“I Was Sold Something That Doesn’t Exist”: 5 Critical Questions to Avoid AI Vendor Deception

The words every procurement professional dreads hearing: "It was clear once we started engaging that we were sold something that doesn't exist." This statement, shared during a recent industry webinar, represents a growing problem as AI hype outpaces reality in the procurement technology market.

With AI budgets increasing and pressure mounting to demonstrate quick wins, vendors are making bold promises about capabilities that exist only in concept or on distant roadmaps. The result? Failed pilots, wasted resources, and damaged relationships that set back legitimate AI adoption by months or years.

The AI Vendor Reality Gap

The procurement AI market is experiencing unprecedented growth, with vendors racing to capture market share by promising transformational capabilities. However, the speed of AI advancement creates a dangerous gap between what vendors claim they can deliver and what actually exists in production-ready form.

Common scenarios causing buyer disappointment:

• Features sold as "available" that are actually in development
• Capabilities demonstrated in controlled environments that don't work with real customer data
• Pilots that require extensive pre-work not disclosed during the sales process
• AI functionality that exists but produces unusable results without perfect data quality

This isn't necessarily intentional deception – many vendors genuinely believe they can deliver promised capabilities quickly. However, the complexity of enterprise AI implementation often reveals gaps between sales promises and technical reality.

Transparent Vendor Practices: Setting the Standard

Some AI-native providers are establishing transparency standards that benefit the entire industry. Level Path, for example, maintains crystal clear communication about current capabilities versus future development plans, building genuine partnerships rather than just closing deals.

Transparent vendors typically:

• Clearly distinguish between available features and roadmap items
• Provide realistic timelines for feature delivery
• Discuss data and infrastructure requirements upfront
• Share specific implementation examples with timelines and scope details
• Acknowledge limitations and potential challenges honestly

5 Critical Questions to Ask Every AI Vendor

1. What Specific AI Capabilities Are Currently Available in Your Solution Today, and Can You Provide a Live Demo?

Why This Matters: Recorded demos can showcase concepts or future functionality rather than current reality. Live demonstrations force vendors to show actual working features rather than polished marketing materials.

Red Flags to Watch For:

• Claims that "everything is available" for your use case
• Reluctance to provide live demonstrations
• Demos that rely heavily on pre-configured scenarios
• Inability to show specific functionality you need

Important Caveat: Even functional AI capabilities require proper data and training to deliver value. A working live demo doesn't guarantee success in your environment without adequate data preparation.

2. Can You Provide Evidence of Successful Deployment of These Capabilities in Similar Procurement Environments?

Critical Follow-up Questions:

• How did they implement the solution?
• What was their actual timeline from start to finish?
• What was the scope of their deployment?
• Are they fully deployed or still in pilot phase?

What to Verify: Evidence of deployment doesn't automatically mean successful, timely, or complete implementation. Some "success stories" may represent pilot programs that started months ago without reaching production, implementations with significantly different scope than your needs, or deployments that required far longer than expected timelines.

3. What Are the Hardware, Software, and Data Requirements for Deploying Your AI Solution?

The Data Requirements Challenge: Data compatibility issues catch most organizations off-guard and represent the most common cause of AI implementation delays or failures.

Real-World Examples:

• Catalog Data Limitations: Organizations with basic catalog data discover AI orchestration tools can only provide simple recommendations ("pencil is in the catalog") rather than intelligent procurement guidance
• Document Format Issues: Contract lifecycle management AI struggles with scanned PDF contracts that appear as images rather than readable text, requiring conversion processes before AI analysis
• Data Quality Prerequisites: AI systems need clean, standardized data to function effectively, often requiring extensive data preparation projects before AI implementation

Questions to Ask:

• What specific data formats does your AI require?
• What data quality standards must be met?
• What infrastructure changes are needed?
• Are there compatibility issues with our existing systems?

4. What Is Your Roadmap for AI Development and How Do You Engage with Clients to Determine Roadmap Priorities?

Why Roadmap Transparency Matters: Understanding development timelines helps distinguish between current capabilities and future promises. Vendors might claim functionality that won't be available until next quarter or next year.

Client Engagement Assessment: Many AI vendors struggle to incorporate client feedback into development priorities due to rapid market changes and technical constraints. Understanding their collaboration process reveals whether you'll have influence over feature development or must accept whatever the vendor prioritizes.

Key Evaluation Criteria:

• How specific are roadmap timelines?
• Do they have a formal client feedback process?
• Can you influence development priorities?
• How do they handle feature requests?
• What happens if promised roadmap items are delayed?

5. What Safeguards Do You Have in Place to Ensure AI Solution Compliance with Data Privacy Requirements and Regulations?

Critical Privacy Considerations:

• How is your data used for AI training?
• Is your data used to train models for other clients?
• What documentation exists for compliance processes?
• How do you handle regulatory requirements specific to your industry?

Training Data Usage Models:

• Client-specific training: Your data trains AI models exclusively for your use
• General training: Your data contributes to models used across all clients
• Hybrid approaches: Different data types receive different treatment

Regulatory Compliance Challenges: Some organizations face industry regulations that prohibit or severely restrict AI usage, making vendor compliance capabilities critical for implementation feasibility.

The Two-Part Success Equation

Even perfect vendor evaluation doesn't guarantee AI success. Implementation requires two critical components:

Vendor Capability (Part 1)

• Functional AI technology that works as demonstrated
• Realistic timelines and scope expectations
• Appropriate support and implementation methodology
• Transparent communication about limitations and requirements

Organizational Readiness (Part 2)

• Clean, accessible data that meets AI requirements
• Well-defined processes including exception handling
• Realistic expectations about implementation complexity
• Adequate resources for data preparation and change management

The Readiness Reality Check: Organizations often focus extensively on vendor capabilities while underestimating their own preparation requirements. AI success demands both competent technology providers and well-prepared implementing organizations.

Implementation Prerequisites

Data Preparation Requirements

• Data Quality: Clean, consistent, standardized information across all relevant systems
• Data Accessibility: Information structured for AI consumption with appropriate labeling
• Data Completeness: All required data elements available or clear plans for acquisition
• Data Governance: Privacy, security, and compliance frameworks established

Process Understanding

• Current State Mapping: Clear documentation of existing workflows and decision points
• Exception Handling: Understanding of non-standard scenarios and edge cases
• Success Metrics: Defined measurements for AI implementation success
• Change Management: Plans for workforce adaptation and training

Technical Infrastructure

• System Integration: Compatibility between AI solutions and existing technology
• Performance Requirements: Adequate computing resources for AI workloads
• Security Framework: Protection mechanisms for AI data and processes
• Monitoring Capabilities: Systems for tracking AI performance and outcomes

Red Flags During Vendor Evaluation

Sales Process Warning Signs:

• Reluctance to provide specific technical details
• Pressure to sign quickly without thorough evaluation
• Inability to connect you with reference customers
• Vague answers about implementation timelines or data requirements
• Claims of universal compatibility without understanding your environment

Technical Demonstration Issues:

• Demos that seem too polished or scripted
• Inability to show functionality relevant to your specific use case
• Evasive answers about data preparation requirements
• Lack of discussion about potential challenges or limitations

Reference and Evidence Problems:

• Unwillingness to provide customer references
• Success stories that lack specific details about scope, timeline, or outcomes
• Reference customers still in pilot phases for extended periods
• Examples from significantly different industries or use cases

Building Successful AI Vendor Partnerships

Establish Clear Expectations Early

• Define specific success criteria and measurement methods
• Document all capabilities and timelines in contracts
• Create milestone-based implementation plans with clear deliverables
• Establish regular communication and review processes

Plan for Implementation Complexity

• Budget adequate time and resources for data preparation
• Identify internal expertise needed for successful deployment
• Plan change management activities for affected users
• Prepare contingency plans for common implementation challenges

Maintain Realistic Timelines

• Factor in data preparation time before AI implementation
• Include training and validation periods in project plans
• Allow buffer time for unexpected technical challenges
• Plan iterative rollouts rather than complete implementations

The Cost of AI Vendor Failures

Direct Financial Impact:

• Wasted pilot budgets and implementation costs
• Delayed benefits from legitimate AI opportunities
• Additional costs for vendor switching and re-implementation
• Opportunity costs from postponed digital transformation

Organizational Consequences:

• Damaged credibility for AI initiatives within the organization
• Reduced stakeholder confidence in procurement technology investments
• Decreased willingness to fund future AI pilot programs
• Lost momentum in digital transformation efforts

Industry Impact:

• Slower overall AI adoption across procurement organizations
• Increased skepticism about AI vendor claims
• Higher due diligence costs for legitimate AI implementations
• Reduced innovation investment as buyers become more conservative

Moving Forward: Building AI Success

Successful AI implementation requires partnership between prepared buyers and capable vendors. Organizations must invest in proper vendor evaluation while simultaneously preparing their own data, processes, and teams for AI deployment.

The goal isn't to avoid AI initiatives due to vendor risks, but to approach them with appropriate diligence and realistic expectations. Transparent vendors who honestly discuss capabilities, limitations, and requirements create the foundation for successful long-term partnerships.

Key Success Factors:

• Thorough vendor evaluation using specific, technical questions
• Honest assessment of organizational readiness and data quality
• Realistic timeline and resource planning for implementation
• Clear contractual definitions of capabilities and deliverables
• Ongoing communication and partnership approach with selected vendors

The procurement profession needs AI success stories to drive continued innovation and investment. Avoiding vendor deception through careful evaluation protects individual organizations while supporting industry-wide AI advancement through successful implementations and satisfied customers.

Ready to evaluate AI vendors with confidence? Contact Wonder Services to learn how our proven vendor evaluation methodology and implementation expertise can help you avoid common pitfalls while identifying AI solutions that deliver real business value.