by Charles Gallaer & Mike McMahan

We have written about the increasing use of artificial intelligence by businesses and pitfalls you should consider. Once you’ve made the decision to implement AI solutions, the next step is choosing an AI vendor. With so many vendors promising cutting-edge solutions, businesses often struggle to distinguish genuine value from overhyped marketing. Vetting AI vendors is critical to ensuring your investment delivers meaningful results while maintaining compliance.

Why Vetting Matters

AI solutions can enhance decision-making, streamline operations, and unlock new opportunities—but only if implemented correctly. An unvetted AI vendor may lead to wasted time, unscalable solutions, or even compliance risks. A thorough vetting process ensures that the chosen vendor aligns with your organization’s goals. It checks that the vendor complies with ethical and regulatory standards. Finally, it confirms that the vendor delivers on its promises.

AI Vendor Vetting Checklist:

1. Define Your Business Needs and Goals
Before engaging with vendors, clearly articulate what you want to achieve with AI. Are you looking to automate processes, gain insights from data, or develop predictive models? Having specific objectives will help you filter out vendors whose offerings don’t align with your needs.

Key Considerations:
• What problem are you solving?
• What success metrics will you use?
• How will AI integrate into existing workflows?

2. Assess the Vendor’s Expertise
Not all AI vendors are created equal. Some specialize in specific industries or technologies, while others offer generalized solutions. Evaluate their expertise by examining case studies, client testimonials, and the experience of their team.

Questions to Ask:
• Does the vendor have experience in your industry?
• What AI technologies or methodologies do they specialize in?
• Do they offer references or examples of past successes?

3. Evaluate the Technology
A vendor’s technology stack and infrastructure are critical. Ensure their AI tools are reliable, scalable, and interoperable with your existing systems. Additionally, investigate whether the AI models are explainable and transparent, especially if the solution involves sensitive data or decision-making processes.

What to Look For:
• Robustness: Can the system handle large volumes of data?
• Scalability: Will it grow with your business?
• Transparency: Can the AI’s outputs be explained and audited?
• Customization: Can the solution be tailored to your needs?

4. Review Data Privacy and Security Practices
AI systems rely on data, often including sensitive or proprietary information. The vendor should demonstrate robust security practices and compliance with relevant regulations such as GDPR or CCPA.

Key Questions:
• How is data stored, processed, and protected?
• Does the vendor comply with data protection regulations?
• Do they offer clear policies on data ownership and usage?

5. Scrutinize Ethical Considerations
AI has ethical implications, from bias in algorithms to unintended consequences in deployment. Choose vendors who prioritize ethical AI practices, including fairness, accountability, and inclusivity.

Checklist:
• Are the models regularly audited for bias?
• How does the vendor address ethical concerns?
• Do they have a clear stance on responsible AI usage?

6. Analyze Cost and ROI
AI solutions can be expensive, so understanding the total cost of ownership (TCO) and potential return on investment (ROI) is crucial. Beyond upfront costs, consider long-term expenses such as maintenance, upgrades, and training.

Evaluate:
• Is the pricing transparent?
• What are the projected savings or revenue gains?
• How does the vendor measure ROI?

7. Test the Solution
A proof of concept (POC) or pilot program can provide valuable insights into the vendor’s solution. This allows you to evaluate its performance, scalability, and ease of integration in a controlled environment.

During the POC:
• Test the AI against real-world scenarios.
• Assess user-friendliness and required training.
• Measure outcomes against predefined success metrics.

Red Flags to Watch For:

• Lack of Transparency: Vague answers about technology, data use, compliance practices, or pricing are a warning sign.
• Overpromising: Be cautious of vendors claiming they can solve all your problems with minimal effort.
• Poor Communication: Slow responses or unclear documentation suggest potential issues with support and reliability.
• No Proven Track Record: Vendors without relevant experience or case studies may not be able to deliver.

Conclusion
Selecting the right AI vendor requires a thoughtful approach. By defining your goals, assessing technical capabilities, and prioritizing ethical and secure practices, you can identify a partner who will drive measurable business value. Remember, the best AI vendor is not the one with the flashiest demo, but the one who understands your needs and delivers solutions that work in the real world.

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Questions? Ask the authors– Charles and Mike have contact information in their bios.