Selecting a data and analytics solution in the age of AI requires evaluating vendors across seven critical areas: company viability, data foundation, AI capabilities, analytics features, security posture, implementation approach, and commercial terms.
This guide provides a structured framework and downloadable question template to help you run an effective procurement and evaluation process.
Download the free RFP question template →
Why your approach to analytics RFPs needs an update
AI is changing how you buy data and analytics solutions. Let’s take for example spend analytics software. Three years ago, the primary question was whether a vendor could classify spend and produce dashboards. Today, the question is whether a platform can serve as the foundation for AI-driven procurement transformation.
According to the 2025 Global CPO Survey from EY, 80% of global CPOs plan to deploy AI in procurement over the next three years. Yet 65% of procurement leaders cite poor data quality as their biggest barrier to scaling AI, according to HFS Research. This creates a critical evaluation challenge: you need to assess not just today's analytics capabilities, but whether a platform can support the AI-native operating model procurement is moving toward.
This guide draws on our experience as the leading procurement intelligence platform for enterprise procurement transformation. We've participated in hundreds of enterprise RFPs and have seen what separates effective evaluations from those that lead to disappointing implementations. The goal here isn't to steer you toward any particular vendor. It's to help you ask the right questions so you can make the best decision for your organization.
Key preparation steps for analytics RFPs
Define your scope and objectives
Procurement analytics solutions vary significantly in their focus areas. Some vendors specialize in spend visibility and classification. Others emphasize sourcing optimization, contract analytics, or supplier risk management. A growing category of AI-native platforms aims to unify these capabilities around a common data foundation.
Before drafting questions, clarify what problem you're solving:
- Visibility gap: You lack a clear, trusted view of enterprise spend across business units and systems
- Savings identification: You need to systematically identify and capture cost reduction opportunities
- Risk management: Supplier risk, compliance gaps, or contract leakage are primary concerns
- Operational efficiency: Manual data work consumes too much of your team's time
- AI enablement: You want to build toward autonomous, AI-assisted procurement operations
Your primary objective will shape how you weight different evaluation criteria. A team focused on basic spend visibility has different priorities than one preparing for AI-native operations.
Align internal stakeholders
The most successful RFPs involve cross-functional alignment before questions go to vendors. At minimum, include perspectives from:
- Procurement leadership: Strategic priorities, success metrics, and change management capacity
- IT/Data teams: Integration requirements, security standards, and technical constraints
- Finance: Reporting needs, audit requirements, and ROI expectations
- End users: Usability requirements and adoption considerations
Internal alignment prevents two common problems: RFPs that ask for capabilities nobody actually needs, and evaluations that overlook requirements that surface late and derail projects.
Gather background information for vendors
Vendors can provide more accurate and useful responses when they understand your context. This context helps vendors tailor their responses rather than providing generic capability descriptions.
Essential Information for Procurement RFPs
| Information Category |
Details to Provide |
| ERP landscape |
Systems, versions, regions, planned changes |
| Data volumes |
Annual spend, invoice/PO counts, supplier count |
| Current tools |
Existing analytics, P2P, sourcing systems |
| Organizational scope |
Business units, regions, user counts by role |
| Timeline |
Decision date, desired go-live, budget cycle |
| Key challenges |
Specific pain points driving the initiative |
Seven Pillars Content
The seven pillars of a modern analytics RFP
Company and Experience
Understanding a vendor's background helps assess their ability to deliver and support the solution long-term, but avoid over-indexing on company size or brand recognition. Some of the most innovative procurement technology comes from focused, well-capitalized specialists rather than legacy suite providers.
Financial Stability & Market Position
A vendor's funding, growth trajectory, and market positioning reveal their staying power. Are they venture-backed with strong investor support? How do they position themselves: as a point solution, part of a suite, or a platform play?
Ask: "What's your funding situation? How are you positioned in the market relative to traditional suite providers?"
Look for: Clear answers about capitalization, growth plans, and strategic direction. Confidence in their market position, not defensive comparisons to competitors.
Customer Base & References
The best predictor of your success is their track record with organizations similar to yours in size, industry, and complexity.
Ask: "Can you provide three references from customers with similar complexity to ours? May I contact them directly?"
Red flag: Vendors who only offer curated case studies or "pre-approved" reference calls. You want to speak with actual users, not marketing contacts.
Product Vision & Roadmap
Where the product is heading, particularly regarding AI and automation, matters as much as current capabilities. You're not just buying today's features; you're buying into a multi-year partnership.
Ask: "What's your product roadmap for the next 12-24 months? How do you incorporate customer feedback into development priorities?"
Look for: Specific plans with clear milestones, not vague promises about "AI innovation." Customer advisory boards and transparent roadmap communication indicate they listen to users.
Data Foundation
The critical question: Can this platform serve as a governed, AI-ready data foundation, or is it primarily a reporting layer on top of messy underlying data?
This distinction determines long-term success. A platform's analytics and AI capabilities are only as good as the data foundation underneath them.
Data Extraction & Integration
Modern enterprises have complex, multi-ERP environments. The platform must connect to all your systems (SAP, Oracle, NetSuite, legacy databases) and handle different schemas, currencies, and data models without requiring expensive custom integration work.
Ask: "Show me how you've handled multi-ERP environments similar to ours. What extraction methods do you use? How do you handle systems with different regional configurations?"
Look for: Pre-built connectors for your specific ERP versions, proven track record with complex environments, and clear explanation of how they maintain data sync as systems change.
Data Harmonization
This is where most platforms fail. Matching supplier records across systems is notoriously difficult because of naming variations, duplicates, and complex corporate hierarchies. The difference between 70% and 95% accuracy is massive in practice.
Ask: "What accuracy rates do you achieve for supplier matching? Can you demonstrate how you handle parent-child supplier relationships and M&A scenarios?"
Look for: Vendors who quote 95%+ automated matching accuracy with specific examples from customers similar to your complexity. Ask for before/after data quality metrics from recent implementations.
Red flag: Vague claims about "industry-leading accuracy" without specific percentages, or vendors who suggest you'll need to do extensive manual matching work.
Data Classification
Taxonomies are critical for spend analysis, but they need to evolve with your business. Many platforms start strong but degrade as your data changes (new products appear, categories shift, suppliers are acquired).
Ask: "What taxonomies do you support? How does your classification approach improve over time? What coverage rates should we expect at go-live versus 12 months later?"
Look for: Machine learning-based classification that gets smarter with use, not static rule-based systems. Ask how they handle new categories and ensure classification doesn't decay over time.
Data Quality & Governance
This is where analytics projects live or die. A one-time cleanse during implementation isn't enough. The platform should identify quality issues proactively, maintain data lineage, and enforce governance policies consistently.
Ask: "How do you identify quality issues proactively? What governance controls exist? How do you maintain data lineage for compliance and debugging?"
Look for: Continuous monitoring, automated quality checks, clear audit trails, and role-based governance controls. The system should flag issues before they impact decisions.
Key Takeaway
The best decision intelligence platforms don't just report on your data. They transform it into a unified, governed foundation that becomes more accurate over time. This is what makes AI and advanced analytics actually work in production, not just in demos.
AI and Automation Capabilities
AI in procurement has moved from marketing buzzword to practical capability. Your RFP should distinguish between different types of AI and assess their enterprise readiness, not just accept vendor claims at face value.
AI Strategy & Architecture
How AI is embedded in the platform reveals whether it's fundamental to the product or a bolt-on feature that will always feel like an afterthought.
Ask: "Is AI a bolt-on feature or fundamental to how the product works? What types of AI/ML technologies do you employ? Can you show me the architecture?"
Look for: AI capabilities built into the core platform architecture, not separate modules. Specific technical details about models used (LLMs, ML, rule-based systems) and how they work together.
AI Agents & Autonomy
The emerging category of "agentic AI" refers to systems that can autonomously complete tasks, not just provide recommendations. This is fundamentally different from chat-based copilots that break after one prompt.
Ask: "What tasks can your AI agents actually perform end-to-end? What guardrails prevent unintended actions? How do agents maintain enterprise context across complex workflows?"
Look for: Specific examples of autonomous workflows: "The agent identifies a maverick purchase, checks policy, finds the preferred supplier, calculates savings opportunity, and creates a recommended action, all without human intervention until approval."
Red flag: Vendors who claim "agent capabilities" but can only demonstrate chatbots or simple automation. Real agents complete multi-step workflows with decision-making, not just answer questions.
Explainability & Trust
Any AI making or influencing procurement decisions must be explainable. Users should be able to trace the data and logic behind every recommendation. This isn't optional; it's essential for enterprise adoption.
Ask: "How do you prevent and detect AI hallucinations? Can users trace the data sources and logic behind recommendations? What happens when the AI is uncertain?"
Look for: Clear lineage from recommendation back to source data, confidence scores that help users understand reliability, and graceful degradation when the AI lacks sufficient context.
Closed-Loop Automation
The highest-value AI doesn't just surface insights. It connects them to execution. Can the platform track opportunities from identification through action to realized outcomes?
Ask: "Can you demonstrate a complete loop: insight generation, recommended action, execution, and outcome tracking? How do you connect to our existing procurement workflows?"
Look for: Bi-directional integration with your P2P, sourcing, and contract systems. The ability to not just recommend "consolidate suppliers" but to track the RFP, award, and realized savings.
AI Governance & Security
This deserves particular attention as AI-specific risks emerge. How customer data is used in training, how models are updated, and how sensitive information is protected all require scrutiny.
Ask: "Is our data used to train shared AI models? What controls prevent sensitive data exposure in AI outputs? How are models validated before deployment?"
Red flag: Vendors who can't clearly explain their AI data policies or who require your data to train models that benefit other customers. Your competitive intelligence shouldn't improve their other clients' results.
Key Takeaway
Enterprise-ready AI requires more than impressive demos. Look for explainable recommendations grounded in your data, agents that complete real workflows with appropriate guardrails, and closed-loop systems that track from insight to measurable outcome.
Analytics and Insights
Traditional analytics capabilities remain important even as AI features get more attention. The most powerful analytics are worthless if people don't actually use them.
Actionable Analytics
Standard dashboards, drill-down capabilities, trend analysis, and savings identification should be table stakes. But surface-level dashboards aren't enough for procurement teams making million-dollar decisions.
Ask: "Can users get to transaction-level detail from executive dashboards? How flexible is ad-hoc analysis? Can I see a power user working in the system, not just executive dashboards?"
Look for: Seamless drill-down from summary to transaction detail, ability to slice data by multiple dimensions simultaneously, and fast query performance even on large datasets.
Reporting & Visualization
Evaluate the balance between out-of-the-box capability and customization flexibility. Some platforms offer limited pre-built reports; others require IT involvement for any changes.
Ask: "What comes out of the box versus requires configuration? Can business users create their own reports, or is IT involvement required? How does the platform integrate with our existing BI tools?"
Look for: Comprehensive pre-built reports that address 80% of common needs, plus self-service tools for business users to create custom views without waiting for IT.
External Data Sources & Intelligence
Procurement intelligence increasingly requires combining your internal data with external sources: supplier risk scores, market benchmarks, ESG ratings, diversity certifications.
Ask: "Which external data capabilities are native versus require additional modules? How do you incorporate third-party risk data, market intelligence, and compliance databases?"
Red flag: Platforms that claim broad external data coverage but charge separately for each source, or that require manual data imports rather than automated enrichment.
Usability
User adoption determines success more than feature completeness. Evaluate the experience for different personas: power analysts who need deep functionality, occasional users who want quick answers, and executives who need key metrics at a glance.
Ask: "Can I see different user types working in the system? What does adoption typically look like in the first 90 days? What training and enablement do you provide?"
Look for: Intuitive navigation, consistent UI patterns, contextual help, and mobile access. Ask about typical adoption rates and what they do to drive usage beyond initial training.
Key Takeaway
Analytics must balance power-user depth with casual-user simplicity. The best platforms make sophisticated analysis accessible to procurement professionals, not just data analysts, while maintaining the flexibility experts need.
Security and Compliance
Security requirements have become more complex as AI capabilities expand. Your RFP should address both traditional security concerns and AI-specific considerations that many organizations miss.
Certifications & Assessments
SOC 2 Type II and relevant industry certifications should be baseline requirements, but don't just accept checkbox claims.
Ask: "Can you provide your most recent SOC 2 Type II report? What other security certifications do you maintain? How often are you audited?"
Look for: Actual audit reports, not just certification logos. Recent assessments (within 12 months) and willingness to share detailed findings with prospects under NDA.
Data Protection
Understanding where data lives, how it's protected, and what happens at contract termination prevents nasty surprises later.
Ask: "Where is our data stored geographically? How is it encrypted in transit and at rest? How is customer data isolated in multi-tenant environments? What's the process for data return or deletion at contract end?"
Look for: Clear data residency options that meet your regulatory requirements, encryption standards (AES-256 or better), logical data isolation with regular penetration testing, and contractual guarantees for data portability.
AI-Specific Security
This is a newer area that many RFPs miss entirely. AI systems introduce unique security challenges: prompt injection attacks, data leakage through model outputs, and training data contamination.
Ask: "How do you secure AI models against prompt injection and adversarial attacks? What prevents sensitive data from appearing in AI outputs? Is our data used to train shared models?"
Red flag: Vendors who don't have clear answers about AI security, or who can't explain how they prevent data leakage between customers in shared AI systems.
Privacy & Compliance
GDPR, CCPA, and industry-specific regulations all have implications for procurement data, which inevitably contains personal information about employees, suppliers, and business partners.
Ask: "How do you handle GDPR/CCPA compliance? What controls exist for personal data in procurement transactions? Can we configure data retention policies to meet our requirements?"
Look for: Built-in privacy controls, configurable retention policies, data subject access request (DSAR) capabilities, and clear DPA (Data Processing Agreement) terms.
Key Takeaway
Security isn't just about certifications. It's about architecture, processes, and transparency. Modern platforms must address AI-specific security challenges alongside traditional concerns, with clear contractual protections for your data.
Implementation and Support
Implementation approach often determines whether a project succeeds or fails. The difference between a 90-day and 9-month implementation isn't just speed. It's methodology, experience, and realistic expectations.
Methodology & Timeline
What does a typical implementation actually look like? What can realistically be achieved in 30, 60, and 90 days? Beware of vendors who promise everything quickly. Data quality work takes time.
Ask: "Walk me through a typical implementation timeline for an organization like ours. What are the major milestones? What's your track record for on-time delivery?"
Look for: Phased approaches that deliver value incrementally: initial data foundation (weeks 1-4), core analytics (weeks 5-8), advanced features and AI agents (weeks 9-12). References who actually went live in the promised timeframe.
Red flag: Vendors who promise "30-day implementations" for complex enterprises, or who can't articulate what happens in each phase. Real data quality work requires iteration.
Resource Requirements
Underestimating internal effort is a common cause of project delays and disappointment. Understanding what the implementation requires from your team prevents surprises.
Ask: "What will this require from our team? How many hours per week for how many people? What expertise do we need internally versus what you provide?"
Look for: Honest estimates that account for data access, stakeholder alignment, and business rule definition. Vendors should distinguish between "must have" and "nice to have" internal resources.
Data Onboarding
How they handle initial data validation and quality assurance reveals their maturity. This is where implementations often stall.
Ask: "How do you approach data validation? What accuracy guarantees exist at go-live? How do you handle issues discovered during data profiling?"
Look for: Structured data profiling and quality assessment, clear success criteria for go-live (e.g., "95% supplier match accuracy"), and processes for continuous improvement post-launch, not just one-time cleansing.
Ongoing Support
Understand the support model clearly. What happens after go-live determines long-term value realization.
Ask: "What are your SLAs for different issue severities? Do we get a dedicated customer success manager or ticket-based support? How are data refreshes and ongoing maintenance handled?"
Look for: Named customer success resources (not just shared pools), clear escalation paths, proactive health monitoring, and regular business reviews to optimize value, not just reactive break-fix support.
Key Takeaway
Fast implementations mean nothing if they don't deliver quality. Look for vendors with repeatable methodologies, realistic timelines, and support models that drive continuous improvement, not just initial deployment.
Commercial Terms
Pricing and contract terms deserve careful attention, particularly given the rapid evolution of AI capabilities. The cheapest option rarely delivers the best value.
Pricing Model
Understand exactly what you're paying for and how costs will evolve as you grow or add capabilities.
Ask: "What's included in the base subscription? Is pricing per user, per spend volume, per module? How does pricing change as we scale or add new regions?"
Look for: Transparent, predictable pricing models that align with your growth plans. Avoid models where adding users or capabilities triggers major cost jumps that create adoption barriers.
Total Cost of Ownership
Base subscription is only part of the picture. Implementation, training, integrations, premium support, and AI features may all carry additional costs.
Ask: "Beyond the subscription fee, what other costs should we budget for? Implementation? Training? Integrations? Premium features? What's the all-in cost for year one versus ongoing?"
Red flag: Vendors with low subscription prices but high implementation fees, or who nickel-and-dime for basic features. Ask for a complete 3-year TCO estimate including all anticipated costs.
Contract Flexibility
What are the terms for pilots or proofs of concept? What termination provisions exist? How does data portability work if you decide to leave?
Ask: "Can we start with a pilot before committing to a multi-year contract? What are the termination terms? How do you handle data export if we don't renew?"
Look for: Pilot programs with clear success criteria and path to full deployment, reasonable termination provisions (not 90-day+ notice periods), and contractual guarantees for data portability in standard formats.
Value Alignment
Some vendors offer outcome-based pricing components or value guarantees. These can align incentives but also add complexity.
Ask: "Do you offer any outcome-based pricing or value guarantees? What performance metrics do you typically commit to?"
Look for: Realistic guarantees tied to measurable outcomes (e.g., "95% data accuracy at go-live" or "identify $X in savings opportunities within 90 days"). Avoid vague promises that can't be verified.
Key Takeaway
Price is what you pay; value is what you get. Focus on total cost of ownership, contract flexibility, and alignment of vendor incentives with your success, not just the lowest subscription fee.
How to structure your RFP document
An effective RFP includes several components beyond the questions themselves:
Background and context
Provide vendors with enough information to respond meaningfully:
- Your organization's profile and industry
- Current procurement operations and technology landscape
- Strategic objectives driving this initiative
- Scope of the engagement (regions, business units, spend categories)
- Timeline and decision process
Clear Instructions
Specify exactly what you want in responses:
- Response format (spreadsheet, document, specific templates)
- Page or word limits for narrative responses
- Required attachments (SOC reports, case studies, pricing)
- Deadline for submissions
- Contact for clarification questions
- Process for demos and follow-up
Key evaluation criteria
Consider sharing how you'll evaluate responses. This helps vendors understand your priorities and provide relevant information. You can adjust weights based on your specific priorities. An organization prioritizing AI enablement might weight Data Foundation and AI Capabilities even higher.
RFP Evaluation Criteria
25%
20%
15%
15%
10%
10%
5%
Analytics & Insights – 15%
Security & Compliance – 15%
Company & Experience – 10%
Implementation & Support – 10%
Adjust based on your priorities: Organizations prioritizing AI enablement might weight Data Foundation and AI Capabilities even higher, while those with established data infrastructure may emphasize Analytics & Insights.
Question design best practices
The quality of your questions determines the quality of vendor responses. A few principles:
- Be specific rather than open-ended: "Describe your solution" invites marketing fluff. "Can you extract data from multiple ERP systems within our organization? Describe the technical approach and any limitations" gets useful information.
- Request evidence, not just claims: Instead of asking if they have "strong" classification accuracy, ask for specific rates and how they're measured. Request customer references, case studies, and proof points.
- Distinguish must-haves from nice-to-haves: If a capability is truly required, say so. This helps vendors self-select and focuses evaluation on what matters.
- Anticipate the AI hype: Every vendor now claims AI capabilities. Your questions should probe beneath the surface: What specific workflows use AI? What training data is involved? What accuracy rates can you document?
- Include scenario-based questions: Present a realistic scenario from your environment and ask how the vendor would address it. This reveals practical capability better than feature checklists.
How to run an effective analytics supplier evaluation
Initial screening
Use initial responses to narrow the field. Look for:
- Responsive, thoughtful answers (not generic marketing copy)
- Evidence supporting claims
- Realistic acknowledgment of limitations
- Alignment with your stated requirements
Consider eliminating vendors who clearly can't meet must-have requirements before investing in detailed evaluation.
Demonstrations
Live demos should go beyond the standard sales presentation:
- Ask to see specific scenarios relevant to your use cases
- Request demos in a sandbox environment with realistic data
- Include end users in demo evaluation, not just project leads
- Probe edge cases and exception handling, not just happy paths
Reference checks
References are valuable but require the right approach:
- Ask for references from organizations similar to yours
- Request references specifically for the capabilities most important to you
- Prepare specific questions rather than relying on open-ended conversation
- Ask about challenges and lessons learned, not just successes
Proof of concept
For significant investments, consider a paid proof of concept with real data. This reveals implementation reality better than any demo or reference call. Define clear success criteria upfront.
Common data and analytics RFP mistakes to avoid
Copying a generic template without customization: RFP questions should reflect your specific requirements, not a one-size-fits-all checklist.
Asking for too much: A 500-question RFP exhausts vendors and evaluators alike. Focus on what actually matters for your decision.
Ignoring data foundation questions: Fancy analytics and AI features are meaningless without quality underlying data. Many organizations learn this the hard way.
Failing to assess AI governance: As AI capabilities become central to procurement platforms, security, explainability, and governance become critical evaluation criteria.
Underweighting implementation and support: The best technology poorly implemented delivers worse results than adequate technology well implemented.
Choosing based on demo polish rather than substance: Some vendors excel at demos but struggle with complex real-world implementations. Probe beneath the surface.
Download the free analytics and data RFP template
We've compiled a comprehensive RFP question template covering all seven evaluation pillars discussed in this guide. The template includes over 120 questions organized by category, with columns for vendor responses and scoring.
What's included:
- Instructions tab: Guidance on customizing and using the template
- Company & Experience: 15 questions on vendor background and market position
- Data Foundation: 23 questions on extraction, integration, classification, and governance
- AI & Automation: 23 questions on AI strategy, agents, explainability, and closed-loop automation
- Analytics & Insights: 23 questions on spend analytics, reporting, and usability
- Security & Compliance: 23 questions on certifications, data protection, and AI security
- Implementation & Support: 22 questions on methodology, training, and ongoing support
- Commercial Terms: 16 questions on pricing, TCO, and contract terms
- Scoring Summary: Template for comparing vendors across weighted criteria
The template is designed to be customized for your specific needs. Remove questions that don't apply, add questions specific to your requirements, and adjust weights based on your priorities.
Bottom line on data and analytics RFPs
Selecting a procurement data and analytics platform is a significant decision with long-term implications. The shift toward AI-native procurement operations makes the underlying data foundation more important than ever. A platform that can't deliver governed, trustworthy data won't be able to support the AI capabilities procurement teams will need in the coming years.
Use this guide and the accompanying template to structure a thorough evaluation. Focus on substance over marketing claims, evidence over promises, and implementation reality over demo polish. The extra effort in the evaluation process pays dividends in avoiding costly mistakes and selecting a platform that will serve your organization well as procurement continues to evolve.
This guide was created by Suplari, the Procurement Intelligence Platform Built for AI. For more resources on procurement transformation, visit suplari.com.
