimension.

Organisations with higher regulatory exposure, particularly in financial services, health, or Commonwealth Government, commonly move weight from functional capability toward governance and Australian context. Organisations in early-stage enterprise AI programmes with less regulatory pressure can weight functional capability and commercial model more heavily.

Common Mistakes When Issuing an Enterprise AI RFP

Certain patterns appear consistently across enterprise AI procurement processes. Recognising them before the RFP is issued tends to be less costly than encountering them during evaluation.

Omitting non-functional gating entirely. Without a gating section, the evaluation proceeds with all vendors regardless of whether they meet mandatory requirements. Organisations commonly encounter disqualifying gaps after significant evaluation effort has been invested, often after a preferred vendor has already been identified.

Accepting policy statements as evidence. Vendor responses to governance and compliance questions frequently cite internal policies, certifications in progress, or general platform commitments rather than demonstrating current capability. RFPs that address this pattern typically specify that evidence requirements apply: not a statement that data is stored in Australia, but a contractual commitment to that effect, and documentation of the infrastructure that supports it.

Not requiring scenario-based cost modelling. A vendor proposal that provides only per-seat base pricing may not provide sufficient visibility into full commercial exposure. Without modelling what costs look like at 500 users, at 1,000 users, and with consumption-based components at projected usage, comparing proposals on a like-for-like basis is difficult. RFPs that address this pattern often request multi-scenario cost models in a specified format to enable comparison.

Treating functional evaluation as the primary differentiator. Functional capability is commonly necessary but not sufficient as a primary differentiator. The vendors who make a shortlist generally have sufficient functional capability to address the use cases in scope. The evaluation differentiates on governance, commercial model, and lifecycle risk. Some procurement practitioners would argue that organisations weighting functional capability at 50% or more may be over-indexing on the dimension least likely to determine long-term success.

Allowing vendor-led reframing of use cases. Some vendors respond to an RFP by reinterpreting the stated use cases to fit their platform's strengths, rather than responding to the use cases as defined. This is a pattern to watch for in evaluation. An RFP that defines use cases with operational specificity, including context, volume, and output requirements, makes it harder for vendors to reframe. Use cases described in vague terms invite reinterpretation.

Not including lifecycle questions. Questions about model updates, deprecation notice, drift monitoring, and change notification practices are absent from most AI RFPs. These questions tend to become significant 12 to 24 months after deployment. Including them gives the organisation an opportunity to assess and compare vendor practices before commitment, and to surface commercial expectations at contract stage.

Using the wrong response format. An RFP that asks open-ended questions without specifying response format produces responses that cannot be compared. Where structured information is needed, RFPs that address this commonly specify a response format: a table for pricing components, a specific architecture diagram format, a structured response to each use case. Comparable responses tend to make evaluation faster and more defensible.

Governance Questions and the Lifecycle Dimension

The governance and lifecycle section is worth examining separately, as it is the section most commonly thinned out or omitted in practice. The reasoning, usually unstated, is that governance questions feel theoretical at procurement stage. The platform has not been deployed yet. There are no governance incidents to point to. The governance questions can be addressed at implementation.

This reasoning tends to have a recognisable outcome. Organisations that defer governance questions to implementation commonly find that the vendor's audit logging does not capture the data the compliance team needs. That model updates are rolled out without adequate notice, breaking downstream workflows. That there is no contractual obligation on the vendor to provide advance notice of deprecations. That the admin controls are less granular than the platform's sales materials implied.

These issues rarely surface at procurement stage. They tend to emerge in the second year of operation, when the platform is more embedded, switching costs are higher, and the organisation's negotiating position is more limited.

The governance questions in Section 6 above exist to surface these issues before commitment. A vendor with genuinely strong governance capability tends to answer them directly and with specificity. A vendor whose governance capability is weaker than its marketing implies tends to provide qualified answers, reference future roadmap items, or redirect to policy documents. The answers commonly distinguish the two.

The Enterprise AI Governance Operating Model sets out the governance structure that these RFP questions are designed to support.

Using This Template

The questions in this article are structured to be adapted directly into an RFP. Procurement teams can draw from each section and incorporate questions into their document, removing or adapting questions that do not apply to the organisation's context and adding use-case-specific questions to the functional capability section.

A common approach is to instruct vendors to answer gating questions with explicit yes or no responses, supported by evidence, with the understanding that failure to meet gating criteria results in removal from the process. This instruction, stated clearly in the RFP, reduces the likelihood that vendors provide qualified responses that imply compliance without confirming it.

The Australian-specific questions in Section 7 may be treated as mandatory requirements in regulated Australian environments, depending on the organisation's operating context, risk profile and regulatory obligations. Vendors operating primarily in US or European markets often provide standard global responses to data residency and compliance questions. Section 7 is structured to draw out Australian-specific commitments rather than global policy statements.

An enterprise AI RFP built around questions of this nature is more likely to produce vendor responses that can be compared, scored, and assessed consistently. That is the practical standard the template is designed to meet.

This article provides general commercial and procurement commentary only and does not constitute legal, financial, or professional advice.