Artificial intelligence is no longer an experimental capability reserved for innovation teams. Across industries, AI platforms now support revenue generation, customer engagement, risk analysis, and core operational decision making. As reliance on AI grows, so does the importance of selecting vendors that can support long-term business objectives without introducing unacceptable risk.
For CIOs and enterprise buyers, AI vendor selection requires a broader lens than traditional software procurement. Beyond performance and functionality, organizations must evaluate vendor stability, operational dependency, and continuity readiness. This article outlines a practical framework for assessing AI vendors through a risk-focused checklist designed for enterprise environments.
Why AI Vendor Evaluation Requires a Different Approach
AI systems introduce unique dependencies that extend beyond application code. Models evolve continuously, data pipelines grow complex, and supporting infrastructure often relies on proprietary tooling or specialized expertise.
These factors increase exposure to vendor-related and operational risk. If a provider exits the market, changes strategic direction, or is unable to continue support, the impact can extend far beyond service disruption.
Enterprise buyers must therefore assess not only what an AI solution does today, but how it can be sustained tomorrow.
The Core AI Vendor Evaluation Checklist
Vendor Viability and Strategic Alignment
CIOs should assess the long-term stability of AI vendors, including financial health, ownership structure, and product roadmap alignment. Vendors supporting business-critical use cases must demonstrate commitment to ongoing development and support.
Transparency Into AI Architecture
Understanding how an AI system is built matters. Buyers should request clarity around model ownership, training processes, dependencies, and infrastructure requirements. Transparency supports informed risk assessment and governance oversight.
Dependency and Lock-In Risk
AI platforms often rely on proprietary frameworks or cloud services. CIOs should evaluate how easily systems can be maintained or transitioned if vendor relationships change.
Documentation and Knowledge Transfer
Enterprise readiness depends on documentation. This includes model descriptions, data handling practices, deployment instructions, and operational guides. Lack of documentation increases continuity risk.
Continuity and Exit Planning
Organizations should require clear continuity provisions that address scenarios where vendor support is interrupted. This is where technology escrow becomes a key control.
The Role of Software Escrow in AI Vendor Risk Management
Software escrow provides a structured mechanism for mitigating vendor dependency risk. By placing critical software assets with a neutral third party, enterprises gain contractual assurance that essential materials will be available under predefined conditions.
For AI systems, escrow arrangements may include source code, model artifacts, training workflows, configuration files, and build instructions. This ensures that enterprises retain a viable path forward if access to vendor support is lost.
An overview of software escrow fundamentals is available at PRAXIS Escrow.
Why Verification Strengthens the Checklist
Escrow without validation can leave gaps unaddressed. Verification services assess whether escrowed materials are complete, current, and usable in practice.
For CIOs, verification transforms escrow into an auditable risk control rather than a contractual formality. PRAXIS Technology Escrow offers verification services designed to align with enterprise governance and emerging technology complexity. Learn more at our Verification and Continuity page.
Integrating the Checklist into Enterprise Governance
AI vendor evaluation should not occur in isolation. CIOs can integrate this checklist into procurement workflows, vendor risk assessments, and ongoing oversight processes. Further, this checklist can become an exhibit to the license agreement and then replicated in the escrow agreement so that complete verification can be performed to ensure all relevant materials are contained in the escrow.
By embedding continuity and dependency considerations early, organizations reduce downstream risk and support responsible AI adoption. Technology escrow solutions tailored for complex environments are available at PRAXIS Technology Escrow.
From Evaluation to Long-Term Resilience
Selecting an AI vendor is not a one-time decision. It establishes a long-term dependency that must be actively managed. A structured evaluation checklist helps CIOs balance innovation with operational resilience while aligning AI initiatives with enterprise risk tolerance.
Organizations that address these considerations upfront position themselves for sustainable growth and stronger stakeholder confidence.
FAQs
AI systems introduce evolving models, data dependencies, and specialized infrastructure that increase operational and continuity risk.
Key risks include vendor failure, loss of support, proprietary dependencies, and lack of access to critical AI assets.
Escrow ensures access to essential software and AI materials if vendor obligations cannot be fulfilled.
Verification confirms that escrowed materials are complete and usable, reducing uncertainty during disruption scenarios.
Escrow is most relevant when AI systems support business-critical operations or regulated activities.
Glossary of Terms
Risk arising from reliance on external providers for artificial intelligence systems and services.
An arrangement in which critical software assets are held by an independent third party to support continuity.
Processes that validate the completeness and usability of escrowed materials.
Reliance on a specific vendor or system to maintain critical business operations.
Preparation to maintain or restore operations during vendor or technology disruptions.
Chris Smith Author
Chris Smith is the Founder and CEO of PRAXIS Technology Escrow and a recognized leader in software and SaaS escrow with more than 20 years of industry experience. He pioneered the first automated escrow solution in 2016, transforming how escrow supports Agile development, SaaS platforms, and emerging technologies.