AI Procurement: What to Ask Vendors Before You Buy

Before buying an AI solution, ask vendors critical questions across four areas. (1) Data & Security: How will our data be used, stored, and protected? Is it used to train your models? (2) Performance & Accuracy: Can you provide case studies and transparent performance metrics for our specific use case? (3) Ethics & Bias: What steps have you taken to audit and mitigate bias in your model? (4) Integration & Support: What is the real implementation timeline and what level of ongoing support do you provide? The most important question: "Do you use our proprietary data to improve models that you sell to our competitors?"

The AI vendor landscape resembles a gold rush. Everyone claims to have AI-powered solutions. Marketing materials overflow with promises of transformation, efficiency, and competitive advantage. But beneath the polished demos and impressive case studies lie critical questions that determine whether a vendor partnership will deliver value or disappointment.

Effective AI procurement goes beyond comparing features and prices. It requires understanding how vendors handle your data, whether their solutions truly fit your needs, and what hidden costs await after signing. The questions you ask before buying shape the relationship's success for years to come.

Beyond the Sales Pitch: The Due Diligence Checklist

Vendor presentations follow predictable patterns. They showcase best-case scenarios, emphasize successful implementations, and gloss over limitations. Your job is to dig deeper, asking uncomfortable questions that reveal the reality behind the marketing.

Due diligence for AI vendors differs from traditional software procurement. AI systems learn from data, making data handling practices crucial. They produce probabilistic outputs, requiring transparency about accuracy and errors. They evolve over time, necessitating clear agreements about updates and improvements. Traditional procurement checklists miss these AI-specific considerations.

The most revealing questions often receive evasive answers. When vendors deflect or provide vague responses, consider it a red flag. Legitimate vendors welcome detailed technical discussions because they've thought through these issues. Those relying on AI hype rather than substance struggle with specifics.

Remember that vendor relationships extend far beyond initial implementation. The questions you ask should probe not just current capabilities but future partnership dynamics. How will the vendor support your growing needs? What happens when problems arise? Who owns the innovations that emerge from using your data? These long-term considerations often matter more than initial functionality.

Category 1: Data Usage, Privacy, and Security

Where Is My Data Stored? Who Owns the Outputs?

Data location matters for regulatory compliance, security, and sovereignty. Many organizations discover too late that their vendor processes data in jurisdictions with different privacy laws or stores it in shared environments with inadequate isolation.

Ask specifically about data residency throughout the entire pipeline. Where is data stored at rest? Which countries does it transit during processing? Can you restrict processing to specific geographic regions? For organizations in regulated industries or those handling European citizen data under GDPR, these aren't theoretical concerns but compliance requirements.

Output ownership proves equally crucial yet often remains ambiguous in contracts. When an AI system trained on your data generates insights, who owns those insights? If the AI creates new intellectual property - perhaps innovative product designs or novel drug compounds - who holds the rights? Clear agreements prevent future disputes and ensure you capture value from your data.

Some vendors claim that aggregated insights from multiple customers improve their AI's performance. While this collaborative learning can add value, understand exactly what information gets shared. Does the vendor use your competitive data to improve services for your rivals? Can you opt out of collaborative learning while maintaining service quality?

Crucial Question: Do You Use My Proprietary Data to Train Your Foundation Model?

This question cuts to the heart of AI vendor relationships. Many vendors build their AI capabilities by training on customer data. Your proprietary information might be improving their models, which they then sell to your competitors.

Foundation models - the large AI systems underlying many vendor solutions - require massive amounts of training data. Vendors have strong incentives to use customer data for model improvement. But your strategic data shouldn't become their product enhancement without your explicit consent and fair value exchange.

Demand clear, written guarantees about data usage. Acceptable approaches include using your data only to improve services specifically for you, anonymizing and aggregating data with explicit permission, or offering reduced pricing in exchange for data usage rights. Unacceptable practices include using your data without consent, training models that benefit competitors, or vague policies that could change without notice.

Watch for contract language that grants vendors broad rights to "improve services" or "enhance product capabilities." These innocent-sounding phrases might authorize extensive data usage. Insist on specific limitations and audit rights to verify compliance.

Category 2: Model Performance and Reliability

How Do You Measure and Prove ROI for Clients Like Us?

Generic case studies impress executives but don't predict your success. Vendors should demonstrate ROI using metrics relevant to your industry, use cases similar to yours, and organizations of comparable size and complexity.

Probe beyond headline numbers. A vendor claiming "90% accuracy" should explain what that means. Accuracy on what tasks? Measured how? Under what conditions? What about the 10% of cases where it's wrong - what happens then? Understanding the full performance picture prevents disappointment when reality doesn't match marketing claims.

Request references from organizations similar to yours, not just the vendor's showcase clients. Talk to customers who've used the system for at least a year, as initial enthusiasm often fades when implementation challenges emerge. Ask specifically about unexpected costs, integration difficulties, and places where the AI fell short of promises.

The best vendors provide performance guarantees with teeth. Service level agreements should specify accuracy thresholds, response times, and uptime requirements. More importantly, they should include meaningful remedies when performance falls short. Credits against future invoices provide little comfort when AI failures disrupt your business.

How Do You Handle Model "Drift" and Ensure Performance Over Time?

AI models degrade as the world changes. Customer behavior evolves. Market conditions shift. New products launch. Without updates, an AI system that performed brilliantly at deployment might fail dramatically months later.

Understanding a vendor's approach to model maintenance reveals their technical sophistication and long-term reliability. How do they detect performance degradation? What triggers retraining? Who bears the cost of updates? Vendors who haven't thought through these issues likely lack experience with production AI systems.

Retraining approaches vary significantly. Some vendors expect you to manually request updates when you notice problems. Others automatically retrain on schedules that might not align with your business cycles. The best approaches continuously monitor performance and adaptively retrain based on detected drift.

Consider also how updates affect your operations. Can models be updated without service interruption? Will updates require re-integration or user retraining? How do you test updates before full deployment? A vendor's update process should minimize disruption while maintaining performance.

Category 3: Ethics, Fairness, and Compliance

Can You Provide Your Model Card or a Similar Transparency Document?

Model cards document AI systems' capabilities, limitations, and appropriate uses. Originally developed by researchers, they've become best practice for responsible AI deployment. Vendors who can't or won't provide transparency documentation likely haven't thoroughly tested their systems.

A comprehensive model card should detail training data sources and characteristics, performance metrics across different populations, known limitations and failure modes, and recommended and discouraged use cases. This transparency helps you assess whether the AI suits your needs and identify potential risks.

Pay particular attention to performance disparities across demographic groups. An AI system that works well on average might fail catastrophically for certain populations. If your customers or employees include diverse groups, these disparities create both ethical and legal risks.

Vendors sometimes claim transparency would reveal trade secrets. While some implementation details deserve protection, basic performance characteristics and limitations don't constitute competitive advantages. Vendors refusing all transparency likely have something to hide.

How Does Your Solution Comply with Regulations Like the EU AI Act?

AI regulation is rapidly evolving globally. The EU AI Act represents just the beginning, with various countries and states developing their own frameworks. Vendors must demonstrate not just current compliance but capacity to adapt as regulations evolve.

Compliance involves more than checking boxes. How does the vendor classify their AI system's risk level? What documentation do they maintain? How do they ensure human oversight where required? Can they support your compliance obligations with necessary data and attestations?

For high-risk applications - those affecting employment, credit, healthcare, or legal decisions - regulatory requirements multiply. Vendors should understand these heightened obligations and have processes ensuring compliance. They should also indemnify you against regulatory penalties resulting from their system's non-compliance.

Consider also sector-specific regulations. Healthcare AI must comply with HIPAA and FDA requirements. Financial services AI faces fair lending laws. Educational AI must respect student privacy laws. Vendors claiming universal solutions often lack deep understanding of sector-specific requirements.

Category 4: Implementation, Support, and Total Cost of Ownership

What Are the Hidden Costs Beyond the License Fee?

The sticker price rarely reflects true AI costs. Like icebergs, the visible license fee represents a small portion of total investment. Understanding hidden costs prevents budget surprises and enables accurate ROI calculations.

Implementation costs often exceed licenses. Professional services for integration, customization, and training add up quickly. Data preparation might require extensive consulting. Custom features demand additional development. Even "plug-and-play" solutions need configuration for your environment.

Operational costs accumulate over time. API call charges for cloud-based services can escalate with usage. Data storage and processing fees grow with volume. Premium support packages become necessary when basic support proves inadequate. These recurring costs compound, potentially dwarfing initial investments.

Scaling costs deserve particular scrutiny. Many vendors offer attractive entry pricing that balloons with success. Per-user pricing punishes adoption. Transaction-based pricing penalizes growth. Understand how costs evolve as your usage expands, and negotiate caps or volume discounts upfront.

What Does the Support Model Look Like Post-Launch?

Launch day marks the beginning, not end, of your vendor relationship. Understanding post-launch support prevents frustration when inevitable issues arise. The quality and availability of support often determines implementation success more than technical capabilities.

Support tiers reveal vendor priorities. Basic support with email-only contact and multi-day response times signals a vendor unprepared for enterprise relationships. Conversely, dedicated account teams with direct access indicate serious partnership commitment. Match support levels to your criticality needs.

Knowledge transfer mechanisms matter as much as break-fix support. How does the vendor help you become self-sufficient? What training do they provide? Can your team access documentation, best practices, and community forums? Vendors invested in customer success enable independence rather than fostering dependence.

Evolution support proves crucial for AI systems. Beyond fixing bugs, how does the vendor help you expand usage, optimize performance, and adopt new capabilities? The best vendors provide regular business reviews, optimization recommendations, and roadmap alignment sessions. They act as partners in your AI journey rather than distant service providers.

Effective AI procurement requires asking hard questions and refusing to accept vague answers. The vendors who welcome your scrutiny, provide detailed responses, and acknowledge limitations honestly make the best partners. Those who deflect, overpromise, or hide behind proprietary claims often deliver disappointment.

Remember that vendor selection shapes your AI journey for years. The questions you ask today determine whether that journey leads to transformation or frustration. Invest the time in thorough due diligence. Your future self will thank you when AI delivers promised value rather than vendor excuses.

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This article is part of the Phoenix Grove Wiki, a collaborative knowledge garden for understanding AI. For more resources on AI implementation and strategy, explore our growing collection of guides and frameworks. It is crucial to note, that you must always do your own research, and make your own decisions. This article is offered for informational purposes only. This article is explicitly not offered as investment, or business advice.