FeatureThe AI Vendor Evaluation Checklist Every Leader Needs

According to a McKinsey & Co. report, 92% of companies plan to invest in generative AI over the next three years. But successfully implementing AI and unlocking its potential for your business starts with a foundation of trust.

One study on closing the AI trust gap revealed that only 55% of employees trust their employer to ensure AI is implemented responsibly. This puts a huge responsibility on leaders to plan for responsible AI governance that upholds users' well-being.

One area that takes center stage is AI vendor selection. As organizations deepen their AI integration, the stakes of vendor selection grow significantly. The wrong vendor can limit scalability opportunities and expose enterprises to operational risks and regulatory fines.

For enterprises, these risks often stem from hidden red flags that are easily overlooked during vendor selection. To avoid costly mistakes, decision-makers need a clear framework for evaluating AI vendors beyond technical specifications. They need to consider data governance practices, migration capabilities and long-term partnership potential, among other key factors. If you’re an AI leader, this guide explains how to evaluate AI vendors by examining the red flags, green lights and must-haves.

Table of Contents

• Red Flags: Warning Signs When Evaluating Potential AI Vendors
• Green Flags: Key Indicators That Signal a Reliable AI Partner
• Must-Haves: Essential Features and Capabilities to Look for in an AI Vendor
• The Evaluation Process: Steps to Effectively Assess Potential AI Vendors
• Making Informed Decisions for Your Organization’s AI Strategy

Red Flags: Warning Signs When Evaluating Potential AI Vendors

While navigating the AI vendor landscape, certain warning signals demand attention. These red flags often indicate deeper issues that might compromise an enterprise's AI initiatives and objectives. Identifying these signals early on helps prevent costly mistakes and relationship challenges.

1. Lack of Transparency Around Data Practices

Enterprises need to understand how a vendor's AI model was trained and ensure that it has been trained on high-quality data from reliable sources. This is particularly important for AI systems that can create content on their own, such as large language models (LLMs).

When evaluating vendors, ask for detailed insights into their datasets, training processes and model cards. Some key questions to ask include: 

• Are they GDPR, CCPA or industry-specific standards-compliant?
• How does the vendor ensure the data is accurate, relevant and free from bias?
• Does the vendor have legal rights to use the data to train their AI models?

2. Weak Policies

Clear, well-documented policies are the basis for trust and accountability. However, most AI vendors do not have them or provide vague boilerplate versions that do not fit enterprise requirements, which is an immediate red flag.

When considering a vendor, review their current compliance, security and risk management policies. Inadequate or missing policy documentation can expose your company to undue risk. At a minimum, the vendor should provide:

• Privacy Policy: How customer data is collected, processed and protected.
• DPA (Data Processing Agreement): This document specifies vendor responsibility regarding data protection.

3. Lack of Ethical AI Practices

AI vendors should be responsible for developing AI that adheres to ethical principles and includes bias mitigation. If an AI vendor can't take a stance on ethical AI or doesn't want to engage in conversations about fairness and accountability, they may be exposing you to long-term risks related to your reputation or regulatory compliance.

Some AI vendors also have lock-in tactics that make it hard to move your data or connect with other systems — intentionally or unintentionally. They often hide this in proprietary file formats and restrictive contracts. A good vendor will provide several integration options and should have a clear exit strategy if you need to switch.

4. Regulatory Compliance Issues

AI is being increasingly regulated, from data privacy to bias mitigation. If vendors don’t build compliance into their products, you're responsible for it. When sourcing AI for your organization, ask for documentation on the standards they follow during development and training.

Assessing a vendor's regulatory compliance status, data governance policies and security measures is important to minimize legal and operational risk.

Michael Bennett, researcher at Boston's Northeastern University, told Forbes that one thing is almost certain: AI laws will be complex to navigate. "You'll almost certainly see more AI regulation, whether it's city ordinance, state law or new federal legislation," said Bennett. "The US is not going to be like the EU; there's probably not going to be one overarching framework in a near-term timeframe. Instead, we can anticipate a growing regulatory thicket that will be very complex for folks to navigate.”

Green Flags: Key Indicators That Signal a Reliable AI Partner

These green flags offer organizations better quality assurance, all with the promise of a vendor relationship that will deliver long-term value.

1. Transparent Data Practices

A trustworthy AI vendor will be transparent about the storing, processing and protection of data. They will communicate their compliance with data privacy regulations like GDPR, HIPAA or CCPA and provide well-defined data ownership, security and governance policies. Clear visibility into how the vendor handles data can help reduce risks from a legal standpoint and build trust.

2. Measurable Results and Proof of Performance

Good vendors won't just tell you their product works; they'll show you. Look for AI partners who share case studies, performance benchmarks and customer success stories outlining how their technology works. They should also be able to set reasonable expectations for you and explain how their models operate in peak and suboptimal conditions.

However, it’s much more important for organizations to develop their own understanding of AI to help them choose the right AI investments. Eamonn O'Neill, CTO at Lemongrass, shared that in the absence of that understanding, organizations need to select a trusted partner to help them navigate these decisions. 

"Typically, simple demos can make solutions seem incredibly capable, but understanding how the provider deals with real-world exceptions and how they delivered customer success will give a much better insight into the viability of their offerings.”

3. Flexible and Scalable Solutions

A good AI vendor has solutions that can scale with your needs. Whether you’re running a small pilot or using AI throughout all of your departments, they should have flexible integration options and API support. The vendor should also provide infrastructure to handle increased data loads and complexity without reinventing the wheel.

4. Strong Customer Support and Ongoing Engagement

AI systems require continuous optimization and monitoring, so vendors with 24/7 technical support, dedicated AI specialists and proactive model maintenance are important for smooth operations. Vendors who view AI adoption as a partnership, providing training materials, best practices and regular check-ins are also a huge green flag.

Must-Haves: Essential Features and Capabilities to Look for in an AI Vendor

Choosing the right AI vendor is more than just selecting a solution that works today – you need a partner who can help you with your long-term AI strategy. Here are three must-have features and capabilities to prioritize when vetting vendors.

1. Enterprise-Grade Security and Compliance

AI deals with sensitive data, so security is paramount. Any credible AI vendor should offer enterprise-grade encryption, access controls and compliance with industry regulations. They should also be open about their data protection practices and provide documentation on handling customer data. Otherwise, you may expose your organization to security breaches and compliance issues.

2. Customizability and Integration Flexibility

No two enterprises will have the same AI needs. The right vendor will provide you with configurable models, API access and the ability to easily integrate with other systems, like a customer relationship management system or customer data platform.  

Avoid vendors that offer a rigid, one-size-fits-all solution, especially those that require complex workarounds. Instead, look for those that provide a modular architecture that plugs into your workflows so that you can get up and running quickly without disrupting your business workflows too much.

According to Derek Ashmore, application transformation principal at Asperitas, customization and integration are critical factors in choosing an AI vendor for long-term success, as they determine how well the AI solution aligns with business needs and scales over time.

“A one-size-fits-all AI solution rarely meets the unique demands of every organization," explained Ashmore. "Customization allows businesses to tailor AI models to their specific workflows, industry challenges and data environments. Vendors offering configurable models, API access and domain-specific training ensure the AI system evolves with business objectives rather than requiring workarounds or manual adjustments.”

Ultimately, he said, poorly integrated AI can lead to data silos, inefficiencies and operational disruptions.

3. Scalability to Support Growth

AI initiatives often start small but quickly grow. Your AI vendor must be able to scale with your organization: handle larger volumes of data, support new use cases and maintain consistent performance as demand increases. Ensure their infrastructure, cloud capabilities and ability to support real-time AI workloads are designed for this.

The Evaluation Process: Steps to Effectively Assess Potential AI Vendors

Choosing the right AI vendor ultimately comes down to your organization's needs, priorities and long-term strategy. A vendor might have an impressive product, but you could have a costly headache if their business practices, data policies or support structure don't hold up.

Here's how to go beyond surface-level claims and conduct a thorough AI vendor evaluation.

1. Have a Checklist for Evaluating Vendors

Before engaging with any AI vendor, create a checklist of must-haves and deal-breakers. This ensures you’re looking at the right factors instead of getting distracted by flashy sales pitches. Some key questions to ask:

• Can their AI solution scale with your company? Do they offer deployment options, such as public or private cloud?
• What country are they headquartered in, and what laws govern their data policies? Are they compliant with GDPR, the EU AI Regulation, etc.?
• Will they support you throughout the AI deployment process? Will your team receive training to ensure you get the most out of the tool?

2. Request a Proposal

Once you've identified potential vendors, schedule a meeting and request a proposal. A well-structured RFP (request for proposals) separates serious vendors from those who sound good on paper. Be specific about your organization’s requirements, including expected outcomes, integration needs, compliance standards and security expectations.

If a vendor dodges key questions or gives generic answers, it might not have the capabilities it claims. Conversely, vendors who provide detailed, transparent responses are more likely to be reliable partners.

3. Conduct Pilot Programs for Performance Testing

Nothing reveals an AI vendor's true capabilities like a real-world test. Before signing a long-term contract, run a pilot program. This lets you see how their system performs with your data, workflows and infrastructure.

Key things to measure:

• Accuracy & Performance: Does the AI deliver the results it promised?
• Integration: How smoothly does it fit into your existing tech stack?
• User Experience: Is it easy for your team to use, or will it require months of training?

A vendor confident in their product should be willing to support a pilot program. If they resist, it could indicate their solution isn't as polished as they claim.

AI expert Dr. Keryn Gold recommended defining what success looks like for the pilot program (e.g., improving detection accuracy to X%, or saving Y hours of manual work per week) and measure against it. Keep the scope manageable — maybe one business unit or one specific process — so that you can learn and iterate quickly.

During the pilot, work closely with the vendor: this will reveal how supportive and collaborative they truly are. If the pilot meets or exceeds expectations, you have tangible evidence to justify scaling up; if it falls short, you can recalibrate or consider alternative solutions with minimal sunk cost.

Making Informed Decisions for Your Organization’s AI Strategy

Picking an AI vendor isn't another "check-the-box" procurement task. It's a decision that can affect your organization and customers for years to come. Overlooking one red flag could expose your business to security vulnerabilities, waste millions of dollars or inject operational chaos into your workflows.

However, with the right decision, you can unlock new untapped value for your organization, drive efficiencies and secure an invaluable competitive advantage. The secret to getting it right lies in how you equip yourself to evaluate potential AI vendors.

When you follow the right checklist and consider all the red flags vis-à-vis green flags, you set your organization on a sacred path to minimal risks and maximum returns from your AI deployment.

AI is evolving fast, and so are the vendors offering it. As an AI leader, take the time to choose wisely — the future of your AI strategy depends on it.