Asking the Right Questions: Evaluating AI Vendor Commitment to Open Standards

Executive Summary & Key Takeaways:

📌 The Problem: Nearly every AI vendor claims their platform is "open" and "supports standards." However, these claims often lack substance ("open-washing"), masking underlying proprietary architectures that lead to lock-in.

🔍 The Challenge: Business leaders need a reliable way to cut through the marketing hype and determine a vendor's genuine commitment to open standards that actually enable flexibility and interoperability.

💡 The Solution: Don't rely on vague claims. Ask specific, evidence-based questions during vendor evaluation and negotiation, focusing on which standards are supported for which functions, data portability, API parity, roadmap commitments, and proof of real-world interoperability.

🎯 Why Read: Get a practical checklist of critical questions and evaluation criteria to rigorously assess AI vendors. Equip yourself to avoid hype, mitigate lock-in risk (a key danger discussed previously), and ensure your chosen partners truly support your need for an adaptable, future-proof AI strategy.

Cheat Sheet: Vetting Vendor Claims on Open Standards

🎯 The Goal: Discern genuine commitment to interoperability vs. superficial "open-washing."

🔍 Key Question Areas: Demand specifics on (1) Which Standards Supported (by name, for which function?), (2) Full Data Portability (any costs/limits?), (3) API Parity (standard vs. proprietary features?), (4) Concrete Roadmap, and (5) Proof (live demos, case studies, references confirming interoperability).

🚩 Red Flags: Vague answers ("We're API-first"), focusing only on proprietary APIs, extra fees for standard data export, lack of clear roadmap, no verifiable proof of interoperability with your key third-party tools.

✅ Action: Use the detailed questions in this article during RFPs, demos, contract negotiations, and ongoing reviews to demand specifics and evidence.

You're evaluating AI platforms or tools, seeking solutions that promise power and flexibility. Every vendor presentation highlights "openness," "interoperability," and "support for standards." It sounds great – exactly what you need to avoid the integration chaos, hidden costs, and vendor lock-in discussed previously.

But how do you verify these claims? In the competitive AI market, "open-washing" – making superficial claims about openness without deep, practical commitment – is unfortunately common.

Accepting vendor claims at face value is a recipe for future headaches. As a leader, you need to equip yourself and your teams to ask the right questions and demand evidence to ensure a vendor's commitment to standards is real and aligns with your strategic need for flexibility, as outlined when we looked at the standards landscape.

Decoding Vendor Claims: Beyond Were Open

Be wary of vague assurances. Statements like these require deeper probing:

🔍 "We're API-first": Which APIs? Are they proprietary or based on open standards? Are the standard APIs as capable as the proprietary ones?

🔍 "We support open source": How? Do they contribute code? Do they ensure their platform integrates smoothly with key open-source tools using standard methods, or just run alongside them?

🔍 "We have an interoperable platform": Interoperable with what? Based on which specific standards? Can they demonstrate this with the other tools you use?

💡 True openness isn't just about having some APIs; it's about a commitment to using broadly adopted, non-proprietary standards for key functions like data exchange, model deployment, and system interaction, enabling genuine choice and flexibility for the customer.

The Litmus Test: Key Questions to Ask Vendors

Incorporate these specific, evidence-focused questions into your RFPs, vendor demos, proof-of-concept evaluations, and contract negotiations:

🔍 Specific Standards Support:

🔦 "Which specific, named open standards (e.g., ONNX for models, Parquet/Avro for data, specific REST API profiles, relevant protocols like MCP) does your platform currently support for the following functions: [List key functions like data ingestion, data export, model deployment, model monitoring, context integration]?"

🔦 "For [Standard X], which version(s) do you support, and what is your process for adopting new versions?"

🔍 Roadmap & Commitment:

🔦 "What is your publicly available roadmap for supporting additional relevant open standards, including timelines?"

🔦 "How does your company contribute to the development or maintenance of the key open standards relevant to your platform?" (Shows commitment vs. just consumption).

🔍 API Access & Parity:

🔦 "Are your APIs based on open standards (e.g., REST, GraphQL with standard schemas) as fully documented, stable, and feature-rich as any proprietary APIs you offer for the same core functions?"

🔦 "Do you provide SDKs and clear examples for interacting with your standard APIs?"

🔍 Data Portability:

🔦 "Can we export 100% of our data – including raw data, processed data, model artifacts, logs, and metadata – in standard, non-proprietary formats (e.g., Parquet, JSON, ONNX)?"

🔦 "Are there any additional costs, technical limitations, or performance impacts associated with exporting data in standard formats versus proprietary ones?"

🔍 Proof of Interoperability:

🔦 "Can you provide live demonstrations or verifiable customer references/case studies showing successful integration of your platform with specific third-party tools [Name key tools in your ecosystem] using only these standard interfaces?"

🔦 "How do you handle situations where a customer needs to integrate with a tool you don't explicitly partner with, relying solely on standard interfaces?"

🔍 Conformance & Certification:

🔦 "How do you test and validate that your implementation correctly conforms to the specifications of the standards you claim to support?"

🔦 "Do you participate in any official conformance testing or certification programs for relevant standards?"

🎯 Key Takeaway: Vague answers to these questions are a major red flag. Demand specific, verifiable details about standards support, data access, and proven interoperability.

Evaluating the Answers: Look for Evidence, Not Just Promises

Getting answers is only the first step. You need to evaluate them critically:

📌 Specificity Matters: Prioritize vendors who can name specific standards, versions, and functionalities over those who give generic assurances.

📌 Review the Documentation: Ask for access to the actual API documentation for their standard interfaces. Is it comprehensive, clear, and actively maintained?

📌 Scrutinize the Roadmap: Look for concrete commitments with timelines, not just vague intentions to "explore" standards support.

📌 Check References: Talk to reference customers specifically about their experience using the standard interfaces for interoperability – did it work as expected? Were there limitations?

📌 Examine Pricing & Contracts: Ensure there are no hidden fees or restrictive clauses related to using standard interfaces or exporting data.

📌 Assess Community Involvement: Active participation in standards bodies or related open-source projects often signals a deeper, more genuine commitment.

Brief Example: Company D evaluated two AI platforms. Vendor A talked extensively about being "open" but couldn't provide clear documentation for standard APIs or references using them for third-party integration. Vendor B provided detailed API docs for ONNX export and Parquet data access, showed a public roadmap for supporting new standard versions, and offered references who confirmed easy data export. Company D chose Vendor B, mitigating future lock-in risk.

Making it Part of Your Process

Don't treat standards evaluation as an afterthought:

✅ Embed Questions in RFPs: Make detailed questions about specific standards support a mandatory part of your vendor selection process.

✅ Mandate Key Standards in Contracts: Where feasible and relevant, include contractual requirements for supporting specific, critical open standards and ensuring data portability.

✅ Conduct Ongoing Vendor Reviews: Don't just evaluate at selection time. Periodically review your existing key vendors' progress against their standards roadmap and commitment.

✅ Empower Your Teams: Ensure your technical architects, procurement specialists, and legal teams understand the strategic importance of standards and are equipped to perform this due diligence.

Conclusion: Demand Proof, Secure Flexibility

In the complex and rapidly evolving AI market, vendor claims of "openness" require rigorous scrutiny. Accepting these claims without verification is how costly vendor lock-in takes root, ultimately hindering your agility and inflating costs.

By arming yourself with specific, evidence-based questions and clear evaluation criteria, you can cut through the hype. Demanding proof of genuine commitment to relevant open standards during vendor selection and management is crucial for ensuring interoperability, maintaining strategic flexibility, and building an AI ecosystem that truly serves your long-term business goals. Don't just hope for openness – demand proof.

Need criteria for evaluating the standards themselves? Revisit: Beyond the Hype: Key Criteria for Evaluating AI Standards...

Return to the main overview: Is Your AI Strategy Built on Sand? Cut Through Integration Chaos.